Asteroid mining

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Overview of the Inner Solar System asteroids up to the Jovian System

Asteroid mining is the hypothetical and technically possible extraction of materials from asteroids and other minor planets, including near-Earth objects.

Research missions focused on asteroid sample return, including Hayabusa, Hayabusa2, OSIRIS-REx, and Tianwen-2, illustrate the challenges of collecting ore from space using current technology. As of 2024, around 127 grams of asteroid material have been successfully brought to Earth from space. Asteroid research missions are complex endeavors that yield a tiny amount of material: less than 100 milligrams for Hayabusa, 5.4 grams for Hayabusa2, and approximately 121.6 grams for OSIRIS-REx, with Tianwen-2 mission currently ongoing. These figures are comparatively negligible when considering the substantial investments and resources allocated to these projects ($300 million for Hayabusa, $800 million for Hayabusa2, $1.16 billion for OSIRIS-REx and $70 million for Tianwen-2).

Notable asteroid mining challenges include the high cost of spaceflight, unreliable identification of asteroids that are suitable for mining, and the challenges of extracting usable material in a space environment.

History

Prior to 1970

Before 1970, asteroid mining existed largely within the realm of science fiction. Publications such as Worlds of If, Scavengers in Space, and Miners in the Sky told stories about the conceived dangers, motives, and experiences of mining asteroids. At the same time, many researchers in academia speculated about the profits that could be gained from asteroid mining, but they lacked the technology to seriously pursue the idea.

The 1970s

In 1969, the Apollo 11 Moon Landing spurred a wave of scientific interest in human space activity far beyond the Earth's orbit. As the decade continued, increasing academic interest surrounded the topic of asteroid mining. A sizeable portion of serious academic consideration was aimed at mining asteroids located closer to Earth than the main asteroid belt. In particular, the asteroid groups Apollo and Amor were considered. These groups were chosen not only because of their proximity to Earth but also because many at the time thought they were rich in raw materials that could be refined.

Despite the wave of interest, many in the space science community were aware of how little was known about asteroids and encouraged a more gradual and systematic approach to asteroid mining.

The 1980s

Academic interest in asteroid mining continued into the 1980s. The idea of targeting the Apollo and Amor asteroid groups still had some popularity. However, by the late 1980s, the interest in the Apollo and Amor asteroid groups was being replaced with interest in the moons of Mars, Phobos and Deimos.

Governmental organizations and space agencies, such as NASA, begin to formulate ideas of how to process materials in space and what to do with the materials that are hypothetically gathered from space.

The 1990s

New reasons emerged for pursuing asteroid mining. These reasons tended to revolve around environmental concerns, such as fears over humans over-consuming the Earth's natural resources and trying to capture energy from the Sun in space.

In the same decade, NASA was trying to establish what materials in asteroids could be valuable for extraction. These materials included free metals, volatiles, and bulk dirt.

The 2010s

After a burst of interest in the 2010s, asteroid mining ambitions shifted to more distant long-term goals, and some 'asteroid mining' companies pivoted to more general-purpose propulsion technology.

On 24 April 2012, at the Seattle, Washington Museum of Flight, a plan was announced by billionaire entrepreneurs to mine asteroids for their resources. The company was called Planetary Resources, and its founders included aerospace entrepreneurs Eric Anderson and Peter Diamandis. The company announced plans to create a propellant depot in space by 2020, aiming to develop the process of splitting water from asteroids into hydrogen and oxygen to replenish satellites and spacecraft. Advisers included film director and explorer James Cameron; investors included Google's chief executive Larry Page, and its executive chairman was Eric Schmidt. Telescope technology proposed to identify and examine candidate asteroids lead to development of the Arkyd family of spacecraft; two prototypes of which were flown in 2015 and 2018. Shortly after, all plans for the Arkyd space telescope technology were abandoned; the company was wound down, its hardware auctioned off, and remaining assets acquired by ConsenSys, a blockchain company.

A year after the appearance of Planetary Resources, similar asteroid mining plans were announced in 2013 by Deep Space Industries; a company established by David Gump, Rick Tumlinson, and others. The initial goal was to visit asteroids with prospecting and sample return spacecraft in 2015 and 2016; and begin mining within ten years. Deep Space Industries later pivoted to developing & selling the propulsion systems that would enable its envisioned asteroid operations, including a successful line of water-propellant thrusters in 2018; and in 2019 was acquired by Bradford Space, a company with a portfolio of earth orbit systems and space flight components.

The 2020s

The 2020s have brought a resurgence of interest, with companies from the United States, Europe, and China renewing their efforts in this ambitious venture. This revival is fueled by a new era of commercial space exploration, significantly driven by SpaceX. SpaceX's development of reusable rocket boosters has substantially lowered the cost of space access, reigniting interest and investment in asteroid mining. A US congressional committee acknowledged this renewed interest by holding a hearing on the topic in December 2023. There are also endeavors to make first-time landings on M-type asteroids to mine metals like iridium which sells for many thousands of dollars per ounce. Private company driven efforts have also given rise to a new culture of secrecy obfuscating which asteroids are identified and targeted for mining missions, whereas previously government-led asteroid research and exploration operated with more transparency.

Minerals in space

The asteroids of the inner Solar System and Jupiter: The belt is located between the orbits of Jupiter and Mars.

Sun   Jupiter trojans   Orbits of planets

Asteroid belt   Hilda asteroids (Hildas)   Near-Earth objects (selection)

Main Asteroid Belt 42 largest asteroids

  Amor asteroid belt

  Apollo asteroid belt

  Aten asteroid belt

See also: List of exceptional asteroids

As resource depletion on Earth becomes more of a concern, the idea of extracting valuable elements from asteroids and transporting them to Earth for profit, or using space-based resources to build solar-power satellites and space habitats, becomes more attractive. Hypothetically, water processed from ice could refuel orbiting propellant depots.

Although asteroids and Earth accreted from the same starting materials, Earth's relatively stronger gravity pulled all heavy siderophilic (iron-loving) elements into its core during its molten youth more than four billion years ago. This left the crust depleted of such valuable elements until a rain of asteroid impacts re-infused the depleted crust with metals like gold, cobalt, iron, manganese, molybdenum, nickel, osmium, palladium, platinum, rhenium, rhodium, ruthenium and tungsten (some flow from core to surface does occur, e.g. at the Bushveld Igneous Complex, a famously rich source of platinum-group metals). Today, these metals are mined from Earth's crust, and they are essential for economic and technological progress. Hence, the geologic history of Earth may very well set the stage for a future of asteroid mining.

In 2006, the Keck Observatory announced that the binary Jupiter trojan 617 Patroclus, and possibly large numbers of other Jupiter trojans, are likely extinct comets and consist largely of water ice. Similarly, Jupiter-family comets, and possibly near-Earth asteroids that are extinct comets, might also provide water. The process of in-situ resource utilization—using materials native to space for propellant, thermal management, tankage, radiation shielding, and other high-mass components of space infrastructure—could lead to radical reductions in its cost. Although whether these cost reductions could be achieved, and if achieved would offset the enormous infrastructure investment required, is unknown.

From the astrobiological perspective, asteroid prospecting could provide scientific data for the search for extraterrestrial intelligence (SETI). Some astrophysicists have suggested that if advanced extraterrestrial civilizations employed asteroid mining long ago, the hallmarks of these activities might be detectable.

An important factor to consider in target selection is orbital economics, in particular the change in velocity (Δv) and travel time to and from the target. More of the extracted native material must be expended as propellant in higher Δv trajectories, thus less returned as payload. Direct Hohmann trajectories are faster than Hohmann trajectories assisted by planetary and/or lunar flybys, which in turn are faster than those of the Interplanetary Transport Network, but the reduction in transfer time comes at the cost of increased Δv requirements.

Mission	Δv (km/s)
Earth surface to LEO	8.0
LEO to near-Earth asteroid	5.5
LEO to lunar surface	6.3
LEO to moons of Mars	8.0

The Easily Recoverable Object (ERO) subclass of Near-Earth asteroids are considered likely candidates for early mining activity. Their low Δv makes them suitable for use in extracting construction materials for near-Earth space-based facilities, greatly reducing the economic cost of transporting supplies into Earth orbit.

