|
The dividing line between this glossary and Algorithms
& data analysis is very fuzzy. In general this one focuses on unstructured data
(or a combination of structured and unstructured), while Algorithms
centers on structured data Finding guide to terms in these glossaries Informatics term
index Site
Map
Informatics includes Bioinformatics Clinical
informatics Drug
discovery informatics IT
infrastructure Ontologies & Taxonomies
are subsets of, and critical tools for Information management &
interpretation
Technologies Microarrays & protein chips
Sequencing
3D technologies: Visual
communications are pervasive in information technology and are a key enabler of
most new emerging media. In this context, the NRC Institute for Information
Technology (NRC-IIT) performs research, development and technology transfer
activities to enable access to 3D information of the real world. Research in the 3D Technologies program focuses on three main areas: Virtualizing Reality
and Visualization, Collaborative Virtual Environments, 3D
Data Mining and Management Institute for Information Technology, National
Research Council, Canada, 3D Technologies artificial intelligence: Algorithms
& data analysis Google = about 1,120,000 July 19,
2002; about 3, 040,000 Oct. 22, 2004
bias: One of the two components of
measurement error (the other one being variance). Bias is a systematic
error that causes the measurement to differ from the correct value. Since bias
is systematic, it affects all experiment replicas the same way.
bibliomining:
The combination of data mining, bibliometrics, statistics, and reporting tools
used to extract patterns of behavior- based artifacts from library systems.
Scott Nicholson, Bibliomining: Data Mining for Libraries, Syracuse Univ. US http://www.bibliomining.com/
collaborative filtering:
Tools that leverage user preferences, patterns, and purchasing behavior to customize organization and navigation systems. [Peter Morville "Software for Information Architects" Argus Center for Information Architecture, 2000]
http://argus-acia.com/strange_connections/current_article.html
Amazon's recommendations based on what other buyers of a specific title are
buying is a familiar example of collaborative filtering. Google = about 21,600
July 19, 2002; about 49,300 Oct. 22, 2004
collaborative
metadata:
A robust increase in both the amount and
quality of metadata is integral to realizing the Semantic Web. The research
reported on in this article addresses this topic of inquiry by investigating the
most effective means for harnessing resource authors' and metadata experts'
knowledge and skills for generating metadata. Jane Greenberg, W. Davenport
Robertson, Semantic web construction: An Inquiry of Authors' Views on
Collaborative Metadata Generation, International Conference DC 2002, Metadata
for e-Communities, Oct. 13- 17, 2003, Florence Italy http://www.bncf.net/dc2002/program/ft/paper5.pdf
Google = about 116
Apr. 24, 2003; about 377 Oct. 22, 2004
computational linguistics:
Computational Linguistics, or Natural Language Processing (NLP), is not a new field. As early as 1946, attempts have been undertaken to use
computers to process natural language. These attempts concentrated mainly on Machine Translation
... the limited performance of these systems made it clear that the underlying
theoretical difficulties of the task had been grossly underestimated, and in the following years and decades much effort was spent on basic
research in formal linguistics. Today, a number of Machine Translation systems are available commercially although there still is no system that
produces fully automatic high- quality translations (and probably there will not be for some time). Human intervention in the form of pre-
and/ or
post-editing is still required in all cases. Another application that has become
commercially viable in the last years is the analysis and synthesis of spoken language, i.e. speech
understanding and speech generation. ... An application that will become at least as important as those already mentioned is the creation, administration, and presentation of texts by
computer. Even reliable access to written texts is a major bottleneck in science and commerce. The amount of textual information is enormous
(and growing incessantly), and the traditional, word- based, information retrieval methods are getting increasingly insufficient as either precision
or recall is always low (i.e. you get either a large number of irrelevant documents together with the relevant ones, or else you fail to get a large
number of the relevant ones in the collection). Linguistically based retrieval methods, taking into account the meaning of sentences as encoded
in the syntactic structure of natural language, promise to be a way out of this quandary.
