scholarly journals A FRAMEWORK FOR SPATIO-TEMPORAL DATA WAREHOUSE

2013 ◽  
Vol 4 (1) ◽  
pp. 146-150
Author(s):  
Lax Maiah ◽  
DR.A.GOVARDHAN DR.A.GOVARDHAN ◽  
DR. C.SUNIL KUMAR
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Data Warehouse ◽  
Data Storage ◽  
Multidimensional Data ◽  
Metadata Management ◽  
Temporal Data ◽  
Application Layer ◽  
Data Process ◽  
Spatio Temporal

Data Warehouse (DW) is topic-oriented, integrated, static datasets which are used to support decision-making. Driven by the constraint of mass spatio-temporal data management and application, Spatio-Temporal Data Warehouse (STDW) was put forward, and many researchers scattered all over the world focused their energy on it.Although the research on STDW is going in depth , there are still many key difficulties to be solved, such as the design principle, system framework, spatio-temporal data model (STDM), spatio-temporal data process (STDP), spatial data mining (SDM) and etc. In this paper, the concept of STDW is discussed, and analyzes the organization model of spatio-temporal data. Based on the above, a framework of STDW is composed of data layer, management layer and application layer. The functions of STDW should include data analysis besides data process and data storage. When users apply certain kind of data services, STDW identifies the right data by metadata management system, then start data processing tool to form a data product which serves the data mining and OLAP. All varieties of distributed databases (DDBs) make up data sources of STDW, including Digital Elevation Model (DEM), Diagnosis-Related Group (DRG), Data Locator Group (DLG), Data Objects Management (DOM), Place Name and other databases in existence. The management layer implements heterogeneous data processing, metadata management and spatio-temporal data storage. The application layer provides data products service, multidimensional data cube, data mining tools and on-line analytical process.

Exploratory spatio-temporal data mining and visualization

2007 ◽  
Vol 18 (3) ◽  
pp. 255-279 ◽  
Author(s):  
P. Compieta ◽  
S. Di Martino ◽  
M. Bertolotto ◽  
F. Ferrucci ◽  
T. Kechadi
Keyword(s):  
Data Mining ◽  
Temporal Data Mining ◽  
Temporal Data ◽  
Spatio Temporal

Towards a predictive model for opal exploration using a spatio-temporal data mining approach

2013 ◽  
Vol 60 (2) ◽  
pp. 217-229 ◽  
Author(s):  
A. S. Merdith ◽  
T. C. W. Landgrebe ◽  
A. Dutkiewicz ◽  
R. D. Müller
Keyword(s):  
Data Mining ◽  
Predictive Model ◽  
Temporal Data Mining ◽  
Temporal Data ◽  
Data Mining Approach ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Data Mining

2018 ◽  
Vol 51 (4) ◽  
pp. 1-41 ◽  
Author(s):  
Gowtham Atluri ◽  
Anuj Karpatne ◽  
Vipin Kumar
Keyword(s):  
Data Mining ◽  
Temporal Data Mining ◽  
Temporal Data ◽  
Spatio Temporal

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

2009 ◽  
pp. 223-229
Author(s):  
Martin Burgard ◽  
Franca Piazza
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Data Warehouse ◽  
Human Resource ◽  
Business Intelligence ◽  
Multidimensional Data ◽  
Business Intelligence Systems ◽  
Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses

2013 ◽  
Vol 9 (2) ◽  
pp. 89-109 ◽  
Author(s):  
Marie-Aude Aufaure ◽  
Alfredo Cuzzocrea ◽  
Cécile Favre ◽  
Patrick Marcel ◽  
Rokia Missaoui
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Query Processing ◽  
Data Warehouse ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
Cooperative Query Answering ◽  
User Centric ◽  
Use Of Knowledge

In this vision paper, the authors discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, the authors first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, the authors propose a user-centric architecture for OLAP query processing, which is the typical applicative interface to data warehouse systems. Finally, the authors propose insights towards cooperative query answering that make use of knowledge management principles and exploit the peculiarities of data warehouses (e.g., multidimensionality, multi-resolution, and so forth).

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