High performance location-based services in a main-memory database

Ray, Suprio; Blanco, Rolando; Goel, Anil Kumar

doi:10.1007/s10707-016-0278-6

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…However, our focus is on designing of consistent index for moving objects not just a concurrency control algorithm. Many recent studies have proposed an index for moving objects and they involved parallelism to improve the response time of query operations 8,51–59 . We have not included such studies in our scope as these studies are based on parallelism rather than concurrency control.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…Many recent studies have proposed an index for moving objects and they involved parallelism to improve the response time of query operations. 8,[51][52][53][54][55][56][57][58][59] We have not included such studies in our scope as these studies are based on parallelism rather than concurrency control. And, focus of this article is on the resolution of conflicts while processing concurrent update and query operations.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

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Exploiting hashing for concurrent query processing and indexing of current location of moving objects

Chaudhry,

Yousaf

2024

Concurrency and Computation

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SummaryDue to recent developments in location‐based services and mobile computing, the need for indices for moving objects has been strengthened to improve the response time of a query operation. With a single index in place for managing both the update and query operations for moving objects, the index needs to be updated each time the object moves. This deteriorates the performance of concurrent query operations. It is critical to handle the conflicts between continuous update and query operations effectively using appropriate concurrency control protocol otherwise, inconsistent results will be reported. Many indices have been proposed in the literature for moving objects but they lack the support for processing concurrent operations. Further, the consistent indices in the literature are based on tree structure that have computationally expensive split/merge operations that can negatively affect the response time of query processing algorithms. Moreover such tree based indices are proposed for historical and future timeline data. As the scope of this article is on current timeline and concurrent continuous query operations, therefore, we exploit state of the art hash‐based indices in the literature and presented the two consistent versions. The comparative analysis of the indices is performed and meaningful findings are also presented along.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Section: Background and Literature Reviewmentioning

confidence: 99%

Exploiting hashing for concurrent query processing and indexing of current location of moving objects

Chaudhry,

Yousaf

2024

Concurrency and Computation

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“…The first involves tree-based indexing methods, such as R-tree, Quad-tree, and their variants, which are based on external storage and are commonly used in traditional spatial database systems [14]. Traditional spatial database systems have been empowered with rich functionalities by using tree-like structure indexes to support efficient multi-dimensional access, but they cannot scale well enough to address large volumes of data and support high throughputs [15,16]. The second consists of hybrid indexing methods that synthesize the advantages of different indexes to compensate for the existing methods [17].…”

Section: Introductionmentioning

confidence: 99%

“…The indexes in the second group aim for the real-time computation and analysis of multi-modal scene data. Generally, with the increase in main-memory capacity, researchers tend to design internal and external collaborative index structures to store the index and hotspot data in the main memory to achieve faster processing speeds [16]. Indexes in this group usually adopt the global-local hybrid pattern to achieve high efficiency during parallel computing.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Graph-Based Spatio-Temporal Indexing Method for Task-Oriented Multi-Modal Scene Data Organization

Feng

Zhu

Liu

et al. 2018

IJGI

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Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the complex relationships of real-time multi-modal scene data; specifically, queries are limited to a certain spatio-temporal range or a small number of selected attributes. This paper proposes a new spatio-temporal indexing method for task-oriented multi-modal scene data organization. First, a hybrid spatio-temporal index architecture is proposed based on the analysis of the characteristics of scene data and the driving forces behind the scene tasks. Second, a graph-based spatio-temporal relation indexing approach, named the spatio-temporal relation graph (STR-graph), is constructed for this architecture. The global graph-based index, internal and external operation mechanisms, and optimization strategy of the STR-graph index are introduced in detail. Finally, index efficiency comparison experiments are conducted, and the results show that the STR-graph performs excellently in index generation and can efficiently address the diverse requirements of different visualization tasks for data scheduling; specifically, the STR-graph is more efficient when addressing complex and uncertain spatio-temporal relation queries.

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“…The third paper in this issue [3] discusses how emerging modern hardware can be leveraged to enable efficient and large-scale GeoStreaming. This paper argues that the key characteristics of the Location-Based Services (LBS) applications include a high rate of timestamped location updates, and many concurrent historical, present and predictive queries.…”

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confidence: 99%

Guest editorial: GeoStreaming

2017

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We are entering the era of Bbig data^thanks to the exponential growth and availability of structured and unstructured data, among which a large amount are real-time streaming data emitted from sensors, imagery and mobile devices. In addition to the temporal nature of stream data, various sources provide stream data that has geographical locations and/or spatial extents, such as geotagging twitter streams, mobile GPS location streams, spatial temporal image streams, and so on. On one hand, this amount of streamed data has been a major propeller to advance the state of the art in geographic information systems. On the other hand, the ability to process, mine, and analyze that massive amount of data in a timely manner prevented researchers from making full use of the incoming stream data. GeoStreaming refers to the ongoing effort in academia and industry to process, mine and analyze stream data with geographic and spatial information.The purpose of this special issue is to showcase some of the recent developments and novel applications of GeoStreaming. The open call for GeoStreaming has attracted nine papers covering broad range of GeoStreaming technologies and applications. After two rounds of peer-reviews by a team of international experts, seven papers were selected to be included in this special issue.We start this issue with two papers focused on system design for mobility data analysis [1,2]. 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 Geoinformatica (2017) 21:231-235

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High performance location-based services in a main-memory database

Cited by 9 publications

References 24 publications

Exploiting hashing for concurrent query processing and indexing of current location of moving objects

Exploiting hashing for concurrent query processing and indexing of current location of moving objects

An Efficient Graph-Based Spatio-Temporal Indexing Method for Task-Oriented Multi-Modal Scene Data Organization

Guest editorial: GeoStreaming

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