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Distributed Mining of Spatial High Utility Itemsets in Very Large Spatiotemporal Databases using Spark In-Memory Computing Architecture | IEEE Conference Publication | IEEE Xplore

Distributed Mining of Spatial High Utility Itemsets in Very Large Spatiotemporal Databases using Spark In-Memory Computing Architecture

Publisher: IEEE

Abstract:

Finding Spatial High Utility Itemsets (SHUIs) in a spatiotemporal database is a challenging problem of great importance in many real-world applications. Most previous wor...View more

Abstract:

Finding Spatial High Utility Itemsets (SHUIs) in a spatiotemporal database is a challenging problem of great importance in many real-world applications. Most previous works focused on the sequential discovery of SHUIs in a database running on a single machine. Consequently, these works are not suitable for big data (or cloud-based) applications as they suffer from the scalability and fault tolerant problems. This paper proposes several novel pruning techniques to reduce the search space and present a more flexible distributed algorithm to find all desired itemsets from the database using Spark in-memory computing architecture. Our algorithm inherits several advantages of Spark, including low communication cost, fault tolerance, and high scalability. Experimental results demonstrate that the proposed algorithm has good scalability and performance on very large databases. Finally, we present a real-world navigation application in which SHUIs generated from the traffic congestion data have been employed to recommend alternative routes to the users.
Date of Conference: 10-13 December 2020
Date Added to IEEE Xplore: 19 March 2021
ISBN Information:
Publisher: IEEE
Conference Location: Atlanta, GA, USA

References

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