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RILCA: Collecting and Analyzing User-Behavior Information in Instant Search Using Relational DBMS

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Book cover Real-Time Business Intelligence and Analytics (BIRTE 2015, BIRTE 2016, BIRTE 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 337))

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Abstract

An instant-search engine computes answers immediately as a user types a query character by character. In this paper, we study how to systematically collect information about user behaviors when they interact with an instant search engine, especially in a real-time environment. We present a solution, called RILCA, which uses front-end techniques to keep track of rich information about user activities. This information provides more insights than methods based on traditional Web servers such as Apache. We store the log records in a relational DBMS system, and leverage the existing powerful capabilities of the DBMS system to analyze the log records efficiently. We study how to use a dashboard to monitor and analyze log records in real time. We conducted experiments on real data sets collected from two live systems to show the benefits and efficiency of these techniques.

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Notes

  1. 1.

    psearch.ics.uci.edu.

  2. 2.

    ipubmed.ics.uci.edu.

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Author information

Authors and Affiliations

  1. University of California, Irvine, CA, 92697, USA

    Taewoo Kim & Chen Li

Authors
  1. Taewoo Kim
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  2. Chen Li
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Corresponding author

Correspondence to Taewoo Kim .

Editor information

Editors and Affiliations

  1. Teradata, Santa Clara, CA, USA

    Malu Castellanos

  2. University of Pittsburgh, Pittsburgh, PA, USA

    Panos K. Chrysanthis

  3. University of Pittsburgh, Pittsburgh, PA, USA

    Konstantinos Pelechrinis

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Kim, T., Li, C. (2019). RILCA: Collecting and Analyzing User-Behavior Information in Instant Search Using Relational DBMS. In: Castellanos, M., Chrysanthis, P., Pelechrinis, K. (eds) Real-Time Business Intelligence and Analytics. BIRTE BIRTE BIRTE 2015 2016 2017. Lecture Notes in Business Information Processing, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-030-24124-7_1

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  • DOI: https://doi.org/10.1007/978-3-030-24124-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24123-0

  • Online ISBN: 978-3-030-24124-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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