Finding tendencies in streaming data using Big Data frequent itemset mining

Fernandez-Basso, Carlos; Francisco-Agra, Abel J.; Martín-Bautista, María J.; Ruiz, M. Dolores

doi:10.1016/j.knosys.2018.09.026

Cited by 40 publications

(21 citation statements)

References 12 publications

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“…environmental or mobile devices) that need to be analyzed in real or near-real time by showing technical challenges and opportunities. A frequent itemset mining based on the Apache Spark Streaming framework has been developed by Fernandez-Basso et al ( 2019 ) for extracting tendencies from continuous data flows using sliding windows. Ahmad et al ( 2017 ) developed an approach based on an online sequence memory algorithm for anomaly detection on streaming data.…”

Section: Analyzing Sn Variabilitymentioning

confidence: 99%

A survey of Big Data dimensions vs Social Networks analysis

2020

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The pervasive diffusion of Social Networks (SN) produced an unprecedented amount of heterogeneous data. Thus, traditional approaches quickly became unpractical for real life applications due their intrinsic properties: large amount of user-generated data (text, video, image and audio), data heterogeneity and high speed generation rate. More in detail, the analysis of user generated data by popular social networks (i.e Facebook (https://www.facebook.com/), Twitter (https://www.twitter.com/), Instagram (https://www.instagram.com/), LinkedIn (https://www.linkedin.com/)) poses quite intriguing challenges for both research and industry communities in the task of analyzing user behavior, user interactions, link evolution, opinion spreading and several other important aspects. This survey will focus on the analyses performed in last two decades on these kind of data w.r.t. the dimensions defined for Big Data paradigm (the so called Big Data 6 V’s).

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Section: Analyzing Sn Variabilitymentioning

confidence: 99%

A survey of Big Data dimensions vs Social Networks analysis

2020

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“…Our future work is to find strategies to select those actionable HUNSRs. In addition, many research studies are based on a quantitative database or fuzzy data and are very useful [35]. But with the development of economy and the progress of Internet technology, the amount of information generated in social media channels (e.g., Twitter, Linkedin, Instagram, etc.)…”

Section: Discussionmentioning

confidence: 99%

e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

Zhang

et al. 2020

Symmetry

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As an important technology in computer science, data mining aims to mine hidden, previously unknown, and potentially valuable patterns from databases.High utility negative sequential rule (HUNSR) mining can provide more comprehensive decision-making information than high utility sequential rule (HUSR) mining by taking non-occurring events into account. HUNSR mining is much more difficult than HUSR mining because of two key intrinsic complexities. One is how to define the HUNSR mining problem and the other is how to calculate the antecedent’s local utility value in a HUNSR, a key issue in calculating the utility-confidence of the HUNSR. To address the intrinsic complexities, we propose a comprehensive algorithm called e-HUNSR and the contributions are as follows. (1) We formalize the problem of HUNSR mining by proposing a series of concepts. (2) We propose a novel data structure to store the related information of HUNSR candidate (HUNSRC) and a method to efficiently calculate the local utility value and utility of HUNSRC’s antecedent. (3) We propose an efficient method to generate HUNSRC based on high utility negative sequential pattern (HUNSP) and a pruning strategy to prune meaningless HUNSRC. To the best of our knowledge, e-HUNSR is the first algorithm to efficiently mine HUNSR. The experimental results on two real-life and 12 synthetic datasets show that e-HUNSR is very efficient.

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“…On one hand, FIM is a collection of items that often appear together. Given the past superiority, FIM has an extensive applicational field from the latest researches, such as extracting useful knowledge from event logs [29], using sliding windows capable of extracting tendencies from continuous data flows [30], directing membrane separator development for microbial fuel cells [31], assisting healthcare researchers in identifying medications and medication combinations that associate with a higher risk of acute kidney injury (AKI) using electronic medical records (EMRs) [32], protecting the cloud servers' privacy of datasets from frequency analysis attack [33], and applying the FP-Growth algorithm in determining frequent itemsets in association with data mining to find customer spending habits in buying goods simultaneously [34]. On the other hand, association rules analyze the possible strong relationship between two items.…”

Section: Association Rule and Its Algorithmmentioning

confidence: 99%

Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis

et al. 2021

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Storage devices in the computer industry have gradually transformed from the hard disk drive (HDD) to the solid-state drive (SSD), of which the key component is error correction in not-and (NAND) flash memory. While NAND flash memory is under development, it is still limited by the “program and erase” cycle (PE cycle). Therefore, the improvement of quality and the formulation of customer service strategy are topics worthy of discussion at this stage. This study is based on computer company A as the research object and collects more than 8000 items of SSD error data of its customers, which are then calculated with data mining and frequent pattern growth (FP-Growth) of the association rule algorithm to identify the association rule of errors by setting the minimum support degree of 90 and the minimum trust degree of 10 as the threshold. According to the rules, three improvement strategies of production control are suggested: (1) use of the association rule to speed up the judgment of the SSD error condition by customer service personnel, (2) a quality strategy, and a (3) customer service strategy.

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Finding tendencies in streaming data using Big Data frequent itemset mining

Cited by 40 publications

References 12 publications

A survey of Big Data dimensions vs Social Networks analysis

A survey of Big Data dimensions vs Social Networks analysis

e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis

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