Distributed Spatial-Keyword Processing

Centralized spatial-keyword processing systems are limited in scale. They cannot handle the massive amounts of spatial-keyword data as they are restricted by the resources of the single machine they run on. The need for scalable processing of spatial-keyword data motivated the development of big SKDMS. The following are three main approaches for processing big spatial-keyword data.

1. 1.

   Applications on top of general-purpose big-data systems, where the processing of spatial-keyword data takes place without any changes to the underlying big-data system.

2. 2.

   Extensions to existing big-data systems, where the underlying big-data system is modified and is extended to be optimized for spatial-keyword processing.

3. 3.

   Dedicated big spatial-keyword management systems, where big SKDMSs are built from scratch for the sole application of spatial-keyword processing.

In this chapter, we start with a brief overview of general-purpose big-data systems. Then, we present the main approaches for big spatial-keyword processing and indexing. Finally, we give examples and case studies on distributed systems that deal with spatial-keyword data.

Authors and Affiliations

1. Purdue University, USA

   Ahmed R. Mahmood & Walid G. Aref

Reprints and permissions

© 2019 Springer Nature Switzerland AG

Policies and ethics