Big data analytics: a survey

Tsai, Chun Wei; Lai, Chin Feng; Chao, Han Chieh; Vasilakos, Athanasios V.

doi:10.1186/s40537-015-0030-3

Cited by 606 publications

(314 citation statements)

References 101 publications

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“…Processing the data where it resides is faster and more efficient then before analyzing transporting it to a centralized system. R. Buse, (2012) noted It will be in the form of computerized information that does not have a data model and hence are not used by data mining [15]. [14] LaValle S, (2011) proposed the task of managing semi-structured data signifies possibly the major data management opportunity which subsequently results in managing relational data.…”

Section: Data Analysingmentioning

confidence: 99%

Data analytic framework for crime sector using open grid rule generation algorithm

Begum¹,

George²

2018

IJET

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Data analytics (DA) is the process of exploring datasets in order to illustrate conclusions about the information they contain. It is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information. A database contains both structured and semi-structured data. The semi-structured data are from different sources. The system is provided with an open grid rule generation for analyzing the data from whole data container. According to this concept, the analysis is much absolute rather than other, data mining technique. The main objective of the proposed study is to provide data having better and significant perspective.

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Section: Data Analysingmentioning

confidence: 99%

Data analytic framework for crime sector using open grid rule generation algorithm

Begum¹,

George²

2018

IJET

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“…It is intended to help users, especially to the organizations to obtain an independent understanding of the strengths and weaknesses of various NoSQL database approaches to supporting applications that process huge volumes of data [24]. Tsai et al (2015) described through survey how the age of big data is now coming.? But the traditional data analytics may not be able to handle such large quantities of data.…”

Section: Classification Of Technologies Of Big Datamentioning

confidence: 99%

A Review on Big Data: Views, Categories and Aspects

Shukla¹,

Alim²

2018

IJCA

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Now-a-days every organization is moving towards on web based application and cope up lots of data sets. Data may be structured, unstructured or semi-structure. These data need processing, analysis and storage in proper format using innovative techniques and methodologies. Big data parameterized into three basic categories Volume, Variety and Velocity. The social media like Facebook, Whatsapp, Twitter, hike. are generating lots of data in a day in the form of textmessages, audio-recording, images, videos etc. The problem which appears is how to manage this huge data in a systematic way because users want quick response on web search or on smart phone access using web apps. In this paper we have studied how big data invoke in different areas like social media, atmosphere, hospitals, research centers etc. We have suggested contributions in big data categories, applications in different area's like in Machine learning, Social Network, Bio-Informatics, Data Mining, and Clouds along with challenges, future scope and storage prospects.

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“…On the other hand, Big data alone has no value, which is why Big data analytics as a sub-field has taken the attention of the academic and commercial sectors, who all strive to obtain value from Big data, for example, [5] highlighted that traditional data analytics is unable to deal with the large scale and complex nature of Big data, which is composed of different types of data. Therefore, the aim of such analytics is to deal with the approaches of processing very large sets of data input, Some of the issues facing Big data's use with DSS is the limited availability of expert personnel in this new field, the high costs of the underlying technologies as they are still in the emerging stage, the difficulty in customizing these new systems according to unique requires without major software development projects.…”

Section: Big Data Analyticsmentioning

confidence: 99%

“…Big data alone has no value, which is why Big data analytics as a sub-field has taken the attention of the academic and commercial sectors, who all strive to obtain value from Big data [5]. The main aim of this analytics approach is to deal with processing very large sets of data input, the limited timeframe for processing streams of data, and the various data types and formats that must be analyzed.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Effective Big data Analytics for Decision Support Systems

Islam¹,

Alfakeeh²,

Nadeem³

2017

ijcna

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-Supporting decision makers requires a good understanding of the various elements that affect the outcomes of a decision. Decision Support Systems have provided decision makers with such insights throughout its history of usage with varying degrees of success. The availability of data sources was a main limitation to what decision support systems can do. Therefore, with the advent of improved analytical methods for Big data sources new opportunities have emerged that can possibly enhance how decision makers analyze their problem and arrive at decisions using information systems. This paper analyzed current related works on both Big data and decision support systems to identify clear elements and factors relevant to the subject and identifying possible ways to enhance their joint usage. Finally, the paper proposes a framework that integrates the key components needed to ensure the quality and relevance of data being analyzed by decision support systems while providing the benefits of insights generated over time from past decisions and positive recommendations.

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Big data analytics: a survey

Cited by 606 publications

References 101 publications

Data analytic framework for crime sector using open grid rule generation algorithm

Data analytic framework for crime sector using open grid rule generation algorithm

A Review on Big Data: Views, Categories and Aspects

A Framework for Effective Big data Analytics for Decision Support Systems

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