TDM 2005
ICDM 2005 Workshop on
Temporal Data Mining: Algorithms, Theory and Applications

Held in conjunction with
The Fifth IEEE International Conference on Data Mining (ICDM'05)

November 27-30, 2005, Houston, Texas, USA

Workshop Program

Workshop Proceedings

Scope and Program

Call for Papers (PDF) or (DOC)

Latest News

Workshop Description

Topics of Interest

Important Dates



Paper Submissions

Submission


Organization

Organizers

Program Committee


Relevant Links

ICDM 2005

TDM Workshop at ICDM 2004

IBM Research

SCS at FIU


Latest News    

[Nov 9, 2005] Workshop Proceedings and Workshop Program are posted on line.

[Oct 16, 2005] We received over 30 submissions this year and we are now in the process of collecting the reviews. Decisions will be made early next week.

[Sept 14, 2005] Due to many requests and the uncertainty of ICDM relocation, the paper submission deadline has been extended to Sept 22, 2005.

 [July 21, 2005] Selected articles (suitably expanded and peer reviewed) from the workshop will be published in a special issue of temporal data mining with DMKD (Data Mining and Knowledge Discovery) Journal. (See the Call for Papers for the Special Issue)

 [July 20, 2005] Authors are also invited to submit short 2 or 3-page papers describing the " work in progress " such as new applications, new approaches, new results, partial experiences, etc. This would provide a chance to present ongoing research that is not yet ready for publication and that has never been presented before, and to get feedback on your work from experienced researchers in the field.
Workshop Description    

This workshop is the second workshop on this theme held annually with the ICDM Conference. Information about the 2004 workshop, including proceedings, can be found at http://www.cs.rochester.edu/u/taoli/workshop. The 2004 workshop was very successful and attracted a mixture of academics and industry practitioners.

Many real-world applications deal with huge amounts of temporal data. Examples include alarms/events and performance measurements generated by distributed computer systems and by telecommunication networks, the web server logs, online transaction logs, financial data, workflow process logs, and sensor data collected from sensor networks. Conventionally, temporal data is classified to either categorical event streams or numerical time series and both types have been intensively studied in data mining and statistics. However, several previously less emphasized aspects of temporal data have proven their importance in emerging applications and posed several challenges calling for more research. In addition, the applications of temporal data analysis, such as web services, information navigation, system management, adaptive workflow management, program behavior analysis, security management and bioinformatics, are enjoying a growing amount of attention. This workshop aims to gather researchers and practitioners to tackle these challenges and attempts to study the common tasks that need to be addressed in practical applications.

The setting of traditional temporal data analysis is to apply one algorithm on a static, regular and relatively small temporal data set. Many practitioners found existing analysis methods inadequate for their real-world data. Many struggle to transform the data in order to apply existing methods or even to reduce the original problems to better studied ones; either ways induce in more preprocessing effort, more artificial parameters and less interpretable results. We believe these new aspects of temporal data deserve theories and algorithms of their own. Some of these new aspects are:

  • Irregularity: Many types of numerical temporal data are not equally paced.

  • Asynchronism: In distributed computing environments like sensor networks, data from different sources tend to be not aligned and hence can not apply synchronous methods.

  • Distributed analysis: A trend in temporal data analysis is to perform data filtering, transformation and analysis as close as possible to the data sources to avoid the prohibitive amount of data being transmitted and analyzed. This new computing paradigm calls for a new theoretical foundation.

  • Streaming Data: Some temporal data is stored only temporally and requires near real-time analysis.

  • Heterogeneous data types: It is very common that temporal data is partly categorical events and partly numerical time series. It remains to be an interesting challenging to best analyze all possible data in a uniform way.

  • Huge Volume: The stream of data can be huge for a long, continuous observation period. Many types of measurements can be obtained from a large number of data sources. This requires designing scalable solutions in analyzing a large volume of temporal data, in terms of both the large number of data points and the large number of types of measurements.

Driven by the new aspects of temporal data, several fundamental problems need to be revisited. Just to name a few of them:

  • Prediction
  • Correlation
  • Regression
  • Benchmarking
  • Periodic Pattern Mining
  • Temporal Association Finding
  • Causality Analysis
  • Sequential Event Patterns
  • Threshold selection
  • Frequency Analysis
  • Anomaly Detection
  • Clustering and Classification
  • Topics of Interest    Top
    In this workshop, we aim to solicit papers that address the aforementioned technical challenges in mining temporal data. Through the workshop, we expect to bring together researchers from both industry and academia with different backgrounds: data mining, machine learning, database, statistical analysis, and application knowledge to propose new ideas, identify promising technologies and pose challenges. The major topics of the workshop include but are not limited to:
    • Temporal data benchmarking
    • Temporal pattern discovery
    • Clustering for temporal data
    • Prediction for temporal data
    • Time series characterization and analysis
    • Statistical analysis of temporal data
    • Accommodating domain knowledge in the temporal mining process
    • Complexity, efficiency and scalability of temporal data mining algorithms
    • Content-based search, retrieval for temporal data
    • Process mining
    • Case studies and applications of temporal data mining, such as
      • Adaptive workflow management
      • Bioinformatics
      • Information navigation
      • Program behavior analysis
      • Security management
      • System management
      • Web services and etc.

    Authors are also invited to submit short 2 or 3-page papers describing the " work in progress " such as new applications, new approaches, new results, partial experiences, etc. This would provide a chance to present ongoing research that is not yet ready for publication and that has never been presented before, and to get feedback on your work from experienced researchers in the field.

    Important Dates    Top

    • September 15, 2005 , September 22, 2005: Electronic submission of full papers
    • October 15, 2005 , October 19, 2005 : Author notification
    • October 22, 2005: Submission of Camera-ready papers
    • November 27, 2005: Workshop in New Orleans, Louisiana, USA

    Paper Submissions    Top

    The electronic submission Web site for research papers is available at: http://www.cs.fiu.edu/TDM2005/myreview/.

    Workshop papers should be prepared in the same format as ICDM conference papers (formatting guidelines of ICDM). The style files are also available here.

    Papers should be no longer than 12 pages (or 5,000 words) inclusive of all references and figures. All papers must be submitted in either PDF (preferred) or postscript. Please ensure that any special fonts used are included in the submitted documents. The workshop proceedings will be published by the ICDM and distributed during the workshop.


    Workshop Co-chairs    Top

    Note: for inquiries please send e-mail to taoli AT cs.fiu.edu.

    Program Committee Members    Top

    • Inderjit Dhillon, University of Texas at Austin
    • Carlotta Domeniconi, George Mason University
    • Christos Faloustos, Carnegie Mellon university
    • Johannes Gehrke, Cornell University
    • Oscar Kipersztok, Boeing Research
    • Wenke Lee, Georgia Institute of Technology
    • Feng Liang, Duke University
    • Bing Liu, University of Illinois at Chicago
    • Mitsunori Ogihara, University of Rochester
    • Srinivasan Parthasarathy, Ohio State University
    • Dennis Shasha, New York University
    • Tong Sun, Xerox Research
    • Hui Xiong, Rutgers University
    • Philip S. Yu, IBM T.J. Watson Research Center
    • Mohammed Zaki, Rensselaer Polytechnic Institute
    • Shenghuo Zhu, NEC Labs America