Information modeling is concerned with the construction of computer-based
symbol structures which capture the meaning of information and organize
it in ways that make it understandable and useful to people. Given
that information is becoming an ubiquitous, abundant and precious
resource, its modeling is serving as a core technology for information
systems engineering. We present a brief history of information modeling
techniques in Computer Science and briefly survey such techniques
developed within Knowledge Representation (Artificial Intelligence),
Data Modeling (Databases), and Requirements Analysis (Software Engineering
and Information Systems). We then offer a characterization of information
modeling techniques which classifies them according to their ontologies,
i.e., the type of application for which they are intended, the set
of abstraction mechanisms (or, structuring principles) they support,
as well as the tools they provide for building, analyzing, and managing
application models. The final component of the paper uses the proposed
characterization to assess particular information modeling techniques
and draw conclusions about the advances that have been achieved
in the field.
- information bases notion - organization ought to reflect contents,
not history - locality principle (organize according to patterns
of use (frequently used items will be used again)) - information
model is collection of symbol structure types, operatons, and integrity
rules (e.g. relational model->table,tuple;add,union,join;no duplicate
keys) - physical (b-tree), logical (relation), conceptual (semantics)
info models - information model is application-specific instance
of data model. Is XML a data model? TT: data model = structure and
query data, so XML seems to fit. It structures data ? maybe not.
And Xquery/XSL are query capability. Schema is the information model.
- four types of ontology: static, dynamic, intentional, social.
Note that my evolution research is none of these. Rather, I apply
a dynamic ontology to any one of these models. In i* that implies
using a metamodel extension to handle that evolution. - abstraction:
generalization (isa), classification (instanceOf), aggregation (partOf)
(similiar to extensional definition?), contextualization, materialization
(similar to instanceOf), normalization, parameterization - three
tool support requirements: analysis, design, management -
%0 Journal Article
%1 mylopoulos98a
%A Mylopoulos, John
%C Oxford, UK, UK
%D 1998
%I Elsevier Science Ltd.
%J Information Systems
%K methodology model mf
%N 3-4
%P 127--155
%R 10.1016/S0306-4379(98)00005-2
%T Information modeling in the time of the revolution
%U http://dx.doi.org/10.1016/S0306-4379(98)00005-2
%V 23
%X Information modeling is concerned with the construction of computer-based
symbol structures which capture the meaning of information and organize
it in ways that make it understandable and useful to people. Given
that information is becoming an ubiquitous, abundant and precious
resource, its modeling is serving as a core technology for information
systems engineering. We present a brief history of information modeling
techniques in Computer Science and briefly survey such techniques
developed within Knowledge Representation (Artificial Intelligence),
Data Modeling (Databases), and Requirements Analysis (Software Engineering
and Information Systems). We then offer a characterization of information
modeling techniques which classifies them according to their ontologies,
i.e., the type of application for which they are intended, the set
of abstraction mechanisms (or, structuring principles) they support,
as well as the tools they provide for building, analyzing, and managing
application models. The final component of the paper uses the proposed
characterization to assess particular information modeling techniques
and draw conclusions about the advances that have been achieved
in the field.
@article{mylopoulos98a,
abstract = {Information modeling is concerned with the construction of computer-based
symbol structures which capture the meaning of information and organize
it in ways that make it understandable and useful to people. Given
that information is becoming an ubiquitous, abundant and precious
resource, its modeling is serving as a core technology for information
systems engineering. We present a brief history of information modeling
techniques in Computer Science and briefly survey such techniques
developed within Knowledge Representation (Artificial Intelligence),
Data Modeling (Databases), and Requirements Analysis (Software Engineering
and Information Systems). We then offer a characterization of information
modeling techniques which classifies them according to their ontologies,
i.e., the type of application for which they are intended, the set
of abstraction mechanisms (or, structuring principles) they support,
as well as the tools they provide for building, analyzing, and managing
application models. The final component of the paper uses the proposed
characterization to assess particular information modeling techniques
and draw conclusions about the advances that have been achieved
in the field.},
added-at = {2006-09-18T06:26:07.000+0200},
address = {Oxford, UK, UK},
author = {Mylopoulos, John},
biburl = {https://www.bibsonomy.org/bibtex/2fc301bff6880afb557cf7b8a7ec8aea8/neilernst},
citeulike-article-id = {577226},
comment = {- information bases notion - organization ought to reflect contents,
not history - locality principle (organize according to patterns
of use (frequently used items will be used again)) - information
model is collection of symbol structure types, operatons, and integrity
rules (e.g. relational model->table,tuple;add,union,join;no duplicate
keys) - physical (b-tree), logical (relation), conceptual (semantics)
info models - information model is application-specific instance
of data model. Is XML a data model? TT: data model = structure and
query data, so XML seems to fit. It structures data ? maybe not.
And Xquery/XSL are query capability. Schema is the information model.
- four types of ontology: static, dynamic, intentional, social.
Note that my evolution research is none of these. Rather, I apply
a dynamic ontology to any one of these models. In i* that implies
using a metamodel extension to handle that evolution. - abstraction:
generalization (isa), classification (instanceOf), aggregation (partOf)
(similiar to extensional definition?), contextualization, materialization
(similar to instanceOf), normalization, parameterization - three
tool support requirements: analysis, design, management -},
description = {Not previously uploaded},
doi = {10.1016/S0306-4379(98)00005-2},
howpublished = {Selected papers from CAISE 97},
interhash = {aaded9a5166cd07cc295d71c811f25d3},
intrahash = {fc301bff6880afb557cf7b8a7ec8aea8},
issn = {0306-4379},
journal = {Information Systems},
keywords = {methodology model mf},
number = {3-4},
pages = {127--155},
priority = {0},
publisher = {Elsevier Science Ltd.},
timestamp = {2006-09-18T06:26:07.000+0200},
title = {Information modeling in the time of the revolution},
url = {http://dx.doi.org/10.1016/S0306-4379(98)00005-2},
volume = 23,
year = 1998
}