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Load-balanced Range Query Workload Partitioning for Compressed Spatial Hierarchical Bitmap (cSHB) Indexes

Description

The spatial databases are used to store geometric objects such as points, lines, polygons. Querying such complex spatial objects becomes a challenging task. Index structures are used to improve the lookup performance of the stored objects in the databases, but

The spatial databases are used to store geometric objects such as points, lines, polygons. Querying such complex spatial objects becomes a challenging task. Index structures are used to improve the lookup performance of the stored objects in the databases, but traditional index structures cannot perform well in case of spatial databases. A significant amount of research is made to ingest, index and query the spatial objects based on different types of spatial queries, such as range, nearest neighbor, and join queries. Compressed Spatial Bitmap Index (cSHB) structure is one such example of indexing and querying approach that supports spatial range query workloads (set of queries). cSHB indexes and many other approaches lack parallel computation. The massive amount of spatial data requires a lot of computation and traditional methods are insufficient to address these issues. Other existing parallel processing approaches lack in load-balancing of parallel tasks which leads to resource overloading bottlenecks.

In this thesis, I propose novel spatial partitioning techniques, Max Containment Clustering and Max Containment Clustering with Separation, to create load-balanced partitions of a range query workload. Each partition takes a similar amount of time to process the spatial queries and reduces the response latency by minimizing the disk access cost and optimizing the bitmap operations. The partitions created are processed in parallel using cSHB indexes. The proposed techniques utilize the block-based organization of bitmaps in the cSHB index and improve the performance of the cSHB index for processing a range query workload.

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Agent

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Date Created
2018

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On Processing Spatial Queries in Graph Database Management Systems

Description

Spatial data is fundamental in many applications like map services, land resource management, etc. Meanwhile, spatial data inherently comes with abundant context information because spatial entities themselves possess different properties, e.g., graph or textual information, etc. Among all these compound

Spatial data is fundamental in many applications like map services, land resource management, etc. Meanwhile, spatial data inherently comes with abundant context information because spatial entities themselves possess different properties, e.g., graph or textual information, etc. Among all these compound spatial data, geospatial graph data is one of the most challenging for the complexity of graph data. Graph data is commonly used to model real scenarios and searching for the matching subgraphs is fundamental in retrieving and analyzing graph data. With the ubiquity of spatial data, vertexes or edges in graphs are enriched with spatial location attributes side by side with other non-spatial attributes. Graph-based applications integrate spatial data into the graph model and provide more spatial-aware services. The co-existence of the graph and spatial data in the same geospatial graph triggers some new applications. To solve new problems in these applications, existing solutions develop an integrated system that incorporates the graph database and spatial database engines. However, existing approaches suffer from the architecture where graph data and spatial data are isolated. In this dissertation, I will explain two indexing frameworks, GeoReach and RisoTree, which can significantly accelerate the queries in geospatial graphs. GeoReach includes a query operator that adds spatial data awareness to a graph database management system. In GeoReach, the neighborhood spatial information is summarized and stored on each vertex in the graph. The summarization includes three different structures according to the location distribution. These spatial summaries are utilized to terminate the graph search early.RisoTree is a hierarchical tree structure where each node is represented by a minimum bounding rectangle (MBR). The MBR of a node is a rectangle that encloses all its children. A key difference between RisoTree and RTree is that RisoTree contains pre-materialized subgraph information to each index node. The subgraph information is utilized during the spatial index search phase to prune search paths that cannot satisfy the query graph pattern. The RisoTree index reduces the search space when the spatial filtering phase is performed with relatively light cost.

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Agent

Created

Date Created
2021