CIKM '93 Proceedings of the second international conference on Information and knowledge management
Bulk-insertions into R-trees using the small-tree-large-tree approach
Proceedings of the 6th ACM international symposium on Advances in geographic information systems
A data model and data structures for moving objects databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Generating spatiotemporal datasets on the WWW
ACM SIGMOD Record
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 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
Moving Objects Databases: Issues and Solutions
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
GBI: A Generalized R-Tree Bulk-Insertion Strategy
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Indexing the Current Positions of Moving Objects Using the Lazy Update R-tree
MDM '02 Proceedings of the Third International Conference on Mobile Data Management
Speeding up construction of PMR quadtree-based spatial indexes
The VLDB Journal — The International Journal on Very Large Data Bases
STRIPES: an efficient index for predicted trajectories
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A generic framework for monitoring continuous spatial queries over moving objects
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A trajectory splitting model for efficient spatio-temporal indexing
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Handling frequent updates of moving objects
Proceedings of the 14th ACM international conference on Information and knowledge management
Indexing the past, present, and anticipated future positions of moving objects
ACM Transactions on Database Systems (TODS)
Supporting frequent updates in R-trees: a bottom-up approach
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Improving performance with bulk-inserts in Oracle R-trees
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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
A benchmark for evaluating moving object indexes
Proceedings of the VLDB Endowment
The vehicle tracking system for analyzing transportation vehicle information
APWeb'06 Proceedings of the 2006 international conference on Advanced Web and Network Technologies, and Applications
A group based insert manner for storing enormous data rapidly in intelligent transportation system
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
GTree: an efficient grid-based index for moving objects
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
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A major issue in LBS (Location Based Service) is the handling of numerous historical moving object data, affecting query performance and service quality in application systems. In order to store and search lots of data rapidly, an effective index structure is required for improving not only the insertion method, but also the search performance. In order to improve the performance of both applications, we propose the GIP^+ (Group Insertion tree with Projection Plus) for historical data management such as the trajectory of a vehicle. This index structure, based on the GIP, employs the separated buffer node method for reducing overlaps. The GIP^+ also uses projection storage for improving search performance by grouping the intersected child node in a node. Additionally, the link between the buffer nodes is designed to directly connect to the next buffer node. To effectively combine these methods and improve the performance, different node levels in the GIP^+ are also arranged for applying the separated buffer node, the projection storage, and the link. The designed historical index structure is useful for inserting and searching data which is arranged on a time axis.