GeoStreams

Brandt, Tobias; Grawunder, Marco

doi:10.1145/3177848

Cited by 5 publications

(3 citation statements)

References 121 publications

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“…For example in Figure 3 , consider two sets of q-grams S 1 , S 2 ⊆ U with D = 12, a sequence of index of one permutation π from U is defined as I = {0,1,...,11}, V 1 , V 2 are two binary (0/1) data vectors for representing locations of the nonzeros in π. We equally divide the sequence I into three bins and find the smallest nonzero element in each bin to generate π(V 1 ) = [0, 5, 8] and π(V 2 ) = [1,6,8]. Finally, we can get three min-wise signatures of S 1 from π(V 1 ) (i.e., 0, 5, 8 ) and three min-wise signatures of S 2 from π(V 2 ) (i.e., 1, 6, 8).…”

Section: Accelerating the Generation Of Min-wise Signaturesmentioning

confidence: 99%

“…Furthermore, Moby Dick [4,5], which is a distributed framework for GeoStreams, has been developed towards efficient real-time managing and monitoring of mobile objects through distributed geo-streaming data processing on large clusters. A more comprehensive introduction about processing GeoStreams is available in [6].…”

Section: Introductionmentioning

confidence: 99%

“…However, for the case that there are multiple keywords in one cut of one keyword node, there still exist multiple min-wise signatures. We can further reduce the space cost by merging multiple min-wise signatures into one min-wise signature based on Equation (6). Let G W be a q-gram set of keyword W, the scheme is illustrated in Figure 2.…”

mentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Indexing Approach for Continuous Spatial Approximate Keyword Queries over Geo-Textual Streaming Data

Deng

Wang

et al. 2019

IJGI

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Current social-network-based and location-based-service applications need to handle continuous spatial approximate keyword queries over geo-textual streaming data of high density. The continuous query is a well-known expensive operation. The optimization of continuous query processing is still an open issue. For geo-textual streaming data, the performance issue is more serious since both location information and textual description need to be matched for each incoming streaming data tuple. The state-of-the-art continuous spatial-keyword query indexing approaches generally lack both support for approximate keyword matching and high-performance processing for geo-textual streaming data. Aiming to tackle this problem, this paper first proposes an indexing approach for efficient supporting of continuous spatial approximate keyword queries by integrating m i n - w i s e signatures into an AP-tree, namely AP-tree + . AP-tree + utilizes the one-permutation m i n - w i s e hashing method to achieve a much lower signature maintenance costs compared with the traditional m i n - w i s e hashing method because it only employs one hashing function instead of dozens. Towards providing a more efficient indexing approach, this paper has explored the feasibility of parallelizing AP-tree + by employing a Graphic Processing Unit (GPU). We mapped the AP-tree + data structure into the GPU’s memory with a variety of one-dimensional arrays to form the GPU-aided AP-tree + . Furthermore, a m i n - w i s e parallel hashing algorithm with a scheme of data parallel and a GPU-CPU data communication method based on a four-stage pipeline way have been used to optimize the performance of the GPU-aided AP-tree + . The experimental results indicate that (1) AP-tree + can reduce the space cost by about 11% compared with MHR-tree, (2) AP-tree + can hold a comparable recall and 5.64× query performance gain compared with MHR-tree while saving 41.66% maintenance cost on average, (3) the GPU-aided AP-tree + can attain an average speedup of 5.76× compared to AP-tree + , and (4) the GPU-CPU data communication scheme can further improve the query performance of the GPU-aided AP-tree + by 39.4%.

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Section: Accelerating the Generation Of Min-wise Signaturesmentioning

confidence: 99%