SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Efficient indexing of the historical, present, and future positions of moving objects
Proceedings of the 6th international conference on Mobile data management
Indexing the past, present, and anticipated future positions of moving objects
ACM Transactions on Database Systems (TODS)
Query and update efficient B+-tree based indexing of moving objects
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Indexing the Past, Present and Future Positions of Moving Objects on Fixed Networks
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 04
Indexing Moving Objects Using Short-Lived Throwaway Indexes
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Trees or grids?: indexing moving objects in main memory
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Parallel main-memory indexing for moving-object query and update workloads
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
MOIST: a scalable and parallel moving object indexer with school tracking
Proceedings of the VLDB Endowment
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The ubiquity of GPS-enabled mobile devices and sensors have led to the explosive growth of time-stamped location data. Consequently Location-Based Services (LBS) has become a popular technology impacting various aspects of our lives. LBS applications are characterized by very high rate of location record updates, and many concurrent historic, present and predictive queries. Commercial LBS providers rely on relational databases to manage their data. However, traditional relational databases do not provide adequate support to meet the growing demands of many LBS systems. Moreover, existing indexing techniques that support historical queries are unable to sustain high update and query throughput as required by many LBS applications. To address this, we propose to exploit in-memory database techniques and present a few key ideas to support high performance commercial LBS. We also introduce a novel in-memory spatio-temporal index in which the spatial domain is organized as grid cells and for each grid cell partial temporal indexes are maintained for moving objects that visited the cell. The partial temporal indexes are implemented as compressed bitmaps. Using fast bitmap operations and utilizing parallelism rendered by multi-core systems, our system offers significantly better performance than traditional relational databases.