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 presentedalong 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.
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 winnerof 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 hasbeen supported by the NSF, the National Institute ofHealth, 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 VLDB10-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 theVLDB Journal, and an editor of the Journal of Spatial Information Science (JOSIS). He has been one of the co-founders and a pastchair of the ACM SIGSPATIAL Special Interest Group.
