Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases
Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database. Most previous studies focused on finding these itemsets in row (temporal) databases and disre...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10034746/ |
| _version_ | 1828032560685383680 |
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| author | Hong N. Dao Penugonda Ravikumar Palla Likhitha Uday Kiran Rage Yutaka Watanobe Incheon Paik |
| author_facet | Hong N. Dao Penugonda Ravikumar Palla Likhitha Uday Kiran Rage Yutaka Watanobe Incheon Paik |
| author_sort | Hong N. Dao |
| collection | DOAJ |
| description | Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database. Most previous studies focused on finding these itemsets in row (temporal) databases and disregarded the occurrences of these itemsets in columnar databases. Furthermore, the naïve approach of transforming a columnar database into a row database and then applying the existing algorithms to find interesting itemsets is not practicable due to computational reasons. With this motivation, this paper proposes a framework to discover stable periodic-frequent itemsets in columnar databases. Our framework employs a novel depth-first search algorithm that compresses a given columnar database into a unified dictionary and mines it recursively to find all stable periodic-frequent itemsets. The dictionary holds the information pertaining to itemsets and their temporal occurrences in a database. Experimental results on six databases demonstrate that the proposed algorithm is computationally efficient and scalable. |
| first_indexed | 2024-04-10T15:07:20Z |
| format | Article |
| id | doaj.art-503677ea9ef34b48a0738b0b8f6940ac |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T15:07:20Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-503677ea9ef34b48a0738b0b8f6940ac2023-02-15T00:00:35ZengIEEEIEEE Access2169-35362023-01-0111125041252410.1109/ACCESS.2023.324131310034746Finding Stable Periodic-Frequent Itemsets in Big Columnar DatabasesHong N. Dao0https://orcid.org/0000-0002-2693-2207Penugonda Ravikumar1https://orcid.org/0000-0001-9124-9781Palla Likhitha2https://orcid.org/0000-0003-3032-9061Uday Kiran Rage3https://orcid.org/0000-0002-5417-0289Yutaka Watanobe4https://orcid.org/0000-0002-0030-3859Incheon Paik5https://orcid.org/0000-0002-7554-8180Department of Computer Science and Engineering, The University of Aizu, Fukushima, Aizuwakamatsu, JapanIIIT-Idupulapaya, RGUKT, Andhra Pradesh, Nuzividu, IndiaNational Institute of Information and Communications Technology, Tokyo, JapanInstitute of Industrial Science, The University of Tokyo, Tokyo, JapanDepartment of Computer Science and Engineering, The University of Aizu, Fukushima, Aizuwakamatsu, JapanDepartment of Computer Science and Engineering, The University of Aizu, Fukushima, Aizuwakamatsu, JapanStable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database. Most previous studies focused on finding these itemsets in row (temporal) databases and disregarded the occurrences of these itemsets in columnar databases. Furthermore, the naïve approach of transforming a columnar database into a row database and then applying the existing algorithms to find interesting itemsets is not practicable due to computational reasons. With this motivation, this paper proposes a framework to discover stable periodic-frequent itemsets in columnar databases. Our framework employs a novel depth-first search algorithm that compresses a given columnar database into a unified dictionary and mines it recursively to find all stable periodic-frequent itemsets. The dictionary holds the information pertaining to itemsets and their temporal occurrences in a database. Experimental results on six databases demonstrate that the proposed algorithm is computationally efficient and scalable.https://ieeexplore.ieee.org/document/10034746/Columnar databasesstable periodic-frequent itemsetitemset mining |
| spellingShingle | Hong N. Dao Penugonda Ravikumar Palla Likhitha Uday Kiran Rage Yutaka Watanobe Incheon Paik Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases IEEE Access Columnar databases stable periodic-frequent itemset itemset mining |
| title | Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases |
| title_full | Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases |
| title_fullStr | Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases |
| title_full_unstemmed | Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases |
| title_short | Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases |
| title_sort | finding stable periodic frequent itemsets in big columnar databases |
| topic | Columnar databases stable periodic-frequent itemset itemset mining |
| url | https://ieeexplore.ieee.org/document/10034746/ |
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