Spatiotemporal query processing for semantic data stream

Eom, Sungkwang; Shin, Sangjin; Lee, Kyong-Ho

doi:10.1109/icosc.2015.7050822

Cited by 13 publications

(6 citation statements)

References 16 publications

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“…Moreover, we plan to relieve the workload overhead of the PA. In connection with our previous work [25], we will improve an RSP engine in the perspective of large-scale applications such as IoT.…”

Section: Discussionmentioning

confidence: 95%

An adaptive plan-based approach to integrating semantic streams with remote RDF data

Chun

Jung

Seo

et al. 2016

Journal of Information Science

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To satisfy a user’s complex requirements, Resource Description Framework (RDF) Stream Processing (RSP) systems envision the fusion of remote RDF data with semantic streams, using common data models to query semantic streams continuously. While streaming data are changing at a high rate and are pushed into RSP systems, the remote RDF data are retrieved from different remote sources. With the growth of SPARQL endpoints that provide access to remote RDF data, RSP systems can easily integrate the remote data with streams. Such integration provides new opportunities for mixing static (or quasi-static) data with streams on a large scale. However, the current RSP systems do not offer any optimisation for the integration. In this article, we present an adaptive plan-based approach to efficiently integrate sematic streams with the static data from a remote source. We create a query execution plan based on temporal constraints among constituent services for the timely acquisition of remote data. To predict the change of remote sources in real time, we propose an adaptive process of detecting a source update, forecasting the update in the future, deciding a new plan to obtain remote data and reacting to a new plan. We extend a SPARQL query with operators for describing the multiple strategies of the proposed adaptive process. Experimental results show that our approach is more efficient than the conventional RSP systems in distributed settings.

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Section: Discussionmentioning

confidence: 95%

An adaptive plan-based approach to integrating semantic streams with remote RDF data

Chun

Jung

Seo

et al. 2016

Journal of Information Science

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“…Constructing a signature matrix on GPU 1 Construction_Procedure (w, T) /* Input : w is one keyword of data tuple, T is a G-AP-tree + */ /* Output: M is a signature matrix */ /* Construct a q-gram-keyword matrix */ 2 Retrieve n min-wise signatures of keywords from T for matching w. 3 Transfer n min-wise signatures into n q-gram-keyword vectors (v 1 , v 2 ,..., v n ) with the characteristic matrix. 4 Compute the min-wise signature of w and transfer it to a q-gram-keyword vector l with the characteristic matrix.…”

Section: Algorithmmentioning

confidence: 99%

“…For example, Galić et al [2] presents a formal framework consisting of data types and operations needed to support geo-streaming data. In [3], a spatio-temporal query language is proposed to process semantic geo-streaming data. 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.…”

Section: Introductionmentioning

confidence: 99%

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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“…Galić et al [7] presented a framework that consists of the types of data and the operations supporting spatial-streaming data. A spatial-temporal language for queries is discussed [8] for processing geo-streaming data. A distributed framework under geo-streams is developed [9], [10] for efficiently supervising motile data objects using distributed geo-streaming data processing in massive clusters while in real-time.…”

Section: Introductionmentioning

confidence: 99%

Efficient processing of continuous spatial-textual queries over geo-textual data stream

Metre

Kharat

2022

IJEECS

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<p>Due to the extensive use of social media and mobile devices, unbounded and massive data is generated continuously. The need to process this big data is increasing day by day. The traditional data processing algorithms fail to cater to the need of processing data generated by various applications such as digital geo-based advertising, and recommendation systems. There has been a high demand to process continuous spatial fuzzy textual queries over data stream of spatial-textual objects with high density by present locationbased and social network-based service applications. For the spatialkeyword data stream, the performance plays a vital role as the geo information and keyword description matching is needed for every incoming streaming object. The various continuous geo-keyword query processing methods normally lack the support for fuzzy keyword matching when processing the objects from the geo-textual data stream. The edit distancebased approach with the adaptive partitioning tree index for the queries is used for fuzzy string matching and it outperforms than the existing approaches in storage cost and query performance cost.</p>

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Spatiotemporal query processing for semantic data stream

Cited by 13 publications

References 16 publications

An adaptive plan-based approach to integrating semantic streams with remote RDF data

An adaptive plan-based approach to integrating semantic streams with remote RDF data

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

Efficient processing of continuous spatial-textual queries over geo-textual data stream

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