Democratic Party mega-donor and energy investor Tom Steyer, Rep. Alexandria Ocasio-Cruz, and Sen. Bernie SandersWikipedia
Since
taking office earlier this month, Congressperson Alexandria
Ocasio-Cortez (D-New York) has captured the public imagination with her
proposal for a “Green New Deal” to create millions of good jobs while
accelerating the transition to clean energy.
Now, a debate has broken out among environmentalists and Democrats over whether a Green New Deal will include or exclude nuclear energy.
Ocasio-Cortez, 29, says “It’s not something that we’ve just ruled out” while her colleague and Green New Deal supporter, Chellie Pingree, 63, (D-Maine), says
“I think on nuclear energy, we all have a general resistance to it,”
and pointed to the 2011 accident in Fukushima, Japan, and nuclear waste.
Pingree is not alone. Progressive heavyweights like Sen. Bernie Sanders, 77, (I-VT) and Sen. Ed Markey, 72, (D-MA), are vehemently anti-nuclear and have supported closing nuclear plants in their home states.
They are backed by Democratic
mega-donors like Tom Steyer, 61, who made his fortune investing in coal,
natural gas, and renewables, and financed a failed effort to close a large nuclear plant in Arizona.
Indeed, from Illinois and New York to South Korea and Taiwan to France and Germany,
progressive politicians ostensibly committed to aggressive action on
climate change have sought to shut down nuclear plants, which would be
replaced almost entirely by fossil fuels.
What gives? Are nuclear plants really so dangerous? Or is something else going on?
As an anti-nuclear activist, I had been told that the accident had killed one million people.
But scientists reporting to the U.N. found
that a total of 28 people who responded to the accident died from acute
radiation syndrome within a few months, and concluded that around 160
people will eventually die from thyroid cancer over their lifetimes.
That’s it. There was no increase in
any other cancer rates — including among the firefighters and
“liquidators” who cleaned up the accident.
Living in a big city is more dangerous than cleaning up Chernobyl
As a result, nuclear is the safest way to make reliable electricity. In fact, the climate scientist James Hansen finds nuclear plants have actually saved1.8 million lives to date by preventing the pollution from the burning of fossil fuels.
What about Fukushima? According to
the World Health Organization, “even in locations within Fukushima
prefecture, the predicted risks remain low and no observable increases
in cancer above natural variation in baseline rates are anticipated.”
In other words, the second worst nuclear disaster in history will have no impact on cancer rates even in the area where the accident occurred.
Does this mean there’s no chance that
trace quantities of radiant particulate matter will harm anyone? Of
course not. It simply means that whatever harm is caused will pale
against all of the other things that cause cancer, from diet and air
pollution to genetics and old age.
Meanwhile, the stress, interrupted medical care, and suicide from the panicky and unnecessary evacuation of Fukushima killed 2,000 people.
That tragedy led independent radiation scientists to conclude that “the evacuation was a mistake...” says Philip Thomas of the University of Bristol, “We would have recommended that nobody be evacuated.”
What about the waste? I used to think
that nuclear waste was a green liquid that occasionally leaked, largely
because I got my information from “The Simpsons.”
In reality, the nuclear waste everyone worries about
are used fuel rods, which are neither liquid nor green, and never hurt
anyone. All of the used fuel from U.S. nuclear plants can fit on a
football field stacked just 50 foot high.
Solar panels produce 300 times more waste
for the amount of energy created than do nuclear plants, according to
simple calculations done by Mark Nelson and Jemin Desai of Environmental
Progress. (DJS -- and that waste is radioactive, just like the exhaust from coal burning plants.)
And, at the end of their life, solar panels are usually destined for landfills,often in poor nations, where workers and residents are at risk of exposure to the panels’ dangerously toxic heavy metals.
Wind and solar generate energy for
just a fraction of the year which is why, when nuclear plants are shut
down, they are mostly replaced with fossil fuels, something even some
anti-nuclear groups arestarting to acknowledge.
