A Scalable Pattern Mining Method Using Apache Spark Platform

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Samiei Samaneh; Joodaki Mehdi; GHADIRI NASSER

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2021 | Issue:7

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Title

A Scalable Pattern Mining Method Using Apache Spark Platform

Author(s)

Samiei Samaneh | Joodaki Mehdi | GHADIRI NASSER

Keywords

Apache Spark

Big Data

Log Files Analysis

Sequential Pattern Mining

Abstract

The amount of data is growing sharply on the Internet. Some data like log files are enormous and entail valuable and precious hidden patterns. In other words, a log file is a set of recorded events that carry beneficial and vital information to develop web server performance, stability server loads, control, and rush up user response operations. However, analyzing massive data take a long time and require powerful hardware. Also, the performance of sequential pattern mining methods is usually unsatisfactory to deal with such data. This paper proposes a novel and advanced parallel method for finding the log file patterns, such as frequent patterns (e. g., URL, IP, Status Code ), how users accessed files, the number of errors, and the most common errors by applying the Apache Spark platform. Experiment results demonstrate that the proposed method's run time on three datasets is significantly less than its four rival pattern mining methods.

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APA: Copy

Samiei, Samaneh, Joodaki, Mehdi, & GHADIRI, NASSER. (2021). A Scalable Pattern Mining Method Using Apache Spark Platform. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949433/en

Vancouver: Copy

Samiei Samaneh, Joodaki Mehdi, GHADIRI NASSER. A Scalable Pattern Mining Method Using Apache Spark Platform. 2021. Available from: https://sid.ir/paper/949433/en

IEEE: Copy

Samaneh Samiei, Mehdi Joodaki, and NASSER GHADIRI, “A Scalable Pattern Mining Method Using Apache Spark Platform,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2021, [Online]. Available: https://sid.ir/paper/949433/en

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