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Recent advances in sensor, high throughput data acquisition, and
digital information storage technologies, have made it possible to acquire
and store large volumes of data in digital form. Advances in computers and
communications, the Internet, and mobile computing have made it possible
for scientists and decision makers, at least in principle,
to access and utilize this data for data-driven knowledge acquisition and
decision making in the respective domains.
Examples of such domains include bioinformatics, monitoring and control
of complex, dynamic, distributed systems (e.g., communication networks,
power systems), among others. Despite the diversity of these domains, they
share several common characteristics:
Different data sources often provide different types of data (e.g., signals
from sensors, relational data, text, images, macromolecular (DNA and protein)
sequences, protein structures, simulations). This calls for sophisticated tools for selective and context-sensitive information extraction and information fusion. Such tools have to be able to bridge the gap in structure and semantics of the respective data and knowledge sources (e.g., using domain-specific ontologies).
Data Repositories of interest are physically distributed. Given the
large amounts of data that is being gathered and stored at these repositories,
and the fact that users are typically interested not in the raw data,
but in results of analysis of the data in a given context, it is desirable to
process the data in a distributed fashion wherever the
data is located and selectively transmit the results of analysis.
This calls for efficient and scalable
analysis tools (e.g. data mining algorithms and decision making
algorithms) with provable
performance guarantees in a distributed setting.
Data sources are often autonomous and the nature of access to data that
is available is often restricted due to privacy and security considerations. Thus, users have a limited view of the data (e.g., in the form of statistical
summaries or results of an agreed-upon set of operations). Thus there is a need
for systematic analysis of the information requirements of data analysis or
decision making algorithms in such environments.
Data sources are dynamic. Given the large amounts of data that need to be processed, this calls for efficient incremental or cumulative algorithms that can update the results of analysis (e.g., a hypthesis generated by a data mining algorithm).
The goals and consequently information needs of users as well as the data sources can change over time. This calls for development of information extraction and fusion algorithms and data mining algorithms that can dynamically adjust to shifting goals and changing constraints.
Translating the advances in data acquisition, storage, and communication technologies into fundamental gains in our ability to utilize the available data for
effective problem solving and decision making in respective domains (e.g., data-driven knowledge discovery in biology, decision support systems using disparate geospatial data sources) presents challenges in several areas of
artifiicial intelligence including machine learning, knowledge representation, and multi-agent systems. Development of effective solutions to this class of problems has to necessarily incorporate recent advances in machine learning, knowledge representation, databases, distributed computing, and related areas.
Against this background, the workshop
seeks to bring together researchers in relevant areas of artificial intelligence and computer science to discuss and exchange recent fundamental as well as applied, theoretical as well as experimental research problems and results on
a number of topics including:
Learning from Distributed Data Sources (types of data fragmentation, alternative formulations of distributed learning problem, information requirements of distributed learning, distributed learning algorithms, performance measures, efficiency and scalability issues).
Learning from Dynamic Data Sources (alternative formulations of the incremental and cumulative learning problems, information requirements of incremental learning, incremental learning algorithms, performance measures, efficiency and scalability issues).
Customizable and Context-Sensitive Information Extraction and Fusion from Distributed, Heterogeneous Data Sources (traditional database techniques for data integration (e.g., views), wrapper and mediator based techniques for handling unstructured and semistructured data, automated generation of domain specific information extraction and information fusion operators, ontologies for information integration).
Architectures and Systems (software agents, multi-agent systems, collaborative learning, collaborative decision-making).
Data and Knowledge Visualization and Decision-Making in Distributed Environments
Applications in internet-based information systems, geo-spatial information systems, communication systems, power grid, information assurance, scientific discovery (e.g., in bioinformatics).
The workshop is of interest to researchers and practitioners in a number of areas of artificial intelligence including machine learning and data mining, information extraction and information fusion, software agents and multi-agent systems
as well as those working related problems in databases and distributed
computing. Current research efforts in Knowledge Discovery from Heterogeneous, Distributed, Dynamic, Autonomous Data and Knowledge Sources would benefit from exchange and
synergistic synthesis of insights, approaches, algorithms, and results from
these disparate areas.
The workshop is open to all members of the AI community. However, the number of participants is strictly limited. Consequently, authors of accepted papers will
be given priority in terms of attendance. All workshop participants must
register for the IJCAI conference. The organizers will make a concerted
effort to ensure a good mix of established researchers,
graduate students and junior researchers, as well as
industrial participants.
The workshop will consist of a small number of invited talks,
presentation of contributed papers, and informal discussions.
The invited talks will give overviews of the key topics (information
extraction and fusion, distributed and incremental learning,
architectures and systems, and selected applications.
These talks will be interspersed with
short presentations of selected contributed papers.
The workshop schedule will allow ample time for informal discussion.
The workshop organizers will identify a set of questions and
discussion topics based on the goals of the workshop (as outlined above)
and the contents of the invited and contributed papers.
The workshop will be organized by Vasant Honavar, Lee Giles, Kyseok Shim, Kristina Lerman, and Yannis Labrou. Vasant Honavar will serve as the primary contact.
Dr. Lee Giles
School of Information Sciences and Technology
Pennsylvania State University
504 Rider Building
120 South Burrowes St.
University Park, PA 16801-3857
Giles@ist.psu.edu
Dr. Kyuseok Shim
Computer Science Department
Korea Advanced Institute of Science and Technology
373-1 Kusong-dong, Yusong-gu
TAEJON 305-701, KOREA
shim@cs.kaist.ac.kr
Dr. Yannis Labrou
Computer Science and Electrical Engineering Department
University of Maryland, Baltimore County
ECS Building, Room 210
1000 Hilltop Circle
Baltimore, MD 21250.
jklabrou@cs.umbc.edu