TDM 2004
ICDM 2004 Workshop on
Temporal Data Mining: Algorithms, Theory and Applications

Held in conjunction with
The Fourth IEEE International Conference on Data Mining (ICDM'04)

November 01-04, 2004, Brighton, UK


Workshop Photos

Workshop Proceedings

Workshop Schedule


Accepted Papers
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Last Modified: 11/1/2004

Scope and Program

Workshop Description

Topics of Interest

Important Dates



Paper Submissions

Submission


Organization

Organizers

Program Committee


Relevant Links

ICDM 2004

IBM Research

Tao Li



Workshop Description    

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, 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. This workshop aims to gather researchers and practitioners to tackle these challenges.

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.

  • Asynchronousness: 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

    Important Dates    Top

    • August 20, 2004 Sept 6, 2004: Electronic submission of full papers
    • September 20, 2004 September 23, 2004: Author notification
    • October 1, 2004: Submission of Camera-ready papers (hard deadline)
    • November 1, 2004: Workshop in Brighton, UK

    Paper Submissions    Top

    All submissions must be made electronically to the workshop contact, Sheng Ma, at SHENGMA AT US DOT IBM DOT COM with subject "TDM 2004". 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
    • Sheng Ma, IBM T.J. Watson Research Center.
    • Tao Li, University of Rochester.
    • Charles Perng, IBM T.J. Watson Research Center.

    Note: for inquiries please send e-mail to SHENGMA AT US DOT IBM DOT COM.

    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
    • Daryl Pregibon, Google
    • Dennis Shasha, New York University
    • Philip S. Yu, IBM T.J. Watson Research Center
    • Mohammed Zaki, Rensselaer Polytechnic Institute
    • Shenghuo Zhu, NEC Labs America