Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework

Sumalatha Saleti

doi:10.1109/ACCESS.2022.3224217

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Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework

2022-01-01

Sumalatha Saleti

T. Jaya Lakshmi

Mohd Wazih Ahmad *

DOI：10.1109/ACCESS.2022.3224217

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Abstract

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Mining high utility sequential patterns is observed to be a significant research in data mining. Several methods mine the sequential patterns while taking utility values into consideration. The patterns of this type can determine the order in which items were purchased, but not the time interval between them. The time interval among items is important for predicting the most useful real-world circumstances, including retail market basket data analysis, stock market fluctuations, DNA sequence analysis, and so on. There are a very few algorithms for mining sequential patterns those consider both the utility and time interval. However, they assume the same threshold for each item, maintaining the same unit profit. Moreover, with the rapid growth in data, the traditional algorithms cannot handle the big data and are not scalable. To handle this problem, we propose a distributed three phase MapReduce framework that considers multiple utilities and suitable for handling big data. The time constraints are pushed into the algorithm instead of pre-defined intervals. Also, the proposed upper bound minimizes the number of candidate patterns during the mining process. The approach has been tested and the experimental results show its efficiency in terms of run time, memory utilization, and scalability.

Keywords:

Upper bound

Sequences

Itemsets

Scalability

Memory management

Big Data

Prediction algorithms

Data mining

MapReduce framework

multiple utility thresholds

sequential pattern mining