FSOLAP: A Fuzzy Logic-based Spatial OLAP Framework for Spatial-Temporal Analytics and Querying

2023-1-3
Keskin, Sinan
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Nowadays, with the rise in sensor technology, the amount of spatial and temporal data increases day by day. Fast, effective, and accurate analysis and prediction of collected data have become more essential than ever. Spatial Online Analytical Processing (SOLAP) emerged to perform data mining on spatial and temporal data that naturally contains the hierarchical structure used in many complex applications. In addition, uncertainty and fuzziness are inherently essential elements of data in many complex data applications, particularly in spatial-temporal database applications. Also, there is always a need to support flexible queries and analyses on uncertain and fuzzy data, due to the nature of the data in these complex spatiotemporal applications. In this study, FSOLAP is proposed as a new fuzzy SOLAP-based framework to compose the benefits of fuzzy logic and SOLAP concepts and is extended with inference capability to the framework to support predictive analytics and spatiotemporal predictive querying. Additionally, while FSOLAP primarily includes historical data and associated queries and analyses, we also describe how to handle predictive fuzzy spatiotemporal queries, which typically require an inference mechanism. The predictive accuracy and resource utilization performance of FSOLAP are compared using real data with some well-known machine learning techniques such as Support Vector Machine, Random Forest, and Fuzzy Random Forest. The extensive experimental results show that the FSOLAP framework for the predictive analysis of various spatiotemporal events using a big meteorological dataset is considerably more accurate and scalable than conventional machine learning techniques.
Fuzzy spatiotemporal data mining, spatiotemporal predictive analytics, fuzzy spatiotemporal OLAP, fuzzy association rule mining, fuzzy knowledge base, fuzzy inference system, fuzzy spatiotemporal querying, fuzzy spatiotemporal predictive querying
https://hdl.handle.net/11511/101865
Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats
S. Keskin, “FSOLAP: A Fuzzy Logic-based Spatial OLAP Framework for Spatial-Temporal Analytics and Querying,” Ph.D. - Doctoral Program, Middle East Technical University, 2023.
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