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GESS: a scalable similarity-join algorithm for mining large data sets in high dimensional spaces
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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PODS '84 Proceedings of the 3rd ACM SIGACT-SIGMOD symposium on Principles of database systems
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Cache Conscious Indexing for Decision-Support in Main Memory
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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WADS '95 Proceedings of the 4th International Workshop on Algorithms and Data Structures
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SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
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The VLDB Journal — The International Journal on Very Large Data Bases
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WISEW '02 Proceedings of the Third International Conference on Web Information Systems Engineering (Workshops) - (WISEw'02)
Analysis of predictive spatio-temporal queries
ACM Transactions on Database Systems (TODS)
Main Memory Evaluation of Monitoring Queries Over Moving Objects
Distributed and Parallel Databases
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SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
STRIPES: an efficient index for predicted trajectories
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Monitoring k-Nearest Neighbor Queries over Moving Objects
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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AGILE: adaptive indexing for context-aware information filters
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VLDB '06 Proceedings of the 32nd international conference on Very large data bases
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Scaling games to epic proportions
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Shakra: tracking and sharing daily activity levels with unaugmented mobile phones
Mobile Networks and Applications
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
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The VLDB Journal — The International Journal on Very Large Data Bases
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ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
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Proceedings of the VLDB Endowment
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With the exponential growth of moving objects data to the Gigabyte range, it has become critical to develop effective techniques for indexing, updating, and querying these massive data sets. To meet the high update rate as well as low query response time requirements of moving object applications, this paper takes a novel approach in moving object indexing. In our approach, we do not require a sophisticated index structure that needs to be adjusted for each incoming update. Rather, we construct conceptually simple short-lived index images that we only keep for a very short period of time (sub-seconds) in main memory. As a consequence, the resulting technique MOVIES supports at the same time high query rates and high update rates, trading this property for query result staleness. Moreover, MOVIES is the first main memory method supporting time-parameterized predictive queries. To support this feature, we present two algorithms: non-predictive MOVIES and predictive MOVIES. We obtain the surprising result that a predictive indexing approach--considered state-of-the-art in an external-memory scenario--does not scale well in a main memory environment. In fact, our results show that MOVIES outperforms state-of-the-art moving object indexes such as a main-memory adapted B x -tree by orders of magnitude w.r.t. update rates and query rates. In our experimental evaluation, we index the complete road network of Germany consisting of 40,000,000 road segments and 38,000,000 nodes. We scale our workload up to 100,000,000 moving objects, 58,000,000 updates per second and 10,000 queries per second, a scenario at a scale unmatched by any previous work.