scholarly journals Knowledge discovery in spatial data by means of ILP

1998 ◽  
pp. 185-193 ◽  
Author(s):  
Luboš Popelínský
Keyword(s):  
Knowledge Discovery ◽  
Spatial Data

Reasoning Temporally Attributed Spatial Entity Knowledge Towards Qualitative Inference of Geographic Process

2019 ◽  
Vol 15 (2) ◽  
pp. 32-53 ◽  
Author(s):  
Jayanthi Ganapathy ◽  
Uma V.
Keyword(s):  
Knowledge Base ◽  
Knowledge Discovery ◽  
Spatial Data ◽  
Spatial Information ◽  
Spatial Knowledge ◽  
Spatial Process ◽  
Formal Representation ◽  
Geographic Process ◽  
Event Based ◽  
Spatial Entity

Knowledge discovery with geo-spatial information processing is of prime importance in geomorphology. The temporal characteristics of evolving geographic features result in geo-spatial events that occur at a specific geographic location. Those events when consecutively occur result in a geo-spatial process that causes a phenomenal change over the period of time. Event and process are essential constituents in geo-spatial dynamism. The geo-spatial data acquired by remote sensing technology is the source of input for knowledge discovery of geographic features. This article performs qualitative inference of geographic process by identifying events causing geo-spatial deformation over time. The evolving geographic features and their types have association with spatial and temporal factors. Event calculus-based spatial knowledge formalism allows reasoning over intervals of time. Hence, representation of Event Attributed Spatial Entity (EASE) Knowledge is proposed. Logical event-based queries are evaluated on the formal representation of EASE Knowledge Base. Event-based queries are executed on the proposed knowledge base and when experimented on, real data sets yielded comprehensive results. Further, the significance of EASE-based spatio-temporal reasoning is proved by evaluating with respect to query processing time and accuracy. The enhancement of EASE with a direction for further development to explore its significance towards prediction is discussed towards the end.

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.

A survey on data mining and knowledge discovery techniques for spatial data

2015 ◽  
Vol 19 (2) ◽  
pp. 265 ◽  
Author(s):  
Majid Shishehgar ◽  
Seyed Nasirodin Mirmohammadi ◽  
Ahmad Reza Ghapanchi
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Spatial Data

Mining Geo-Referenced Databases

2011 ◽  
pp. 113-150 ◽  
Author(s):  
Maribel Yasmina Santos ◽  
Luís Alfredo Amaral
Keyword(s):  
Knowledge Discovery ◽  
Spatial Data ◽  
Domain Knowledge ◽  
Relational Databases ◽  
Spatial Reasoning ◽  
Spatial Relations ◽  
Semantic Knowledge ◽  
Knowledge Discovery In Databases ◽  
Data Sets ◽  
Qualitative Spatial Reasoning

Knowledge discovery in databases is a process that aims at the discovery of associations within data sets. The analysis of geo-referenced data demands a particular approach in this process. This chapter presents a new approach to the process of knowledge discovery, in which qualitative geographic identifiers give the positional aspects of geographic data. Those identifiers are manipulated using qualitative reasoning principles, which allows for the inference of new spatial relations required for the data mining step of the knowledge discovery process. The efficacy and usefulness of the implemented system — Padrão — has been tested with a bank dataset. The results support that traditional knowledge discovery systems, developed for relational databases and not having semantic knowledge linked to spatial data, can be used in the process of knowledge discovery in geo-referenced databases, since some of this semantic knowledge and the principles of qualitative spatial reasoning are available as spatial domain knowledge.

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.

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