Processing and optimizing main memory spatial-keyword queries

Taesung Lee, Seung Won Hwang, Jin Woo Park, Sanghoon Lee, Sameh Elnikety, Yuxiong He

Research output: Chapter in Book/Report/Conference proceedingChapter

21 Citations (Scopus)


Important cloud services rely on spatial-keyword queries, containing a spatial predicate and arbitrary boolean keyword queries. In particular, we study the processing of such queries in main memory to support short response times. In contrast,current state-of-theart spatial-keyword indexes and relational engines are designed for different assumptions. Rather than building a new spatial-keyword index, we employ a cost-based optimizer to process these queries using a spatial index and a keyword index. We address several technical challenges to achieve this goal. We introduce three operators as the building blocks to construct plans for main memory query processing. We then develop a cost model for the operators and query plans. We introduce five optimization techniques that efficiently reduce the search space and produce a query plan with low cost. The optimization techniques are computationally efficient, and they identify a query plan with a formal approximation guarantee under the common independence assumption. Furthermore, we extend the framework to exploit interesting orders. We implement the query optimizer to empirically validate our proposed approach using real-life datasets. The evaluation shows that the optimizations provide significant reduction in the average and tail latency of query processing: 7- to 11-fold reduction over using a single index in terms of 99th percentile response time. In addition, this approach outperforms existing spatial-keyword indexes, and DBMS query optimizers for both average and high-percentile response times.

Original languageEnglish
Title of host publicationProceedings of the VLDB Endowment
PublisherAssociation for Computing Machinery
Number of pages12
Publication statusPublished - 2016
Event42nd International Conference on Very Large Data Bases, VLDB 2016 - Delhi, India
Duration: 2016 Sept 52016 Sept 9

Publication series

NameProceedings of the VLDB Endowment
ISSN (Electronic)2150-8097


Other42nd International Conference on Very Large Data Bases, VLDB 2016

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computer Science(all)


Dive into the research topics of 'Processing and optimizing main memory spatial-keyword queries'. Together they form a unique fingerprint.

Cite this