Overview

Text data that is associated with location data has become ubiquitous. A tweet is an example of this type of data, where the text in a tweet is associated with the location where the tweet has been issued. We use the term spatial-keyword data to refer to this type of data. Spatial-keyword data is being generated at massive scale. Almost all online transactions have an associated spatial trace. The spatial trace is derived from GPS coordinates, IP addresses, or cell-phone-tower locations. Hundreds of millions or even billions of spatial keyword objects are being generated daily. Spatial-keyword data has numerous applications that require efficient processing and management of massive amounts of spatial-keyword data. This book starts by overviewing some important applications of spatial-keyword data, and demonstrates the scale at which spatial-keyword data is being generated. Then, it formalizes and classifies the various types of queries that execute over spatial-keyword data. Next, it discusses important and desirable properties of spatial-keyword query languages that are needed to express queries over spatial-keyword data. As will be illustrated, existing spatial-keyword query languages vary in the types of spatial-keyword queries that they can support. There are many systems that process spatial-keyword queries. Systems differ from each other in various aspects, e.g., whether the system is batch-oriented or stream-based, and whether the system is centralized or distributed. Moreover, spatial-keyword systems vary in the types of queries that they support. Finally, systems vary in the types of indexing techniques that they adopt. This book provides an overview of the main spatial-keyword data-management systems (SKDMSs), and classifies them according to their features. Moreover, the book describes the main approaches adopted when indexing spatial-keyword data in the centralized and distributed settings. Several case studies of {SKDMSs} are presented along with the applications and query types that these {SKDMSs} are targeted for and the indexing techniques they utilize for processing their queries. Optimizing the performance and the query processing of {SKDMSs} still has many research challenges and open problems. The book concludes with a discussion about several important and open research-problems in the domain of scalable spatial-keyword processing.

Full Product Details

9781681734873

Table of Contents

Reviews

Author Information

Ahmed R. Mahmood is a Ph.D. candidate at the Department of Computer Science, Purdue University. His research interests are big data, database systems, spatial, spatial-keyword, and distributed stream processing. He is the first-place winner of the 2017 ACM SIGSPATIAL student research competition. He has been awarded the Purdue CS Teaching Fellowship, the Teaching Academy Graduate Teaching Award, and the Raymond Boyce Graduate Teacher Award. Ahmed is the main designer and developer of Tornado, the first distributed spatial-keyword stream processing system. He published several scholarly articles in the area of spatial and spatial-keyword processing in top venues including ACM SIGSPATIAL, ICDE, and VLDB. Walid G. Aref is a professor of Computer Science at Purdue. His research interests are in the areas of database systems, spatial and spatio-temporal data systems, data streaming, indexing, and query processing techniques. His research has been supported by the NSF, the National Institute of Health, Purdue Research Foundation, Qatar National Research Foundation, CERIAS, Panasonic, and Microsoft Corp. In 2001, he received the CAREER Award from the National Science Foundation and in 2004, he received a Purdue University Faculty Scholar award. Walid is an IEEE Fellow. He has received several best-paper awards including the 2016 VLDB 10-Year Best-Paper award. Walid is the Editor-in-Chief of the ACM Transactions of Spatial Algorithms and Systems (TSAS), and has been an associate editor of the ACM Transactions of Database Systems (TODS), an editor of the VLDB Journal, and an editor of the Journal of Spatial Information Science (JOSIS). He has been one of the co-founders and a past chair of the ACM SIGSPATIAL Special Interest Group. H. V. Jagadish is Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science, and Distinguished Scientist at the Institute for Data Science, at the University of Michigan in Ann Arbor. Prior to 1999, he was Head of the Database Research Department at AT&T Labs, Florham Park, NJ. Professor Jagadish is well known for his broad-ranging research on information management, and has approximately 200 major papers and 37 patents. He is a fellow of the ACM, The First Society in Computing, (since 2003) and serves on the board of the Computing Research Association (since 2009). He has been an Associate Editor for the ACM Transactions on Database Systems (1992-1995), Program Chair of the ACM SIGMOD annual conference (1996), Program Chair of the ISMB conference (2005), a trustee of the VLDB (Very Large DataBase) foundation (2004-2009), Founding Editor-in-Chief of the Proceedings of the VLDB Endowment (2008-2014), and Program Chair of the VLDB Conference (2014). Among his many awards, he won the ACM SIGMOD Contributions Award in 2013 and the David E Liddle Research Excellence Award (at the University of Michigan) in 2008.

Tab Content 6

Customer Reviews

Recent Reviews

Countries Available