NOSQL DATABASES AS A DATA WAREHOUSE FOR DECISION SUPPORT SYSTEMS

2017 ◽  
Vol 185 (3) ◽  
pp. 124-131
Author(s):  
Jarosław KURPANIK
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Data Warehouse ◽  
Data Storage ◽  
Relational Databases ◽  
Support Systems ◽  
Large Data ◽  
Daily Basis ◽  
Nosql Databases ◽  
Big Data Storage

Nowadays, to some extent decision support systems are forced to base their operation on large data warehouses whose analysis is difficult and time consuming. This is why where data are stored becomes vital. The use of an efficient and productive data warehouse for this purpose can significantly improve application/system operation. Currently one of the most common solutions used in Big Data storage and quick processing are non-relational databases NoSQL. They are a relatively new solution, however, their development is dy-namic and their market share is increased on a daily basis, which means that it worth in-vestigating what they offer.

Using OCL to Model Constraints in Data Warehouses

2013 ◽  
pp. 212-224
Author(s):  
François Pinet ◽  
Myoung-Ah Kang ◽  
Kamal Boulil ◽  
Sandro Bimonte ◽  
Gil De Sousa ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Data Warehouse ◽  
Support Systems ◽  
General Information ◽  
Data Warehouses ◽  
Object Constraint Language ◽  
Software Applications ◽  
Constraint Language ◽  
Transactional Databases

Recent research works propose using Object-Oriented (OO) approaches, such as UML to model data warehouses. This paper overviews these recent OO techniques, describing the facts and different analysis dimensions of the data. The authors propose a tutorial of the Object Constraint Language (OCL) and show how this language can be used to specify constraints in OO-based models of data warehouses. Previously, OCL has been only applied to describe constraints in software applications and transactional databases. As such, the authors demonstrate in this paper how to use OCL to represent the different types of data warehouse constraints. This paper helps researchers working in the fields of business intelligence and decision support systems, who wish to learn about the major possibilities that OCL offer in the context of data warehouses. The authors also provide general information about the possible types of implementation of multi-dimensional models and their constraints.

Enhancing Decision Support Systems with Spatial Capabilities

2011 ◽  
pp. 2542-2557
Author(s):  
Marcus Costa Sampaio ◽  
Cláudio de Souza Baptista ◽  
André Gomes de Sousa ◽  
Fabiana Ferreira do Nascimento
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Data Warehouse ◽  
Spatial Data ◽  
Support Systems ◽  
Data Cube ◽  
Related Data ◽  
Spatial Dimensions ◽  
Object Relational ◽  
Long Time

This chapter introduces spatial dimensions and measures as a means of enhancing decision support systems with spatial capabilities. By some way or other, spatial related data has been used for a long time; however, spatial dimensions have not been fully exploited. It is presented a data model that tightly integrates data warehouse and geographical information systems — so characterizing a spatial data warehouse (SDW) — ; more precisely, the focus is on a formalization of SDW concepts, on a spatial-aware data cube using object-relational technology, and on issues underlying a SDW — specially regarding spatial data aggregation operations. Finally, the MapWarehouse prototype is presented aiming to validate the ideas proposed. The authors believe that SDW allows for the efficient processing of queries that use, jointly, spatial and numerical temporal data (e.g., temporal series from summarized spatial and numerical measures).

Modelling Spatial Decision Support Systems Focused on the Development of a Data Warehouse

2014 ◽  
Vol 6 (3) ◽  
pp. 43-64
Author(s):  
Concepción M. Gascueña ◽  
Rafael Guadalupe
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Data Warehouse ◽  
Support Systems ◽  
Spatial Decision Support ◽  
Model Driven ◽  
Engineering Software ◽  
Development Methodology ◽  
Software Engineering Process ◽  
Integration Problems

Nowadays, organizations have plenty of data stored in DB databases, which contain invaluable information. Decision Support Systems DSS provide the support needed to manage this information and planning medium and long-term “the modus operandi” of these organizations. Despite the growing importance of these systems, most proposals do not include its total development, mostly limiting itself on the development of isolated parts, which often have serious integration problems. Hence, methodologies that include models and processes that consider every factor are necessary. This paper will try to fill this void as it proposes an approach for developing spatial DSS driven by the development of their associated Data Warehouse DW, without forgetting its other components. To the end of framing the proposal different Engineering Software focus (The Software Engineering Process and Model Driven Architecture) are used, and coupling with the DB development methodology, (and both of them adapted to DW peculiarities). Finally, an example illustrates the proposal.

