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Querying and Mining Strings Made Easy

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Advanced Data Mining and Applications (ADMA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10604))

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Abstract

With the advent of large string datasets in several scientific and business applications, there is a growing need to perform ad-hoc analysis on strings. Currently, strings are stored, managed, and queried using procedural codes. This limits users to certain operations supported by existing procedural applications and requires manual query planning with limited tuning opportunities. This paper presents StarQL, a generic and declarative query language for strings. StarQL is based on a native string data model that allows StarQL to support a large variety of string operations and provide semantic-based query optimization. String analytic queries are too intricate to be solved on one machine. Therefore, we propose a scalable and efficient data structure that allows StarQL implementations to handle large sets of strings and utilize large computing infrastructures. Our evaluation shows that StarQL is able to express workloads of application-specific tools, such as BLAST and KAT in bioinformatics, and to mine Wikipedia text for interesting patterns using declarative queries. Furthermore, the StarQL query optimizer shows an order of magnitude reduction in query execution time.

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Notes

  1. 1.

    ftp://ftp.ncbi.nih.gov/genbank/gbrel.txt.

  2. 2.

    http://cancergenome.nih.gov.

  3. 3.

    https://www.skatelescope.org.

  4. 4.

    http://www.tgac.ac.uk/KAT/.

  5. 5.

    http://cloud.kaust.edu.sa/Pages/stardb.aspx.

  6. 6.

    ftp://ftp.ncbi.nih.gov/blast/db/FASTA/igSeqNt.gz.

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Author information

Authors and Affiliations

  1. Saudi Aramco, Dhahran, Saudi Arabia

    Majed Sahli

  2. Qatar Computing Research Institute, HBKU, Doha, Qatar

    Essam Mansour

  3. KAUST, Thuwal, Saudi Arabia

    Panos Kalnis

Authors
  1. Majed Sahli
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  2. Essam Mansour
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  3. Panos Kalnis
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Corresponding author

Correspondence to Majed Sahli .

Editor information

Editors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Gao Cong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

  3. Macquarie University, Sydney, New South Wales, Australia

    Wei Emma Zhang

  4. Wuhan University, Wuhan, China

    Chengliang Li

  5. Nanyang Technological University, Singapore, Singapore

    Aixin Sun

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Sahli, M., Mansour, E., Kalnis, P. (2017). Querying and Mining Strings Made Easy. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_1

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  • DOI: https://doi.org/10.1007/978-3-319-69179-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69178-7

  • Online ISBN: 978-3-319-69179-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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