Applications of FP-Growth and Apriori Algorithm for Mining Fuzzified Spatial Dataset

Spatial data, also called geospatial data, is term needed to describe data linked to or containing knowledgeable data about a particular location on Earth’s surface. Spatial data mining's primary goal is to uncover hidden complicated information from spatial & non-spatial information in spite of their enormous quantity and find the spatial relations density. Spatial Data Mining techniques, however, continue to be an expansion of individuals utilized in standard data mining. Spatial Data is an extremely challenging area since enormous quantities of spatial data have been obtained from the remote sensed to the GIS (Geographic Information Systems), ecological estimation, computer cartography, planning and many more. In a given paper, we only focus on an essential type of spatial vagueness termed as spatial fuzziness. Spatial fuzziness intakes the property of several spatial objects in certainty which don’t contain boundaries of sharp type and interiors or whose boundaries as well as interiors can't be determined in precise form. This paper provides the method for finding fuzzy spatial data of association rule. Association rules provided valuable data in the assessment of important correlations observed in big databases. Compared to the previous research work, the current approach for there search highlights the superiority over the same dataset in terms of time taken and generated rules. The rules generated tell about the occurrence of attributes. The results show that the current research is more efficient than that of the previous work and also less time-consuming.

New Design Approach to Handle Spatial Vagueness in Spatial OLAP Datacubes

Spatial OLAP (SOLAP) technologies are dedicated to multidimensional analysis of large volumes of (spatial) data. Spatal data are subject to different types of uncertainty, in particular spatial vagueness. Although several researches propose new models to cope with spatial vagueness, their integration in SOLAP systems is still in an embryonic state. Also, analyzing multidimensional data with metadata brought by the exploitation of the new models can be too complex and demanding for decision makers. To help reduce spatial vagueness consequences on the exactness of SOLAP analysis queries, the authors present a new approach for designing SOLAP datacubes based on end-users' tolerance to the risks of misinterpretation of fact data. An experimentation of the new approach on agri-environmental data is also proposed.

New Design Approach to Handle Spatial Vagueness in Spatial OLAP Datacubes

Spatial-OLAP (SOLAP) technologies are dedicated to multidimensional analysis of large volumes of (spatial) data. Spatial data are subject to different types of uncertainty, in particular spatial vagueness. Although several researches propose new models to cope with spatial vagueness, their integration in SOLAP systems is still in an embryonic state. Also, analyzing multidimensional data with metadata brought by the exploitation of the new models can be too complex and demanding for decision-makers. To help reduce spatial vagueness consequences on the exactness of SOLAP analysis queries, the authors present a new approach for designing SOLAP datacubes based on end-users' tolerance to the risks of misinterpretation of fact data. An experimentation of the new approach on agri-environmental data is also proposed.

An UML Profile and SOLAP Datacubes Multidimensional Schemas Transformation Process for Datacubes Risk-Aware Design

Spatial Data Warehouses (SDWs) and Spatial On-Line Analytical Processing (SOLAP) systems are new technologies for the integration and the analysis of huge volume of data with spatial reference. Spatial vagueness is often neglected in these types of systems and the data and analysis results are considered reliable. In a previous work, the authors provided a new design method for SOLAP datacubes that allows the handling of vague spatial data analysis issues. The method consists of tailoring SOLAP datacubes schemas to end-users tolerance levels to identified potential risks of misinterpretation they encounter when exploiting datacubes containing vague spatial data. It this paper, the authors further their previous proposal by presenting different formal tools to support their method: it is an UML profile providing stereotypes needed to add vague, risks and tolerance levels information on datacubes schemas plus the formal definition of SOLAP datacubes schemas transformation process and functions.

New Design Approach to Handle Spatial Vagueness in Spatial OLAP Datacubes

Spatial-OLAP (SOLAP) technologies are dedicated to multidimensional analysis of large volumes of (spatial) data. Spatial data are subject to different types of uncertainty, in particular spatial vagueness. Although several researches propose new models to cope with spatial vagueness, their integration in SOLAP systems is still in an embryonic state. Also, analyzing multidimensional data with metadata brought by the exploitation of the new models can be too complex and demanding for decision-makers. To help reduce spatial vagueness consequences on the exactness of SOLAP analysis queries, the authors present a new approach for designing SOLAP datacubes based on end-users' tolerance to the risks of misinterpretation of fact data. An experimentation of the new approach on agri-environmental data is also proposed.

Fuzzy Spatial Data Types for Spatial Uncertainty Management in Databases

Spatial database systems and geographical information systems are currently only able to support geographical applications that deal with crisp spatial objects, that is, objects whose extent, shape, and boundary are precisely determined. Examples are land parcels, school districts, and state territories. However, many new, emerging applications are interested in modeling and processing geographic data that are inherently characterized by spatial vagueness or spatial indeterminacy. This requires novel concepts due to the lack of adequate approaches and systems. In this chapter, we focus on an important kind of spatial vagueness called spatial fuzziness. Spatial fuzziness captures the property of many spatial objects in reality that do not have sharp boundaries and interiors or whose boundaries and interiors cannot be precisely determined. We will designate this kind of entities as fuzzy spatial objects. Examples are polluted areas, temperature zones, and lakes. We propose an abstract, formal, and conceptual model of so-called fuzzy spatial data types (that is, a fuzzy spatial algebra) introducing fuzzy points, fuzzy lines, and fuzzy regions in the two-dimensional Euclidean space. This chapter provides a definition of their structure and semantics, which is supposed to serve as a specification of their implementation. Furthermore, we introduce fuzzy spatial set operations like fuzzy union, fuzzy intersection, and fuzzy difference, as well as fuzzy topological predicates as they are useful in fuzzy spatial joins and fuzzy spatial selections. We also sketch implementation strategies for the whole type system and show their integration into databases. An outlook on future research challenges rounds out the chapter.