Computational Linguistics FAQ, http://www2.ling.su.se/DaLi/cl_faq/index.htm
Linguistics, natural language, and
computational linguistics Meta- Index, Stanford Univ. US
http://www-nlp.stanford.edu/links/linguistics.html
Google = about 97,100 July 19,
2002, about 283,000 Oct. 22, 2004
DAML DARPA Agent Markup Language:
The goal of the DAML effort is to develop a language and tools to facilitate the concept of the semantic web.
http://www.daml.org/
Related term: OIL
DAML + OIL http://www.w3.org/TR/daml+oil-walkthru/
data cleaning, data integration: Algorithms
& data analysis Google = "data cleaning"
about 12,200; about 22,500 July 3, 2003
"data integration" about 175,000 July 19,
2002; about 306, 000 July 3, 2003; about 817,000 Mar. 22, 2004; about 2,940,000
June 22, 2007
data
conversion: Originally
data conversion was primarily a matter of moving text and database files from
one medium to another, one hardware platform to another, one operating system
environment to another. But as text and database representations became more
sophisticated it became apparent that application interoperability was going to
be the overriding issue of concern. Company History, Data Conversion Lab http://www.dclab.com/company_history.asp
Glossary,
DCL Labs http://www.dclab.com/glossary.asp
30+ definitions
data
management methods: Algorithms & data analysis
has automated methods, methods in this glossary generally
combine human and automated methods.
data mapping:
Wikipedia http://en.wikipedia.org/wiki/Data_mapping
Google = about 26,700 Aug. 20, 2002;
about 55,000 July 26, 2004; about 208,000 Nov 27, 2006
data
quality: A vital consideration for
data analysis and interpretation. While people are still reeling from the
vast amount of data becoming available, they need to brace themselves to both
discard low quality data and handle much more at the same time.
Data quality glossary, Graham Rind, GRC Data Intelligence, http://www.dqglossary.com/
6,700 terms.
data visualization: The
classical definition of visualization is as follows: the formation of mental
visual images, the act or process of interpreting in visual terms or of putting
into visual form. A new definition is a tool or method for interpreting image
data fed into a computer and for generating images from complex
multi-dimensional data sets (1987). Definitions and
Rationale for Visualisation, D. Scott
Brown, SIGGRAPH, 1999 http://www.siggraph.org/education/materials/HyperVis/visgoals/visgoal2.htm
includes information on data visualization. Related term: information visualization;
Broader term: visualization
databases: Bioinformatics; Databases & software directory
deep web:
http://en.wikipedia.org/wiki/Deep_Web
Google = about 10,200 Aug. 17, 2002;
about 42,900 Oct. 22, 2004 Related term: invisible web
description logic:
Has
existed as a field for a few decades yet only somewhat recently has appeared to
transform from an area of academic interest to an area of broad interest. This
paper provides a brief historical perspective of description logic developments
that have impacted DL usability to include communities beyond universities and
research labs. Deborah L.
McGuinness. ``Description Logics Emerge from Ivory Towers''. Stanford
Knowledge Systems Laboratory Technical Report KSL-01-08 2001. In the Proceedings
of the International Workshop on Description Logics. Stanford, CA, August 2001.http://www.ksl.stanford.edu/people/dlm/papers/dls-emerge-abstract.html
The main effort of the research in knowledge
representation is providing theories and systems for expressing structured
knowledge and for accessing and reasoning with it in a principled way. Description
Logics are considered the most important knowledge representation formalism
unifying and giving a logical basis to the well known traditions of Frame- based
systems, Semantic Networks and KL- ONE-like languages, Object- Oriented
representations, Semantic data models, and Type systems. [Description Logic
Knowledge Representation] http://dl.kr.org/
disambiguate:
Make less ambiguous, clarify,
elucidate. Google = about 33,100 July 19,
2002; about 65,300 Oct. 22, 2004, about 340,000 Nov 18, 2009
domain expertise: Wikipedia
http://en.wikipedia.org/wiki/Domain_expert
http://en.wikipedia.org/wiki/Domain_knowledge
Google = about 25,500 Dec. 18, 2002;
about 68,500 Oct. 22, 2004; about 785,000 June 22, 2007; about 1, 120,000 Nov
18, 2009
DTDs
Document Type Definitions:
The National
Center for Biotechnology Information (NCBI) of the National
Library of Medicine (NLM) created the Journal Archiving and Interchange
Document Type Definition (DTD) with the intent of providing a common format in
which publishers and archives can exchange journal content. http://dtd.nlm.nih.gov/
Dublin Core Metadata Initiative:
An open forum engaged in the development of interoperable online
metadata standards that support a broad range of purposes and business models. The original workshop for the Initiative was held in Dublin, Ohio [OCLC] in 1995.
http://dublincore.org/
evolvability:
Tim Berners Lee defines http://www.w3.org/Talks/1998/0415-Evolvability/slide3-1.htm
Wikipedia http://en.wikipedia.org/wiki/Evolvability
Google = evolvability about 8,210
July 19, 2002; about 21,400 Oct. 22, 2004; about 51,000 Nov 18, 2009 See
also under interoperability
federated databases:
An integrated repository data from of multiple, possibly heterogeneous, data sources presented with consistent and
coherent semantics. They do not usually contain any summary data, and all of the data resides only at the data source (i.e. no local storage).
