A review on big data based parallel and distributed approaches of pattern mining

Kumar, Sunil; Mohbey, Krishna Kumar

doi:10.1016/j.jksuci.2019.09.006

Cited by 42 publications

(18 citation statements)

References 80 publications

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“…It is time efficient, good privacy protection, and better utility If we talk about large database then it is not feasible to grasp FP-tree in main memory. This can be solved by partitioning FP-tree into smaller database and construct FP-tree for all these [10] This paper gives idea about parallel and distributed mining to overcome difficulty that arises to extract pattern from large-scale data Further work can be done on discussed challenges of parallel and distributed mining [11] The issues of quality of data at same level and the issues of security of data at the same level are focused inside this paper. The context of big data imposed the challenges and cause problems for quality and security In future to assess and improve big data quality we can implement it via secure process [12] Firstly authors introduce big data security technique.…”

Section: Proposed Methodsmentioning

confidence: 99%

Design and Development of an Algorithm to Secure Big Data on Cloud Computing

Shah

Dave²

2021

ICT With Intelligent Applications

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In any organization, data is coming from various online sources and various offline sources. It is very difficult to equipment huge amount of data. Big data is one of the technologies who tackle huge amount of data easily. Cloud computing is a platform for big data. Although there are vast number of advantages for big data and cloud computing, they failed to make their place in people heart as people are anxious about security of their data. They are huge number of advantages we gain if we move big data on cloud computing like on-demand service availability, availability of data and information on Internet, resources grouping, easy to manage, easy to analyze large volume of big data, and most important is cost effective. Although there are numerous advantages to moving big data on cloud computing, we cannot avoid challenges to big data and cloud computing. The various challenges to big data are cost, data quality, rapid change, skilled man power requirement, infrastructure need, moving data onto big data platform, need for synchronization across data sources, and most important is security of data. These all challenges are not going to be easily solved; various researches are going on to solve all these problems. In this paper, we provide review about various research done in the area of security of big data on cloud computing, and finally, we provide research plan for our project regarding security of big data on cloud computing.

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Section: Proposed Methodsmentioning

confidence: 99%

Design and Development of an Algorithm to Secure Big Data on Cloud Computing

Shah

Dave²

2021

ICT With Intelligent Applications

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“…20 The issues seen in FPM by the researchers includes classification, outlier analysis, and clustering in many fields including spatiotemporal and biological data analysis, along with software bug detection applications. 21 In order to overcome the issues of FPM, many researchers have introduced numerous algorithms, research works, and surveys related to FPM enhancing techniques. Nevertheless, due to its inability for mining the massive dataset with increase in numbers advances stay immobile and entailed to be resolved in the direction of recitation of present FPM algorithms.…”

Section: Motivationmentioning

confidence: 99%

A Parallel Direct‐Vertical Map Reduce Programming model for an effective frequent pattern mining in a dispersed environment

Devi¹

2021

Concurrency and Computation

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Data mining methods face various problems when extracting the data analysis process.These problems are solved based on the frequent pattern mining (FPM). In the data mining area, the frequent pattern (FP) extraction is a dominant embassy while processing the databases. Despite the fact that it has proven its importance in various mining provinces, the consistent research work of researchers has provided numerous efforts in this environment through substantial innovations. Based on the expected challenges of pattern mining, the time and memory consumption while mining the hidden FP increases over time by the conventional FPM approaches. This article proposes a Parallel Direct-Vertical Map Reduce Programming model to effectively extract the FPM in a dispersed environment and analyzed using the Sales transaction dataset as UCI Machine Learning Repository. The proposed model has two phases of working, namely, (i) mapping and (ii) reduction. Here, the proposed Parallel Direct-Vertical method is utilized while mapping. The parallelism of the proposed model is implemented and executed based on the number of clusters of databases in architecture-based software and its final outcome shows that the proposed model is more efficient than the existing methods.

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“…From another perspective, it is common to use multi-thread and computing power with multicore architecture [12] in supporting data processing. This raises the suspicion that multi-thread in [13] and [14] can produce better performance also in getting this frequent itemset, as well as a challenge on how to determine the best performance process architecture that is applied to which subprocesses as threads and how big is the increase for the best multi-thread architecture in a single server environment, where an experiment in multi-node server environment was proposed by [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Analysis of frequent itemset generation based on trie data structure in Apriori algorithm

Hodijah

Setijohatmo

2021

TELKOMNIKA

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Apriori is one technique of data mining association rules that aims to extract correlations between sets of items in the transaction database. The main problem with the Apriori algorithm is the process of scanning databases repeatedly to generate itemset candidates. This research examines the combination of pruning by using the trieapproach and multi-thread implementation in three algorithms to obtain frequent itemset. Trie is a data structure in the form of an ordered tree to store a set of strings where every node in the tree contains the same prefix. The use of a full combination trie (different from frequent pattern (FP) tree using links) allows the implementation of arrays and the hash calculation to achieve the addressing of itemset combination. In this research, the measure to get the address is called Hash-node calculation used to update support value. For these three alternatives, run time processing is analyzed based on the number of itemset combinations and transaction data at a certain minimum support value. The experimental results show that an algorithm thatexploits resource capabilities by applying multi-threadperforms almost seven times betterthanan algorithm implemented in single-thread in calculating hash-node. The fastest run time of the multi-thread approach is 43 minutes with 150-itemset combinations on 100,000 transaction data.

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A review on big data based parallel and distributed approaches of pattern mining

Cited by 42 publications

References 80 publications

Design and Development of an Algorithm to Secure Big Data on Cloud Computing

Design and Development of an Algorithm to Secure Big Data on Cloud Computing

A Parallel Direct‐Vertical Map Reduce Programming model for an effective frequent pattern mining in a dispersed environment

Analysis of frequent itemset generation based on trie data structure in Apriori algorithm

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