Scalable Processing of Spatial-keyword Queries
Date and Time of the talk: April 15 2021, 9:30 AM EDT
Information of the Speaker
Walid G. Aref, Purdue University and Alexandria University-Egypt
Walid G. Aref got his Ph.D. from UMD in 1993. His research interests are in extending the functionality of database systems in support of emerging applications, e.g., spatial, spatio- temporal, graph, biological, and sensor databases. He is also interested in query processing, indexing, data streaming, and geographic information systems (GIS). Walid’s research has been supported by the National Science Foundation, the National Institute of Health, Purdue 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 a member of Purdue’s CERIAS. He is the Editor-in-Chief of the ACM Transactions of Spatial Algorithms and Systems (ACM TSAS), an editorial board member of the Journal of Spatial Information Science (JOSIS), and has served as an editor of the VLDB Journal and the ACM Transactions of Database Systems (ACM TODS). Walid has won several best paper awards including the 2016 VLDB ten-year best paper award. He is a Fellow of the IEEE, and a member of the ACM. Between 2011 and 2014, Walid has served as the chair of the ACM Special Interest Group on Spatial Information (SIGSPATIAL).
Walid acknowledges the support of the National Science Foundation under Grant Numbers III-1815796 and IIS-1910216.
Ahmed R. Mahmood, Google LLC
Ahmed R. Mahmood is a software engineer at Google LLC. Ahmed is a member of the F1 Query team working on scalable query processing and spatial data management. Ahmed got his Ph.D. at the Department of Computer Science, Purdue University. His research interests included scalable data management, distributed stream processing, and spatial-keyword data processing.
The widespread use of GPS-enabled cellular devices, i.e., smart phones, led to the popularity of numerous mobile applications, e.g., social networks, micro-blogs, mobile web search, and crowd-powered reviews. These applications generate large amounts of geo-tagged textual data, i.e., spatial-keyword data. This data needs to be processed and queried at an unprecedented scale. This has led to the development of various scalable spatial-keyword processing systems. These systems are designed to ingest, store, index, and query massive amounts of spatial-keyword data. We present recent research efforts in the area of scalable spatial-keyword processing. We describe the main models for scalable spatial-keyword processing, and list the popular spatial-keyword queries. Then, we present the approaches that have been adopted in scalable spatial-keyword processing systems with special attention to data indexing, adaptive data partitioning, and query processing.