Visual models for spatial knowledge discovery

2000 ◽  
Author(s):  
Nelly M. Kovalevskaya ◽  
Georgy L. Gimel'farb
Keyword(s):  
Knowledge Discovery ◽  
Spatial Knowledge ◽  
Visual Models

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.

METHODS OF SOLVING PROBLEMS OF DATA ANALYSIS USING ANALYTICAL VISUAL MODELS

2017 ◽  
Vol 9 (4) ◽  
pp. 78-88 ◽  
Author(s):  
A.A. Zakharova ◽  
E.V. Vekhter ◽  
A.V. Shklyar
Keyword(s):  
Data Analysis ◽  
Visual Models

Analisis Data Pembayaran Kredit Nasabah Bank Menggunakan Metode Data Mining

2013 ◽  
Vol 4 (1) ◽  
pp. 18-27
Author(s):  
Ira Melissa ◽  
Raymond S. Oetama
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Database

Data mining adalah analisis atau pengamatan terhadap kumpulan data yang besar dengan tujuan untuk menemukan hubungan tak terduga dan untuk meringkas data dengan cara yang lebih mudah dimengerti dan bermanfaat bagi pemilik data. Data mining merupakan proses inti dalam Knowledge Discovery in Database (KDD). Metode data mining digunakan untuk menganalisis data pembayaran kredit peminjam pembayaran kredit. Berdasarkan pola pembayaran kredit peminjam yang dihasilkan, dapat dilihat parameter-parameter kredit yang memiliki keterkaitan dan paling berpengaruh terhadap pembayaran angsuran kredit. Kata kunci—data mining, outlier, multikolonieritas, Anova

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