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[Accepted Papers] [Workshop Program] [Program Committee]
The need for mining multiple information
sources (MMIS) is almost ubiquitous and applications abound in all spheres,
including in business domains, government/enterprise organizations, and many
scientific disciplines. For example, biological and biomedical research studies
microarray data of different species. Although genomes of different species are
not identical, they are not entirely independent: even remote species exhibit
correlations in their genomes. An interesting research problem that arises here
is how to unify heterogeneous data sources from different species to facilitate
data mining. As another example, in business intelligence, movie rental
companies (such as Netflix) usually maintain and utilize at least two major
datasets, namely the movie data and the customer data. This allows us to build
models for different purposes, e.g,
predicting in which types of movies customers are interested, or identifying
high-attrition customers. A more interesting problem is how to leverage
information from other sources, such as the actor data, for multi-task
learning.
While most data
mining algorithms are conceived for mining data from a single source, the need
to develop general theories, frameworks, data structures, and heuristics, for
mining multiple heterogeneous information sources that share dependency has
become more and more crucial. Unleashing the full power of multiple information
sources is, however, a very challenging task, considering that the schema of
different data collections might be very different (data heterogeneity),
the distributions and patterns underlying different data sources may undergo
continuous changes (concept evolving),
and mining tasks for each data source might also be different (mining
diversity). Although several approaches for utilizing multiple information
sources have been proposed, these methods are usually rather ad-hoc and do not
address adequately some of the most fundamental research issues in this field:
(1) Harnessing Complex Data Relationships:
Multiple information sources represent a collection of highly correlated data,
issues such as data integration, data integration, model integration, and model
transferring across different domains, play fundamental roles in supporting KDD
from multiple information sources; (2) Integrative
and Cooperative Mining: For heterogeneous information sources with diverse
mining tasks, our goal is to unify such data to generate enhanced global
models, as well as help individual data collections to
best achieve their respective mining goals; and (3) Differentiation and Correlation: Differentiate and coordinate the
difference between data sources at the knowledge level is one crucial step for
users to gain a high-level understanding of their data.
The aim of
this workshop is to bring together data mining experts to advance research on
pattern discovery from multiple information sources, and identify current needs for such purposes. Representative issues to be
addressed include but are not limited to:
We solicit two types of papers: Research paper and Application paper (8 pages for all submissions inclusive of all
references and figures).
Research papers should focus on new designs,
algorithms, and solutions for mining multiple information sources, whereas Application papers may provide
frameworks and systems related to real-world multi-source mining applications.
Alternatively, the authors can submit a data
track application paper (2 pages) which purely discusses real-world
multi-source data and related research topics. A copy of multi-source data must
be submitted (through email) for verification. If there is any copyright issue
related to the submitted data, the authors should clearly mention this issue in
the submission.
All papers should be submitted in
IEEE proceedings format (two columns). Please follow the IEEE Computer Society Press Proceedings Author
Guidelines at http://www.computer.org/portal/pages/cscps/cps/final/icdm06.xml
The workshop proceedings will be
published by the IEEE Digital Library and distributed during the workshop
For submission of
the paper, please use submission system Here
If you are experiencing any
difficulties, please contact workshop co-chairs. Upon the receiving of
each submission, the workshop co-chairs will organize the peer-review process
immediately.