2022 Fiscal Year Research-status Report
Exploring Novel Mathematical Models and Efficient Algorithms to Discover Periodic Spatial Patterns in Irregular Spatiotemporal Big Data
| Project/Area Number |
21K12034
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| Research Institution | The University of Aizu |
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Principal Investigator |
Rage Uday・Kiran 会津大学, コンピュータ理工学部, 准教授 (20874324)
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| Co-Investigator(Kenkyū-buntansha) |
是津 耕司 国立研究開発法人情報通信研究機構, ユニバーサルコミュニケーション研究所統合ビッグデータ研究センター, 研究センター長 (40415857)
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| Project Period (FY) |
2021-04-01 – 2025-03-31
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| Keywords | Big data / high utility patterns / spatial information / data mining |
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| Outline of Annual Research Achievements |
High utility itemset mining is an important knowledge discovery in data mining. Exicisting studies ignored the spatial information of the items in the database and tried to find hidden patterns. We have observed that ignoring the spatial information results either in missing useful information or generating suprious information that is not useful to the experts. In this year, we have tested our cliam, which is important to capture the spatial information of the items to find patterns that have high value. The work was published in Applied Intelligence Journal.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
We were successful in downloading the air pollution data of various sensors in Japan, USA, and other countries. We were able to test the distribution of characteritics of the data. We found that existing high utility itemset mining algorithms were inadequate to find spatially interesting patterns as they ignored the spatial information. We proposed a new model and an efficient algorithm to discover spatially interesting patterns having high value. Our work also published in Applied Intelligence journal.
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| Strategy for Future Research Activity |
1. This year we will model the air pollution has uncertain data generated by the sensors and study the current limitations. Next, we will analyze the results and make claim, followed by introducing a new model and algorithm to find spatially interesting patterns in uncertain data.
2. We will also study how to model air pollution as fuzzy dataset to extract useful information
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| Causes of Carryover |
We will write necessary programs (ETL-techniques) to analyze air pollution data and make the decision on importance of model the air pollution data as uncertain data and/or fuzzy data.We will develop models to capture the hidden information uncertain/fuzzy data. We will investigate algorithms to extract information.
This year we will using the Kakenhi grant on development of ETL-techniques (Miscellaneous) and article processing fees.
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