Why, then, are Democratic members of Congress like Rep. Pingree, who
support a Green New Deal, squeamish about nuclear energy? And why does
it seem like Baby Boomers are so much more anxious than Millennials like
Ocasio-Cortez?
The Climate Bomb
In the 1950s and 60s, Democratic politicians were mostly pro-nuclear energy. In fact, men like Sen. Al Gore Sr. and California Gov. Edmund Brown Sr. felt the government wasn’t doing enough to promote civilian nuclear energy.
Was it the 1979 accident at Three Mile
Island that turned liberal Baby Boomers against nuclear energy? That’s
the oft-repeated story — and it’s wrong. The New Left,
environmentalists, and Baby Boom liberals started turning against nuclear energy in the early 1960s.
Various reasons were given but the underlying fear was the association with nuclear weapons.
The invention of nuclear weapons
created deep psychological stress and trauma. For the first time our
history, Americans were threatened militarily by external enemies and
could do nothing about it, given the revolutionary nature of nuclear
weapons.
In the 50s and 60s, Hollywood produced
film after film about monsters, ants, and lizards made large from
nuclear weapons radiation that the film’s heroes — often scientists and
the military — would, in the end, vanquish and kill.
According to critic Susan Sontag, the films were a way for audiences to resolve, however temporarily, the psychological discomfort created by the bomb.
Meanwhile, the U.S. government
sponsored propaganda showing nuclear blasts and gave instructions on how
to survive nuclear war that frightened the public, especially Baby
Boomer children.
Until the mid-1960s, schools taught children to "duck and cover" in case of nuclear war.US Civil Defense
When Rep. Pingree was in kindergarten
in the early 1960s, many American schools were still requiring children
to practice “duck and cover” in case of nuclear war. And when she was
seven years old, the world came closer than it ever has to nuclear war,
during the 1962 Cuban Missile Scare.
When psychologists interviewed young
adults like Rep. Pingree in the 1970s, they found lasting memories of
nuclear terror, including in their nightmares.
“Nuclear weapons had become almost inseparable in the minds of many young people from overwhelming death itself,” notes Weart.
Left and Right resolved their anxieties in different ways.
American conservatives sought “nuclear superiority” over the Soviet
Union, and missile defense, so we could supposedly fight and “win” a
nuclear war. American liberals sought nuclear disarmament so we could
supposedly eliminate the risk of nuclear war.
Neither approach worked. It’s
impossible to “win” a full-scale nuclear war without suffering an
unacceptably large death toll. Meanwhile, it proved impossible to ban
nuclear weapons; warheads are too small and easy to hide. Even if
humankind got rid of them all, any two nations that went to war would
race to build a nuclear weapon and use it on the other.
Frustrated by their inability to ban
the bomb, progressives displaced their anxieties onto nuclear power
plants, and sought to halt their construction in the 1970s and 1980s.
In the 1970s, Amory Lovins and other environmentalists called for
a “soft energy path” focused on radical reductions in energy
consumption, renewable energies, and the decentralized use of fossil
fuels, particularly coal, in order to move away from nuclear energy.
Lovins in 1976 claimed that the “unilateral
adoption of a soft energy path by the United States can go a long way
to control nuclear proliferation-perhaps to eliminate it entirely... the
genie is not wholly out of the bottle yet — thousands of [nuclear]
reactors are planned for a few decades hence, tens of thousands
thereafter-and the cork sits unnoticed in our hands.”
The anti-nuclear movement was hugely successful. It managed to kill or get cancelled half of the nuclear plants that utilities had planned to build. In their place, utilities built coal plants.
The anxiety created by nuclear weapons
didn’t go away but died down with the fall of the Soviet Union in 1991.
What emerged in its place was fear of climate change — a problem for
which progressives already had an answer: radical reductions in energy
consumption, renewables and the decentralized use of fossil fuels,
particularly natural gas.
Over the next two decades, progressive
love for renewables grew as strong as progressive hatred of nuclear.
The goal for Baby Boomers traumatized by the Bomb was nothing less than
healing humankind’s relationship with Nature.
