Comparative analysis of SpatialHadoop and GeoSpark for geospatial big data analytics

Lenka, Rakesh Kumar; Barik, Rabindra K.; Gupta, Neelesh; Ali, Syed Mohd; Rath, Amiya Kumar; Dubey, Harishchandra

doi:10.1109/ic3i.2016.7918013

Cited by 46 publications

(25 citation statements)

References 14 publications

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“…BIG DATA Big Data is defined by its following 5 properties i.e. Volume, Variety, Veracity, Value, and Velocity [3]. The data growth has been exponential in the past few years, this statement can be supported by the statement made by Eric Schmidt (executive chairmen google) "From dawn of civilization until 2003, humankind generated five exabyte of data.…”

Section: Introductionmentioning

confidence: 61%

Fair: A Hadoop-based Hybrid Model for Faculty Information Retrieval System

Gupta¹,

Lenka²,

Barik³

et al. 2017

Preprint

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Abstract:In era of ever-expanding data and knowledge, we lack a centralized system that maps all the faculties to their research works. This problem has not been addressed in the past and it becomes challenging for students to connect with the right faculty of their domain. Since we have so many colleges and faculties this lies in the category of big data problem. In this paper, we present a model which works on the distributed computing environment to tackle big data. The proposed model uses apache spark as an execution engine and hive as database. The results are visualized with the help of Tableau that is connected to Apache Hive to achieve distributed computing.

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Section: Introductionmentioning

confidence: 61%

Fair: A Hadoop-based Hybrid Model for Faculty Information Retrieval System

Gupta¹,

Lenka²,

Barik³

et al. 2017

Preprint

Self Cite

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“…The only contributions in this regard are [16,17,8]. In [16,17], SpatialHadoop is compared with SpatialSpark and GeoSpark, respectively, for spatial join query processing.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…In [16,17], SpatialHadoop is compared with SpatialSpark and GeoSpark, respectively, for spatial join query processing. In [8], SpatialHadoop is compared with GeoSpark with respect to the architectural point of view. Motivated by these observations, and since KCPQ [7] is implemented in SpatialHadoop (its adaptation to εDJQ is straightforward), and in LocationSpark neither KCPQ nor εDJQ have been implemented yet, we design and implement both DJQs in LocationSpark.…”

Section: Related Work and Motivationmentioning

confidence: 99%

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A Comparison of Distributed Spatial Data Management Systems for Processing Distance Join Queries

García-García

Corral

Iribarne

et al. 2017

Advances in Databases and Information Systems

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Abstract. Due to the ubiquitous use of spatial data applications and the large amounts of spatial data that these applications generate, the processing of large-scale distance joins in distributed systems is becoming increasingly popular. Two of the most studied distance join queries are the K Closest Pair Query (KCPQ) and the ε Distance Join Query (εDJQ). The KCPQ finds the K closest pairs of points from two datasets and the εDJQ finds all the possible pairs of points from two datasets, that are within a distance threshold ε of each other. Distributed clusterbased computing systems can be classified in Hadoop-based and Sparkbased systems. Based on this classification, in this paper, we compare two of the most current and leading distributed spatial data management systems, namely SpatialHadoop and LocationSpark, by evaluating the performance of existing and newly proposed parallel and distributed distance join query algorithms in different situations with big real-world datasets. As a general conclusion, while SpatialHadoop is more mature and robust system, LocationSpark is the winner with respect to the total execution time.

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“…Big data are data those distribution, diversity, scale and timeliness require the use of new technical architectures and analytics to enable insights that unlock new sources of business value. Big data have included data sets with sizes beyond the ability of commonly used software tools to capture, accurate, manage and process data within an acceptable elapsed time [12,13]. Big data can come in multiple forms.…”

Section: Geohealth Big Datamentioning

confidence: 99%

SOA-FOG: Secure service-oriented edge computing architecture for smart health big data analytics

Barik

Dubey

Mankodiya

2017

2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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The smart health paradigms employ Internet-connected wearables for telemonitoring, diagnosis for providing inexpensive healthcare solutions. Fog computing reduces latency and increases throughput by processing data near the body sensor network. In this paper, we proposed a secure serviceorientated edge computing architecture that is validated on recently released public dataset. Results and discussions support the applicability of proposed architecture for smart health applications. We proposed SoA-Fog i.e. a three-tier secure framework for efficient management of health data using fog devices. It discuss the security aspects in client layer, fog layer and the cloud layer. We design the prototype by using win-win spiral model with use case and sequence diagram. Overlay analysis was performed using proposed framework on malaria vector borne disease positive maps of Maharastra state in India from 2011 to 2014. The mobile clients were taken as test case. We performed comparative analysis between proposed secure fog framework and state-of-the art cloud-based framework.

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Comparative analysis of SpatialHadoop and GeoSpark for geospatial big data analytics

Cited by 46 publications

References 14 publications

Fair: A Hadoop-based Hybrid Model for Faculty Information Retrieval System

Fair: A Hadoop-based Hybrid Model for Faculty Information Retrieval System

A Comparison of Distributed Spatial Data Management Systems for Processing Distance Join Queries

SOA-FOG: Secure service-oriented edge computing architecture for smart health big data analytics

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