Geospatial data streams: Formal framework and implementation

Galić, Zdravko; Baranović, Mirta; Križanović, Krešimir; Mešković, Emir

doi:10.1016/j.datak.2014.02.002

Cited by 16 publications

(7 citation statements)

References 9 publications

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“…TelegraphCQ (online) [14] Possible link for offline with Hermes(PostGis) [74] -Sharing paradigm (Psoup) [16] -Adaptive processing (eddy) [10] -Dynamic scheduling [86] -ST Sampling [76] -Windows aggregation [73] -Windows at different granularity level [72] -Index at different granularity level [77] Flux [81] SCUBA [62] CAPE [78] -Plan migration [97] -Adaptive scheduling [85] -Cluster sharing paradigm [62] -Clustersheddy [63] -Aggregation D-CAPE [84] GeoInsight [48] Microsoft StreamInsight [7] -Fusing horizontal & vertical [7] -Stream partitionning knn range queries [56] -Event-based [7] -Views derived from archive in-memory [48] -Native support for ST stream [6] Infosphere Streams ITS [12] SPADE [7] -scheduling component [89] -operator fusing [49] -basic PE (Processing Element) -map-matching [6] -shortest path -Datafaflow [12] Zaghreb laboratory works -TelegraphCQ -Implementation in java -General framework for MO [30] -Uncertainty handling -Trajectory buffering [55] thors proposes to deal with moving objects in a online way only by processing data in-memory, but this work doesn't take care of distribution aspects.…”

Section: Kostas Patroumpas Workmentioning

confidence: 99%

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Design principles of a stream-based framework for mobility analysis

Salmon

Ray

2016

Geoinformatica

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Trajectory analysis is of crucial importance in several fields as social analysis, zoology, climatology or traffic monitoring. Over the last decade, the number of mobile systems and devices recording their positions has grown significantly generating a deluge of spatial and temporal data to analyze. This increasing volume of data raises numerous issues in terms of storage, processing and extraction of information. Previous works considering movement analysis have been mainly oriented towards either archived data processing and mining or continuous handling of incoming streams. The research developed in this paper introduces the design principles of a holistic approach combining real-time processing and archived data analysis to process mobility data "on the fly". This solution aims to provide better results comparing to both purely offline and online approaches. This research considers distributed data and processing to be more efficient. The design principles are applied to maritime traffic analysis and a few representative examples are introduced to demonstrate the relevance of our approach.

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Section: Kostas Patroumpas Workmentioning

confidence: 99%

“…In [30] the authors suggest a general framework to deal with moving objects knowing about the limits of TelegraphCQ in [71] and inspired by the algebra defined in [96]. It can be used as a basis for developing a geospatial DSMS but doesn't take into account management and query repartition aspect.…”

Section: Kostas Patroumpas Workmentioning

confidence: 99%

Design principles of a stream-based framework for mobility analysis

Salmon

Ray

2016

Geoinformatica

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“…This can be seen in techniques defined for queries for superset containment using the ordered keyword trie [6]. 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.…”

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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“…A number of successful attempts have been made for the challenge that current GIS applications face. 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.…”

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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Geospatial data streams: Formal framework and implementation

Cited by 16 publications

References 9 publications

Design principles of a stream-based framework for mobility analysis

Design principles of a stream-based framework for mobility analysis

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

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

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