Mining Spatial Association Rules to Automatic Grouping of Spatial Data Objects Using Multiple Kernel-Based Probabilistic Clustering

2017 ◽  
Vol 26 (3) ◽  
pp. 561-572 ◽  
Author(s):  
Y. Jayababu ◽  
G.P.S. Varma ◽  
A. Govardhan
Keyword(s):  
Association Rules ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Association ◽  
Kernel Functions ◽  
Geographical Information ◽  
Probabilistic Clustering ◽  
Multiple Kernel ◽  
Data Objects ◽  
Automatic Grouping

AbstractWith the extensive application of spatial databases to various fields ranging from remote sensing to geographical information systems, computer cartography, environmental assessment, and planning, discovery of interesting and hidden knowledge in the spatial databases is a considerable chore for classifying and using the spatial data and knowledge bases. The literature presents different spatial data mining methods to mine knowledge from spatial databases. In this paper, spatial association rules are mined to automatic grouping of spatial data objects using a candidate generation process with three constraint measures, such as support, confidence, and lift. Then, the proposed multiple kernel-based probabilistic clustering is applied to the associate vector to further group the spatial data objects. Here, membership probability based on probabilistic distance is used with multiple kernels, where exponential and tangential kernel functions are utilized. The performance of the proposed method is analyzed with three data sets of three different geometry types using the number of rules and clustering accuracy. From the experimentation, the results proved that the proposed multi-kernel probabilistic clustering algorithm achieved better accuracy as compared with the existing probabilistic clustering.

Data Discovery Approaches for Vague Spatial Data

Data Mining ◽  
2013 ◽  
pp. 50-65
Author(s):  
Frederick E. Petry
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
Association Rules ◽  
Rough Set ◽  
Fuzzy Set ◽  
Spatial Data ◽  
Spatial Databases ◽  
Uncertain Data ◽  
Rule Extraction ◽  
Data Discovery

This chapter focuses on the application of the discovery of association rules in approaches vague spatial databases. The background of data mining and uncertainty representations using rough set and fuzzy set techniques is provided. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets is described. Finally, an example of rule extraction for both types of uncertainty representations is given.

Spatial and Spatiotemporal Data Types as a Foundation for Representing Space-Time Data in GIS

2017 ◽  
pp. 1-28 ◽  
Author(s):  
Markus Schneider
Keyword(s):  
Spatial Data ◽  
Geographical Information Systems ◽  
Moving Objects ◽  
Spatial Databases ◽  
Query Languages ◽  
Geographical Information ◽  
Spatiotemporal Data ◽  
Data Types ◽  
Time Data ◽  
Use Of Data

A data type comprises a set of homogeneous values together with a collection of operations defined on them. This chapter emphasizes the importance of crisp spatial data types, fuzzy spatial data types, and spatiotemporal data types for representing static, vague, and time-varying geometries in Geographical Information Systems (GIS). These data types provide a fundamental abstraction for modeling the geometric structure of crisp spatial, fuzzy spatial, and moving objects in space and time as well as their relationships, properties, and operations. The goal of this chapter is to provide an overview and description of these data types and their operations that have been proposed in research and can be found in GIS, spatial databases, moving objects databases, and other spatial software tools. The use of data types, operations, and predicates will be illustrated by their embedding into query languages.

Privacy Protection in Outsourced Spatial Databases

2016 ◽  
Vol 10 (03) ◽  
pp. 347-363
Author(s):  
Ibrahim Kamel ◽  
Mohammed N. Ba-Hutair
Keyword(s):  
Spatial Data ◽  
Spatial Databases ◽  
Range Queries ◽  
Security Issue ◽  
Simulation Experiments ◽  
Encrypted Data ◽  
Space Filling Curve ◽  
Data Objects ◽  
Filling Curve ◽  
Entire Dataset