Lawrence Berkeley Lab "Advanced Computational
Structural Genomics" Glossary Related term: Information management
& interpretation semantic data integration
federated information systems.
Their main characteristic is that they
are constructed as an integrating layer over existing legacy applications and
databases. They can be broadly classified in three dimensions: the degree of
autonomy they allow in integrated components, the degree of heterogeneity
between components they can cope with, and whether or not they support
distribution. Whereas the communication and interoperation problem has come into
a stage of applicable solutions over the past decade, semantic data integration
has not become similarly clear. Susanne Busse et. al "Federated
Information Systems: Concepts, Terminology and Architecture"
Computergestützte Informations Systeme CIS, Berlin, Germany 1999 http://citeseer.ist.psu.edu/busse99federated.html
fractal nature of the web: http://www.w3.org/DesignIssues/Fractal.html
Tim Berners- Lee, Commentary on architecture, Fractal nature of the web, first
draft
Society
has to be fractal - people want to be involved on a lot of different levels. The
need for things that are local and special will create enclaves. And those will
give us the diversity of ideas we need to survive. Tim Berners Lee, in "The
father of the web", Evan Schwartz, Wired Mar. 1997 http://www.wired.com/wired/archive/5.03/ff_father_pr.html
granularity:
Wikipedia http://en.wikipedia.org/wiki/Granularity
<jargon, parallel> The size of the units of code under consideration in some
context The term generally refers to the level of detail at which code is considered, e.g. "You can specify the granularity for this profiling tool". The most common computing use is in parallelism where "fine grain parallelism" means individual tasks are relatively small in terms of code size and execution time, "coarse grain" is the opposite. You talk about the "granularity" of the parallelism. The smaller the granularity, the greater the potential for parallelism and hence speed- up but the greater the overheads of synchronisation and communication.
FOLDOC 1997 http://www.swif.uniba.it/lei/foldop/foldoc.cgi?granularity
The extent to which a system contains separate components (like granules). The more components in a system - or the greater the granularity - the more flexible it is. [Webopedia]
http://www.webopedia.com/TERM/g/granularity.html
Level of detail seems to be the essence of granularity.
Google = about 250,000 July 19, 2002;
about 454,000 Oct. 22, 2004; about 2,170,000 Nov 18, 2009
informatics:
A field of study that focuses on the use of technology for improving access
to and utilization of information. AHIMA e-HIMTM Work Group on
Computer-Assisted Coding. "Delving into Computer-assisted Coding. Appendix
G: Glossary of Terms" Journal of AHIMA 75, no.10 (Nov-Dec 2004): web
extra. http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_025042.hcsp?dDocName=bok1_025042
Narrower terms: bioinformatics;
cheminformatics;
Computers &
computing clinical
informatics, molecular informatics, Biomaterials
matinformatics research
informatics; Drug
discovery & development life sciences informatics, Intellectual
property & legal; patinformatics; Molecular
imaging image informatics; pharmacoinformatics,
pharmainformatics Proteomics
protein informatics
information -- how
much? How Much Information 2003,
School of Information Science and Systems, Univ. of California, Berkeley, 2003 http://www.sims.berkeley.edu/research/projects/how-much-info-2003/index.htm
information architecture: "Involves the design of organization, labeling, navigation, and searching systems to help people find and manage information more successfully."
Lou Rosenfeld, Peter Morville interview quoted in Mark Hurst "About
Information Architecture, Apr. 3, 2000] http://www.goodexperience.com/columns/040300infoarch.html
Google = about 132,000 July 19, 2002;
about 258,000 July 3, 2003; about 622,000 Oct. 22, 2004; about 5,760,000 Nov 18,
2009
Information architecture glossary,
Kat Hagedorn, Argus Associates, 2000, 60 + definitions http://argus-acia.com/white_papers/iaglossary.html
information ecology: Wikipedia
http://en.wikipedia.org/wiki/Information_ecology
The Information Ecology group (formerly
the Physical Language Workshop) explores ways to connect our physical
environments with information resources. Through the use of low-cost, ubiquitous
technologies, we are creating seamless and pervasive ways to interact with our
information—and with each other. We focus on projects that harness the ecology
of consumer electronics and sensor devices—present and future—to more
smoothly mediate the boundaries between the physical and information worlds we
inhabit. MIT Media Lab Design
Ecology/Information Ecology 2009 http://eco.media.mit.edu/
Google =
about 11,100 Oct. 22, 2004; about 70,200 Nov 18, 2009
information extraction:
Automated ways of extracting unstructured or partially structured information from
machine readable files. Compare with information retrieval. Google = about 43,100 July 19, 2002;
about 590,000 Nov 18, 2009 Related
terms: natural language
processing, term extraction
information harvesting: See under
Knowledge Discovery in Databases KDD Google = about 871 July 19, 2002;
about 1,230 July 3, 2003; about 1,730 Oct. 22, 2004; about 1,140,000 June 22,
2007
information
integration: Our research group is developing
intelligent techniques to enable rapid and efficient information integration.
