Book Chapter10.1007/978-3-030-29029-0_7
Exploring Bit Arrays for Join Processing in Spatial Data Streams
Wendy Osborn
- 05 Sep 2019
- pp 73-85
8
TL;DR: This work explores the use of a Bloom-filter inspired representation of a spatial object using a bit array as an inspired representation for spatial joins in spatial data streams.
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Abstract: In this paper, the use of bit arrays for processing spatial joins in spatial data streams is explored. Although spatial joins between objects have been explored in other contexts, such as centralized and distributed systems, they have not been explored in great detail in spatial data streams. This work explores the use of a Bloom-filter (i.e., bit array) inspired representation of a spatial object. Strategies for both mapping objects to bit arrays, and processing spatial joins using the bit arrays in a data stream environment are presented. The strategies are evaluated and compared with spatial (non-bit) join approaches. Performance improvements are identified, and areas of improvement are also identified.
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Citations
Data Stream Query Processing on Mobile Devices
Mitra Kazemzadeh,Wendy Osborn +1 more
- 12 Oct 2020
TL;DR: This paper proposes an architecture and framework for stream data management and query processing on a mobile device that only keeps a minimal number of features and presents details on an implementation of this framework for a currency conversion application, which demonstrates the utility of the framework.
4
An Extension Proposal of AntOR for Parallel Computing
Delfín Rupérez Cañas,Ana Lucila Sandoval Orozco,Luis Javier García Villalba +2 more
- 15 Dec 2011
TL;DR: This article proposes an extension of AntOR that using programming multiprocessor architectures based on shared memory protocol, allows to run tasks in parallel using threads, being applicable this parallelization in the route discovery phase, route local repair process and link failure notification.
1
Shedding strategies for optimizing join processing in spatial data streams
TL;DR: In this paper , the authors proposed several strategies for object shedding for spatial data stream query processing, which employ spatial data properties, object properties and non-spatial properties in order to decide which objects to discard when space is needed.
1
Unbounded Spatial Data Stream Query Processing using Spatial Semijoins
Wendy Osborn
- 01 Mar 2021
TL;DR: Two strategies for spatial data stream join processing are proposed where the spatial extent of the spatial object stream is not required to be known in advance, and both estimate the common region that is shared by two or more spatial data streams in order to process the spatial join.
1
Join processing in unbounded spatial data streams
TL;DR: This work explores a particular issue with spatial join processing in spatial data streams - the lack of a bounded region of space from which the spatial objects are generated, and two strategies are proposed.
1
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