Distributed Indexing Methods for Moving Objects based on Spark Stream
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Generally, existing parallel main-memory spatial index structures to avoid the trade-off between query freshness and CPU cost uses light-weight locking techniques. However, still, the lock based methods have some limits such as thrashing which is a well-known problem in lock based methods. In this paper, we propose a distributed index structure for moving objects exploiting the parallelism in multiple machines. The proposed index is a lock free multi-version concurrency technique based on the D-Stream model of Spark Stream. The proposed method exploits the multiversion nature of D-Stream of Spark Streaming.
Abstract
Generally, existing parallel main-memory spatial index structures to avoid the trade-off between query freshness and CPU cost uses light-weight locking techniques. However, still, the lock based methods have some limits such as thrashing which is a well-known problem in lock based methods. In this paper, we propose a distributed index structure for moving objects exploiting the parallelism in multiple machines. The proposed index is a lock free multi-version concurrency technique based on the D-Stream model of Spark Stream. The proposed method exploits the multiversion nature of D-Stream of Spark Streaming.
참고문헌 (11)
- K. Kim, "Optimizing Multidimensional Index Trees for Main Memory Access", SIGMOD Rec, 30(2), pp.139~150 (2001)
- L. Biveinis, "Main-memory Operation Buffering for Efficient R-tree Update", Proc. VLDB, pp.591~602 (2007)
- J. Dittrich, "Indexing Moving Objects using Short-lived Throwaway Indexes", Proc. SSTD, pp.189~207 (2009)
- D. Šidlauskas, "Parallel Mainmemory Indexing for Moving-object Query and Update Workloads", Proc. ACM SIGMOD, pp.37~48 (2012)
- M. Zaharia, "Discretized Streams: An Efficient and Fault-tolerant Model for Stream Processing on Large Clusters", Proc. USENIX on Hot Topics in Cloud Computing, pp.10~ (2012)
- A. Akdogan, "ToSS-it: A Cloud-Based Throwaway Spatial Index Structure for Dynamic Location Data", Proc. MDM, pp.249~258 (2014)
- W. Lu, "Efficient Processing of k Nearest Neighbor Joins using MapReduce", Proc. VLDB, pp.1016~1027 (2012)
- C. Zhang, "Efficient parallel kNN Joins for Large Data in MapReduce", Proc. EDBT, pp.38~39 (2012)
- Z. Deng, "Parallel Processing of Dynamic Continuous Queries over Streaming Data Flows", IEEE Transactions on Parallel and Distributed Systems, 26(3), pp.834~846 (2015)
- C. S. Jensen, "GPU-Based Computing of Repeated Range Queries over Moving Objects", Proc. Euromicro International Conference on Parallel, Distributed and Network-Based Processing, pp.640~647 (2014)
- F. Lettich, "Manycore Processing of Repeated Range Queries over Massive Moving", CoRR (2014)