The focus of our research has been on the technologies required for constructing
distributed, integrated applications from online sources. This research
includes: Information
Extraction: Machine learning techniques for extracting information from
online sources; Source
Modeling: Constructing a semantic model of wrapped sources so that they can
be automatically integrated with other sources; Record
Linkage: Learning how to align records across sources; Data
Integration: Generating plans to automatically integrate data across
sources; Plan Execution:
Representing, defining, and efficiently executing integration plans in the Web
environment; Constraint-based
Integration Interactive constraint-based planning and integration for
the Web environment. Information Integration Research Group, Intelligent Systems
Division, Information Sciences Institute (ISI), University of Southern
California http://www.isi.edu/integration/
Google =
about 4,430,000 July 3, 2003; about 1,080,000 June 22, 2007; about 1, 160,000
Nov 18, 2009
information overload: Biomedicine is in the middle of revolutionary advances. Genome projects, microassay methods like DNA chips, advanced radiation sources for crystallography and other instrumentation, as well as new imaging methods, have exceeded all expectations, and in the process have generated a dramatic information overload that requires new resources for handling, analyzing and interpreting data. Delays in the exploitation of the discoveries will be costly in terms of health benefits for individuals and will adversely affect the economic edge of the country.
Opportunities in Molecular Biomedicine in the Era of Teraflop Computing: March 3 & 4, 1999, Rockville, MD, NIH Resource for Macromolecular Modeling and Bioinformatics Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-
Champaign
Many
of today's problems stem from information overload and there is a desperate need
for innovative software that can wade through the morass of information and
present visually what we know. The development of such tools will depend
critically on further interactions between the computer scientists and the
biologists so that the tools address the right questions, but are designed in a
flexible and computationally efficient manner. It is my hope that we will
see these solutions published in the biological or computational literature.
Richard J. Roberts, The early days of bioinformatics publishing, Bioinformatics
16 (1): 2-4, 2000
"Information overload" is not an overstatement these days. One of the biggest challenges is to deal with the tidal wave of data, filter out extraneous
noise and poor quality data, and assimilate and integrate information on a
previously unimagined scale Google = about 118,000
July 19, 2002; about 249,000 Oct. 22, 2004; about 1,480,000 Nov 18, 2009
Where's
my stuff? Ways to help with information overload, Mary Chitty, SLA
presentation June 10, 2002, Los Angeles CA
Wikipedia http://en.wikipedia.org/wiki/Information_overload
information
retrieval: Wikipedia http://en.wikipedia.org/wiki/Information_retrieval
information visualization: The direct
visualization of a representation of selected features or elements of complex
multi- dimensional data. Data that can be used to create a visualization
includes text, image data, sound, voice, video - and of course, all kinds of
numerical data. Our visual analysis systems also provide the tools to interact
with the data that has been visualized so that users can explore, discover and
learn. Users do not look at static images, but can subset the data, run queries,
do time sequence studies and create categories and correlations of data type.
Pacific
Northwest National Lab, About Visualization at PNNL, 1999 http://www.pnl.gov/infoviz/
Google = about 28,100 July 19, 2002;
about 94,200 Oct. 22, 2004; about 1,330, 000 Nov 18, 2009 Related term: data visualization; Broader
term: visualization
Information visualization resources on
the web, 2002 http://graphics.stanford.edu/courses/cs348c-96-fall/resources.html
Wikipedia http://en.wikipedia.org/wiki/Information_visualization
invisible web:
Those parts of the web which are inaccessible to current search engines. A straightforward example
was PubMed/ Medline
(until Google started indexing it.) You still can't usually access proprietary (fee- based) databases
such as Thomson Dialog or Lexis- Nexis. except directly. Until fairly recently PDF documents and PowerPoint slides were inaccessible to search engines.
