Spatio-temporal Data Warehouses Research Articles

Open Spatiotemporal Data Warehouse for Agriculture Production Analytics

Business Intelligence (BI) technology with Extract, Transform, and Loading process, Data Warehouse, and OLAP have demonstrated the ability of information and knowledge generation for supporting decision making. In the last decade, the advancement of the Web 2.0 technology is improving the accessibility of web of data across the cloud. Linked Open Data, Linked Open Statistical Data, and Open Government Data is increasing massively, creating a more significant computer-recognizable data available for sharing. In agricultural production analytics, data resources with high availability and accessibility is a primary requirement. However, today’s data accessibility for production analytics is limited in the 2 or 3-stars open data format and rarely has attributes for spatiotemporal analytics. The new data warehouse concept has a new approach to combine the openness of data resources with mobility or spatiotemporal data in nature. This new approach could help the decision-makers to use external data to make a crucial decision more intuitive and flexible. This paper proposed the development of a spatiotemporal data warehouse with an integration process using service-oriented architecture and open data sources. The data sources are originating from the Village and Rural Area Information System (SIDeKa) that capture the agricultural production transaction in a daily manner. This paper also describes the way to spatiotemporal analytics for agricultural production using a new spatiotemporal data warehouse approach. The experiment results, by executing six relevant spatiotemporal query samples on DW with fact table contains 324096 tuples with temporal integer/float for each tuple, 4495 tuples of field dimension with geographic data as polygons, 80 tuples of village dimension, dozens of tuples of the district, subdistrict, province dimensions. The DW time dimension contains 3653 tuples representing a date for ten years, proved that this new approach has a convenient, simple model, and expressive performance for supporting executive to make decisions on agriculture production analytics based on spatiotemporal data. This research also underlines the prospects for scaling and nurturing the spatiotemporal data warehouse initiative.

Difference Operators in Simulation Data Warehouses

In analyzing observation data and simulation results, there are frequent demands for comparing more than one data on the same subject to detect any differences between them. For example, comparison of observation data for an object in a certain spatial domain at different times or comparison of spatial simulation data with different parameters. Therefore, this paper proposes the difference operator in spatio-temporal data warehouses, which store temporal and spatial observation data and simulation data. The requirements for the difference operator are summarized, and the approaches to implement them are presented. In addition, the proposed approach is applied to the mass evacuation of simulation data in a tsunami disaster, and its effectiveness is verified. Extensions of the difference operator and their applications are also discussed.

Data integration as the key to building a decision support system for groundwater management: Case of Saiss aquifers, Morocco

The widespread of Information Technology tools and the increasing of data production in groundwater management allowed the development of different information systems. Even if this field requires various disciplines, organizations and authorities in charge of groundwater have often worked with operational and specific information systems focusing on a single discipline. Driven by the need to integrate all available data from distributed sources to get relevant information at different levels of the decision-making process, many researches try to deal with this issue. This paper aims to show the role of data integration in building spatiotemporal data warehouse for supporting decision making in groundwater governance and management. The paper presents the approach adopted in this work which consider the geographical unit 'water point' as the model heart and the axis of groundwater data analysis. In order to illustrate this approach, Saiss aquifers have been chosen as a study area. Actually, Saiss aquifer system comprises two superimposed and connected aquifers. These aquifers play a significant role in the socio-economic development of Fez–Meknes region through the supply of drinking water and the satisfaction of irrigation water demand. From the 80s, with decreasing precipitation and increasing groundwater use, Saiss aquifers have known strong overexploitation. Spatiotemporal data warehouse of Saiss aquifers constitutes a platform for developing decision support systems, geo-analytical tools, and intelligent mobile applications, able to quick response to multidimensional queries of groundwater managers and users.

Hair-Oriented Data Model for Spatio-Temporal Data Mining

Spatio-temporal data are complex in terms of number of attributes for spatial and temporal values, and the data are changing towards time. Traditional method to mining the spatio-temporal data is the fact that the data is stored in data warehouse in un-normalization form as union of spatial and temporal data know as tabular data warehouse. A Hair-Oriented Data Model (HODM) has been proved as a suitable data model for spatio-temporal data. It has reduced the file size and decreased query execution time. The spatio-temporal data stored using the HODM known as Hair-Oriented Data warehouse. However, this paper aims to presents a method to develop spatio-temporal data mining model using the Hair-Oriented data warehouse. The Hair-Oriented data model also provide with various functions for easy maintenance on spatio-temporal data warehouse. Experiment conducted using Climate-change spatio-temporal data set benchmark. Two Climate-change spatio-temporal models been developed using regression and k-nearest neighbor techniques. The performance of the Hair-Oriented Data Warehouse is evaluated by comparing its performance with traditional tabular data warehouse. The result shows that developing data mining spatio-temporal model using Hair-Oriented data warehouse is faster compare using the tabular data warehouse, therefore it can be concluded that the Hair-Oriented Data Model is suitable for Spatio-temporal data mining.

A FRAMEWORK FOR SPATIO-TEMPORAL DATA WAREHOUSE

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.

Spatio-temporal data warehouses using an adaptive cell-based approach

Most of the framework for supporting OLAP operations over immense amounts of spatio-temporal data is based on multi-tree structures. The multi-tree frameworks, however, are hardly applicable to spatio-temporal OLAP in practice, due mainly to high management costs and low query efficiency. To overcome the limitations of such multi-tree frameworks, we propose a new approach called ST -Cube ( spatio-temporal cube), which is an adaptive cell-based, total-ordered and prefix-summed cube for spatio-temporal data warehouses. Our extensive performance studies show that the ST -Cube requires less space and achieves higher query performance than multi-tree frameworks, under various operational conditions.

A TOOL FOR COLLECTING, QUERYING AND MINING MACROSEISMIC DATA

SEISMO-SURFER is a tool for collecting, querying and mining seismic data being developed in Java programming language using Oracle database system. The objective is to combine recent research trends and results in the fields of spatial and spatio-temporal databases, data warehouses and data mining, as well as well established visualization techniques for geographical information. The database of the tool is automatically updated from remote sources while existing possibilities allow the querying on different earthquakes parameters, the analysis of the data for extraction of useful information and the graphical representation of the results via maps, charts etc. In the present work, we extend SEISMO-SURFER to include macroseismic data collected by the Geodynamic Institute and filled in a relative database. More specifically, the seismic parameters of the strong earthquakes, stored into the SEISMO-SURFER database, are linked to the macroseismic intensities observed at different sites. Administrative information for each site, local surface geology, tectonic lines, damage photographs and detailed descriptions from newspapers are also included. University of Piraeus and Geodynamic Institute are working together to continuously update and develop SEISMO-SURFER, concerning the data included, the variety of parameters stored and the mining algorithms supported for exploiting knowledge.