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In most spatial data management applications, objects are represented in terms of their coordinates in a 2-dimensional space and search queries in this space are processed using spatial index structures. On the other hand, bitmap-based indexing, especially thanks to the compression opportunities bitmaps provide, has been shown to be highly effective for query processing workloads including selection and aggregation operations. In this paper, we show that bitmapbased indexing can also be highly effective for managing spatial data sets. More specifically, we propose a novel compressed spatial hierarchical bitmap (cSHB) index structure to support spatial range queries. We consider query workloads involving multiple range queries over spatial data and introduce and consider the problem of bitmap selection for identifying the appropriate subset of the bitmap files for processing the given spatial range query workload. We develop cost models for compressed domain range query processing and present query planning algorithms that not only select index nodes for query processing, but also associate appropriate bitwise logical operations to identify the data objects satisfying the range queries in the given workload. Experiment results confirm the efficiency and effectiveness of the proposed compressed spatial hierarchical bitmap (cSHB) index structure and the range query planning algorithms in supporting spatial range query workloads.

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Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many diverse application domains. Locality Sensitive Hashing (LSH) is one of the most popular techniques for finding approximate nearest neighbor searches in high-dimensional spaces. The main benefits of LSH are its sub-linear query performance and theoretical guarantees on the query accuracy. In this survey paper, we provide a review of state-of-the-art LSH and Distributed LSH techniques. Most importantly, unlike any other prior survey, we present how Locality Sensitive Hashing is utilized in different application domains.CCS Concepts: • General and reference → Surveys and overviews.

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