Google = about 17,300 July 19, 2002;
about 278,000 Oct. 22, 2004; about 802,000 Nov 18, 2009 Related
terms: deep web, semantic web
Invisible or Deep Web: What it is,
How to find it, and Its inherent ambiguity http://www.lib.berkeley.edu/TeachingLib/Guides/Internet/InvisibleWeb.html
just in time information:
90,200 websites were found with this phrase by Google on
May 23, 2007. An increasing need as we are deluged with information and data -- and still need time to reflect, discuss and think about
what all these mean. Google = about
2,900 March 14, 2002, about 3,400 July 19, 2002; about 51,600 Feb. 21, 2006; about 88,400 May 7, 2007;
about 781,000 Nov 18, 2009
Just-In-Time Information Retrieval. Bradley J. Rhodes. Ph.D. Dissertation, MIT Media Lab, May 2000. Just in time retrieval agents Bradley J. Rhodes
http://www.bradleyrhodes.com/Papers/rhodes-phd-JITIR.pdf
Related terms: information overload, remembrance agents;
Bioinformatics modularity
knowledge integration: Wikipedia http://en.wikipedia.org/wiki/Knowledge_integration
Related terms: ontologies,
semantics
knowledge management:
Systematic approach to acquiring, analyzing, storing, and disseminating
information related to products, manufacturing processes, and components ICH Q10
http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm073517.pdf Related terms: ontologies, paraphrase problem, taxonomies
Google = about 826,000 July 19, 2002;
about 3,520,000 Oct. 22, 2004; about 11,000,000 Nov 18, 2009,
about 33,400,000 Feb 15, 2011
KM
Glossary, GOTCHA, Univ. of California
Berkeley, 1999 About 50 terms. http://sims.berkeley.edu/courses/is213/s99/Projects/P9/web_site/glossary.htm
Virtual Library: Knowledge Management, May 2000
http://www.brint.com/km/ Definition, articles, white papers, interviews, business and technology library, periodicals and publications, “out of box thinking”, “movers and shakers”, “think tank”, calendar of events, emerging topics.
Wikipedia
http://en.wikipedia.org/wiki/Knowledge_management
lexical
semantics:
http://en.wikipedia.org/wiki/Lexical_semantics
lexicon:
A
machine- readable dictionary that may contain a good deal of additional
information about the properties of the words, notated in a form that parsers
can utilize. Bob Futrelle, A brief introduction to NLP, BIONLP.org, , Computer Science,
Northeastern Univ., US, 2002 http://www.ccs.neu.edu/home/futrelle/bionlp/intro.html
A linguistics term (words and their definitions), an
artificial intelligence term. Sometimes a synonym for glossary or dictionary.
Google = about 768,000 July 19, 2002;
about 1,960,000 Oct. 22, 2004
linked
data:
Linked
Data is about using the Web to connect related data that wasn’t previously
linked, or using the Web to lower the barriers to linking data currently linked
using other methods. http://linkeddata.org/
Linked data glossary http://linkeddata.org/glossary
machine-readable: See under
metadata Google= about 303,000 July 19, 2002;
about 535,000 Oct. 22, 2004
machine-understandable: See under
metadata Google= about
3,730 July 19, 2002; about 8,950 July 14, 2004
markup languages: Computers
& computing Google = about 639,000 Aug. 9, 2002;
about 170,000 Oct. 22, 2004
mash-up
http://en.wikipedia.org/wiki/Mashup_(web_application_hybrid)
Google
= about 22,100,000 Oct. 27, 2006; about 20,400,000 Nov 18, 2009
Medbiquitous
Consortium: Technology standards
based on XML and web services. http://www.medbiq.org/index.html
metadata:
Could elevate the status of the web from machine- readable to something we might call machine- understandable. Metadata is "data about data" or specifically in our current context "data describing web resources." The distinction between "data" and "metadata" is not an absolute one; it is a distinction created primarily by a particular application ("one application's metadata is another application's data"). [W3C, "Introduction to RDF Metadata" 1997]
http://www.w3.org/TR/NOTE-rdf-simple-intro
Metadata is machine understandable
information for the web. The W3C
Metadata Activity addressed the combined needs of several groups for a
common framework to express assertions about information on the Web, and was
superceded by the W3C Semantic Web Activity.
[W3C, Metadata and Resource Description, W3C Technology and Society Domain,
2001]http://www.w3.org/Metadata/
Google = about 1,640,000 July 19, 2002;
about 4,850,000 Oct. 22, 2004; about 25,600,000 May 9, 2005; about
62,700,000 May 7, 2007 Narrower
terms: Dublin Core Metadata Initiative, faceted metadata Related terms: interoperability, RDF, semantic web
organizational
informatics: A field which studies the development and
use of computerized information systems and communication systems in
organizations. It includes social studies of their conception, design, effective
implementation within organizations, maintenance, use, organizational value,
conditions that foster risks of failures, and their effects for people and an
organization's clients. It is an intellectually rich and practical research area.