The table above shows a comparison of Δv requirements for various missions. In terms of propulsion energy requirements, a mission to a near-Earth asteroid compares favorably to alternative mining missions.

An example of a potential target for an early asteroid mining expedition is 4660 Nereus, expected to be mainly enstatite. This body has a very low Δv compared to lifting materials from the surface of the Moon. However, it would require a much longer round-trip to return the material.

Multiple types of asteroids have been identified but the three main types would include the C-type, S-type, and M-type asteroids:

• C-type asteroids have a high abundance of water which is not currently of use for mining, but could be used in an exploration effort beyond the asteroid. Mission costs could be reduced by using the available water from the asteroid. C-type asteroids also have high amounts of organic carbon, phosphorus, and other key ingredients for fertilizer which could be used to grow food.
• S-type asteroids carry little water but are more attractive because they contain numerous metals, including nickel, cobalt, and more valuable metals, such as gold, platinum, and rhodium. A small 10-meter S-type asteroid contains about 650,000 kg (1,433,000 lb) of metal with 50 kg (110 lb) in the form of rare metals like platinum and gold.
• M-type asteroids are rare but contain up to 10 times more metal than S-types.

A class of "easily retrievable objects" (EROs) was identified by a group of researchers in 2013. Twelve asteroids made up the initially identified group, all of which could be potentially mined with present-day rocket technology. Of 9,000 asteroids searched in the NEO database, these twelve could all be brought into an Earth-accessible orbit by changing their velocity by less than 500 meters per second (1,800 km/h; 1,100 mph). The dozen asteroids range in size from 2 to 20 meters (10 to 70 ft).

Mining considerations

There are four options for mining:

1. In-space manufacturing (ISM), which may be enabled by biomining.
2. Bring raw asteroidal material to Earth for use.
3. Process asteroidal material on-site to bring back only processed materials, and perhaps produce propellant for the return trip.
4. Transport the asteroid to a safe orbit around the Moon or Earth or to a space station. This can hypothetically allow for most materials to be used and not wasted.

Processing in situ for the purpose of extracting high-value minerals will reduce the energy requirements for transporting the materials, although the processing facilities must first be transported to the mining site. In situ mining will involve drilling boreholes and injecting hot fluid/gas and allow the useful material to react or melt with the solvent and extract the solute. Due to the weak gravitational fields of asteroids, any activities, like drilling, will cause large disturbances and form dust clouds. These might be confined by some dome or bubble barrier. Or else some means of rapidly dissipating any dust could be provided.

Mining operations require special equipment to handle the extraction and processing of ore in outer space. The machinery will need to be anchored to the body, but once in place, the ore can be moved about more readily due to the lack of gravity. However, no techniques for refining ore in zero gravity currently exist. Docking with an asteroid might be performed using a harpoon-like process, where a projectile would penetrate the surface to serve as an anchor; then an attached cable would be used to winch the vehicle to the surface, if the asteroid is both penetrable and rigid enough for a harpoon to be effective.

Due to the distance from Earth to an asteroid selected for mining, the round-trip time for communications will be several minutes or more, except during occasional close approaches to Earth by near-Earth asteroids. Thus any mining equipment will either need to be highly automated, or a human presence will be needed nearby. Humans would also be useful for troubleshooting problems and for maintaining the equipment. On the other hand, multi-minute communications delays have not prevented the success of robotic exploration of Mars, and automated systems would be much less expensive to build and deploy.

Economics

Currently, the quality of the ore and the consequent cost and mass of equipment required to extract it are unknown and can only be speculated on. Some economic analyses indicate that the cost of returning asteroidal materials to Earth far outweighs their market value, and that asteroid mining will not attract private investment at current commodity prices and space transportation costs. Other studies suggest large profit by using solar power. Potential markets for materials can be identified and profit generated if extraction cost is brought down. For example, the delivery of multiple tonnes of water to low Earth orbit for rocket fuel preparation for space tourism could generate significant profit if space tourism itself proves profitable.

In 1997, it was speculated that a relatively small metallic asteroid with a diameter of 1.6 km (1 mi) contains more than US$20 trillion worth of industrial and precious metals. A comparatively small M-type asteroid with a mean diameter of 1 km (0.62 mi) could contain more than two billion metric tons of iron–nickel ore, or two to three times the world production of 2004. The asteroid 16 Psyche is believed to contain 1.7×10¹⁹ kg of nickel–iron, which could supply the world production requirement for several million years. A small portion of the extracted material would also be precious metals.

Not all mined materials from asteroids would be cost-effective, especially for the potential return of economic amounts of material to Earth. For potential return to Earth, platinum is considered very rare in terrestrial geologic formations and therefore is potentially worth bringing some quantity for terrestrial use. Nickel, on the other hand, is quite abundant on Earth and being mined in many terrestrial locations, so the high cost of asteroid mining may not make it economically viable.

Although Planetary Resources indicated in 2012 that the platinum from a 30-meter-long (98 ft) asteroid could be worth US$25–50 billion, an economist remarked any outside source of precious metals could lower prices sufficiently to possibly doom the venture by rapidly increasing the available supply of such metals.

Development of an infrastructure for altering asteroid orbits could offer a large return on investment.

Scarcity

Main article: Natural resource § Classification

See also: Steady-state economy § Pushing some of the terrestrial limits into outer space

Scarcity is a fundamental economic problem of humans having seemingly unlimited wants in a world of limited resources. Since Earth's resources are finite, the relative abundance of asteroidal ore gives asteroid mining the potential to provide nearly unlimited resources, which could essentially eliminate scarcity for those materials.

The idea of exhausting resources is not new. In 1798, Thomas Malthus wrote, because resources are ultimately limited, the exponential growth in a population would result in falls in income per capita until poverty and starvation would result as a constricting factor on population. Malthus posited this 228 years ago, and no sign has yet emerged of the Malthus effect regarding raw materials.

• Proven reserves are deposits of mineral resources that are already discovered and known to be economically extractable under present or similar demand, price and other economic and technological conditions.
• Conditional reserves are discovered deposits that are not yet economically viable.
• Indicated reserves are less intensively measured deposits whose data is derived from surveys and geological projections. Hypothetical reserves and speculative resources make up this group of reserves.
• Inferred reserves are deposits that have been located but not yet exploited.

Continued development in asteroid mining techniques and technology may help to increase mineral discoveries. As the cost of extracting mineral resources, especially platinum group metals, on Earth rises, the cost of extracting the same resources from celestial bodies declines due to technological innovations around space exploration.

Asteroid tracking catalogs such as Asterank estimate about 700 known asteroids with a value exceeding US$100 trillion each.

Financial feasibility

Space ventures are high-risk, with long lead times and heavy capital investment, and that is no different for asteroid-mining projects. These types of ventures could be funded through private investment or through government investment. For a commercial venture, it can be profitable as long as the revenue earned is greater than total costs (costs for extraction and costs for marketing). The costs involving an asteroid-mining venture were estimated to be around US$100 billion in 1996.

There are six categories of cost considered for an asteroid mining venture:

1. Research and development costs
2. Exploration and prospecting costs
3. Construction and infrastructure development costs
4. Operational and engineering costs
5. Environmental costs
6. Time cost

Determining financial feasibility is best represented through net present value. One requirement needed for financial feasibility is a high return on investment estimating around 30%. Example calculation assumes for simplicity that the only valuable material on asteroids is platinum. On 16 August 2016, platinum was valued at $1157 per ounce or $37,000 per kilogram. At a price of $1,340, for a 10% return on investment, 173,400 kg (5,575,000 ozt) of platinum would have to be extracted for every 1,155,000 tons of asteroid ore. For a 50% return on investment 1,703,000 kg (54,750,000 ozt) of platinum would have to be extracted for every 11,350,000 tons of asteroid ore. This analysis assumes that doubling the supply of platinum to the market (5.13 million ounces in 2014) would have no effect on the price of platinum. An economics-based assessment would conclude increasing the supply of platinum without an obvious increase in demand will drive prices downward.