Top Management's Role in Promoting Decision Support Systems Efficiency

2021 ◽  
pp. 1409-1429
Author(s):  
Abdullah Ibrahim Alkraiji
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Data Warehouse ◽  
Support Systems ◽  
Quality Information ◽  
Top Management ◽  
Governmental Organizations ◽  
High Quality Information ◽  
Effective Use

Despite overwhelmingly positive reviews for decision support systems, the IS literature has produced inconsistent results regarding the role of top management and the effectiveness of these systems. IS researchers are concerned with there being a widening gap between research and practice, leading to the current study, focusing on the relevance of these two constituencies. This study employs the Delphi methodology in relation to Saudi Arabia to investigate the reality of the decision support systems in governmental organizations and the diverse issues related to making effective use of them by increasing the role of top management. The findings revealed that there is an absence of a role for IT in the decision-making process, and that there is a lack of robust data warehouse systems capable of supporting organizations' top management with high-quality information. The study revealed various required reforms of various governmental and institutional arrangements and obligational aspects of the efficiency of decision support systems.

scholarly journals Decision Support Systems (DSS): A survey

1984 ◽  
Vol 15 (4) ◽  
pp. 189-196 ◽  
Author(s):  
H. W. Ittmann
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Large Data ◽  
Data Bases ◽  
Practical Applications ◽  
The Past ◽  
Technological Developments ◽  
Computer Based

Interest in aiding and supporting decision-making through the use of computers has been stimulated by technological developments in areas such as personal computers, computer networks, large data bases, colour graphics and computer-based models. These uses are known as Decision Support Systems (DSS) and imply computer systems designed to extend managers' capabilities, and at the same time to integrate these uses into existing managerial activities and needs. During the past few years there have been rapid international developments in the DSS field. In South Africa this trend is also noticeable, although on a smaller scale. The object of this article is to give a survey of Decision Support Systems by defining the concept and by showing how it relates to other fields, and to present a framework of the typical functions and components of such a system. Some practical applications are discussed for illustrative purposes.

Enhancing Decision Support Systems with Spatial Capabilities

2011 ◽  
pp. 94-116
Author(s):  
Marcus Costa Sampaio ◽  
Cláudio de Souza Baptita ◽  
André Gomes de Sousa ◽  
Fabiana Ferreira do Nascimento
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Data Warehouse ◽  
Spatial Data ◽  
Support Systems ◽  
Data Cube ◽  
Related Data ◽  
Spatial Dimensions ◽  
Object Relational ◽  
Long Time

This chapter introduces spatial dimensions and measures as a means of enhancing decision support systems with spatial capabilities. By some way or other, spatial related data has been used for a long time; however, spatial dimensions have not been fully exploited. It is presented a data model that tightly integrates data warehouse and geographical information systems — so characterizing a spatial data warehouse (SDW) — ; more precisely, the focus is on a formalization of SDW concepts, on a spatial-aware data cube using object-relational technology, and on issues underlying a SDW — specially regarding spatial data aggregation operations. Finally, the MapWarehouse prototype is presented aiming to validate the ideas proposed. The authors believe that SDW allows for the efficient processing of queries that use, jointly, spatial and numerical temporal data (e.g., temporal series from summarized spatial and numerical measures).

Data Warehouse Facilitating Evidence-Based Medicine

2010 ◽  
pp. 174-207 ◽  
Author(s):  
Nevena Stolba ◽  
Tho Manh Nguyen ◽  
A Min Tjoa
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Decision Support Systems ◽  
Knowledge Base ◽  
Data Warehouse ◽  
Support Systems ◽  
Medical Data ◽  
Decision Makers ◽  
Evidence Based ◽  
Healthcare Decision

In the past, much effort of healthcare decision support systems were focused on the data acquisition and storage, in order to allow the use of this data at some later point in time. Medical data was used in static manner, for analytical purposes, in order to verify the undertaken decisions. Due to the immense volumes of medical data, the architecture of the future healthcare decision support systems focus more on interoperability than on integration. With the raising need for the creation of unified knowledge base, the federated approach to distributed data warehouses (DWH) is getting increasing attention. The exploitation of evidence-based guidelines becomes a priority concern, as the awareness of the importance of knowledge management rises. Consequently, interoperability between medical information systems is becoming a necessity in modern health care. Under strong security measures, health care organizations are striking to unite and share their (partly very high sensitive) data assets in order to achieve a wider knowledge base and to provide a matured decision support service for the decision makers. Ontological integration of the very complex and heterogeneous medical data structures is a challenging task. The authors’ objective is to point out the advantages of the deployment of a federated data warehouse approach for the integration of the wide range of different medical data sources and for distribution of evidence-based clinical knowledge, to support clinical decision makers, primarily clinicians at the point of care.

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