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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...
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...
Data clustering is usually time-consuming since it by default needs to iteratively aggregate and process large volume of data. Approximate aggregation based on sample provides fast and quality ensured results. In this paper, we propose to leverage approximation techniques to data clustering to obtain the trade-off between clustering efficiency and result quality, along with online accuracy estimation...
The data placement strategy greatly affects the efficiency of MapReduce. The current strategy only takes the map phase into account to optimize the map time. But the ignored shuffle phase may increase the total running time significantly in many jobs. We propose a new data placement strategy, named OPTAS, which optimizes both the map and shuffle phases to reduce their total time. However, the huge...
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