New Design Principles for Effective Knowledge Discovery from Big Data

Gosain, Anjana; Chugh, Nikita

doi:10.5120/16886-6904

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…Gosain and Chugh [18] exh ibited two new standards dependent upon security furthermore auspicious Investigation for KDD starting with enormous data. The guideline particular case gave those issue fro m claiming timeliness.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Big Data Value Using Knowledge Discovery Techniques

Abdrabo¹,

Elmogy²,

ElTaweel³

et al. 2016

IJITCS

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The world has been drowned by floods of data due to technological development. Consequently, the Big Data term has gotten the expression to portray the gigantic sum. Different sorts of quick data are doubling every second. We have to profit fro m this enormous surge of data to convert it to knowledge. Knowledge Discovery (KDD) can enhance detecting the value of Big Data based on some techniques and technologies like Hadoop, MapReduce, and NoSQL. The use of Big Data value is critical in different fields. This survey discusses the expansion of data that led the world to Big Data exp ression. Big Data has distinctive characteristics as volume, variety, velocity, value, veracity, variab ility, viscosity, virality, amb iguity, and complexity. We will describe the connection between Big Data and KDD techniques to reach data value. Big Data applications that are applied by big organizat ions will be discussed. Characteristics of big data will be introduced, which represent a significant challenge for Big Data management. Finally, some of the impo rtant future directions in Big Data field will be presented.

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Section: Related Workmentioning

confidence: 99%

Enhancing Big Data Value Using Knowledge Discovery Techniques

Abdrabo¹,

Elmogy²,

ElTaweel³

et al. 2016

IJITCS

View full text Add to dashboard Cite

show abstract

Big Data Techniques For Supporting Accurate Predictions of Energy Production From Renewable Sources

Ceci

Corizzo

Fumarola

et al. 2014

Proceedings of the 19th International Database Engineering &Amp; Applications Symposium on - IDEAS '15

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Predicting the output power of renewable energy production plants distributed on a wide territory is a really valuable goal, both for marketing and energy management purposes. Vi-POC (Virtual Power Operating Center) project aims at designing and implementing a prototype which is able to achieve this goal. Due to the heterogeneity and the high volume of data, it is necessary to exploit suitable Big Data analysis techniques in order to perform a quick and secure access to data that cannot be obtained with traditional approaches for data management. In this paper, we describe Vi-POC -a distributed system for storing huge amounts of data, gathered from energy production plants and weather prediction services. We use HBase over Hadoop framework on a cluster of commodity servers in order to provide a system that can be used as a basis for running machine learning algorithms. Indeed, we perform one-day ahead forecast of PV energy production based on Artificial Neural Networks in two learning settings, that is, structured and nonstructured output prediction. Preliminary experimental results confirm the validity of the approach, also when compared with a baseline approach.

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New Design Principles for Effective Knowledge Discovery from Big Data

Cited by 2 publications

References 10 publications

Enhancing Big Data Value Using Knowledge Discovery Techniques

Enhancing Big Data Value Using Knowledge Discovery Techniques

Big Data Techniques For Supporting Accurate Predictions of Energy Production From Renewable Sources

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