In the same way that grocery shoppers
believed products marketed as “natural” were healthier than other
products, progressive Democrats came to believe that products marketed
as “renewable” were better for the environment.
That turned out to be horrifyingly wrong. Solar farms require 450 times more land and nine times more materials (and thus mining and waste) as nuclear plants. Wind turbines may make a bat species go extinct and are a grave threat to migratory and raptor bird species.
But, backed by fossil-renewable energy investors like Tom Steyer, and Wall Street tycoons that stand to profit from complex carbon trading schemes, environmental groups like NRDC and EDF work with Democratic politicians like Bernie Sanders to a fossil-renewables fuel mix to replace nuclear plants.
Given who’s paying for those efforts, it’s not surprising that the spread of solar and wind is locking-in fossil fuels and raising electricity prices, which has made dealing with climate change harder, not easier.
The Courage to Lead?
Where does that leave Millennials like Rep. Ocasio-Cortez?
The good news is that she is smart. She makes an effort to get the science right. She evenwon a prize for her microbiology research.
And Ocasio-Cortez is courageous. She
stood up to the Democratic Party elite and joined a protest in the
office of House Speaker Nancy Pelosi, 78.
But will Ocasio-Cortez make an effort to learn the facts about nuclear and renewables?
And will she be courageous enough to stand up to Democratic Party elders like Bernie Sanders and mega-donors like Tom Steyer?
A data model is an abstract model that organizes elements of data
and standardizes how they relate to one another and to properties of
the real world entities. For instance, a data model may specify that the
data element representing a car be composed of a number of other
elements which, in turn, represent the color and size of the car and
define its owner.
The term data model is used in two distinct but closely
related senses. Sometimes it refers to an abstract formalization of the
objects and relationships found in a particular application domain, for
example the customers, products, and orders found in a manufacturing
organization. At other times it refers to a set of concepts used in
defining such formalizations: for example concepts such as entities,
attributes, relations, or tables. So the "data model" of a banking
application may be defined using the entity-relationship "data model".
This article uses the term in both senses.
Overview
of data modeling context: Data model is based on Data, Data
relationship, Data semantic and Data constraint. A data model provides
the details of information to be stored, and is of primary use when the final product is the generation of computer software code for an application or the preparation of a functional specification to aid a computer software make-or-buy decision. The figure is an example of the interaction between process and data models.
A data model explicitly determines the structure of data. Data models are specified in a data modeling notation, which is often graphical in form.
Managing large quantities of structured and unstructured data is a primary function of information systems.
Data models describe the structure, manipulation and integrity aspects
of the data stored in data management systems such as relational
databases. They typically do not describe unstructured data, such as word processing documents, email messages, pictures, digital audio, and video.
The role of data models
How data models deliver benefit
The main aim of data models is to support the development of information systems
by providing the definition and format of data. According to West and
Fowler (1999) "if this is done consistently across systems then
compatibility of data can be achieved. If the same data structures are
used to store and access data then different applications can share
data. The results of this are indicated above. However, systems and
interfaces often cost more than they should, to build, operate, and
maintain. They may also constrain the business rather than support it. A
major cause is that the quality of the data models implemented in
systems and interfaces is poor".
"Business rules, specific to how things are done in a particular
place, are often fixed in the structure of a data model. This means
that small changes in the way business is conducted lead to large
changes in computer systems and interfaces".
"Entity types are often not identified, or incorrectly identified.
This can lead to replication of data, data structure, and functionality,
together with the attendant costs of that duplication in development
and maintenance".
"Data models for different systems are arbitrarily different. The
result of this is that complex interfaces are required between systems
that share data. These interfaces can account for between 25-70% of the
cost of current systems".
"Data cannot be shared electronically with customers and suppliers,
because the structure and meaning of data has not been standardized. For
example, engineering design data and drawings for process plant are
still sometimes exchanged on paper".
The reason for these problems is a lack of standards that will ensure
that data models will both meet business needs and be consistent.
A data model explicitly determines the structure of data. Typical
applications of data models include database models, design of
information systems, and enabling exchange of data. Usually data models
are specified in a data modeling language.
