Paper Info
Title
On supporting compilation in spatial query engines: (vision paper).
Abstract
Today's 'Big' spatial computing and analytics are largely processed in-memory. Still, evaluation in prominent spatial query engines is neither fully optimized for modern-class platforms nor taking full advantage of compilation (i.e., generating low-level query code). Query compilation has been rapidly rising inside in-memory relational database management systems (RDBMSs) achieving remarkable speedups; how can we bring similar benefits to spatial query engines? In this research, we bring in proven Programming Languages (PL) approaches e.g., partial evaluation, generative programming, etc. and leverage the power of modern hardware to extend query compilation inside spatial query engines. We envision a fully compiled spatial query engine that is efficient and feasible to implement in a high-level language. We describe LB2-Spatial; a prototype for a fully compiled spatial query engine that employs generative and multi-stage programming to realize query compilation. Furthermore, we discuss challenges, and conduct a preliminary experiment to highlight potential gains of compilation. Finally, we sketch potential avenues for supporting spatial query compilation in Postgres/ PostGIS; a traditional RDBMS and Spark/ Spark SQL; a main-memory cluster computing framework.
Year
DOI
Venue
2016
10.1145/2996913.2996945
SIGSPATIAL/GIS
Keywords
Field
DocType
Spatial Query Compilation
Query optimization,Data mining,Query language,RDF query language,Information retrieval,Query expansion,Computer science,Sargable,Web query classification,Query by Example,Spatial query,Database
Conference
Citations 
PageRank 
References 
0
0.34
9
Authors
2
Name
Order
Citations
PageRank
Ruby Y. Tahboub1143.74
Tiark Rompf274345.86