Leveraging Big Data Analytics Utilizing Hadoop Framework in Sports Science

The rapid deployment of Phasor Measurement Units (PMUs) in power systems globally is leading to Big Data challenges. New high performance computing techniques are now required to process an ever increasing volume of data from PMUs. To that extent the Hadoop framework, an open source implementation of the MapReduce computing model, is gaining momentum for Big Data analytics in smart grid applications. However, Hadoop has over 190 configuration parameters, which can have a significant impact on the performance of the Hadoop framework. This paper presents an Enhanced Parallel Detrended Fluctuation Analysis (EPDFA) algorithm for scalable analytics on massive volumes of PMU data. The novel EPDFA algorithm builds on an enhanced Hadoop platform whose configuration parameters are optimized by Gene Expression Programming. Experimental results show that the EPDFA is 29 times faster than the sequential DFA in processing PMU data and 1.87 times faster than a parallel DFA, which utilizes the default Hadoop configuration settings.

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Analyzing and Exploring the Impact of Big Data Analytics in Sports Science

2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN) ◽

10.1109/indo-taiwanican48429.2020.9181320 ◽

2020 ◽

Author(s):

Gagandeep Kaur ◽

Gagandeep Jagdev

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Sports Science ◽

The Impact

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Big data analytics-recommendation system with Hadoop Framework

2016 International Conference on Inventive Computation Technologies (ICICT) ◽

10.1109/inventive.2016.7830109 ◽

2016 ◽

Cited By ~ 1

Author(s):

Sayali D. Kadam ◽

Dilip Motwani ◽

Siddhesh A. Vaidya

Keyword(s):

Big Data ◽

Data Analytics ◽

Recommendation System ◽

Big Data Analytics ◽

Hadoop Framework

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A Cost Effective Virtual Cluster with Hadoop Framework for Big Data Analytics

International Journal of Database Theory and Application ◽

10.14257/ijdta.2015.8.6.18 ◽

2015 ◽

Vol 8 (6) ◽

pp. 199-214

Author(s):

Seraj Al Mahmud Mostafa ◽

A. B. M Moniruzzaman

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Cost Effective ◽

Virtual Cluster ◽

Hadoop Framework

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Hadoop Framework for Big Data Analytics to Analyze Attacks on Business Data

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b7410.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 3995-4000

Keyword(s):

Big Data ◽

Data Security ◽

Data Analytics ◽

Big Data Analytics ◽

Data Sets ◽

Business Data ◽

Analytical Tools ◽

Security Analytics ◽

Day By Day ◽

Hadoop Framework

Big Data consist large volumes of data sets with various formats i.e., structured, unstructured and semi structured. Big Data requires security because day by day attackers attack on it in different manner. Big Data Security Analytics analyses Big Data for finding various threats and complex attacks. By increasing the number of targeting attacks on data and one side rapid growing of data, it is too difficult to analyze accurately. The Security Analytics Systems are used the untrusted data. So, strong security analytical tools are required to analyze the data. The organizations and industries exchange the data through networks dynamically, so this may become more vulnerable for data misusing and theft. Attackers are more advanced in the attacking on data that the existing security mechanisms are not identified before damaging. At present, the collecting and analyzing various attacks is major challenging task for Security Analytics Systems, to take suitable decision. In this research paper, we have addressed about Hadoop tool that how it analyses Big Data and how Big Data Security Analytics is applied to analyze the various threats and securing the business data more accurately.

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Big Data Analytics: The Next Big Thing

The Management Accountant Journal ◽

10.33516/maj.v54i5.20-24p ◽

2019 ◽

Vol 54 (5) ◽

pp. 20

Author(s):

Dheeraj Kumar Pradhan

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

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Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

WiSt - Wirtschaftswissenschaftliches Studium ◽

10.15358/0340-1650-2020-5-11 ◽

2020 ◽

Vol 49 (5) ◽

pp. 11-17

Author(s):

Thomas Wrona ◽

Pauline Reinecke

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

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