Organizations have come to realize that storing their databases in the Cloud rather than in-house data centers is cheaper and more flexible. However, companies are still concerned about the privacy and the security of their data. Encrypting the whole database before uploading it to the Cloud solves the security issue. But querying the database requires downloading and decrypting the entire dataset, which is impractical. This paper proposes a new scheme for protecting the privacy and integrity of spatial data stored in the Cloud while being able to execute range queries efficiently. Data objects are encrypted and sorted using Z-order space-filling curve. An index is built on top of the encrypted data to be utilized by the Service Provider to identify and retrieve a superset of data objects that contains the answers to the query. Many simulation experiments have been carried out to measure the performance of the proposed scheme in terms of the redundancy in data retrieved. The experimental results show that the proposed scheme outperforms the most recent scheme by Kim et al. in terms of data redundancy.

scholarly journals Evaluation methods for association rules in spatial knowlegde base

2014 ◽  
Vol II-4 ◽  
pp. 53-58
Author(s):  
X. Niu ◽  
X. Ji
Keyword(s):  
Association Rules ◽  
Spatial Data ◽  
Evaluation System ◽  
Spatial Database ◽  
Spatial Association ◽  
Evaluation Criteria ◽  
Evaluation Process ◽  
Evaluation Methods ◽  
Spatial Knowledge ◽  
Automatic Data

Association rule is an important model in data mining. It describes the relationship between predicates in transactions, makes the expression of knowledge hidden in data more specific and clear. While the developing and applying of remote sensing technology and automatic data collection tools in recent decades, tremendous amounts of spatial and non-spatial data have been collected and stored in large spatial database, so association rules mining from spatial database becomes a significant research area with extensive applications. How to find effective, reliable and interesting association rules from vast information for helping people analyze and make decision has become a significant issue. Evaluation methods measure spatial association rules with evaluation criteria. On the basis of analyzing the existing evaluation criteria, this paper improved the novelty evaluation method, built a spatial knowledge base, and proposed a new evaluation process based on the support-confidence evaluation system. Finally, the feasibility of the new evaluation process was validated by an experiment with real-world geographical spatial data.

Geographic Knowledge Discovery in Multiple Spatial Databases

2017 ◽  
pp. 344-366 ◽  
Author(s):  
Tahar Mehenni
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Association ◽  
Spatial Data Mining ◽  
Research Field ◽  
Geographic Knowledge ◽  
Geographic Knowledge Discovery ◽  
Active Research

Voluminous geographic data have been, and continue to be, collected from various Geographic Information Systems (GIS) applications such as Global Positioning Systems (GPS) and high-resolution remote sensing. For these applications, huge amount of data is maintained in multiple disparate databases and different in spatial data type, file formats, data schema, access mechanism, etc. Spatial data mining and knowledge discovery has emerged as an active research field that focuses on the development of theory, methodology, and practice for the extraction of useful information and knowledge from massive and complex spatial databases. This chapter highlights recent theoretical and applied research in geographic knowledge discovery and spatial data mining in a distributed environment where spatial data are dispersed in multiple sites. The author will present in this chapter, an overall picture of how spatial multi-database mining is achieved through several common spatial data-mining tasks, including spatial cluster analysis, spatial association rule and spatial classification.

Direction-based Spatial Skyline for Retrieving Arbitrary-Shaped Surrounding Objects

2019 ◽  
Vol 63 (11) ◽  
pp. 1668-1688
Author(s):  
Bojie Shen ◽  
Saiful Islam ◽  
David Taniar
Keyword(s):  
Spatial Data ◽  
Spatial Databases ◽  
Data Indexing ◽  
Potential Applications ◽  
Tree Data ◽  
Data Pruning ◽  
Data Objects ◽  
Rotationally Invariant ◽  
Pruning Techniques ◽  
Spatial Skyline

Abstract Retrieval of arbitrary-shaped surrounding data objects has many potential applications in spatial databases including nearby arbitrary-shaped object-of-interests retrieval surrounding a user. In this paper, we propose directional zone concept to determine directional similarity among spatial data objects. Then, we propose a novel query, called direction-based spatial skyline (DSS), which retrieves non-dominated arbitrary-shaped surrounding data objects in spatial databases for a user. The proposed DSS query is rotationally invariant as well as fair. We develop efficient algorithms for processing DSS queries in spatial databases by designing novel data pruning techniques using R-Tree data indexing scheme. Finally, we demonstrate the effectiveness and efficiency of our approach by conducting extensive experiments with real datasets.