"Social Informatics" Indiana Univ, School of Library & Information
Science http://www.slis.indiana.edu/SI/oi1.html
Narrower
term: social informatics Related term:
knowledge management Google = about 153 July 19, 2002;
about 211 Oct. 22, 2004 pattern,
pattern language: Patterns, discussion FAQ http://g.oswego.edu/dl/pd-FAQ/pd-FAQ.html
precision:
Percentage of unrelated material excluded by a specific query or search statement. Related
terms: Genetic testing
analytical specificity, clinical specificity
Compare recall
query contraction: Needed when a
search engine retrieves thousands of citations. May consist of additional
(Boolean AND terms) or different (Boolean OR). Google = about 26 July 19, 2002;
about 130 Oct. 22, 2004
query expansion: Adding new and/ or
different terms to a search statement (particularly when a search engine or
database retrieve no hits). Often uses Boolean OR. Google = about 7,500 July 19, 2002;
about 21,300 Oct. 22, 2004 Related terms: ontologies, taxonomies
RDF
Resource Description Framework: Integrates a
variety of applications from library catalogs and world- wide directories to
syndication and aggregation of news, software, and content to personal
collections of music, photos, and events using XML as an interchange syntax. The
RDF specifications provide a lightweight ontology system to support the exchange
of knowledge on the Web. W3C Semantic Web Activity, accessed May 5,
2005 http://www.w3.org/RDF/
recall:
The percentage of applicable material retrieved by a specific query or search statement. Compare precision. Related term:
Genetic testing sensitivity
relevance:
Percentage of truly related material retrieved by a specific query or search statement. Related terms: precision
Genetic testing &
diagnostics analytical specificity, clinical specificity. Compare recall
remembrance agents:
A set of applications that watch over a user's shoulder and suggest information relevant to the current situation. While query- based memory aids help with direct
recall, remembrance agents are an augmented associative memory. [Bradley Rhodes, Remembrance Agents Because serendipity is too important to be left to chance...,
2001http://www.bradleyrhodes.com/Papers/rhodes-phd-JITIR.pdf
Google = about 673 July 19, 2002;
about 549 Oct. 22, 2004 Related
terms: collaborative filtering, just in time information
Rosetta: A systems- level design
language developed to address requirements specification for systems- on- chip
designs. Rosetta specifically addresses problems associated with heterogeneity
and complexity in current systems. Specifically, Rosetta allows designers to
develop and integrate specifications written in multiple semantic models to
provide language and semantic support for concurrent engineering of electronic
systems. Accellera Rosetta Standards Committee Homepage, EDA Industry
Working Groups, 2002
http://www.eda.org/slds-rosetta/
SOAP Simple Object Access Protocol:
A
lightweight protocol for exchange of information in a decentralized, distributed
environment. SOAP, W3C 1.1 http://www.w3.org/TR/SOAP/
semantic:
Ontologies &
taxonomies
social
informatics: Social Informatics (SI) refers to the body of research
and study that examines social aspects of computerization, including the roles
of information technology in social and organizational change, the uses of
information technologies in social contexts, and the ways that the social
organization of information technologies is influenced by social forces and
social practices. http://rkcsi.indiana.edu/
A serviceable working conception of "social informatics" is that it identifies a body of research that examines the social aspects of computerization. A more formal definition is "the interdisciplinary study of the design, uses and consequences of information technologies that takes into account their interaction with institutional and cultural contexts."
