TY - JOUR
T1 - FSOLAP
T2 - A fuzzy logic-based spatial OLAP framework for effective predictive analytics
AU - Keskin, Sinan
AU - Yazıcı, Adnan
N1 - Funding Information:
We thank the Turkish State Meteorological Service for providing the meteorological data used in the study. The source code of this study is available at https://github.com/skeskin19/solapfuzzyframework. All authors approved the final version of the manuscript.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2023/3/1
Y1 - 2023/3/1
N2 - 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. 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. 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 analytics of various spatiotemporal events in big meteorological databases is considerably more accurate and scalable than using conventional machine learning techniques.
AB - 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. 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. 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 analytics of various spatiotemporal events in big meteorological databases is considerably more accurate and scalable than using conventional machine learning techniques.
KW - Fuzzy association rule mining
KW - Fuzzy inference system
KW - Fuzzy knowledge base
KW - Fuzzy spatiotemporal data mining
KW - Fuzzy spatiotemporal OLAP
KW - Spatiotemporal predictive analytics
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U2 - 10.1016/j.eswa.2022.118961
DO - 10.1016/j.eswa.2022.118961
M3 - Article
AN - SCOPUS:85140073654
SN - 0957-4174
VL - 213
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 118961
ER -