Technical ISI Research: Opportunities in Knowledge
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Technical ISI Research: Opportunities in Knowledge






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Technical ISI Research: Opportunities in Knowledge Technical ISI Research: Opportunities in Knowledge Presentation Transcript

  • Technical ISI Research: Opportunities in Knowledge & Data Engineering Xindong Wu Department of Computer Science University of Vermont USA xwu @ cs . uvm . edu
  • Knowledge and Data Engineering Topics from ISI ’05 CFP
    • Information Sharing and Data Mining
      • Deception and intent detection
      • Agents and collaborative systems for intelligence sharing
    • Infrastructure Protection and Emergency Responses
      • Intrusion detection
      • Emergency response and management
    • Terrorism Informatics
      • Terrorism knowledge portals and databases
      • Social network analysis.
  • What Is Data Mining?
    • T he discovery of knowledge (in the form of rules, trees, frequent patterns etc.) from large volumes of data .
    • A hot field : 15 “data mining” conferences in 2003, including KDD, ICDM, SDM, IDA, PKDD and PAKDD, excluding IJCAI, COMPSTAT, SIGMOD and other more general conferences.
  • Main Activities in Data Mining
    • Conferences:
      • The birth of data mining/KDD: 1989 IJCAI Workshop on Knowledge Discovery in Databases
        • 1991-1994 Workshops on Knowledge Discovery in Databases
      • 1995 – date: International Conferences on Knowledge Discovery in Databases and Data Mining ( KDD )
      • 2001 – date: IEEE ICDM and SIAM-DM (SDM)
      • Several regional conferences, incl. PAKDD (since 1997) & PKDD (since 1997)
    • Journals:
      • Data Mining and Knowledge Discovery (DMKD, since 1997)
      • Knowledge and Information Systems (KAIS, since 1999)
      • IEEE Transactions on Knowledge and Data Engineering (TKDE)
      • Many others, incl. TPAMI, ML, IDA, …
  • IEEE ISI-2005 Panel on Technical ISI Research, May 19, 2005 ACM KDD vs. IEEE ICDM
  • TKDE Topics Related to ISI
    • Data Mining:
      • outlier detection
      • personalization
    • Database and Data Modeling
      • secure databases
    • Knowledge Engineering and Intelligent Systems
      • information extraction
    • Emerging Applications
      • privacy
      • security
      • social networks and graph analysis
  • Main Topics in Data Mining
    • Association analysis (frequent patterns)
    • Classification (trees, Bayesian methods, etc)
    • Clustering and outlier analysis
    • Sequential and spatial patterns, and time-series analysis
    • Text and Web mining
    • Data visualization and visual data mining.
  • Fundamental Knowledge Engineering/AI Techniques in Data Mining
    • Knowledge representation . Data mining seeks to discover interesting patterns from large volumes of data. These patterns can take various forms, such as association rules, classification rules, and decision trees.
    • Knowledge acquisition . The discovery process shares various heuristic algorithms and methods with machine learning for the same purpose of knowledge acquisition from data or learning from examples.
    • Knowledge inference . The patterns discovered from data need to be verified in various applications so deduction of mining results is an essential technique in data mining applications.
  • Some Research Directions
    • Web mining (incl. Web structures, usage analysis, authoritative pages, and document classification)
    • Intelligent data analysis in domain-specific applications (such as bioinformatics and ISI)
    • Mining with data streams (in continuous, real-time, dynamic data environments)
    • Integrated, intelligent data mining environments and tools (incl. induction, deduction, and heuristic computation).
  • How to Publish ISI Research at ICDM and TKDE?
    • ICDM and TKDE both look for technological contributions
    • ICDM and TKDE are both very tough, expecting best results in their respective research field
    • Reading and citing relevant papers from ICDM/KDD and TKDE is a must
    • A possible way to publish in both ICDM/KDD and TKDE:
      • Submit to ICDM/KDD to get (quick) feedback
      • Expand and submit to TKDE if positive feedback from ICDM/KDD, with at least 30% new material.
  • How to Publish ISI Research at ICDM and TKDE (2)
    • How about ISI application papers?
      • Application papers are always invited, but innovations are necessary. A case of an innovative application must be presented, for the ICDM/TKDE audience.
    • How about data analysis w/o large volumes of data?
      • Experiments on large databases are not always required, but relevance to mining/discovery must be established.
    • Most important of all: the uniqueness of your research in the field!
      • You work has to be (1) technically sound, (2) relevant, (3) original, (4) significant, and (5) well clarified.
  • Concluding Remarks
    • ISI research needs knowledge and data engineering systems and tools.
    • Data mining conferences (such as KDD and ICDM) are good forums to publish ISI research, but they expect technological contributions and/or innovative applications.
    • TKDE publishes “well-defined theoretical results and empirical studies that have potential impact on the acquisition, management, storage, and graceful degeneration of knowledge and data, as well as in provision of knowledge and data services.”