Nine-areas-tree-bit-patterns-based method for continuous range queries over moving objects

Chen, Hue-Ling; Chang, Youngho

doi:10.1049/iet-sen.2009.0085

Cited by 6 publications

(6 citation statements)

References 16 publications

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“…But, these methods have poor query performance due to dynamic nature of data and overlapping of data in tree nodes as it requires traversing the tree on more than one path to process the query. To index the continuous queries, some tree structures without overlapping are proposed (Park, 2007 ;Chen, 2011 ). The KDB-tree is used to index RFID queries (Park, 2007) which are continuous in nature.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(Kalashnikov, 2004) used hierarchical grid cells to organize skewed queries to accelerate continuous range queries to process over dynamic spatial objects. But it suffers from high cost of storage and time for creation and updating of indexing structures (Chen, 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The gridbased methods use cell approach (Bohm, 2007;Kalashnikov, 2004) for indexing spatial queries and it divides the indexing pace into equal-sized grid. The grid-based approach results in better search and update time than the tree-based approaches (Kalashnikov, 2004) but it incurs high construction cost for indexing queries (Chen, 2011). Now days, due to dynamic nature of location-based queries, various query indexing approaches for continuous range queries (Gedik, 2006 ;Li & Karras, 2012) are suggested.…”

Section: Introductionmentioning

confidence: 99%

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Location based Continuous Query Processing over Geo-streaming Data

Metre¹

2021

TURCOMAT

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In recent years, many data-intensive and location based applications have emerged that need to process stream data in applications such as network monitoring, telecommunications data management, and sensor networks. Unlike regular queries, a continuous query exists for certain period of time and need to be continuously processed during this time. The algorithms used for data processing for the traditional database systems are not suited to tackle complex and various continuous queries over dynamic streaming data. The indexing for finite queries is preferred to indexing on infinite data to avoid expensive operations of index maintenance. Previous related work focused on moving queries on static objects or static queries on moving object. But now-a-days queries as well as objects are dynamic. So, hybrid indexing for queries significantly reduces the space costs and scales well with the increasing data. To deal with the speed of unbounded data, it is necessary to use data parallelism in query processing. The data parallelism in query processing offers better performance, availability and scalability.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Location based Continuous Query Processing over Geo-streaming Data

Metre¹

2021

TURCOMAT

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“…Too large of a size may cause too many moving objects residing in one cell to decrease the query performance of the indexing structure. On the contrary, too small of one can lead to the high costs of space and time for building and rebuilding their indexing structures [28].…”

Section: Related Workmentioning

confidence: 99%

G-ML-Octree: An Update-Efficient Index Structure for Simulating 3D Moving Objects Across GPUs

Deng

Wang

Han

et al. 2018

IEEE Trans. Parallel Distrib. Syst.

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In real simulation applications, simulations often involve large volumes of three-dimensinal (3D) moving objects. With the rapid growth of the scale of simulation-problem domains, it has become a key requirement to efficiently manage massive 3D moving objects. Conventional indexing approaches for managing 3D moving objects during simulations generally suffer from excessive update costs. Aiming to this problem, this paper first proposes an update-efficient indexing structure by fusing a loose Octree and one update-memo structure, namely ML-Octree. ML-Octree significantly reduces the update costs of one simulation involving massive 3D moving objects. Towards providing a more efficient indexing approach, this paper has explored the feasibility of paralleling ML-Octree by employing Graphic Processing Unit (GPU). A load-balancing scheme is used to further improve the update performance of the GPU-aided ML-Octree. Finally, a distributed GPU-aided ML-Octree is proposed for largescale simulations. The experimental results indicate that (1) ML-Octree can acquire the update-performance gain of an order of magnitude similar to that of Octree, (2) the GPU-aided ML-Octree can accelerate 5.07× faster than a parallel ML-Octree with 8 CPU threads on average, (3) the load-balance scheme can improve GPU-aided ML-Octree by 2.3× on average, and (4) the distributed GPU-aided ML-Octree can efficiently support large-scale simulations.

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“…By location detection devices (e.g. global postioning system devices), the moving objects can get their current locations [2]. Then, this information is reported to the server via wireless network or other communication network.…”

Section: Introductionmentioning

confidence: 99%

Dual‐expansion indexing for moving objects

Shen

Chang

2014

IET softw.

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With the development of wireless communications and mobile computing technologies, the applications of moving objects have been developed in many topics, for example, traffic monitoring. Such applications need to track the current and near-future locations of the moving objects. This motivates the development of spatial-temporal indices to support efficient querying about such locations of the moving objects. Therefore, in this study, the authors propose a dual-expansion indexing (DEI) to support the current and near-future prediction of the moving objects. To filter out more number of the data blocks that do not contain the final result, the query region can be expanded in each of eight directions individually. To further reduce the number of the data blocks that should be examined, the qualified data blocks can be expanded according to the direction towards the query region. Moreover, only the objects moving to the query region will be checked in the query process of DEI. Therefore our method can reduce more number of retrieved data blocks and that of input/output operations than the existing method. Experimental results show that the query process of DEI is more efficient than that of the existing method.

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Nine-areas-tree-bit-patterns-based method for continuous range queries over moving objects

Cited by 6 publications

References 16 publications

Location based Continuous Query Processing over Geo-streaming Data

Location based Continuous Query Processing over Geo-streaming Data

G-ML-Octree: An Update-Efficient Index Structure for Simulating 3D Moving Objects Across GPUs

Dual‐expansion indexing for moving objects

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