A Survey on Big Data, Mining: (Tools, Techniques, Applications and Notable Uses)

Oweis, Nour E.; Owais, Suhail S. J.; George, W. H. S.; M, Suliman; Snåšel, Václav

doi:10.1007/978-3-319-21206-7_10

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…This approach can be easily applied to many commodity machines to deal with big data. Finally, the work presented in this paper is in agreement with the published literature [1,9,10], and concludes that the traditional data processing tools and its applications are incapableof handling the current huge and complex data, such as, managing big data mining, and the newest industrial age of the IoT. Therefore, the parallel algorithm is the suitable solution for the big data mining techniques.…”

Section: Introductionsupporting

confidence: 78%

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A Novel Mapreduce Lift Association Rule Mining Algorithm (Mrlar) for Big Data

Oweis¹,

Fouad²,

Oweis³

et al. 2016

ijacsa

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Abstract-Big Data mining is an analytic process used to discover the hidden knowledge and patterns from a massive, complex, and multi-dimensional dataset. Single-processor's memory and CPU resources are very limited, which makes the algorithm performance ineffective. Recently, there has been renewed interest in using association rule mining (ARM) in Big Data to uncover relationships between what seems to be unrelated. However, the traditional discovery ARM techniques are unable to handle this huge amount of data. Therefore, there is a vital need to scalable and parallel strategies for ARM based on Big Data approaches. This paper develops a novel MapReduce framework for an association rule algorithm based on Lift interestingness measurement (MRLAR) which can handle massive datasets with a large number of nodes. The experimental result shows the efficiency of the proposed algorithm to measure the correlations between itemsets through integrating the uses of MapReduce and LIM instead of depending on confidence.

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Section: Introductionsupporting

confidence: 78%

“…Usually, these applications create a huge stream of non-stop data that is currently well-known as big data, denoted as "Big Data" [1]. Big Data is a massive set of data that is too complex to be managed by traditional applications.…”

Section: Introductionmentioning

confidence: 99%

A Novel Mapreduce Lift Association Rule Mining Algorithm (Mrlar) for Big Data

Oweis¹,

Fouad²,

Oweis³

et al. 2016

ijacsa

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“…There have been excellent efforts to develop tools, methods, and programming models to store, process, and analyze Big Data [3,31,41]. The full-text search on the Big Data is a challenging and interesting problem which recently gained attention.…”

Section: Related Workmentioning

confidence: 99%

Big Data Full-Text Search Index Minimization Using Text Summarization

Iqbal¹,

Malik²,

Bukhari³

et al. 2021

ITC

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An efficient full-text search is achieved by indexing the raw data with an additional 20 to 30 percent storagecost. In the context of Big Data, this additional storage space is huge and introduces challenges to entertainfull-text search queries with good performance. It also incurs overhead to store, manage, and update the largesize index. In this paper, we propose and evaluate a method to minimize the index size to offer full-text searchover Big Data using an automatic extractive-based text summarization method. To evaluate the effectivenessof the proposed approach, we used two real-world datasets. We indexed actual and summarized datasets usingApache Lucene and studied average simple overlapping, Spearman’s rho correlation, and average rankingscore measures of search results obtained using different search queries. Our experimental evaluation showsthat automatic text summarization is an effective method to reduce the index size significantly. We obtained amaximum of 82% reduction in index size with 42% higher relevance of the search results using the proposedsolution to minimize the full-text index size.

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