Three perspectives
The ANSI/SPARC three level architecture.
This shows that a data model can be an external model (or view), a
conceptual model, or a physical model. This is not the only way to look
at data models, but it is a useful way, particularly when comparing
models.
A data model instance may be one of three kinds according to ANSI in 1975:
Conceptual data model :
describes the semantics of a domain, being the scope of the model. For
example, it may be a model of the interest area of an organization or
industry. This consists of entity classes, representing kinds of things
of significance in the domain, and relationship assertions about
associations between pairs of entity classes. A conceptual schema
specifies the kinds of facts or propositions that can be expressed using
the model. In that sense, it defines the allowed expressions in an
artificial 'language' with a scope that is limited by the scope of the
model.
Logical data model :
describes the semantics, as represented by a particular data
manipulation technology. This consists of descriptions of tables and
columns, object oriented classes, and XML tags, among other things.
Physical data model : describes the physical means by which data are stored. This is concerned with partitions, CPUs, tablespaces, and the like.
The significance of this approach, according to ANSI, is that it
allows the three perspectives to be relatively independent of each
other. Storage technology can change without affecting either the
logical or the conceptual model. The table/column structure can change
without (necessarily) affecting the conceptual model. In each case, of
course, the structures must remain consistent with the other model. The
table/column structure may be different from a direct translation of
the entity classes and attributes, but it must ultimately carry out the
objectives of the conceptual entity class structure. Early phases of
many software development projects emphasize the design of a conceptual data model. Such a design can be detailed into a logical data model. In later stages, this model may be translated into physical data model. However, it is also possible to implement a conceptual model directly.
History
One of the earliest pioneering works in modelling information systems was done by Young and Kent (1958), who argued for "a precise and abstract way of specifying the informational and time characteristics of a data processing problem". They wanted to create "a notation that should enable the analyst to organize the problem around any piece of hardware".
Their work was a first effort to create an abstract specification and
invariant basis for designing different alternative implementations
using different hardware components. A next step in IS modelling was
taken by CODASYL,
an IT industry consortium formed in 1959, who essentially aimed at the
same thing as Young and Kent: the development of "a proper structure for
machine independent problem definition language, at the system level of
data processing". This led to the development of a specific IS information algebra.
In the 1970s entity relationship modeling emerged as a new type of conceptual data modeling, originally proposed in 1976 by Peter Chen. Entity relationship models were being used in the first stage of information system design during the requirements analysis to describe information needs or the type of information that is to be stored in a database. This technique can describe any ontology, i.e., an overview and classification of concepts and their relationships, for a certain area of interest.
In the 1970s G.M. Nijssen developed "Natural Language Information Analysis Method" (NIAM) method, and developed this in the 1980s in cooperation with Terry Halpin into Object-Role Modeling
(ORM). However, it was Terry Halpin's 1989 PhD thesis that created the
formal foundation on which Object-Role Modeling is based.
Bill Kent, in his 1978 book Data and Reality,
compared a data model to a map of a territory, emphasizing that in the
real world, "highways are not painted red, rivers don't have county
lines running down the middle, and you can't see contour lines on a
mountain". In contrast to other researchers who tried to create models
that were mathematically clean and elegant, Kent emphasized the
essential messiness of the real world, and the task of the data modeller
to create order out of chaos without excessively distorting the truth.
In the 1980s, according to Jan L. Harrington (2000), "the development of the object-oriented
paradigm brought about a fundamental change in the way we look at data
and the procedures that operate on data. Traditionally, data and
procedures have been stored separately: the data and their relationship
in a database, the procedures in an application program. Object
orientation, however, combined an entity's procedure with its data."
Types of data models
Database model
A database model is a specification describing how a database is structured and used.
Several such models have been suggested. Common models include:
This may not strictly qualify as a data model. The flat (or table)
model consists of a single, two-dimensional array of data elements,
where all members of a given column are assumed to be similar values,
and all members of a row are assumed to be related to one another.
