Lahar demonstration: warehousing Markovian streams

Authors:
Julie Letchner;Christopher Ré;Magdalena Balazinska;Matthai Philipose
Affiliations:
University of Washington;University of Washington;University of Washington;Intel Research Seattle
Venue:
Proceedings of the VLDB Endowment
Year:
2009

Citing 3
Cited 1

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Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

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Abstract

Lahar is a warehousing system for Markovian streams---a common class of uncertain data streams produced via inference on probabilistic models. Example Markovian streams include text inferred from speech, location streams inferred from GPS or RFID readings, and human activity streams inferred from sensor data. Lahar supports OLAP-style queries on Markovian stream archives by leveraging novel approximation and indexing techniques that efficiently manipulate stream probabilities. This demonstration allows users to interactively query a warehouse of imprecise text streams inferred automatically from audio podcasts. Through this interaction, the demo introduces users to the challenges of Markovian stream processing as well as technical contributions developed to address these challenges.