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Locality Sensitive Hashing (LSH) is a widely used similarity search technique for many web services, such as content-based retrieval services for images and videos. Due to its popularity, much research effort has been devoted to improving the search quality, and the indexing and query performance of LSH. However, most existing variants of LSH can only run on single node, which limits their applicability...
This paper proposes the GraphF abstraction which exploits Adaptive Radix Tree for efficient graph indexing with lower storage cost. Leveraging the GraphF abstraction, we implement a separate graph computation engine on Spark. Experiments showed that on average GraphF outperforms GraphX and PowerGraph by up to 8.1X and 3.6X separately in execution time both for real world and for synthetic graphs....
Locality Sensitive Hashing (LSH) is an important indexing technique for approximate similarity search in high-dimensional spaces. An obvious limitation of LSH approaches is the lack of capability and scalability to deal with massive data. This paper proposes a distributed variant of LSH called Spark-LSH, which is implemented on Apache Spark, a well-known distributed computing framework. We design...
With the fast development of location-based services and geo-tagging, spatial keyword queries that retrieve objects satisfying both spatial and keyword conditions are gaining in prevalence. A hybrid index that integrates a spatial index (e.g., the R-tree or its variations) with a keyword filter offers a promising approach for processing such queries efficiently. However, it is still an open problem...
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