Embedding an Extra Layer of Data Compression Scheme for Efficient Management of Big-Data

Pal, Sayan Rudra; Das, Indranil; Majumder, Suvajit; Gupta, Amit; Bhattacharya, Indrajit

doi:10.1007/978-81-322-2247-7_71

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…Spatial support has also been extended to NoSQLbased solutions. [7], [8] adopt Geohash to handle spatial data in HBase. HGrid [23] builds a hybrid index structure, combining a quad-tree and a grid as primary and secondary indices in HBase.…”

Section: Related Workmentioning

confidence: 99%

“…T-Drive was generated by 30,000 taxis in Beijing during Feb. 2 to Feb. 8,2008 within Beijing. The total number of records in this dataset is 17,762,390.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…However, they do not have native support for geographic data and spatial queries, which is required in a database of geographic data. Some methods [7], [8] use Geohash * * , a linearization technique that transforms two-dimensional spatial data into a one-dimensional hashcode, to handle spatial data in HBase. However, there are two major limitations of Geohash which they do not address.…”

Section: Introductionmentioning

confidence: 99%

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G-HBase: A High Performance Geographical Database Based on HBase

Takasu

2018

IEICE Trans. Inf. & Syst.

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SUMMARYWith the recent explosion of geographic data generated by smartphones, sensors, and satellites, a data storage that can handle the massive volume of data and support high-computational spatial queries is becoming essential. Although key-value stores efficiently handle large-scale data, they are not equipped with effective functions for supporting geographic data. To solve this problem, in this paper, we present G-HBase, a high-performance geographical database based on HBase, a standard keyvalue store. To index geographic data, we first use Geohash as the rowkey in HBase. Then, we present a novel partitioning method, namely binary Geohash rectangle partitioning, to support spatial queries. Our extensive experiments on real datasets have demonstrated an improved performance with k nearest neighbors and range query in G-HBase when compared with SpatialHadoop, a state-of-the-art framework with native support for spatial data. We also observed that performance of spatial join in G-HBase is on par with SpatialHadoop and outperforms SJMR algorithm in HBase.

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Section: Related Workmentioning

confidence: 99%

“…T-Drive was generated by 30,000 taxis in Beijing during Feb. 2 to Feb. 8,2008 within Beijing. The total number of records in this dataset is 17,762,390.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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G-HBase: A High Performance Geographical Database Based on HBase

Takasu

2018

IEICE Trans. Inf. & Syst.

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“…Venkatesan, Arunkumar, and Prabhavathy (2015) proposed a novel co-located classifier utilising the CP-Tree algorithm to handle complex spatial landslide big data. Pal et al (2015) proposed a method that employs an extra layer of compression while storing location data in the form of latitude-longitude (lat-long) pairs to the HBase database. The location data in a mobile network is big-data, as an un-interrupted gathering of such information adds copious data inputs.…”

Section: Introductionmentioning

confidence: 99%

Promoting Data Science in Disaster Risk Reduction: Glimpses from the Global South

Das

Dutta

2023

J Data Sci. Info. Citation Studies

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There are coordinated attempts to use Data Science in disaster risk reduction (DRR) across the world. Different special interest groups are formed within the data science community to formulate different action plans and strategies to use data science in the DRR. Some examples of community-driven initiatives are the CODATA Task Group FAIR Data for Disaster Risk Research (FAIR-DRR), CODATA Task Group Linked Open Data for Global Disaster Risk Research (LODGD), and ISC Integrated Research on Disaster Risk (IRDR). This chapter highlights the data science approaches, models, frameworks, and stakeholders mapping while dealing with the data collection, analysis, and reporting for strengthening data-driven decision-making at different levels. The chapter also explores the FAIR Data Principles that aim at achieving the findable, accessible, interoperable, and reusable data resources at the institutional and national levels. FAIR Data ensures various data users increasingly engage with data related to disasters. We also discuss some case studies and success stories from the Global South, which may inspire other countries in data-driven decision-making while dealing with natural disasters. The much-discussed "Data, Information, Knowledge and Action" (DIKA) model is highlighted in this chapter. For example, the recently launched "Data-Knowledge-Action for Urban Systems" podcast series of CODATA and CEPT Research and Development Foundation (CRDF) in Ahmedabad, talks about how the DIKA model helps in creating intelligent systems for DRR using scientific and quantitative data. On a podcast in related Series, the DRR practitioners and data science researchers discussed the use of big data to predict natural disasters. This chapter highlights notable examples of how big data helped predict natural disasters across the world, particularly in the Global South.

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An Efficient Distributed Index for Geospatial Databases

Van

Takasu

2015

Lecture Notes in Computer Science

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Embedding an Extra Layer of Data Compression Scheme for Efficient Management of Big-Data

Cited by 4 publications

References 7 publications

G-HBase: A High Performance Geographical Database Based on HBase

G-HBase: A High Performance Geographical Database Based on HBase

Promoting Data Science in Disaster Risk Reduction: Glimpses from the Global South

An Efficient Distributed Index for Geospatial Databases

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