TY - JOUR
T1 - On multi-type reverse nearest neighbor search
AU - Ma, Xiaobin
AU - Zhang, Chengyang
AU - Shekhar, Shashi
AU - Huang, Yan
AU - Xiong, Hui
N1 - Funding Information:
We formalized a multi-type reverse nearest neighbor problem (MTRNN) and developed an MTRNN query algorithm by exploiting a two-step R-tree node-level pruning strategy. In the coarse-level pruning step, we generate closed and open pruning regions which can be used to prune an entire R-tree or part of an R-tree for a given query. In the refinement step, the remaining data points are evaluated and the true MTRNN points are identified for the given query. We compared the MTRNN algorithm with a traditional RNN query method in terms of number of feature types, number of points in each feature type and queried data set. The experiment results show that our algorithm not only returns MTRNNs within a reasonable time but that MTRNN results differ significantly from results of traditional RNN queries. This finding has important implications for decision-making algorithms used in business and confirms the value of further investigation of MTRNN query approaches. As for future work, we plan to introduce different weight factors for different feature types into the objective function. It is important for some business applications since in reality the influence of different feature types may be different for different types of applications. We are then interested in the application of MTRNN queries on road networks using road network distance or other types of spatio-temporal databases. We believe that the computational complexity of MTRNN queries will rise dramatically when applied to such databases. Therefore, we plan to explore off-line techniques for pre-computing of intermediate results of MTRNN queries as well as indexing, data structure, and other methods for efficient storage and retrieval of results. Another direction to extend our MTRNN work is to design some good heuristic approaches to find approximate results as well as design corresponding measurements to evaluate the results that sufficiently capture the problem's complexity. Xiaobin Ma has received his B.S. degree in Computer Science from Tianjin University in 1993 and M.S. in Computer Science from University of Minnesota in 2005. He is currently a software engineer at Oracle Corporation. His research interests include spatial databases, location based services, database query optimization and materialized view processing. Chengyang Zang has received his B.S. degree in Industrial Automation from University of Science and Technolgy, Beijing in 2000 and Ph.D. degree in Computer Science from University of North Texas, Denton, TX, USA. He is currently a software engineer at Teradata Corporation. His research interests include streaming data management, location based services, spatio-temporal databases and database query optimization. Shashi Shekhar received the B. Tech degree in Computer Science from the Indian Institute of Technology, Kanpur, India, in 1985, the M.S. degree in Business Administration and the Ph.D. degree in Computer Science from the University of California, Berkeley, CA, USA, in 1989. He is a McKnight Distinguished University Professor the University of Minnesota, Minneapolis, MN, USA. His research interests include spatial databases, spatial data mining, geographic and information systems (GIS), and intelligent transportation systems. He is a co-author of a textbook on Spatial Databases (Prentice Hall, 2003, isbn 0-13-017480-7), co-edited an Encyclopedia of GIS (Springer, 2008, isbn 978-0-387-30858-6) and has published over 260 research papers in peer-reviewed journals, books, and conferences, and workshops. He is serving as a co-Editor-in-Chief for Geo-Informatica: An International Journal on Advances in Computer Sc. for GIS, a series editor for the Springer Briefs in GIS, a general co-chair for Intl. Symposium on Spatial and Temporal Databases (2011) and a program co-chair for Intl. Conf. on Geographic Information Science (2012). He served on the editorial boards of IEEE Transactions on Knowledge and Data Engineering as well as the IEEE-CS Computer Science & Engineering Practice Board. He also served as a program co-chair of the ACM Intl. Workshop on Advances in Geographic Information Systems, 1996. He is serving on the National Academy's Future Work-force for Geospatial Intelligence Committee (2011). He served as a member of the mapping science committee of the National Research Council National Academy of Sciences (2004-9), as well as the Board of Directors of University Consortium on GIS (2003-2004). Dr. Shekhar received the IEEE Technical Achievement Award (2006). He is a Fellow of the IEEE Computer Society, a Fellow of the American Association for Advancement to Science, as well as a member of the ACM. Yan Huang received her B.S. degree in Computer Science from Beijing University, Beijing, China, in 1997 and Ph.D. degree in, Computer Science from University of Minnesota, Twin-cities, MN, USA, in 2003. She is currently an associate professor at the Computer Science and Engineering department of University of North Texas, Denton, TX, USA. Her research interests include spatio-temporal Databases and mining, geo-stream data processing, and mobile computing. Hui Xiong is currently an Associate Professor in the Management Science and Information Systems department at Rutgers University. He received the B.E. degree from the University of Science and Technology of China — (USTC), China, the M.S. degree from the National University of Singapore — (NUS), Singapore, and the Ph.D. degree from the University of Minnesota — (UMN), USA. His general area of research is data and knowledge engineering, with a focus on developing effective and efficient data analysis techniques for emerging data intensive applications. He has published over 90 technical papers in peer-reviewed journals and conference proceedings. He is a co-editor of Clustering and Information Retrieval (Kluwer Academic Publishers, 2003) and a co-Editor-in-Chief of Encyclopedia of GIS (Springer, 2008). He is an Associate Editor of the Knowledge and Information Systems journal and has served regularly in the organization committees and the program committees of a number of international conferences and workshops. He is a senior member of the ACM and IEEE.
PY - 2011/11
Y1 - 2011/11
N2 - This paper presents a study of the Multi-Type Reverse Nearest Neighbor (MTRNN) query problem. Traditionally, a reverse nearest neighbor (RNN) query finds all the objects that have the query point as their nearest neighbor. In contrast, an MTRNN query finds all the objects that have the query point in their multi-type nearest neighbors. Existing RNN queries find an influence set by considering only one feature type. However, the influence from multiple feature types is often critical for strategic decision making in many business scenarios, such as site selection for a new shopping center. To that end, we first formalize the notion of the MTRNN query by considering the influence of multiple feature types. We also propose R-tree based algorithms to find the influence set for a given query point and multiple feature types. Finally, experimental results are provided to show the strength of the proposed algorithms as well as design decisions related to performance tuning.
AB - This paper presents a study of the Multi-Type Reverse Nearest Neighbor (MTRNN) query problem. Traditionally, a reverse nearest neighbor (RNN) query finds all the objects that have the query point as their nearest neighbor. In contrast, an MTRNN query finds all the objects that have the query point in their multi-type nearest neighbors. Existing RNN queries find an influence set by considering only one feature type. However, the influence from multiple feature types is often critical for strategic decision making in many business scenarios, such as site selection for a new shopping center. To that end, we first formalize the notion of the MTRNN query by considering the influence of multiple feature types. We also propose R-tree based algorithms to find the influence set for a given query point and multiple feature types. Finally, experimental results are provided to show the strength of the proposed algorithms as well as design decisions related to performance tuning.
KW - Location-based service
KW - Reverse nearest neighbor search
KW - Spatial database
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UR - http://www.scopus.com/inward/citedby.url?scp=80052303158&partnerID=8YFLogxK
U2 - 10.1016/j.datak.2011.06.003
DO - 10.1016/j.datak.2011.06.003
M3 - Article
AN - SCOPUS:80052303158
SN - 0169-023X
VL - 70
SP - 955
EP - 983
JO - Data and Knowledge Engineering
JF - Data and Knowledge Engineering
IS - 11
ER -