Pro Apache Hadoop

Wadkar, Sameer; Siddalingaiah, Madhu; Venner, Jason

doi:10.1007/978-1-4302-4864-4

Cited by 19 publications

(13 citation statements)

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“…The third approach for managing point clouds involves the use of big data tools, which can be characterized by their ability to scale out by distributing the data over a cluster of several nodes [37]. Because the bottleneck in data intensive applications are the I/O operations, big data tools strive to parallelize these operations so that each node uses the share of data residing in its local disk.…”

Section: The Big Data Approachmentioning

confidence: 99%

A Simple Semantic-Based Data Storage Layout for Querying Point Clouds

El-Mahgary

Virtanen

Hyyppä

2020

IJGI

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The importance of being able to separate the semantics from the actual (X,Y,Z) coordinates in a point cloud has been actively brought up in recent research. However, there is still no widely used or accepted data layout paradigm on how to efficiently store and manage such semantic point cloud data. In this paper, we present a simple data layout that makes use the semantics and that allows for quick queries. The underlying idea is especially suited for a programming approach (e.g., queries programmed via Python) but we also present an even simpler implementation of the underlying technique on a well known relational database management system (RDBMS), namely, PostgreSQL. The obtained query results suggest that the presented approach can be successfully used to handle point and range queries on large points clouds.

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Section: The Big Data Approachmentioning

confidence: 99%

A Simple Semantic-Based Data Storage Layout for Querying Point Clouds

El-Mahgary

Virtanen

Hyyppä

2020

IJGI

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“…The TORNADO Server hosts TORNADO Web Service, VSAS and video stream processing modules. This server also hosts Ambari Server [45]. The Agent-1 deploys HDFS Name Node [4], Zookeeper Server [46], Yarn Resource Manager [47] and Spark2 History Server [5].…”

Section: Experimental Environmentmentioning

confidence: 99%

TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud

Alam

Lee

2020

Sensors

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In the recent past, the number of surveillance cameras placed in the public has increased significantly, and an enormous amount of visual data is produced at an alarming rate. Resultantly, there is a demand for a distributed system for video analytics. However, a majority of existing research on video analytics focuses on improving video content management and rely on a traditional client/server framework. In this paper, we develop a scalable and flexible framework called TORNADO on top of general-purpose big data technologies for intelligent video big data analytics in the cloud. The proposed framework acquires video streams from device-independent data-sources utilizing distributed streams and file management systems. High-level abstractions are provided to allow the researcher to develop and deploy video analytics algorithms and services in the cloud under the as-a-service paradigm. Furthermore, a unified IR Middleware has been proposed to orchestrate the intermediate results being generated during video big data analytics in the cloud. We report results demonstrating the performance of the proposed framework and the viability of its usage in terms of better scalability, less fault-tolerance, and better performance.

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“…Architectural models also should be designed to support Big Data knowledge Discovery process. Keeping in view four architectures are studied that provides computational support andenable processing ofenormousquantities of data within a sensible time periodagreeing to S. Wadkar et al [23]:  "Massively parallel processing" (MPP) database system including"EMC's Greenplum" and "IBM's Netezza".  "In-memory database systems"for example"Oracle Exalytics", "SAP's HANA" and "Spark ".…”

Section: Big Data Infrastructure (Bdi)mentioning

confidence: 99%

Big Data Knowledge Discovery Platforms: A 360 Degree Perspective

Singh,

Pant

2019

IJEAT

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Big Datais a buzzword affecting nearly every domain and providing different set new opportunity for the development of knowledge discovery process. Although it comes with challengeslike abundance, extensiveness and diversity, timeliness and dynamism, messiness and vagueness, and with an uncertainty as all the data generated does not relates to any specific question and can be associated with another process or activity. To address these challenges are certainly cannot be handled by the traditional infrastructure, platforms and frameworks. New analytical techniques and high performance computing architecture came into picture to handle this explosion. These platforms and architecture are giving a cutting edge to the Big Data Knowledge Discovery process by using Artificial Intelligence, Machine Learning and Expert systems. This study encompasses a comprehensive review of Big Data analytical platforms and frameworks with their comparative analysis. A Knowledge Discovery architecture for Big Data Analytics is also proposed while considering the fundamental aspect of gaining insights from Big Data sets and focus of this analysis is to provide the open challenges associated with these techniques and future research directions.

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Pro Apache Hadoop

Cited by 19 publications

References 0 publications

A Simple Semantic-Based Data Storage Layout for Querying Point Clouds

A Simple Semantic-Based Data Storage Layout for Querying Point Clouds

TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud

Big Data Knowledge Discovery Platforms: A 360 Degree Perspective

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