Querying and Mining Strings Made Easy

Majed Sahli, Essam Mansour, Panos Kalnis

Research output: Chapter in Book/Report/Conference proceedingChapter

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.
Original languageEnglish (US)
Title of host publicationAdvanced Data Mining and Applications
PublisherSpringer Nature
Pages3-17
Number of pages15
ISBN (Print)9783319691787
DOIs
StatePublished - Oct 14 2017

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