An Efficient Quad-Tree Based Index Structure for Cloud Data Management

Ding, Linlin; Qiao, Bo; Wang, Guoren; Chen, Chen

doi:10.1007/978-3-642-23535-1_22

Cited by 24 publications

(13 citation statements)

References 10 publications

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“…Finally, an adaptive strategy to optimize index performance is proposed. A novel quadtree based multi-dimensional index structure is proposed in [20] for efficient data management and query processing in cloud computing systems. It also adopts two index levels.…”

Section: Related Workmentioning

confidence: 99%

A Multi-dimensional Index Structure Based on Improved VA-file and CAN in the Cloud

Cheng

Sun

et al. 2014

Int. J. Autom. Comput.

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Currently, the cloud computing systems use simple key-value data processing, which cannot support similarity search effectively due to lack of efficient index structures, and with the increase of dimensionality, the existing tree-like index structures could lead to the problem of "the curse of dimensionality". In this paper, a novel VF-CAN indexing scheme is proposed. VF-CAN integrates content addressable network (CAN) based routing protocol and the improved vector approximation file (VA-file) index. There are two index levels in this scheme: global index and local index. The local index VAK-file is built for the data in each storage node. VAK-file is the k-means clustering result of VA-file approximation vectors according to their degree of proximity. Each cluster forms a separate local index file and each file stores the approximate vectors that are contained in the cluster. The vector of each cluster center is stored in the cluster center information file of corresponding storage node. In the global index, storage nodes are organized into an overlay network CAN, and in order to reduce the cost of calculation, only clustering information of local index is issued to the entire overlay network through the CAN interface. The experimental results show that VF-CAN reduces the index storage space and improves query performance effectively.

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Section: Related Workmentioning

confidence: 99%

A Multi-dimensional Index Structure Based on Improved VA-file and CAN in the Cloud

Cheng

Sun

et al. 2014

Int. J. Autom. Comput.

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“…RT-CAN [8], QT-Chord [3], EMINC [10] and A-Tree [7] are four types of multi ple-dimensional indexes suitable for cloud data management systems. RT-CAN integrates CAN based routing protocol and the R-tree based indexing scheme to support efficient multi-dimensional query processing in a Cloud system.…”

Section: Query Optimization Through Indexesmentioning

confidence: 99%

Fast Multi-fields Query Processing in Bigtable Based Cloud Systems

Wang

Meng

2013

Web-Age Information Management

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Abstract.With the rapid increase of data sizes, enterprise applications are migrating their backend data management and analytic systems into cloud based data management systems.Bigtable is among one of the major data models used by cloud storage systems as their storage layer. Such systems provide high scalability and schema flexibility, and support efficient point and range based queries based on rowkeys. However, Bigtable based systems have limited support on non-rowkey based queries and multiple-fields based queries, due to much overhead on invoking extra scanning of data. In this paper, we develop a system TNBGR(Telecom Network Browsing Gateway Records) on managing and querying large scale telecommunication data. TNBGR is built on top of HBase and MapReduce, with a focus on optimizing multi-fields query processing. TNBGR provides a novel application and system resource aware data allocation strategy to minimize data access through multi-layer region partitioning, resource parameterization, and balanced region distribution.The query composition dynamically updates application parameters based on tracked system statistics and automatically translates queries for MapReduce. Through additional query optimization by improving region locality, TNBGR achieves high efficiency on supporting multi-field queries. The experimental results show that our solution improves the performance of the queries by about 5 and 18 times respectively.

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“…In order to improve the query efficiency and eliminate the bottleneck of the centralized index paradigm, the computer nodes are organized into overlay networks such as CAN [5]. Based on the above index framework, Wang et al [6] , Ding et al [2] and Zhang et al [8] proposed other different index solutions respectively. Wang et al [6] built one R-tree to index the local data on each compute node, and organized the compute nodes into a CAN overlay network, the global index was constructed by selecting portion of the local R-tree index nodes to publish into the CAN overlay network.…”

Section: Related Workmentioning

confidence: 99%

“…Zhang et al [8] used the K-d tree for local data, and in the global index level he adopted the centralized index scheme by using R-tree to organize the portion of the local K-d tree nodes. Ding et al [2] used MX-CIF quad tree as the local index and Chord overlay network as the global index.…”

Section: Related Workmentioning

confidence: 99%

An efficient index for massive IOT data in cloud environment

Rao

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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The Internet of Things (IOT) has been widely applied in many fields, while the IOT data are always large volume, update frequently and inherently multi-dimensional, these characteristics bring big challenges to the traditional DBMSs. The traditional DBMSs have rich functionality and can deal with multi-attributes access efficiently, they can not scale good enough to deal with large volume data and can not support high insert throughput. The cloud-based database systems have good scalability, but they don't support multidimensional access natively.In order to deal with the large volume of IOT data, we propose an update and query efficient index framework (UQE-Index) based on key-value store that can support high insert throughput and provide efficient multi-dimensional query simultaneously.We implemented a prototype based on HBase and did comprehensive experiments to test our solution's scalability and efficiency.

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An Efficient Quad-Tree Based Index Structure for Cloud Data Management

Cited by 24 publications

References 10 publications

A Multi-dimensional Index Structure Based on Improved VA-file and CAN in the Cloud

A Multi-dimensional Index Structure Based on Improved VA-file and CAN in the Cloud

Fast Multi-fields Query Processing in Bigtable Based Cloud Systems

An efficient index for massive IOT data in cloud environment

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