The hierarchical model is similar to the network model except that
links in the hierarchical model form a tree structure,while the network
model allows arbitrary graph.
This model organizes data using two fundamental constructs, called
records and sets. Records contain fields, and sets define one-to-many
relationships between records: one owner, many members.The network data
model is an abstraction of the design concept used in the implementation
of databases.
This is a database model based on first-order predicate logic. Its core
idea is to describe a database as a collection of predicates over a
finite set of predicate variables, describing constraints on the
possible values and combinations of values.The power of the relational
data model lies in its mathematical foundations and a simple user-level
paradigm.
A method of data modeling that has been defined as "attribute free",
and "fact based". The result is a verifiably correct system, from which
other common artifacts, such as ERD, UML, and semantic models may be
derived. Associations between data objects are described during the
database design procedure, such that normalization is an inevitable
result of the process.
The simplest style of data warehouse schema. The star schema
consists of a few "fact tables" (possibly only one, justifying the name)
referencing any number of "dimension tables". The star schema is
considered an important special case of the snowflake schema.
A data structure diagram (DSD) is a diagram and data model used to describe conceptual data models by providing graphical notations which document entities and their relationships, and the constraints that bind them. The basic graphic elements of DSDs are boxes, representing entities, and arrows, representing relationships. Data structure diagrams are most useful for documenting complex data entities.
Data structure diagrams are an extension of the entity-relationship model (ER model). In DSDs, attributes
are specified inside the entity boxes rather than outside of them,
while relationships are drawn as boxes composed of attributes which
specify the constraints that bind entities together. DSDs differ from
the ER model in that the ER model focuses on the relationships between
different entities, whereas DSDs focus on the relationships of the
elements within an entity and enable users to fully see the links and
relationships between each entity.
There are several styles for representing data structure diagrams, with the notable difference in the manner of defining cardinality. The choices are between arrow heads, inverted arrow heads (crow's feet), or numerical representation of the cardinality.
Example of an IDEF1X Entity relationship diagrams used to model IDEF1X itself
Entity-relationship model
An entity-relationship model (ERM), sometimes referred to as an
entity-relationship diagram (ERD), could be used to represent an
abstract conceptual data model (or semantic data model or physical data model) used in software engineering to represent structured data. There are several notations used for ERMs. Like DSD's, attributes
are specified inside the entity boxes rather than outside of them,
while relationships are drawn as lines, with the relationship
constraints as descriptions on the line. The E-R model, while robust,
can become visually cumbersome when representing entities with several
attributes.
There are several styles for representing data structure
diagrams, with the notable difference in the manner of defining
cardinality. The choices are between arrow heads, inverted arrow heads
(crow's feet), or numerical representation of the cardinality.
Geographic data model
A data model in Geographic information systems is a mathematical construct for representing geographic objects or surfaces as data. For example,
the vector data model represents geography as collections of points, lines, and polygons;
the raster data model represent geography as cell matrices that store numeric values;
and the Triangulated irregular network (TIN) data model represents geography as sets of contiguous, nonoverlapping triangles.
Groups relate to process of making a map
NGMDB data model applications
NGMDB databases linked together
Representing 3D map information
Generic data model
Generic data models are generalizations of conventional data models.
They define standardized general relation types, together with the kinds
of things that may be related by such a relation type. Generic data
models are developed as an approach to solve some shortcomings of
conventional data models. For example, different modelers usually
produce different conventional data models of the same domain. This can
lead to difficulty in bringing the models of different people together
and is an obstacle for data exchange and data integration. Invariably,
however, this difference is attributable to different levels of
abstraction in the models and differences in the kinds of facts that can
be instantiated (the semantic expression capabilities of the models).
The modelers need to communicate and agree on certain elements which are
to be rendered more concretely, in order to make the differences less
significant.
Semantic data model
Semantic data models
A semantic data model in software engineering is a technique to
define the meaning of data within the context of its interrelationships
with other data. A semantic data model is an abstraction which defines
how the stored symbols relate to the real world. A semantic data model is sometimes called a conceptual data model.