The financial feasibility of asteroid mining with regards to different technical parameters has been presented by Sonter and more recently by Hein et al. They have specifically explored the case where platinum is brought from space to Earth and estimate that economically viable asteroid mining for this specific case would be rather challenging.

Decreases in the price of space access matter. The start of operational use of the low-cost-per-kilogram-in-orbit Spacex Falcon Heavy launch vehicle in 2018 is projected by astronomer Martin Elvis to have increased the extent of economically minable near-Earth asteroids from hundreds to thousands. With the increased availability of several kilometers per second of delta-v that Falcon Heavy provides, it increases the number of NEAs accessible from 3 percent to around 45 percent.

Precedent for joint investment by multiple parties into a long-term venture to mine commodities may be found in the legal concept of a mining partnership, which exists in the state laws of multiple US states including California. In a mining partnership, "[Each] member of a mining partnership shares in the profits and losses thereof in the proportion which the interest or share he or she owns in the mine bears to the whole partnership capital or whole number of shares."

Mining the Asteroid Belt from Mars

See also: Category:Main-belt_asteroids, Amor asteroids, and Apollo asteroids

Since Mars is much closer to the asteroid belt than Earth is, it would take less Delta-v to get to the asteroid belt and return minerals to Mars. One hypothesis is that the origin of the Moons of Mars (Phobos and Deimos) are actually asteroid captures from the asteroid belt. 16 Psyche in the main belt could have over $10,000 Quadrillion United States dollar worth of minerals. NASA is planning a mission for 10 October 2023 for the Psyche orbiter to launch and get to the asteroid by August 2029 to study. 511 Davida could have $27 quadrillion worth of minerals and resources. Using the moon Phobos to launch spacecraft is energetically favorable and a useful location from which to dispatch missions to main belt asteroids. Mining the asteroid belt from Mars and its moons could help in the Colonization of Mars.

Phobos as a space elevator for Mars

Space elevator Phobos

Phobos is synchronously orbiting Mars, where the same face stays facing the planet at ~6,028 km above the Martian surface. A space elevator could extend from Phobos to Mars 6,000 km, about 28 kilometers from the surface, and just out of the atmosphere of Mars. A similar space elevator cable could extend out 6,000 km the opposite direction that would counterbalance Phobos. In total the space elevator would extend over 12,000 km which would be below Areostationary orbit of Mars (17,032 km). A rocket launch would be needed to get the rocket and cargo to the beginning of the space elevator 28 km above the surface. The surface of Mars is rotating at 0.25 km/s at the equator and the bottom of the space elevator would be rotating around Mars at 0.77 km/s, so only 0.52 km/s of Delta-v would be needed to get to the space elevator. Phobos orbits at 2.15 km/s and the outer most part of the space elevator would rotate around Mars at 3.52 km/s.

Regulation and safety

Space law involves a specific set of international treaties, along with national statutory laws. The system and framework for international and domestic laws have emerged in part through the United Nations Office for Outer Space Affairs. The rules, terms and agreements that space law authorities consider to be part of the active body of international space law are the five international space treaties and five UN declarations. Approximately 100 nations and institutions were involved in negotiations. The space treaties cover many major issues such as arms control, non-appropriation of space, freedom of exploration, liability for damages, safety and rescue of astronauts and spacecraft, prevention of harmful interference with space activities and the environment, notification and registration of space activities, and the settlement of disputes. In exchange for assurances from the space power, the nonspacefaring nations acquiesced to U.S. and Soviet proposals to treat outer space as a commons (res communis) territory which belonged to no one state.

Asteroid mining in particular is covered by both international treaties—for example, the Outer Space Treaty—and national statutory laws—for example, specific legislative acts in the United States and Luxembourg.

Varying degrees of criticism exist regarding international space law. Some critics accept the Outer Space Treaty, but reject the Moon Agreement. The Outer Space Treaty allows private property rights for outer space natural resources once removed from the surface, subsurface or subsoil of the Moon and other celestial bodies in outer space. Thus, international space law is capable of managing newly emerging space mining activities, private space transportation, commercial spaceports and commercial space stations, habitats and settlements. Space mining involving the extraction and removal of natural resources from their natural location is allowable under the Outer Space Treaty. Once removed, those natural resources can be reduced to possession, sold, traded and explored or used for scientific purposes. International space law allows space mining, specifically the extraction of natural resources. It is generally understood within the space law authorities that extracting space resources is allowable, even by private companies for profit. However, international space law prohibits property rights over territories and outer space land.

Astrophysicists Carl Sagan and Steven J. Ostro raised the concern altering the trajectories of asteroids near Earth might pose a collision hazard threat. They concluded that orbit engineering has both opportunities and dangers: if controls instituted on orbit-manipulation technology were too tight, future spacefaring could be hampered, but if they were too loose, human civilization would be at risk.

The Outer Space Treaty

Main article: Outer Space Treaty

Outer Space Treaty:

  Parties

  Signatories

  Non-parties

After ten years of negotiations between nearly 100 nations, the Outer Space Treaty opened for signature on 27 January 1966. It entered into force as the constitution for outer space on 10 October 1967. The Outer Space Treaty was well received; it was ratified by ninety-six nations and signed by an additional twenty-seven states. The outcome has been that the basic foundation of international space law consists of five (arguably four) international space treaties, along with various written resolutions and declarations. The main international treaty is the Outer Space Treaty of 1967; it is generally viewed as the "Constitution" for outer space. By ratifying the Outer Space Treaty of 1967, ninety-eight nations agreed that outer space would belong to the "province of mankind", that all nations would have the freedom to "use" and "explore" outer space, and that both these provisions must be done in a way to "benefit all mankind".

The province of mankind principle and the other key terms have not yet been specifically defined. Critics have complained that the Outer Space Treaty is vague. Yet, international space law has worked well and has served space commercial industries and interests for many decades. The taking away and extraction of Moon rocks, for example, has been treated as being legally permissible.

The framers of Outer Space Treaty initially focused on solidifying broad terms first, with the intent to create more specific legal provisions later (Griffin, 1981: 733–734). This is why the members of the COPUOS later expanded the Outer Space Treaty norms by articulating more specific understandings which are found in the "three supplemental agreements" – the Rescue and Return Agreement of 1968, the Liability Convention of 1973, and the Registration Convention of 1976.

Hobe (2007) explains that the Outer Space Treaty "explicitly and implicitly prohibits only the acquisition of territorial property rights" but extracting space resources is allowable. It is generally understood within the space law authorities that extracting space resources is allowable, even by private companies for profit. However, international space law prohibits property rights over territories and outer space land. Hobe further explains that there is no mention of "the question of the extraction of natural resources which means that such use is allowed under the Outer Space Treaty" (2007: 211). He also points out that there is an unsettled question regarding the division of benefits from outer space resources in accordance with Article, paragraph 1 of the Outer Space Treaty.

The Moon Agreement

Main article: Moon Agreement

Participation in the Moon Treaty

  Parties

  Signatories

  Non-parties

The Moon Agreement was signed on 18 December 1979, as part of the United Nations Charter and it entered into force in 1984 after a five state ratification consensus procedure, agreed upon by the members of the United Nations Committee on Peaceful Uses of Outer Space (COPUOS). As of September 2019, only 18 nations have signed or ratified the treaty. The other three outer space treaties experienced a high level of international cooperation in terms of signage and ratification, but the Moon Treaty went further than them, by defining the Common Heritage concept in more detail and by imposing specific obligations on the parties engaged in the exploration and/or exploitation of outer space. The Moon Treaty explicitly designates the Moon and its natural resources as part of the Common Heritage of Mankind.

The Article 11 establishes that lunar resources are "not subject to national appropriation by claim of sovereignty, by means of use or occupation, or by any other means". However, exploitation of resources is suggested to be allowed if it is "governed by an international regime" (Article 11.5), but the rules of such regime have not yet been established. S. Neil Hosenball, the NASA General Counsel and chief US negotiator for the Moon Treaty, cautioned in 2018 that negotiation of the rules of the international regime should be delayed until the feasibility of exploitation of lunar resources has been established.