... Social informatics has been a subject of systematic analytical and critical research for the last 25 years. Unfortunately, social informatics studies are scattered in the journals of several different fields, including computer science, information systems, information science and some social sciences. Each of these fields uses somewhat different nomenclature. This diversity of communication outlets and specialized terminologies makes it hard for many
non- specialists (and even specialists) to locate important studies. Rob Kling,
What is social informatics and why does it matter? D-Lib 5(1): Jan. 1999 http://www.dlib.org/dlib/january99/kling/01kling.html
Red Rock Eater News Service, Phil Agre, UCLA,
US http://polaris.gseis.ucla.edu/pagre/rre.html
Social informatics HomePage http://www.slis.indiana.edu/SI/
soft
computing: Principal constituents of soft
computing (SC) are fuzzy logic (FL), neural network theory (NN) and
probabilistic reasoning (PR), with the latter subsuming belief networks,
evolutionary computing including DNA computing, chaos theory and parts of
learning theory.... Differs from conventional (hard) computing in that, unlike hard
computing, it is tolerant of imprecision, uncertainty and partial truth. In
effect, the role model for soft computing is the human mind. The guiding
principle of soft computing is: Exploit the tolerance for imprecision,
uncertainty and partial truth to achieve tractability, robustness and low
solution cost. Lotfi A. Zadeh, What is BISC? Berkeley Initiative on Soft
Computing, http://www-bisc.cs.berkeley.edu/bisc/bisc.memo.html#what_is_sc
subsumption:
http://ai.eecs.umich.edu/cogarch0/subsump/
Google = about 30,800 July 19, 2002;
about 80,500 Oct. 22, 2004; about 159,000 May 2, 2005
syntactic,
syntax:
Ontologies & taxonomies
term extraction:
Robert Futrelle, Northeastern Univ., 2001 http://www.ccs.neu.edu/home/futrelle/bionlp/psb2001/psb01-tutorial-bib1.htm
Google
- about 49,900 Nov 18, 2009
See related information extraction
term mining:
Term Mining in Biomedicine, Sophia Ananiadou - University of Manchester,
2007 http://talks.cam.ac.uk/talk/index/6769
Google = about 1,990
June 16, 2003; about 2,980 Oct. 22, 2004; about 40,100 June 22, 2007
text
categorisation: See Algorithms
& data analysis under support vector machines Google = about 902 "text
categorization" 9,220 July 19, 2002 about 27,100 Oct. 22, 2004
text mining: Usually
data mining technologies mine knowledge from data with well-formed schemes such
as relational tables. But, text data don't have such scheme, and information is
described freely in the documents. Therefore, we focus on Natural Language
Processing (NLP) technologies to extract such information. Using NLP
technologies, documents are transformed into a collection of concepts, described
using terms discovered in the text. Usually, "text
mining" is used to indicate a text search technique. But, we think of text
mining as having more functions. Text mining technologies extract more
information than just picking up keywords from texts: facts, author's
intentions, their expectations, and their claims. Tokyo Research Lab, IBM,
Text Mining http://www.trl.ibm.com/projects/textmining/index_e.htm Using data mining on unstructured data, such as the
biomedical literature. Related terms: natural language processing; Algorithms
& data analysis: support vector machines
Google = about 20,600 July 19, 2002
about 39,300 July 3, 2003; about 113,000 Oct. 22, 2004; about 1,110,000 June
22, 2007
Text Mining
Glossary, ComputerWorld, 2004 http://www.computerworld.com/s/article/93967/Sidebar_Text_Mining_Glossary
Includes Categorization, clustering, extraction, keyword search, natural
language processing, taxonomy, and visualization. unstructured data: Generally free text, natural language.
Related term: natural language
processing. Compare structured. Google = about 21,200 July 19, 2002
variance: One of the two components of
measurement error (the other one being bias). Variance results from
uncontrolled (or uncontrollable) variation that occurs in biological samples,
experimental procedures, and arrays themselves;
visualization:
A method of computing by which the enormous bandwidth and
processing power of the human visual (eye- brain) system becomes an integral
part of extracting knowledge from complex data. It utilizes graphics and
imaging techniques as well as knowledge of both data management and the human
visual system. Lloyd Trenish, Visualization for Deep Thunder, IBM
Research, 2002 http://www.research.ibm.com/weather/vis/w_vis.htm
Use of computer-
generated graphics to make the
information more accessible and interactive. Related term data mining Narrower terms:
data
visualization, information visualization; Algorithms
& data analysis dendogram, heat map, profile chart
visualisation:
As
the quantity of data produced by simulations grows, so does the difficulty of
extracting useful information. It is now clear that in many applications visual
methods are the only practical way of extracting information from the data.
Computer graphics and scientific visualisation techniques have become more
important in the last few years with the increased availability of computing
resource and of visualisation tools. Visualisation is becoming one of the
key tools for problem solving both in traditional areas such as visualisation of
complex flow and in new applications areas like the planning of surgical
operations using 3-D recontruction of anatomical sites using diagnostic images
or the development of highly-realistic aeroplane simulators for pilot
training. DIRECT Development of an Interdisciplinary Roundtable for
Emerging Computer Technologies, Edinburgh University, Scotland http://www.epcc.ed.ac.uk/DIRECT/vect.html
Definitions and
Rationale for Visualisation, D. Scott
Brown, SIGGRAPH, 1999 http://www.siggraph.org/education/materials/HyperVis/visgoals/visgoal2.htm
W3C World Wide Web Consortium: Develops
interoperable technologies (specifications, guidelines, software, and tools) to
lead the Web to its full potential. W3C is a forum for information, commerce,
communication, and collective understanding. http://www.w3.org/
web:
The genome community was an early adopter of the Web, finding in it a way to publish
its vast accumulation of data, and to express the rich interconnectedness of biological information. The Web is the home of primary data, of
genome maps, of expression data, of DNA and
protein sequences, of X-ray crystallographic structures, and of the genome project's huge outpouring of publications. ... However the Web is much more than a static repository of information. The Web is increasingly being used as a front end for sophisticated analytic software. Sequence similarity search engines, protein structural motif finders, exon identifiers, and even mapping programs have all been integrated into the Web. Java applets are adding rapidly to Web browsers' capabilities, enabling pages to be far more interactive than the original click- fetch- click interface.
