Abstract:
Nowadays, with the rise of sensor technology, the amount of spatial and temporal data
is increasing day by day. Modeling data in a structured way and performing effective and efficient
complex queries has become more essential than ever. Online analytical processing (OLAP), devel oped for this purpose, provides appropriate data structures and supports querying multidimensional
numeric and alphanumeric data. However, uncertainty and fuzziness are inherent in the data in
many complex database applications, especially in spatiotemporal database applications. Therefore,
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. FSOLAP is a new framework
based on fuzzy logic technologies and spatial online analytical processing (SOLAP). In this study, we
use crisp measures as input for this framework, apply fuzzy operations to obtain the membership
functions and fuzzy classes, and then generate fuzzy association rules. Therefore, FSOLAP does
not need to use predefined sets of fuzzy inputs. This paper presents the method used to model the
FSOLAP and manage various types of complex and fuzzy spatiotemporal queries using the FSOLAP
framework. In this context, we describe how to handle non-spatial and fuzzy spatial queries, as well
as spatiotemporal fuzzy query types. 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