The objection to the treaty by the spacefaring nations is held to be the requirement that extracted resources (and the technology used to that end) must be shared with other nations. The similar regime in the United Nations Convention on the Law of the Sea is believed to impede the development of such industries on the seabed.

The United States, the Russian Federation, and the People's Republic of China (PRC) have neither signed, acceded to, nor ratified the Moon Agreement.

Legal regimes of some countries

Luxembourg

In February 2016, the Government of Luxembourg said that it would attempt to "jump-start an industrial sector to mine asteroid resources in space" by, among other things, creating a "legal framework" and regulatory incentives for companies involved in the industry. By June 2016, it announced that it would "invest more than US$200 million in research, technology demonstration, and in the direct purchase of equity in companies relocating to Luxembourg". In 2017, it became the "first European country to pass a law conferring to companies the ownership of any resources they extract from space", and remained active in advancing space resource public policy in 2018.

In 2017, Japan, Portugal, and the UAE entered into cooperation agreements with Luxembourg for mining operations in celestial bodies.

In 2018, the Luxembourg Space Agency was created. It provides private companies and organizations working on asteroid mining with financial support.

United States

Some nations are beginning to promulgate legal regimes for extraterrestrial resource extraction. For example, the United States "SPACE Act of 2015"—facilitating private development of space resources consistent with US international treaty obligations—passed the US House of Representatives in July 2015. In November 2015 it passed the United States Senate. On 25 November U.S. President Barack Obama signed the H.R.2262 – U.S. Commercial Space Launch Competitiveness Act into law. The law recognizes the right of U.S. citizens to own space resources they obtain and encourages the commercial exploration and use of resources from asteroids. According to the article § 51303 of the law:

A United States citizen engaged in commercial recovery of an asteroid resource or a space resource under this chapter shall be entitled to any asteroid resource or space resource obtained, including to possess, own, transport, use, and sell the asteroid resource or space resource obtained in accordance with applicable law, including the international obligations of the United States

On 6 April 2020 U.S. President Donald Trump signed the Executive Order on Encouraging International Support for the Recovery and Use of Space Resources. According to the Order:

• Americans should have the right to engage in commercial exploration, recovery, and use of resources in outer space
• the US does not view space as a "global commons"
• the US opposes the Moon Agreement

Environmental impact

A positive impact of asteroid mining has been conjectured as being an enabler of transferring industrial activities into space, such as energy generation. A quantitative analysis of the potential environmental benefits of water and platinum mining in space has been developed, where potentially large benefits could materialize, depending on the ratio of material mined in space and mass launched into space.

Asteroid mining, or off-Earth Mining (OEM), is occasionally promoted as a sustainable alternative to terrestrial extraction, with the potential to reduce ecological degradation on Earth. Metals such as platinum and palladium, which are comparatively scarce on Earth but more abundant in some near-Earth asteroids (NEAs) such as 16 Psyche are likely to be primary targets for future resource return missions. However, growing academic and environmental scrutiny suggests this narrative may oversimplify the complex, and often negative, environmental implications of OEM.

Space debris

Mining on asteroids is expected to generate large amounts of dust due to the fine-grained nature of regolith on these bodies. This dust is not only abrasive, due to a high glass content, but can also be sticky, clinging to equipment and spacesuits. Previous missions, such as all 6 Apollo missions (11, 12, 14, 15, 16, and 17) reported serious issues with lunar dust (similar dust can occur on asteroids) interfering with mechanical systems, visibility, and even posing health risks to astronauts. Similar challenges are anticipated during asteroid mining, where dust may travel significant distances and impact nearby operations. Managing this risk will be crucial for the environmental and technical success of future OEM activities.

Asteroid mining has the potential to worsen the existing issue of space debris, particularly if large-scale operations are introduced without adequate regulation. These missions are likely to involve multiple spacecraft, automated mining systems, and transportation vehicles, all of which carry the risk of contributing additional debris to orbit. Fragments of rock, dust, or equipment failures during extraction or transit phases could increase congestion in already crowded orbital pathways. This would heighten the risk of in-orbit collisions, contributing to what is known as the Kessler syndrome, a scenario where debris collisions generate more debris, leading to a self-perpetuating cascade effect. Kessler's Syndrome poses serious risks to satellite functionality, potentially disrupting essential services and utilities and significantly impacting global stability. According to the European Space Agency over 36,000 objects larger than 10 cm are currently being tracked in Earth's orbit, and so if mitigation strategies are not put in place, asteroid mining could significantly impact the long-term safety and sustainability of space activities.

Contamination of celestial bodies

Although OEM will differ in many ways from operations on Earth, the risk of contamination from spills or accidents remains an important concern. On Earth, spills from mining and processing have caused long-term environmental damage that has often been difficult to reverse. It's crucial that similar risks are taken seriously in space, with strong safeguards and contingency plans in place from the outset.

Rare earth mining on Earth has severe health and environmental consequences, including radioactive contamination of waterways, increased rates of cancer in affected communities, arsenic poisoning, and long-term degradation of soil and water systems. While these impacts are terrestrial, the same extractive logic based on environmental sacrifice and regulatory avoidance, could be extended to off-Earth contexts. If left unregulated, OEM could lead to similar disregard for the integrity of planetary bodies, treating them as consequence-free zones for contamination.

Several asteroids are thought to be relatively untouched since the early formation of the solar system, making them valuable targets for scientific research. These bodies may contain important clues about the distribution of water, the presence of organic compounds, and the conditions under which planets formed.

Planetary protection is a set of international guidelines designed to prevent harmful contamination of celestial bodies. For example, although most asteroids are not expected to support life, the accidental introduction of Earth-based microbes or substances could still compromise their natural state. The Committee on Space Research (COSPAR) also outlines procedures to minimise biological contamination, but enforcement may become increasingly difficult as commercial missions expand into deep space.

Unsustainable mining techniques

Mining techniques, such as surface excavation, thermal extraction and electrostatic separation could permanently disturb their physical and chemical makeup, limiting future opportunities for scientific study.

I. Pneumatic excavation is considered one of the least sustainable techniques due to its high energy requirements and potential to generate hazardous debris in microgravity environments.

II. Thermal and chemical extraction can be extremely energy-intensive and may leave behind harmful by-products, raising concerns about long-term environmental impacts.

III. Electrostatic separation, while effective in theory, poses sustainability challenges in space due to its significant power demands and sensitivity to environmental conditions.

Landscape changes

The geology and geomorphology of celestial bodies offer important insights into the history of the Solar System and the formation of asteroids, moons and terrestrial planets. Changes to these features because of OEM could be detrimental to scientific research. Without flowing water, landscapes on bodies such as the Moon change very slowly, shaped mainly by meteorite impacts. This means that any anthropogenic changes could be effectively permanent or at least, long-term.

The scale of OEM proposals varies; some may involve extensive regolith excavation, potentially altering key geomorphological features, while others may have minimal impact. Effects on geological formations such as layers, hollows and caverns should be considered.

On Earth, mining often leads to temporary or permanent landscape changes, and sites suitable for OEM may also be targeted for future human settlement. Irreversible alterations could reduce the habitability of these areas. Therefore, OEM planning should consider how landscape changes might be minimised, reversed, or adapted to support post-mining uses.

Carbon emissions and atmospheric impact

Although asteroid mining takes place beyond Earth's atmosphere, it still carries significant environmental consequences here on Earth, particularly in relation to carbon emissions. The process relies heavily on regular rocket launches, which currently emit pollutants such as black carbon, water vapour, and nitrogen oxides into the stratosphere. These particles can disrupt atmospheric chemistry and contribute to ozone layer depletion and radiative forcing, both of which are linked to climate change. Unlike emissions released at lower altitudes, pollutants in the upper atmosphere remain for longer periods due to the lack of rain. As demand for space-based operations grows, including those related to asteroid mining, the environmental burden of launch emissions could become increasingly significant. Unregulated growth in the space sector may lead to measurable impacts on Earth's climate systems over time.

Demonstrating technological capacity

See also: List of minor planets and comets visited by spacecraft

Missions demonstrating technological capacity and capability are precursors enabling the complex solutions necessary for extra-terrestrial resource exploitation and mining.

Space mission firsts by country

Technological "stepping stones" comprise capabilities including flying by the object, orbiting the object, landing on the object, roving on the surface of the object, and returning a sample from an exterrestrial object. Here are the list of "first" successful missions by country:

Nation	Flyby	Orbit	Land	Rover	Return sample
Moon
China	Chang'e 1 (2007)	Chang'e 1 (2007)	Chang'e 3 (2013)	Chang'e 3 (2013)	Chang'e 5 (2020)
European Union		SMART-1 (2003)
India	Chandrayaan-1 (2008)	Chandrayaan-1 (2008)	Chandrayaan-3 (2023)	Pragyan (2023)
Japan	Hiten (1990)	Hiten (1992)	SLIM (2024)	LEV-1 (2024)
Soviet Union	Luna 1 (1959)	Luna 10 (1966)	Lunokhod 1 (1970)	Lunokhod 1 (1970)	Luna 16 (1970)
United States	Pioneer 4 (1959)	Lunar Orbiter 1 (1966)	Surveyor 1 (1966)	Apollo 15 (1971)	Apollo 11 (1969)
Planet (e.g. Mars, Venus, etc.)
China	Tianwen-1 (2021)	Tianwen-1 (2021)	Tianwen-1 (2021)	Zhurong (2021)
Soviet Union	Venera 1 (1961)	Mars 2 (1971)	Venera 7 (1970)
United States	Mariner 2 (1962)	Mariner 9 (1971)	Viking 1 (1976)	Sojourner (1997)
Minor planet, asteroid, comet
China	Chang'e 2 (2012)
European Union	ICE (1985)	Rosetta (2014)	Rosetta (2014)
Japan	Suisei (1986)	Hayabusa (2005)	Hayabusa (2005)		Hayabusa (2010)
Soviet Union	Vega 1 (1986)
United States	ICE (1985)	NEAR (1997)	NEAR (2001)		Stardust (2006)

Additional completed and ongoing missions

• Hayabusa2 (completed) – JAXA asteroid sample return mission (arrived at the target in 2018, returned sample in 2020)
• OSIRIS-REx (completed) – NASA asteroid sample return mission (launched on 8 September 2016, arrived at target 2020, returned sample on 24 September 2023)
• Tianwen-2 (ongoing) – ongoing CNSA asteroid sample return mission (will arrive at the target in 2026, will return sample in 2027)

Proposed Missions

Many missions have been initiated by both sovereign and commercial players to advance technologies necessary to support extra-planetary resource exploitation, including mining, as shown in the table below. For purposes of tracking technology development, this table includes missions with lunar, asteroid, planetary, and comet mission targets.

Nation	Organization	Inception Date	Mission	Purpose	Status
United States	SpaceDev	1997	Near Earth Asteroid Prospector	asteroid prospecting	Canceled
Russia	Roskomos	2009	Fobos-Grunt 2	sample return mission to Phobos	stranded in earth orbit (Nov 2011)
United States	NASA	2012-09	Robotic Asteroid Prospector	examine and evaluate the feasibility of asteroid mining in terms of means, methods, and systems.	canceled (Apr 2018)
United States	Kepler Energy and Space Engineering	2013-05	Cornucopia	automated mining to collect 40 tons of asteroid regolith and return to low Earth orbit by 2020.	status unknown
United States	NASA	2018	VIPER rover	prospect for lunar resources	canceled (Jul 2024)
United States	AstroForge	2022-05	Brokkr-1, Odin, and Vestri	develop technologies & spacecraft for prospecting, mining, and refining platinum from near-earth asteroids	TBD

Other precursor activities

Asteroid cataloging

To support the cataloging of potentially dangerous asteroids, NASA announced in September 2019 that a space-based infrared telescope will be developed and launched. NASA/JPL is developing the NEO Surveyor mission with budget from NASA's Planetary Defense Coordination Office, within the Planetary Science Division. Launch is planned for June 2028.

Private organizations including the B612 Foundation have conducted related research to help detect asteroids that could one day strike Earth, and find the technological means to divert their path to avoid such collisions. Plans have included a design and build a privately financed asteroid-finding space telescope, Sentinel in 2013. When private fundraising did not achieve goals, the program was canceled and the Foundation pursued alternate approaches using a constellation of much smaller spacecraft. In August 2023, the Asteroid Institute, a program of the B612 foundation, announced the availability of the Asteroid Discovery Analysis and Mapping (ADAM) platform to enable ready public access to asteroid orbit data and related resources.

In fiction

An astronaut mining an asteroid in the video game Space Engineers

See also: Asteroids in fiction

The first mention of asteroid mining in science fiction is regarded to be Garrett P. Serviss' story Edison's Conquest of Mars, published in the New York Evening Journal in 1898. Several science-fiction video games include asteroid mining.

Sentientism

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Sentientism

Sentientism (or sentiocentrism) is an ethical philosophy that places sentience at the center of moral concern. It holds that moral consideration extends to all sentient beings. Gradualist sentientism assigns moral consideration based on the degree of sentience.

Sentientists argue that assigning different levels of moral consideration based solely on species membership, rather than morally relevant attributes like sentience, constitutes a form of unjustified discrimination known as speciesism.

Etymology

The term sentientism was used by John Rodman in 1977 who referred to Peter Singer's philosophy as "a kind of zoöcentric sentientism". Andrew Linzey defined the term in 1980 to denote an attitude that arbitrarily favours sentients over non-sentients.

History

English utilitarian philosopher Jeremy Bentham (1748–1832), early proponent of sentientism

The 18th-century utilitarian philosopher Jeremy Bentham was among the first to argue for sentientism. He maintained that any individual who is capable of subjective experience should be considered a moral subject. Members of species who are able to experience pleasure and pain are thus included in the category. In his Introduction to the Principles of Morals and Legislation, Bentham made a comparison between slavery and sadism toward humans and non-human animals:

The French have already discovered that the blackness of the skin is no reason why a human being should be abandoned without redress to the caprice of a tormentor [see Louis XIV's Code Noir] ... What else is it that should trace the insuperable line? Is it the faculty of reason, or, perhaps, the faculty of discourse? But a full-grown horse or dog is beyond comparison a more rational, as well as a more conversable animal, than an infant of a day, or a week, or even a month, old. But suppose the case were otherwise, what would it avail? The question is not Can they reason? nor, Can they talk? but, Can they suffer?

— Jeremy Bentham, Introduction to the Principles of Morals and Legislation, (1823), 2nd edition, Chapter 17, footnote

The late 19th- and early 20th-century American philosopher J. Howard Moore, in Better-World Philosophy (1899), described every sentient being as existing in a constant state of struggle. He argued that what aids them in their struggle can be called good and what opposes them can be called bad. Moore believed that only sentient beings can make such moral judgements because they are the only parts of the universe which can experience pleasure and suffering. As a result, he argued that sentience and ethics are inseparable and therefore every sentient piece of the universe has an intrinsic ethical relationship to every other sentient part, but not the insentient parts. Moore used the term "zoocentricism" to describe the belief that universal consideration and care should be given to all sentient beings; he believed that this was too difficult for humans to comprehend in their current stage of development.

Other prominent philosophers discussing or defending sentientism include Joel Feinberg, Peter Singer, Tom Regan, and Mary Anne Warren.

Concept

Sentientism posits that sentience is the necessary and sufficient condition in order to belong to the moral community. Other organisms, therefore, aside from humans are morally important in their own right. According to the concept, there are organisms that have some subjective experience, which include self-awareness, rationality as well as the capacity to experience pain and suffering.

There are sources that consider sentientism as a modification of traditional ethic, which holds that moral concern must be extended to sentient animals.

Peter Singer provides the following justification of sentientism:

The capacity for suffering and enjoying things is a prerequisite for having interests at all, a condition that must be satisfied before we can speak of interests in any meaningful way. It would be nonsense to say that it was not in the interests of a stone to be kicked along the road by a child. A stone does not have interests because it cannot suffer. Nothing that we can do to it could possibly make any difference to its welfare. A mouse, on the other hand, does have an interest in not being tormented, because mice will suffer if they are treated in this way. If a being suffers, there can be no moral justification for refusing to take that suffering into consideration. No matter what the nature of the being, the principle of equality requires that the suffering be counted equally with the like suffering – in so far as rough comparisons can be made – of any other being. If a being is not capable of suffering, or of experiencing enjoyment or happiness, there is nothing to be taken into account. This is why the limit of sentience (...) is the only defensible boundary of concern for the interests of others.

— Peter Singer, Practical Ethics (2011), 3rd edition, Cambridge University Press, p. 50

Utilitarian philosophers such as Singer care about the well-being of sentient non-human animals as well as humans. They reject speciesism, defined by Singer as a "prejudice or attitude of bias in favour of the interests of members of one’s own species and against those of members of other species". Singer considers speciesism to be a form of arbitrary discrimination similar to racism or sexism.

Sentientists are opposed to human-centered ethics, but they may nevertheless identify as humanists, as humanism does not imply caring only for humans.

Gradualist sentientism proposes that the value of sentient beings is relative to their degree of sentience, which is assumed to increase with the cognitive, emotional and social complexity.

Criticism

John Rodman criticized the sentientist approach, commenting "the rest of nature is left in a state of thinghood, having no intrinsic worth, acquiring instrumental value only as resources for the well-being of an elite of sentient beings".

The sentientism of Peter Singer and others has been criticized for holding the view that only sentient creatures have moral standing because they have interests. A human corpse for example may deserve respect and proper treatment even though it lacks sentience and can no longer be harmed. The claim that only sentient beings have interests has also been questioned as a person in a coma is not sentient but is still being cared for. Philosopher Gregory Bassham has written that "many environmentalists today reject sentientism and claim instead that all living things, both plants and animals, have moral standing".

A biocentrist may argue that valuing lifeforms that have sentience more than other lifeforms is just as arbitrary as doing the same with any other trait.

Data mining

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Data_mining

Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

The term "data mining" is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support systems, including artificial intelligence (e.g., machine learning) and business intelligence. Often the more general terms (large scale) data analysis and analytics—or, when referring to actual methods, artificial intelligence and machine learning—are more appropriate.

The actual data mining task is the semi-automatic or automatic analysis of massive quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, although they do belong to the overall KDD process as additional steps.

The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large volume of data.

The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.

Etymology

In the 1960s, statisticians and economists used terms like data fishing or data dredging to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis. The term "data mining" was used in a similarly critical way by economist Michael Lovell in an article published in the Review of Economic Studies in 1983. Lovell indicates that the practice "masquerades under a variety of aliases, ranging from "experimentation" (positive) to "fishing" or "snooping" (negative).

The term data mining appeared around 1990 in the database community, with generally positive connotations. For a short time in 1980s, the phrase "database mining"™, was used, but since it was trademarked by HNC, a San Diego–based company, to pitch their Database Mining Workstation; researchers consequently turned to data mining. Other terms used include data archaeology, information harvesting, information discovery, knowledge extraction, etc. Gregory Piatetsky-Shapiro coined the term "knowledge discovery in databases" for the first workshop on the same topic (KDD-1989) and this term became more popular in the AI and machine learning communities. However, the term data mining became more popular in the business and press communities. Currently, the terms data mining and knowledge discovery are used interchangeably.

Background

The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology have dramatically increased data collection, storage, and manipulation ability. As data sets have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, specially in the field of machine learning, such as neural networks, cluster analysis, genetic algorithms (1950s), decision trees and decision rules (1960s), and support vector machines (1990s). Data mining is the process of applying these methods with the intention of uncovering hidden patterns. in large data sets. It bridges the gap from applied statistics and artificial intelligence (which usually provide the mathematical background) to database management by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever-larger data sets.

Process

The knowledge discovery in databases (KDD) process is commonly defined with the stages:

1. Selection
2. Pre-processing
3. Transformation
4. Data mining
5. Interpretation/evaluation.

It exists, however, in many variations on this theme, such as the Cross-Industry Standard Process for Data Mining (CRISP-DM) which defines six phases:

1. Business understanding
2. Data understanding
3. Data preparation
4. Modeling
5. Evaluation
6. Deployment

or a simplified process such as (1) Pre-processing, (2) Data Mining, and (3) Results Validation.

Polls conducted in 2002, 2004, 2007 and 2014 show that the CRISP-DM methodology is the leading methodology used by data miners.

The only other data mining standard named in these polls was SEMMA. However, 3–4 times as many people reported using CRISP-DM. Several teams of researchers have published reviews of data mining process models, and Azevedo and Santos conducted a comparison of CRISP-DM and SEMMA in 2008.

Pre-processing

Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before data mining. The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data.

Data mining

Data mining involves six common classes of tasks:

• Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation due to being out of standard range.
• Association rule learning (dependency modeling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
• Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
• Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".
• Regression – attempts to find a function that models the data with the least error that is, for estimating the relationships among data or datasets.
• Summarization – providing a more compact representation of the data set, including visualization and report generation.

Results validation

An example of data produced by data dredging through a bot operated by statistician Tyler Vigen, apparently showing a close link between the best word winning a spelling bee competition and the number of people in the United States killed by venomous spiders

Data mining can unintentionally be misused, producing results that appear to be significant but which do not actually predict future behavior and cannot be reproduced on a new sample of data, therefore bearing little use. This is sometimes caused by investigating too many hypotheses and not performing proper statistical hypothesis testing. A simple version of this problem in machine learning is known as overfitting, but the same problem can arise at different phases of the process and thus a train/test split—when applicable at all—may not be sufficient to prevent this from happening.

The final step of knowledge discovery from data is to verify that the patterns produced by the data mining algorithms occur in the wider data set. Not all patterns found by the algorithms are necessarily valid. It is common for data mining algorithms to find patterns in the training set which are not present in the general data set. This is called overfitting. To overcome this, the evaluation uses a test set of data on which the data mining algorithm was not trained. The learned patterns are applied to this test set, and the resulting output is compared to the desired output. For example, a data mining algorithm trying to distinguish "spam" from "legitimate" e-mails would be trained on a training set of sample e-mails. Once trained, the learned patterns would be applied to the test set of e-mails on which it had not been trained. The accuracy of the patterns can then be measured from how many e-mails they correctly classify. Several statistical methods may be used to evaluate the algorithm, such as ROC curves.

If the learned patterns do not meet the desired standards, it is necessary to re-evaluate and change the pre-processing and data mining steps. If the learned patterns do meet the desired standards, then the final step is to interpret the learned patterns and turn them into knowledge.

Research

The premier professional body in the field is the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining (SIGKDD). Since 1989, this ACM SIG has hosted an annual international conference and published its proceedings, and since 1999 it has published a biannual academic journal titled "SIGKDD Explorations".

Computer science conferences on data mining include:

• CIKM Conference – ACM Conference on Information and Knowledge Management
• European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
• KDD Conference – ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Data mining topics are also present in many data management/database conferences such as the ICDE Conference, SIGMOD Conference and International Conference on Very Large Data Bases.

Standards

There have been some efforts to define standards for the data mining process, for example, the 1999 European Cross-Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004 Java Data Mining standard (JDM 1.0). Development on successors to these processes (CRISP-DM 2.0 and JDM 2.0) was active in 2006 but has stalled since. JDM 2.0 was withdrawn without reaching a final draft.

For exchanging the extracted models—in particular for use in predictive analytics—the key standard is the Predictive Model Markup Language (PMML), which is an XML-based language developed by the Data Mining Group (DMG) and supported as exchange format by many data mining applications. As the name suggests, it only covers prediction models, a particular data mining task of high importance to business applications. However, extensions to cover (for example) subspace clustering have been proposed independently of the DMG.

Notable uses

Main article: Examples of data mining

See also: Category:Applied data mining

Data mining is used wherever there is digital data available. Notable examples of data mining can be found throughout business, medicine, science, finance, construction, and surveillance.

Privacy concerns and ethics

While the term "data mining" itself may have no ethical implications, it is often associated with the mining of information in relation to user behavior (ethical and otherwise).

The ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics. In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program or in ADVISE, has raised privacy concerns.