Geographic Knowledge Discovery in Multiple Spatial Databases

2018 ◽  
pp. 121-143
Author(s):  
Tahar Mehenni
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Association ◽  
Spatial Data Mining ◽  
Research Field ◽  
Geographic Knowledge ◽  
Geographic Knowledge Discovery ◽  
Active Research

Voluminous geographic data have been, and continue to be, collected from various Geographic Information Systems (GIS) applications such as Global Positioning Systems (GPS) and high-resolution remote sensing. For these applications, huge amount of data is maintained in multiple disparate databases and different in spatial data type, file formats, data schema, access mechanism, etc. Spatial data mining and knowledge discovery has emerged as an active research field that focuses on the development of theory, methodology, and practice for the extraction of useful information and knowledge from massive and complex spatial databases. This chapter highlights recent theoretical and applied research in geographic knowledge discovery and spatial data mining in a distributed environment where spatial data are dispersed in multiple sites. The author will present in this chapter, an overall picture of how spatial multi-database mining is achieved through several common spatial data-mining tasks, including spatial cluster analysis, spatial association rule and spatial classification.

GML Topology Data Storage Schema Design

2007 ◽  
Vol 11 (6) ◽  
pp. 701-708 ◽  
Author(s):  
Yuzhen Li ◽  
◽  
Jianming Lu ◽  
Jihong Guan ◽  
Mingying Fan ◽  
...  
Keyword(s):  
Data Storage ◽  
Spatial Data ◽  
Information Exchange ◽  
Spatial Databases ◽  
Geographical Information ◽  
Markup Language ◽  
Oracle Spatial ◽  
Geographical Data ◽  
Schema Design ◽  
Extensible Markup

Geography Markup Language (GML) was developed to standardize the representation of geographical data in extensible markup language (XML), which facilitates geographical information exchange and sharing. Increasing amounts of geographical data are being presented in GML as its use widens, raising the question of how to store GML data efficiently to facilitate its management and retrieval. We analyze topology data in GML and propose storing nonspatial and spatial data from GML documents in spatial databases (e.g, Oracle Spatial, DB2 Spatial, and PostGIS/PostgreSQL.). We then use an example to analyze the topology relation.

Data Discovery Approaches for Vague Spatial Data

2011 ◽  
pp. 342-360 ◽  
Author(s):  
Frederick E. Petry
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
Association Rules ◽  
Rough Set ◽  
Fuzzy Set ◽  
Spatial Data ◽  
Spatial Databases ◽  
Uncertain Data ◽  
Rule Extraction ◽  
Data Discovery

This chapter focuses on the application of the discovery of association rules in approaches vague spatial databases. The background of data mining and uncertainty representations using rough set and fuzzy set techniques is provided. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets is described. Finally, an example of rule extraction for both types of uncertainty representations is given.

Spatial Network Databases

2009 ◽  
pp. 307-315 ◽  
Author(s):  
Michael Vassilakopoulos
Keyword(s):  
Information Systems ◽  
Spatial Data ◽  
Computer Aided Design ◽  
Geographical Information Systems ◽  
Large Scale ◽  
Spatial Databases ◽  
Query Language ◽  
Geographical Information ◽  
Data Types ◽  
Large Scale Integration

A Spatial Database is a database that offers spatial data types, a query language with spatial predicates, spatial indexing techniques, and efficient processing of spatial queries. All these fields have attracted the focus of researchers over the past 25 years. The main reason for studying spatial databases has been applications that emerged during this period, such as Geographical Information Systems, Computer-Aided Design, Very Large Scale Integration design, Multimedia Information Systems, and so forth. In parallel, the field of temporal databases, databases that deal with the management of timevarying data, attracted the research community since numerous database applications (i.e., Banking, Personnel Management, Transportation Scheduling) involve the notion of time.

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