Solutions for decision support in university management

The paper proposes an overview of decision support systems in order to define the role of a system to assist decision in university management. The authors present new technologies and the basic concepts of multidimensional data analysis using models of business processes within the universities. Based on information provided by scientific literature and on the authors’ experience, the study aims to define selection criteria in choosing a development environment for designing a support system dedicated to university management. The contributions consist in designing a data warehouse model and models of OLAP analysis to assist decision in university management.university management, decision support, multidimensional analysis, data warehouse, OLAP

Business intelligence and the telecommunications industry : can business intelligence lead to higher profits?

Organizations are finding it increasingly difficult to increase profits as competition in the marketplace continually pressurizes margins. Organizations will have to do more to enjoy sustainable profits in the future and information technology could arguably be the key to assisting management with the task of increasing profits on a sustainable basis. Business intelligence (BI) could be the competitive advantage for organizations to increase profitability. South Africa is faced with an unemployment rate of over 40% and it is not desirable that costs are contained by reducing staff. It is clear that innovative ideas should be looked at to ensure that organizations continue to make profits. Information management programmes offer the necessary tools to ensure that efficient and strategic decisions are made

Creating Business Intelligence through Machine Learning: An Effective Business Decision Making Tool

Growing technological progressions have given rise to many issues concerning the contemporary decision making in business, which is a difficult phenomenon without Business Intelligence/ Machine Learning. The linking of machine learning with business intelligence is not only pivotal for business decision making but also for the business intelligence in totality, owing to the reason that in absence of machine learning, decision making couldn’t take place efficaciously. Machines need to learn, re-learn, and then only they can help your learning process. The below paper seeks to make this concept simple/ easy by removing the ambiguities using a general framework. In order to prove the impact of machine learning on business intelligence, we need to forecast the trends, what is going around the world – business has to stay updated, then only it can be a successful endeavour.  The paper posits the basic theories and definitions of business intelligence and machine learning. To learn from the past and forecast the future trends, many companies are adopting business intelligence tools and systems. Companies have understood the brilliance of enforcing achievements of the goals defined by their business strategies through business intelligence concepts and with the help of machine learning. It describes the insights on the role and requirement of real time BI by examining the business needs. Keywords: Business Intelligence (BI); Machine Learning (ML); Artificial Neural Networks (ANN); Self-Organizing Maps (SOM); Data Mining (DM); Data Warehousing (DW)

On the Application of Data Mining to Official Data

Retrieving valuable knowledge and statistical patterns from official data has a great potential in supporting strategic policy making. Data Mining (DM) techniques are well-known for providing flexible and efficient analytical tools for data processing. In this paper, we provide an introduction to applications of DM to official statistics and flag the important issues and challenges. Considering recent advancements in software projects for DM, we propose intelligent data control system design and specifications as an example of DM application in official data processing.Data mining, Official data, Intelligent data control system

BCAS: A Web-enabled and GIS-based Decision Support System for the Diagnosis and Treatment of Breast Cancer

For decades, geographical variations in cancer rates have been observed but the precise determinants of such geographic differences in breast cancer development are unclear. Various statistical models have been proposed. Applications of these models, however, require that the data be assembled from a variety of sources, converted into the statistical models’ parameters and delivered effectively to researchers and policy makers. A web-enabled and GIS-based system can be developed to provide the needed functionality. This article overviews the conceptual web-enabled and GIS-based system (BCAS), illustrates the system’s use in diagnosing and treating breast cancer and examines the potential benefits and implications for breast cancer research and practice

Graph BI & analytics: current state and future challenges

In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI and analytics framework.Peer ReviewedPostprint (author's final draft

Incorporation of ontologies in data warehouse/business intelligence systems - A systematic literature review

Semantic Web (SW) techniques, such as ontologies, are used in Information Systems (IS) to cope with the growing need for sharing and reusing data and knowledge in various research areas. Despite the increasing emphasis on unstructured data analysis in IS, structured data and its analysis remain critical for organizational performance management. This systematic literature review aims at analyzing the incorporation and impact of ontologies in Data Warehouse/Business Intelligence (DW/BI) systems, contributing to the current literature by providing a classification of works based on the field of each case study, SW techniques used, and the authors’ motivations for using them, with a focus on DW/BI design, development and exploration tasks. A search strategy was developed, including the definition of keywords, inclusion and exclusion criteria, and the selection of search engines. Ontologies are mainly defined using the Ontology Web Language standard to support multiple DW/BI tasks, such as Dimensional Modeling, Requirement Analysis, Extract-Transform-Load, and BI Application Design. Reviewed authors present a variety of motivations for ontology-driven solutions in DW/BI, such as eliminating or solving data heterogeneity/semantics problems, increasing interoperability, facilitating integration, or providing semantic content for requirements and data analysis. Further, implications for practice and research agenda are indicated.info:eu-repo/semantics/publishedVersio

Ontology based data warehousing for mining of heterogeneous and multidimensional data sources

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals