ARGUS: Rete + DBMS = Efficient Persistent Profile Matching on Large-Volume Data Streams

  • Chun Jin
  • Jaime Carbonell
  • Phil Hayes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3488)


Efficient processing of complex streaming data presents multiple challenges, especially when combined with intelligent detection of hidden anomalies in real time. We label such systems Stream Anomaly Monitoring Systems (SAMS), and describe the CMU/Dynamix ARGUS system as a new kind of SAMS to detect rare but high value patterns combining streaming and historical data. Such patterns may correspond to hidden precursors of terrorist activity, or early indicators of the onset of a dangerous disease, such as a SARS outbreak. Our method starts from an extension of the RETE algorithm for matching streaming data against multiple complex persistent queries, and proceeds beyond to transitivity inferences, conditional intermediate result materialization, and other such techniques to obtain both accuracy and efficiency, as demonstrated by the evaluation results outperforming classical techniques such as a modern DMBS.


High Data Rate Incremental Evaluation Continuous Query Transitivity Inference Computation Sharing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Chun Jin
    • 1
  • Jaime Carbonell
    • 1
  • Phil Hayes
    • 2
  1. 1.Language Technologies Institute, School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA
  2. 2.Dynamix TechnologiesWexfordUSA

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