IEEE Workshop on
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Invited Talk: "Scientific Data Integration: From the Big Picture to some Gory Details"
Invited
Talk Slides
Online Workshop
Proceedings
Background:
Recent
advances in high performance
computing, high speed and high bandwidth communication, massive
storage, and software (e.g., web services) that can be remotely invoked
on the Internet present unprecedented opportunities in data-driven
knowledge acquisition in a broad range of applications in virtually all
areas of human endeavor including collaborative cross-disciplinary
discovery in e-science, bioinformatics, e-government, environmental
informatics, health informatics, security informatics, e-business,
education, social informatics, among others. Given the explosive growth
in the number and diversity of potentially useful information sources
in many domains, there is an urgent need for sound approaches to
integrative and collaborative analysis and interpretation of
distributed, autonomous (and hence, inevitably semantically
heterogeneous) data sources.
Machine learning offers some of the most cost-effective approaches to
automated or semi-automated knowledge acquisition (discovery of
features, correlations, and other complex relationships and hypotheses
that describe potentially interesting regularities from large data
sets) in many data rich application domains. However, the applicability
of current approaches to machine learning in emerging data rich
application domains presents several challenges in practice:
- Centralized access to data (assumed by most machine learning algorithms) is infeasible because of the large size and/or access restrictions imposed by the autonomous data sources. Hence, there is a need for knowledge acquisition systems that can perform the necessary analysis of data at the locations where the data and the computational resources are available and transmit the results of analysis (knowledge acquired from the data) to the locations where they are needed.
- Ontological commitments associated with a data source (that is, assumptions concerning the objects that exist in the world, the properties or attributes of the objects, the possible values of attributes, and their intended meaning) are determined by the intended use of the data repository (at design time). In addition, data sources that are created for use in one context often find use in other contexts or applications. Therefore, semantic differences among autonomous data sources are simply unavoidable. Because users often need to analyze data in different contexts from different perspectives, there is no single privileged ontology that can serve all users, or for that matter, even a single user, in every context. Effective use of multiple sources of data in a given context requires reconciliation of such semantic differences from the user’s point of view.
- Explicitly associating ontologies with data repositories results in partially specified data, i.e., data that are described in terms of attribute values at different levels of abstraction. For example, the program of study a student in a data source can be specified as Graduate Program (higher level of abstraction), while the program of study of a different student in the same data source (or even a different data source) can be specified as Doctoral Program (lower level of abstraction).
Topics of Interest:
The workshop seeks to bring together researchers in
relevant areas of artificial intelligence (machine learning, data
mining, knowledge representation, ontologies), information
systems (information integration, databases, semantic Web), distributed
computing (service-oriented computing) and selected application areas
(e.g., bioinformatics, security informatics, environmental informatics)
to address several questions such as:
- What are some of the research challenges presented by emerging data-rich application domains such as bioinformatics, health informatics, security informatics, social informatics, environmental informatics?
- How can we perform knowledge discovery from distributed data (assuming different types of data fragmentation, e.g., horizontal or vertical data fragmentation; different hypothesis classes, e.g., naïve Bayes, decision tree, support vector machine classifiers; different performance criteria, e.g., accuracy versus complexity versus reliability of the model generated, etc.)?
- How can we make semantically heterogeneous data sources self-describing (e.g., by explicitly associating ontologies with data sources and mappings between them) in order to help collaborative scientific discovery from autonomous information sources?
- How can we represent, manipulate, and reason with ontologies and mappings between ontologies?
- How can we learn ontologies from data (e.g., attribute value taxonomies)?
- How can we learn mappings between semantically heterogeneous data source schemas and between their associated ontologies?
- How can we perform knowledge discovery in the presence of ontologies (e.g., attribute value taxonomies) and partially specified data (data that are described at different levels of abstraction within an ontology)?
- How can we achieve online query relaxation when an initial query posed to the data sources fails (i.e., returns no tuples)? That is, how do we perform a query-driven mining of the individual sources that will result in knowledge that can be used for query relaxation?
Submission Requirements:
Postscript or PDF versions of papers, no more than 10 pages long (including figures, tables, and references) in the ICDM camera-ready format (IEEE 2-column format), should be submitted electronically to kadash-icdm05@cs.iastate.edu by October 9, 2005. Each paper will be rigorously refereed by at least 2 reviewers for technical soundness, originality, and clarity of presentation. Accepted papers will be included in informal workshop proceedings published by ICDM and distributed at the workshop.Important Dates:
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Paper submission |
October |
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Notification of acceptance |
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Camera ready papers |
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Workshop date |
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Organizers:
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Doina Caragea
226 Atanasoff Hall
Department of Computer Science
Iowa State University
Ames, IA 50011-1040 USA
dcaragea@cs.iastate.edu
Phone: 1-515-292-3704
Vasant Honavar
226 Atanasoff Hall
Department of Computer Science
Iowa State University
Ames, IA 50011-1040 USA
honavar@cs.iastate.edu
Phone: 1-515-294-4377
Ion Muslea
Language Weaver, Inc.
4640 Admiralty Way
Suite 1210
Marina del Rey, CA 90292
imuslea@languageweaver.com
Phone: 1-310-437-7300
Raghu Ramakrishnan
Department of Computer Sciences
University of Wisconsin-Madison
1210 West Dayton Street
Madison, WI 53706-1685 USA
raghu@cs.wisc.edu
Phone: 1-608-262-9759
Program Committee:
- Naoki Abe - IBM
- Liviu Badea – ICI, Romania
- Doina Caragea - Iowa State University
- Marie desJardins - University of Maryland, Baltimore County
- C. Lee Giles - Pennsylvania State University
- Vasant Honavar - Iowa State University
- Hillol Kargupta – University of Maryland, Baltimore County
- Sally McClean -- University of Ulster at Coleraine
- Bamshad Mobasher – DePaul University
- Ion Muslea - Language Weaver, Inc.
- C. David Page Jr. – University of Wisconsin, Madison
- Alexandrin Popescul - Ask Jeeves, Inc.
- Raghu Ramakrishnan - University of Wisconsin-Madison
- Steffen Staab -- University of Koblenz