Lincoln D. Stein "Introduction to Human Genome Computing via the World Wide Web", Cold Spring Harbor Lab,
1998 Related terms: fractal nature of the
web, weblike Narrower terms: semantic web, web portals, web
services
web service interoperability: Web services
technology has the promise to provide a new level of interoperability between
software applications. It should be no wonder then that there is a rush by
platform providers, software developers, and utility providers to enable their
software with SOAP, WSDL, and UDDI capabilities. http://www-106.ibm.com/developerworks/webservices/library/ws-inter.html
Google = "web service
interoperability" about 412 "web services interoperability"
about 9,620 July 19, 2002; about 283,000 Nov 17, 2006
web services: The goal of the Web Services Activity
is to develop a set of technologies in order to bring Web services to their full
potential. W3C "Web Services Activity 2002 http://www.w3.org/2002/ws/
Google = about 2,110,000 July 19, 2002;
about 122,000,000 Nov 17, 2006
Web services
glossary, W3C, http://www.w3.org/TR/ws-gloss/
webizing: "Webizing Existing
Systems" Tim Berners-Lee, last updated 2001 http://www.w3.org/DesignIssues/Webize
weblike: Tim Berners- Lee, Ralph
Swick, Semantic web Amsterdam, 2000 May 16 http://www.w3.org/2000/Talks/0516-sweb-tbl/slide3-1.html
Tim
Berners- Lee writes in his account of coming up with the idea of the web
Weaving the Web about "learning to think in a weblike way". I don't know that I can claim to approach this yet, but the more that I write and research this glossary on and for the web, the more insight I'm getting into what he might mean. Metaphors
like "shooting at a moving target" and like Wayne Gretzky
"skating to where the puck is going to be" are helpful images.
Google = about 3,020
July 19, 2002; about 5,510 Oct. 22, 2004; about 75,700 Nov 17, 2006 "web like" about 788,000,000 Nov 17, 2006
workflows:
A collaborative environment where scientists can safely publish their workflows
and experiment plans, share them with groups and find those of others.
Workflows, other digital objects and collections (called Packs) can now
be swapped, sorted and searched like photos and videos on the Web. ...
myExperiment makes it really easy for the next generation of scientists to
contribute to a pool of scientific workflows, build communities and form
relationships. It enables scientists to share, reuse and repurpose workflows and
reduce time-to-experiment, share expertise and avoid reinvention. myExperiment
http://www.myexperiment.org/
XML eXtensible Markup Language :
The universal format for structured documents and data on the Web.
W3C, "Extensible Markup Language (XML)" http://www.w3.org/XML/
Bibliography
Barnes, Ken et. al, Microsoft Lexicon or Microspeak made easier,
1995- 1998, 150 +
terms. http://www.cinepad.com/mslex.htm
FOLDOC Free On-line Dictionary of Computing, Denis Howe, 2007.
14,400+ terms. http://foldoc.org/
Barnes,
Ken et. al, Microsoft Lexicon or Microspeak made easier, 1995- 1998, 150 +
terms. http://www.cinepad.com/mslex.htm
Schneider, Tom and
Karen Lewis, Glossary for Molecular Information Theory and the Delila System, Lab of Computational and Experimental Biology, NCI
Frederick, US, 2004. 100+ definitions. http://www.lecb.ncifcrf.gov/~toms/glossary.html
W3C Glossary and
Dictionary http://www.w3.org/2003/glossary/
Webopedia http://www.webopedia.com/
whatis.com Information Technology encyclopedia. About 3,000 + definitions.
http://whatis.techtarget.com/
XML
Glossary http://www.softwareag.com/xml/about/glossary.htm
Alpha
glossary index
IUPAC definitions are reprinted with the permission of
the International Union of Pure and Applied Chemistry.
How
to look for other unfamiliar terms
|
|