The logical data structure of a database management system (DBMS), whether hierarchical, network, or relational, cannot totally satisfy the requirements
for a conceptual definition of data because it is limited in scope and
biased toward the implementation strategy employed by the DBMS.
Therefore, the need to define data from a conceptual view
has led to the development of semantic data modeling techniques. That
is, techniques to define the meaning of data within the context of its
interrelationships with other data. As illustrated in the figure. The
real world, in terms of resources, ideas, events, etc., are symbolically
defined within physical data stores. A semantic data model is an
abstraction which defines how the stored symbols relate to the real
world. Thus, the model must be a true representation of the real world.
Data model topics
Data architecture
Data architecture is the design of data for use in defining the
target state and the subsequent planning needed to hit the target state.
It is usually one of several architecture domains that form the pillars of an enterprise architecture or solution architecture.
A data architecture describes the data structures used by a
business and/or its applications. There are descriptions of data in
storage and data in motion; descriptions of data stores, data groups and
data items; and mappings of those data artifacts to data qualities,
applications, locations, etc.
Essential to realizing the target state, Data architecture
describes how data is processed, stored, and utilized in a given system.
It provides criteria for data processing operations that make it
possible to design data flows and also control the flow of data in the
system.
Data modeling
The data modeling process
Data modeling in software engineering
is the process of creating a data model by applying formal data model
descriptions using data modeling techniques. Data modeling is a
technique for defining business requirements for a database. It is sometimes called database modeling because a data model is eventually implemented in a database.
The figure illustrates the way data models are developed and used today. A conceptual data model is developed based on the data requirements for the application that is being developed, perhaps in the context of an activity model.
The data model will normally consist of entity types, attributes,
relationships, integrity rules, and the definitions of those objects.
This is then used as the start point for interface or database design.
Data properties
Some important properties of data for which requirements need to be met are:
definition-related properties
relevance: the usefulness of the data in the context of your business.
clarity: the availability of a clear and shared definition for the data.
consistency: the compatibility of the same type of data from different sources.
Some important properties of data
content-related properties
timeliness: the availability of data at the time required and how up to date that data is.
accuracy: how close to the truth the data is.
properties related to both definition and content
completeness: how much of the required data is available.
accessibility: where, how, and to whom the data is available or not available (e.g. security).
cost: the cost incurred in obtaining the data, and making it available for use.
Data organization
Another kind of data model describes how to organize data using a database management system
or other data management technology. It describes, for example,
relational tables and columns or object-oriented classes and attributes.
Such a data model is sometimes referred to as the physical data model,
but in the original ANSI three schema architecture, it is called
"logical". In that architecture, the physical model describes the
storage media (cylinders, tracks, and tablespaces). Ideally, this model
is derived from the more conceptual data model described above. It may
differ, however, to account for constraints like processing capacity and
usage patterns.
While data analysis is a common term for data modeling, the activity actually has more in common with the ideas and methods of synthesis (inferring general concepts from particular instances) than it does with analysis (identifying component concepts from more general ones). {Presumably we call ourselves systems analysts because no one can say systems synthesists.}
Data modeling strives to bring the data structures of interest
together into a cohesive, inseparable, whole by eliminating unnecessary
data redundancies and by relating data structures with relationships.
A binary tree, a simple type of branching linked data structure
A data structure is a way of storing data in a computer so that it
can be used efficiently. It is an organization of mathematical and
logical concepts of data. Often a carefully chosen data structure will
allow the most efficientalgorithm to be used. The choice of the data structure often begins from the choice of an abstract data type.
A data model describes the structure of the data within a given
domain and, by implication, the underlying structure of that domain
itself. This means that a data model in fact specifies a dedicated grammar
for a dedicated artificial language for that domain. A data model
represents classes of entities (kinds of things) about which a company
wishes to hold information, the attributes of that information, and
relationships among those entities and (often implicit) relationships
among those attributes. The model describes the organization of the data
to some extent irrespective of how data might be represented in a
computer system.
