Data storage optimization strategy in distributed column-oriented database by considering spatial adjacency

Zheng, Kun; Gu, Danpeng; Fang, Fan Tao; Zhang, Miao; Zheng, Kang; Li, Qi

doi:10.1007/s10586-017-1081-3

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…al. paper stated that Apache Hadoop is a framework that facilates parallelization, remote execution, data distribution, load balancing, or fault tolerance while working with SBD [29]. Other then Apache Hadoop; Pregel [18], GraphLab [17], Power-Graph [27], HaLoop [3], PrIter [26], and CIEL [17] also provide solution for processing SBD.…”

Section: B Data Processing Methods For Heterogeneous Data and Big Damentioning

confidence: 99%

Future Prospects and Challenges of Big Data with Big Data Problems

2019

IJITEE

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As we aware, every second, petabytes of data is being generated from various sources as mentioned in previous para at a rapid speed, other sources may be stock market transactions data, sales and marketing data, sensors data, web documents, internet images, movies, multimedia data and lot many. These Big Data are as volume, variety, velocity, veracity and value in 5 V’s, which explains the complexity of Big Data. Due to adoption of Big Data analytics, there is demand of efficient technologies to manage heterogeneous data]. 21st century has marked the advent of Rich-Data concept. Different societal application such as disaster management, urban planning & monitoring, health hazards etc. has reinvigorated the significance of Geographic Information Systems (GIS). Newer and advance technologies are generating means to produce location-based dataset called as geospatial datasets. Increasing influx of user-generated data has amplified geospatial data and has outdid conventional computation requirement limits. The similitude of Big data and data in consideration has evolved the concept and appellation as “Spatial Big Data” (SBD). This paper is an effort towards enlistment and analysis of the different SBD concepts and technologies in the contemporary time. The main endeavor of this paper is to critically analyze the different technologies in the present day and identify the existing technical inadequacies in the existing systems

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Section: B Data Processing Methods For Heterogeneous Data and Big Damentioning

confidence: 99%

Future Prospects and Challenges of Big Data with Big Data Problems

2019

IJITEE

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“…Using this disk-based R-tree, processing a spatial query is to scan a NoSQL table. As the query range expands, the performance of the scan operation endures a serious degradation from the increasing invalid fragment queries [58,59].…”

Section: Spatial-query Processing On Nosql Databasesmentioning

confidence: 99%

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

Sun

Jin

2023

Sustainability

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Geospatial databases are basic tools to collect, index, and manage georeferenced data indicators in sustainability research for efficient, long-term analysis. NoSQL databases are increasingly applied to manage the ever-growing massive spatial vector data (SVD) with their changeable data schemas, agile scalability, and fast query response time. Spatial queries are basic operations in geospatial databases. According to Green information technology, an efficient spatial index can accelerate query processing and save power consumption for ubiquitous spatial applications. Current solutions tend to pursue it by indexing spatial objects with space-filling curves or geohash on NoSQL databases. As for the performance-wise R-tree family, they are mainly used in slow disk-based spatial access methods on NoSQL databases that incur high loading and searching costs. Therefore, performing spatial queries efficiently with the R-tree family on NoSQL databases remains a challenge. In this paper, an in-memory balanced and distributed R*-tree index named the BDRST index is proposed and implemented on HBase for efficient spatial-query processing of massive SVD. The BDRST index stores and distributes serialized R*-trees to HBase regions in association with SVD partitions in the same table. Moreover, an efficient optimized server-side parallel processing framework is presented for real-time R*-tree instantiation and query processing. Through extensive experiments on real-world land-use data sets, the performance of our method is tested, including index building, index quality, spatial queries, and applications. Our proposed method outperforms other state-of-the-art solutions, saving between 27.36% and 95.94% on average execution time for the above operations. Experimental results show the capability of the BDRST index to support spatial queries over large-scale SVD, and our method provides a solution for efficient sustainability research that involves massive georeferenced data.

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“…Some storage-oriented systems provided spatial indexing mechanisms for efficient basic spatial querying. For example, [73] applies a simple mechanism for supporting range-alike queries. It consists of providing a unique key for each range set, indexing the key which can then be used as a predicate in the range query.…”

Section: Spatial Query Optimizersmentioning

confidence: 99%

Big Spatial Data Management for the Internet of Things: A Survey

et al. 2020

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The high abundance of IoT devices have caused an unprecedented accumulation of avalanches of georeferenced IoT spatial data that if could be analyzed correctly would unleash important information. This can feed decision support systems for better decision making and strategic planning regarding important aspects of our lives that depend heavily on LBSs. Several spatial data management systems for IoT data in Cloud has recently gained momentum. However, the literature is still missing a comprehensive survey that conceptualize a convenient framework that classify those frameworks under appropriate categories. In this survey paper, we focus on the management of big geospatial data that are generated by IoT data sources. We also define a conceptual framework and match the woks of the recent literature with it. We then identify future research frontiers in the field depending on the surveyed works.

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Data storage optimization strategy in distributed column-oriented database by considering spatial adjacency

Cited by 12 publications

References 17 publications

Future Prospects and Challenges of Big Data with Big Data Problems

Future Prospects and Challenges of Big Data with Big Data Problems

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

Big Spatial Data Management for the Internet of Things: A Survey

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