Data mining requires data preparation which uncovers information or patterns which compromise confidentiality and privacy obligations. A common way for this to occur is through data aggregation. Data aggregation involves combining data together (possibly from various sources) in a way that facilitates analysis (but that also might make identification of private, individual-level data deducible or otherwise apparent). The threat to an individual's privacy comes into play when the data, once compiled, cause the data miner, or anyone who has access to the newly compiled data set, to be able to identify specific individuals, especially when the data were originally anonymous.

Data may also be modified so as to become anonymous, so that individuals may not readily be identified. However, even "anonymized" data sets can potentially contain enough information to allow identification of individuals, as occurred when journalists were able to find several individuals based on a set of search histories that were inadvertently released by AOL.

The inadvertent revelation of personally identifiable information leading to the provider violates Fair Information Practices. This indiscretion can cause financial, emotional, or bodily harm to the indicated individual. In one instance of privacy violation, the patrons of Walgreens filed a lawsuit against the company in 2011 for selling prescription information to data mining companies who in turn provided the data to pharmaceutical companies.

Situation in Europe

Europe has rather strong privacy laws, and efforts are underway to further strengthen the rights of the consumers. However, the U.S.–E.U. Safe Harbor Principles, developed between 1998 and 2000, currently effectively expose European users to privacy exploitation by U.S. companies. As a consequence of Edward Snowden's global surveillance disclosure, there has been increased discussion to revoke this agreement, as in particular the data will be fully exposed to the National Security Agency, and attempts to reach an agreement with the United States have failed.

In the United Kingdom in particular there have been cases of corporations using data mining as a way to target certain groups of customers forcing them to pay unfairly high prices. These groups tend to be people of lower socio-economic status who are not savvy to the ways they can be exploited in digital market places.

Situation in the United States

In the United States, privacy concerns have been addressed by the US Congress via the passage of regulatory controls such as the Health Insurance Portability and Accountability Act (HIPAA). The HIPAA requires individuals to give their "informed consent" regarding information they provide and its intended present and future uses. According to an article in Biotech Business Week, "'[i]n practice, HIPAA may not offer any greater protection than the longstanding regulations in the research arena,' says the AAHC. More importantly, the rule's goal of protection through informed consent is approaching a level of incomprehensibility to average individuals." This underscores the necessity for data anonymity in data aggregation and mining practices.

U.S. information privacy legislation such as HIPAA and the Family Educational Rights and Privacy Act (FERPA) applies only to the specific areas that each such law addresses. The use of data mining by the majority of businesses in the U.S. is not controlled by any legislation.

Copyright law

Situation in Europe

European Union

Even if there is no copyright in a dataset, the European Union recognises a Database right, so data mining becomes subject to intellectual property owners' rights that are protected by the Database Directive. Under European copyright database laws, the mining of in-copyright works (such as by web mining) without the permission of the copyright owner is permitted under Articles 3 and 4 of the 2019 Directive on Copyright in the Digital Single Market. A specific TDM exception for scientific research is described in article 3, whereas a more general exception described in article 4 only applies if the copyright holder has not opted out.

The European Commission facilitated stakeholder discussion on text and data mining in 2013, under the title of Licences for Europe. The focus on the solution to this legal issue, such as licensing rather than limitations and exceptions, led to representatives of universities, researchers, libraries, civil society groups and open access publishers to leave the stakeholder dialogue in May 2013.

United Kingdom

On the recommendation of the Hargreaves review, this led to the UK government to amend its copyright law in 2014 to allow content mining as a limitation and exception. The UK was the second country in the world to do so after Japan, which introduced an exception in 2009 for data mining. However, due to the restriction of the Information Society Directive (2001), the UK exception only allows content mining for non-commercial purposes. UK copyright law also does not allow this provision to be overridden by contractual terms and conditions.

Switzerland

Since 2020, also Switzerland has been regulating data mining by allowing it in the research field under certain conditions laid down by art. 24d of the Swiss Copyright Act. This new article entered into force on 1 April 2020.

Situation in the United States

US copyright law, and in particular its provision for fair use, upholds the legality of content mining in America, and other fair use countries such as Israel, Taiwan and South Korea. As content mining is transformative, that is it does not supplant the original work, it is viewed as being lawful under fair use. For example, as part of the Google Book settlement the presiding judge on the case ruled that Google's digitization project of in-copyright books was lawful, in part because of the transformative uses that the digitization project displayed—one being text and data mining.

Software

See also: Category:Data mining and machine learning software

Free open-source data mining software and applications

The following applications are available under free/open-source licenses. Public access to application source code is also available.

• Carrot2: Text and search results clustering framework.
• Chemicalize.org: A chemical structure miner and web search engine.
• ELKI: A university research project with advanced cluster analysis and outlier detection methods written in the Java language.
• GATE: a natural language processing and language engineering tool.
• KNIME: The Konstanz Information Miner, a user-friendly and comprehensive data analytics framework.
• Massive Online Analysis (MOA): a real-time big data stream mining with concept drift tool in the Java programming language.
• MEPX: cross-platform tool for regression and classification problems based on a Genetic Programming variant.
• mlpack: a collection of ready-to-use machine learning algorithms written in the C++ language.
• NLTK (Natural Language Toolkit): A suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Python language.
• OpenNN: Open neural networks library.
• Orange: A component-based data mining and machine learning software suite written in the Python language.
• PSPP: Data mining and statistics software under the GNU Project similar to SPSS
• R: A programming language and software environment for statistical computing, data mining, and graphics. It is part of the GNU Project.
• scikit-learn: An open-source machine learning library for the Python programming language;
• Torch: An open-source deep learning library for the Lua programming language and scientific computing framework with wide support for machine learning algorithms (development of it moved mostly to the much more used Python-based PyTorch)
• UIMA: The UIMA (Unstructured Information Management Architecture) is a component framework for analyzing unstructured content such as text, audio and video – originally developed by IBM.
• Weka: A suite of machine learning software applications written in the Java programming language.

Proprietary data-mining software and applications

The following applications are available under proprietary licenses.

• Angoss KnowledgeSTUDIO: data mining tool
• LIONsolver: an integrated software application for data mining, business intelligence, and modeling that implements the Learning and Intelligent OptimizatioN (LION) approach.
• PolyAnalyst: data and text mining software by Megaputer Intelligence.
• Microsoft Analysis Services: data mining software provided by Microsoft.
• NetOwl: suite of multilingual text and entity analytics products that enable data mining.
• Oracle Data Mining: data mining software by Oracle Corporation.
• PSeven: platform for automation of engineering simulation and analysis, multidisciplinary optimization and data mining provided by DATADVANCE.
• Qlucore Omics Explorer: data mining software.
• RapidMiner: An environment for machine learning and data mining experiments.
• SAS Enterprise Miner: data mining software provided by the SAS Institute.
• SPSS Modeler: data mining software provided by IBM.
• STATISTICA Data Miner: data mining software provided by StatSoft.
• Tanagra: Visualization-oriented data mining software, also for teaching.
• Vertica: data mining software provided by Hewlett-Packard.
• Google Cloud Platform: automated custom ML models managed by Google.
• Amazon SageMaker: managed service provided by Amazon for creating & productionising custom ML models.

Autopoiesis

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Autopoiesis 

3D representation of a living cell during the process of mitosis, example of an autopoietic system

The term autopoiesis (from Greek αὐτo- (auto) 'self' and ποίησις (poiesis) 'creation, production'), one of several current theories of life, refers to a system capable of producing and maintaining itself by creating its own parts. The term was introduced in the 1972 publication Autopoiesis and Cognition: The Realization of the Living by Chilean biologists Humberto Maturana and Francisco Varela to define the self-maintaining chemistry of living cells.

The concept has since been applied to the fields of cognition, neurobiology, systems theory, architecture and sociology. Niklas Luhmann briefly introduced the concept of autopoiesis to organizational theory.

Overview

Maturana describes how he invented the word autopoiesis:

"... one day, while talking with a friend (José Bulnes) about an essay of his on Don Quixote de la Mancha, in which he analyzed Don Quixote's dilemma of whether to follow the path of arms (praxis, action) or the path of letters (poiesis, creation, production), and his eventual choice of the path of praxis deferring any attempt at poiesis, I understood for the first time the power of the word 'poiesis' and invented the word that we needed: autopoiesis. This was a word without a history, a word that could directly mean what takes place in the dynamics of the autonomy proper to living systems."

— Humberto Maturana, Autopoiesis and Cognition, Introduction, page xvii

Maturana and Varela explain that:

"An autopoietic machine is a machine organized (defined as a unity) as a network of processes of production (transformation and destruction) of components which: (i) through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produced them; and (ii) constitute it (the machine) as a concrete unity in space in which they (the components) exist by specifying the topological domain of its realization as such a network."

They describe the "space defined by an autopoietic system" as "self-contained", a space that "cannot be described by using dimensions that define another space. When we refer to our interactions with a concrete autopoietic system, however, we project this system on the space of our manipulations and make a description of this projection."

Meaning

Autopoiesis was originally presented as a system description that was said to define and explain the nature of living systems. A canonical example of an autopoietic system is the biological cell. The eukaryotic cell, for example, is made of various biochemical components such as nucleic acids and proteins, and is organized into bounded structures such as the cell nucleus, various organelles, a cell membrane and cytoskeleton. These structures, based on an internal flow of molecules and energy, produce the components which, in turn, continue to maintain the organized bounded structure that gives rise to these components.

An autopoietic system is to be contrasted with an allopoietic system, such as a car factory, which uses raw materials (components) to generate a car (an organized structure) which is something other than itself (the factory). However, if the system is extended from the factory to include components in the factory's "environment", such as supply chains, plant / equipment, workers, dealerships, customers, contracts, competitors, cars, spare parts, and so on, then as a total viable system it could be considered to be autopoietic.

Autopoiesis in biological systems can be viewed as a network of constraints that work to maintain themselves. This concept has been called organizational closure or constraint closure and is closely related to the study of autocatalytic chemical networks where constraints are reactions required to sustain life.

Though others have often used the term as a synonym for self-organization, Maturana himself stated he would "[n]ever use the notion of self-organization ... Operationally it is impossible. That is, if the organization of a thing changes, the thing changes". Moreover, an autopoietic system is autonomous and operationally closed, in the sense that there are sufficient processes within it to maintain the whole. Autopoietic systems are "structurally coupled" with their medium, embedded in a dynamic of changes that can be recalled as sensory-motor coupling. This continuous dynamic is considered as a rudimentary form of knowledge or cognition and can be observed throughout life-forms.

An application of the concept of autopoiesis to sociology can be found in Niklas Luhmann's Systems Theory, which was subsequently adapted by Bob Jessop in his studies of the capitalist state system. Marjatta Maula adapted the concept of autopoiesis in a business context. The theory of autopoiesis has also been applied in the context of legal systems by not only Niklas Luhmann, but also Gunther Teubner. Patrik Schumacher has applied the term to refer to the 'discursive self-referential making of architecture.'  Varela eventually further applied autopoesis to develop models of mind, brain, and behavior called non-representationalist, enactive, embodied cognitive neuroscience, culminating in neurophenomenology.

In the context of textual studies, Jerome McGann argues that texts are "autopoietic mechanisms operating as self-generating feedback systems that cannot be separated from those who manipulate and use them". Citing Maturana and Varela, he defines an autopoietic system as "a closed topological space that 'continuously generates and specifies its own organization through its operation as a system of production of its own components, and does this in an endless turnover of components'", concluding that "Autopoietic systems are thus distinguished from allopoietic systems, which are Cartesian and which 'have as the product of their functioning something different from themselves'". Coding and markup appear allopoietic", McGann argues, but are generative parts of the system they serve to maintain, and thus language and print or electronic technology are autopoietic systems.

The philosopher Slavoj Žižek, in his discussion of Hegel, argues:

"Hegel is – to use today's terms – the ultimate thinker of autopoiesis, of the process of the emergence of necessary features out of chaotic contingency, the thinker of contingency's gradual self-organisation, of the gradual rise of order out of chaos."^[18]

Relation to complexity

Autopoiesis can be defined as the ratio between the complexity of a system and the complexity of its environment.

This generalized view of autopoiesis considers systems as self-producing not in terms of their physical components, but in terms of its organization, which can be measured in terms of information and complexity. In other words, we can describe autopoietic systems as those producing more of their own complexity than the one produced by their environment.

— Carlos Gershenson, "Requisite Variety, Autopoiesis, and Self-organization"

Autopoiesis has been proposed as a potential mechanism of abiogenesis, by which molecules evolved into more complex cells that could support the development of life.

Comparison with other theories of life

Autopoiesis is just one of several current theories of life, including the chemoton of Tibor Gánti, the hypercycle of Manfred Eigen and Peter Schuster, the (M,R) systems of Robert Rosen, and the autocatalytic sets of Stuart Kauffman, similar to an earlier proposal by Freeman Dyson. All of these (including autopoiesis) found their original inspiration in Erwin Schrödinger's book What is Life? but at first they appear to have little in common with one another, largely because the authors did not communicate with one another, and none of them made any reference in their principal publications to any of the other theories. Nonetheless, there are more similarities than may be obvious at first sight, for example between Gánti and Rosen. Until recently there have been almost no attempts to compare the different theories and discuss them together.

Relation to cognition

An extensive discussion of the connection of autopoiesis to cognition is provided by Evan Thompson in his 2007 publication, Mind in Life. The basic notion of autopoiesis as involving constructive interaction with the environment is extended to include cognition. Initially, Maturana defined cognition as behavior of an organism "with relevance to the maintenance of itself". However, computer models that are self-maintaining but non-cognitive have been devised, so some additional restrictions are needed, and the suggestion is that the maintenance process, to be cognitive, involves readjustment of the internal workings of the system in some metabolic process. On this basis it is claimed that autopoiesis is a necessary but not a sufficient condition for cognition. Thompson wrote that this distinction may or may not be fruitful, but what matters is that living systems involve autopoiesis and (if it is necessary to add this point) cognition as well. It can be noted that this definition of 'cognition' is restricted, and does not necessarily entail any awareness or consciousness by the living system. With the publication of The Embodied Mind in 1991, Varela, Thompson and Rosch applied autopoesis to make non-representationalist, and enactive models of mind, brain and behavior, which further developed embodied cognitive neuroscience, later culminating in neurophenomenology.

Relation to consciousness

The connection of autopoiesis to cognition, or if necessary, of living systems to cognition, is an objective assessment ascertainable by observation of a living system.

One question that arises is about the connection between cognition seen in this manner and consciousness. The separation of cognition and consciousness recognizes that the organism may be unaware of the substratum where decisions are made. What is the connection between these realms? Thompson refers to this issue as the "explanatory gap", and one aspect of it is the hard problem of consciousness, how and why we have qualia.

A second question is whether autopoiesis can provide a bridge between these concepts. Thompson discusses this issue from the standpoint of enactivism. An autopoietic cell actively relates to its environment. Its sensory responses trigger motor behavior governed by autopoiesis, and this behavior (it is claimed) is a simplified version of a nervous system behavior. The further claim is that real-time interactions like this require attention, and an implication of attention is awareness.

Criticism

There are multiple criticisms of the use of the term in both its original context, as an attempt to define and explain the living, and its various expanded usages, such as applying it to self-organizing systems in general or social systems in particular. Critics have argued that the concept and its theory fail to define or explain living systems and that, because of the extreme language of self-referentiality it uses without any external reference, it is really an attempt to give substantiation to Maturana's radical constructivist or solipsistic epistemology, or what Danilo Zolo has called instead a "desolate theology". An example is the assertion by Maturana and Varela that "We do not see what we do not see and what we do not see does not exist".

According to Razeto-Barry, the influence of Autopoiesis and Cognition: The Realization of the Living in mainstream biology has proven to be limited. Razeto-Barry believes that autopoiesis is not commonly used as the criterion for life.

Zoologist and philosopher Donna Haraway also criticizes the usage of the term, arguing that "nothing makes itself; nothing is really autopoietic or self-organizing", and suggests the use of sympoiesis, meaning "making-with", instead.