The entities represented by a data model can be the tangible
entities, but models that include such concrete entity classes tend to
change over time. Robust data models often identify abstractions
of such entities. For example, a data model might include an entity
class called "Person", representing all the people who interact with an
organization. Such an abstract entity
class is typically more appropriate than ones called "Vendor" or
"Employee", which identify specific roles played by those people.
A data model instance is created by applying a data model theory.
This is typically done to solve some business enterprise requirement.
Business requirements are normally captured by a semantic logical data model.
This is transformed into a physical data model instance from which is
generated a physical database. For example, a data modeler may use a
data modeling tool to create an entity-relationship model of the corporate data repository of some business enterprise. This model is transformed into a relational model, which in turn generates a relational database.
Patterns
Patterns are common data modeling structures that occur in many data models.
Related models
Data flow diagram
Data Flow Diagram example
A data flow diagram (DFD) is a graphical representation of the "flow" of data through an information system. It differs from the flowchart as it shows the data flow instead of the control flow of the program. A data flow diagram can also be used for the visualization of data processing (structured design). Data flow diagrams were invented by Larry Constantine, the original developer of structured design, based on Martin and Estrin's "data flow graph" model of computation.
It is common practice to draw a context-level Data flow diagram first which shows the interaction between the system and outside entities. The DFD
is designed to show how a system is divided into smaller portions and
to highlight the flow of data between those parts. This context-level
Data flow diagram is then "exploded" to show more detail of the system
being modeled
An Information model is not a type of data model, but more or less an
alternative model. Within the field of software engineering both a data
model and an information model can be abstract, formal representations
of entity types that includes their properties, relationships and the
operations that can be performed on them. The entity types in the model
may be kinds of real-world objects, such as devices in a network, or
they may themselves be abstract, such as for the entities used in a
billing system. Typically, they are used to model a constrained domain
that can be described by a closed set of entity types, properties,
relationships and operations.
According to Lee (1999) an information model is a representation of concepts, relationships, constraints, rules, and operations to specify data semantics
for a chosen domain of discourse. It can provide sharable, stable, and
organized structure of information requirements for the domain context. More in general the term information model
is used for models of individual things, such as facilities, buildings,
process plants, etc. In those cases the concept is specialised to Facility Information Model, Building Information Model,
Plant Information Model, etc. Such an information model is an
integration of a model of the facility with the data and documents about
the facility.
An information model provides formalism to the description of a
problem domain without constraining how that description is mapped to an
actual implementation in software. There may be many mappings of the
information model. Such mappings are called data models, irrespective of
whether they are object models (e.g. using UML), entity relationship models or XML schemas.
An object model in computer science is a collection of objects or
classes through which a program can examine and manipulate some specific
parts of its world. In other words, the object-oriented interface to
some service or system. Such an interface is said to be the object model of the represented service or system. For example, the Document Object Model (DOM) is a collection of objects that represent a page in a web browser, used by script programs to examine and dynamically change the page. There is a Microsoft Excel object model for controlling Microsoft Excel from another program, and the ASCOM Telescope Driver is an object model for controlling an astronomical telescope.
Example of the application of Object-Role Modeling in a "Schema for Geologic Surface", Stephen M. Richard (1999)
Object-Role Modeling (ORM) is a method for conceptual modeling, and can be used as a tool for information and rules analysis.
Object-Role Modeling is a fact-oriented method for performing systems analysis
at the conceptual level. The quality of a database application depends
critically on its design. To help ensure correctness, clarity,
adaptability and productivity, information systems are best specified
first at the conceptual level, using concepts and language that people
can readily understand.
The conceptual design may include data, process and behavioral
perspectives, and the actual DBMS used to implement the design might be
based on one of many logical data models (relational, hierarchic,
network, object-oriented etc.).
Unified Modeling Language models
The Unified Modeling Language (UML) is a standardized general-purpose modeling language in the field of software engineering. It is a graphical language for visualizing, specifying, constructing, and documenting the artifacts of a software-intensive system. The Unified Modeling Language offers a standard way to write a system's blueprints, including:
