Big data (lost) in the cloud

Martino, Beniamino Di; Aversa, Rocco; Cretella, Giuseppina; Esposito, Antonio; Kołodziej, Joanna

doi:10.1504/ijbdi.2014.063840

Cited by 43 publications

(18 citation statements)

References 20 publications

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“…Enterprises benefit from big data reduction methods in multiple manners. They perform preprocessing information to reduced big data streams before entering in cloud computing systems (Di Martino et al, 2014). They perform dimension reduction methods to address the curse of dimensionality and determine the substantially relevant big data sets (Zhai et al, 2014).…”

Section: Big Data Reduction: Key To Value Creationmentioning

confidence: 99%

Big data reduction framework for value creation in sustainable enterprises

Rehman

Chang

Batool

et al. 2016

International Journal of Information Management

224

146

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Value creation is a major sustainability factor for enterprises, in addition to profit maximization and revenue generation. Modern enterprises collect big data from various inbound and outbound data sources. The inbound data sources handle data generated from the results of business operations, such as manufacturing, supply chain management, marketing, and human resource management, among others. Outbound data sources handle customer-generated data which are acquired directly or indirectly from customers, market analysis, surveys, product reviews, and transactional histories. However, cloud service utilization costs increase because of big data analytics and value creation activities for enterprises and customers. This article presents a novel concept of big data reduction at the customer end in which early data reduction operations are performed to achieve multiple objectives, such as a) lowering the service utilization cost, b) enhancing the trust between customers and enterprises, c) preserving privacy of customers, d) enabling secure data sharing, and e) delegating data sharing control to customers. We also propose a framework for early data reduction at customer end and present a business model for end-toend data reduction in enterprise applications. The article further presents a business model canvas and maps the future application areas with its nine components. Finally, the article discusses the technology adoption challenges for value creation through big data reduction in enterprise applications.

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Section: Big Data Reduction: Key To Value Creationmentioning

confidence: 99%

Big data reduction framework for value creation in sustainable enterprises

Rehman

Chang

Batool

et al. 2016

International Journal of Information Management

224

146

View full text Add to dashboard Cite

show abstract

“…Indeed, global rules are usually privileged instead of specific rule so the many of the class are abandoned throughout the model building. Thus, Standard learning techniques do not consider the dissimilarity among the amount of samples fit in to dissimilar classes [13]. However, the classes which are under-represented may constitute important cases to identify.…”

Section: Imbalanced Big Datamentioning

confidence: 99%

Parallel processing on Big Data in the context of Machine Learning and Hadoop Ecosystem: A Survey

Brahmane¹,

R²

2018

IJET

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Emergent Big Data applications have become gradually more essential. In reality, a lot of institutes, businesses and in general entire society from diverse segments depend more and more on information take out from enormous quantity of raw information, statistics and numbers. On the other hand, in Big Data perspective, customary information methods and policies are not as much of capable. They prove a time-consuming receptiveness and are short of quantifiability, measurability, presentation and accurateness. To solve the composite Big Data constraints and difficulties, a large amount effort has been carried out. As an effect, different categories of packages, distributions and technologies have been developed. In this paper an evaluation is done, this studies recent technologies developed for Big Data. It aims to assist to choose and adopt the exact combination of diverse Big Data technologies according to their technological, scientific needs and particular applications requirements. It provides not only a worldwide sight of most important Big Data technologies but also relationship according to special organizational, classifications levels

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“…Data preprocessing is the second important phase of big data processing, and it must be preprocessed before storage at large-scale infrastructures [19]. This approach helps in big data reduction and also extracts the meta-data for further processing.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…The aforementioned methods first extract the semantics and linked structures from the unstructured datasets and then apply graph theory for network optimization. Conversely, some methods to reduce big data during the data collection process are also proposed in the recent literature [19][20][21]. In this study, we presented a detailed discussion of these data reduction methods.…”

Section: Introductionmentioning

confidence: 99%

Big Data Reduction Methods: A Survey

et al. 2016

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Research on big data analytics is entering in the new phase called fast data where multiple gigabytes of data arrive in the big data systems every second. Modern big data systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity in the acquired data and consequently give rise to the 6Vs of big data. The reduced and relevant data streams are perceived to be more useful than collecting raw, redundant, inconsistent, and noisy data. Another perspective for big data reduction is that the million variables big datasets cause the curse of dimensionality which requires unbounded computational resources to uncover actionable knowledge patterns. This article presents a review of methods that are used for big data reduction. It also presents a detailed taxonomic discussion of big data reduction methods including the network theory, big data compression, dimension reduction, redundancy elimination, data mining, and machine learning methods. In addition, the open research issues pertinent to the big data reduction are also highlighted.

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Big data (lost) in the cloud

Cited by 43 publications

References 20 publications

Big data reduction framework for value creation in sustainable enterprises

Big data reduction framework for value creation in sustainable enterprises

Parallel processing on Big Data in the context of Machine Learning and Hadoop Ecosystem: A Survey

Big Data Reduction Methods: A Survey

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