scholarly journals Estado actual de temáticas para el análisis espacial en la toma de decisiones [Current status of thematic for spatial analysis in decision making]

2014 ◽  
Author(s):  
Christiam Alejandro Niño Peña ◽  
Gustavo Cáceres Castellanos
Keyword(s):  
Data Mining ◽  
Spatial Analysis ◽  
Business Intelligence ◽  
Spatial Data ◽  
Value Added ◽  
Spatial Data Mining ◽  
Spatial Knowledge ◽  
Data Cube ◽  
Current Status ◽  
Data Warehouses

Resumen Las necesidades de las organizaciones para cumplir sus metas y llegar a tomar las mejores decisiones han planteado retos en cuanto al análisis de la información, ya sea por competitividad, valor agregado, costos o ganancia; tener una base de datos de todos los movimientos transaccionales realizados en un histórico de tiempo se volvió algo muy importante. Sumado a esto, surgió un factor que permitiría tener control sobre el lugar de los sucesos y así situar los esfuerzos donde en realidad se necesitan, este factor se identifica con el espacio geográfico almacenado en las bases de datos como tipo de dato geométrico o espacial. A partir de este tipo se crean nuevas formas de análisis y manejo de los datos como la inteligencia de negocios, las bodegas de datos, las consultas, modelado y minería de datos enfocados en el descubrimiento de conocimiento espacial. De esta forma, el objetivo principal del artículo es exponer, desde la revisión documental, los proyectos y hallazgos actuales sobre las temáticas del análisis espacial. Palabras Claves: Bodegas de datos espaciales, Cubos de datos espaciales, Inteligencia de negocios espaciales, Minería de datos espaciales, Procesamiento analítico en línea espacial. Abstract The needs of organizations to meet their goals and get to make the best decisions have posed new challenges for the analysis of the information, either by competitiveness, value added, cost or profit; having a database of all movements transactions made in a historical time became something very important. Added to this emerged a factor that would allowing control over the place of events and in this manner situate efforts where you actually needing, this factor is identified with the geographical space stored in the databases as geometric type data or spatial. From this type are created new forms of analysis and data management as business intelligence, data warehouses, queries, modeling, and data mining pointed on the discovery of spatial knowledge. Thus, the main objective of the article is to present the projects and current findings of research in the topics of spatial analysis. Keywords: Spatial data warehouses, Spatial data cube, Spatial business intelligence, Spatial data mining, Processing spatial analytical online.

A Survey of Spatial Data Mining Methods Databases and Statistics Point of Views

2011 ◽  
pp. 229-242 ◽  
Author(s):  
Karine Zeitouni
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Spatial Analysis ◽  
Geographic Information Systems ◽  
Spatial Statistics ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Data Mining ◽  
Geographic Information ◽  
Mining Methods

This chapter reviews the data mining methods that are combined with Geographic Information Systems (GIS) for carrying out spatial analysis of geographic data. We will first look at data mining functions as applied to such data and then highlight their specificity compared with their application to classical data. We will go on to describe the research that is currently going on in this area, pointing out that there are two approaches: the first comes from learning on spatial databases, while the second is based on spatial statistics. We will conclude by discussing the main differences between these two approaches and the elements they have in common.

scholarly journals SPDM in Social Media for the Development of Business

2020 ◽  
Vol 9 (5) ◽  
pp. 2088-2091
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Spatial Data ◽  
Daily Life ◽  
Spatial Data Mining ◽  
Spatial Knowledge ◽  
Process Data ◽  
Collective Information ◽  
Modern Era

In Modern era Social Media(SM) is a place of communication with collective information. Spatial Data Mining(SPDM) are acknowledged as mining of spatial knowledge among attractive pattern from various forms of Spatial Data. SPDM focus to theory and Methodology and process for extracting useful information through spatial data. Spatial Data are attributes of neighbors of selected object. SM and their role played in daily life increased considerably over the last few years. To examine collaboration among friends in a SM pattern of relationship is essential, for development of digitalized businesses in trading process. Data extracted with SPDM is utilized with KNG technique to identify the highly recommended product among clustered users. This paper illustrate the betterment of KNG while compared with KNN process using Spatial Data for the development of Business.

scholarly journals Utilizing MapReduce to Improve Probe-Car Track Data Mining

2018 ◽  
Vol 7 (7) ◽  
pp. 287 ◽  
Author(s):  
Li Zheng ◽  
Meng Sun ◽  
Yuejun Luo ◽  
Xiangbo Song ◽  
Chaowei Yang ◽  
...  
Keyword(s):  
Data Mining ◽  
Traffic Safety ◽  
Spatial Data ◽  
Road Traffic ◽  
Spatial Data Mining ◽  
Tracking Data ◽  
Mining System ◽  
Computational Performance ◽  
True Path ◽  
Probe Car

With the rapidly increasing popularization of the automobile, challenges and greater demands have come to the fore, including traffic congestion, energy crises, traffic safety, and environmental pollution. To address these challenges and demands, enhanced data support and advanced data collection methods are crucial and highly in need. A probe-car serves as an important and effective way to obtain real-time urban road traffic status in the international Intelligent Transportation System (ITS), and probe-car technology provides the corresponding solution through advanced navigation data, offering more possibilities to address the above problems. In addition, massive spatial data-mining technologies associated with probe-car tracking data have emerged. This paper discusses the major problems of spatial data-mining technologies for probe-car tracking data, such as true path restoration and the close correlation of spatial data. To address the road-matching issue in massive probe-car tracking data caused by the strong correlation combining road topology with map matching, this paper presents a MapReduce-based technology in the second spatial data model. The experimental results demonstrate that by implementing the proposed spatial data-mining system on distributed parallel computing, the computational performance was effectively improved by five times and the hardware requirements were significantly reduced.

scholarly journals SPASIAL DATA MINING MENGGUNAKAN MODEL SAR-KRIGING

2011 ◽  
Vol 5 (3) ◽  
pp. 52
Author(s):  
Atje Setiawan ◽  
Rudi Rosadi
Keyword(s):  
Data Mining ◽  
Elementary School ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Education Quality ◽  
Percentage Error ◽  
Kriging Method ◽  
National Education ◽  
Software Application ◽  
Average Percentage

The region of Indonesia is very sparse and it has a variation condition in social, economic and culture, so the problem in education quality at many locations is an interesting topic to be studied. Database used in this research is Base Survey of National Education 2003, while a spatial data is presented by district coordinate as a least analysis unit. The aim of this research is to study and to apply spatial data mining to predict education quality at elementary and junior high schools using SAR-Kriging method which combines an expansion SAR and Kriging method. Spatial data mining process has three stages. preprocessing, process of data mining, and post processing.For processing data and checking model, we built software application of Spatial Data Mining using SAR-Kriging method. An application is used to predict education quality at unsample locations at some cities at DIY Province.  The result shows that SAR-Kriging method for some cities at DIY for elementary school has an average percentage error 6.43%. We can conclude that for elementary school, SAR-Kriging method can be used as a fitted model. Keywords—  Expansion SAR, SAR-Kriging, quality education

Spatial data mining to support pandemic preparedness

2006 ◽  
Vol 8 (1) ◽  
pp. 80-82 ◽  
Author(s):  
Chris Bailey-Kellogg ◽  
Naren Ramakrishnan ◽  
Madhav V. Marathe
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Pandemic Preparedness
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