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

Enhancing Data Warehouse management through semi-automatic data integration and complex graph generation

2013 - 2014Strategic information is one of the main assets for many organizations and, in the next future, it will become increasingly more important to enable the decisionmakers answer questions about their business, such as how to increase their profitability. A proper decision-making process is benefited by information that is frequently scattered among several heterogeneous databases. Such databases may come from several organization systems and even from external sources. As a result, organization managers have to deal with the issue of integrating several databases from independent data sources containing semantic differences and no specific or canonical concept description. Data Warehouse Systems were born to integrate such kind of heterogeneous data in order to be successively extracted and analyzed according to the manager’s needs and business plans. Besides being difficult and onerous to design, integrate and build, Data Warehouse Systems present another issue related to the difficulty to represent multidimensional information typical of the result of OLAP operations, such as aggregations on data cubes, extraction of sub-cubes or rotations of the data axis, through easy to understand views... [edited by author]XIII n.s

Heterogeneous data source integration for smart grid ecosystems based on metadata mining

The arrival of new technologies related to smart grids and the resulting ecosystem of applications andmanagement systems pose many new problems. The databases of the traditional grid and the variousinitiatives related to new technologies have given rise to many different management systems with several formats and different architectures. A heterogeneous data source integration system is necessary toupdate these systems for the new smart grid reality. Additionally, it is necessary to take advantage of theinformation smart grids provide. In this paper, the authors propose a heterogeneous data source integration based on IEC standards and metadata mining. Additionally, an automatic data mining framework isapplied to model the integrated information.Ministerio de Economía y Competitividad TEC2013-40767-

Modelling and Simulation of a Decision Support System Prototype Built on an Improved Data Warehousing Architecture for the School of Postgraduate, MAUTECH, Yola – Nigeria

A Data Warehouse (DW) is constructed with the goal of storing and providing all the relevant information that is generated along the heterogeneous databases of an organization. The development and management of precise and up-to-date information concerning academic staff, department, faculty, student’s academic record etc. is critically important in the management of a university. This study has become necessary because, data warehousing is a new field, a small number of investigations has been done regarding the features of academic data analysis and report. At present, data warehousing is among the best solution for gathering and maintaining data for decision making.  Therefore, the aim of this paper is to develop a DW prototype model for the School of Postgraduate Studies’ (SPGS) programmes of Modibbo Adama University of Technology (MAUTEC), Yola. The objective of the study is to model and simulate a decision support system that is capable of querying the prototype DW database model to generate reports as output in order to help administrative decision making of the SPGS MAUTEC, Yola. The study has provided relevant literatures in relation to the subject matter. In the methodology, a secondary, field and case study research were conducted. The software engineering development methodology considered was the “Realistic Waterfall Model”. The findings of this paper provide a DW prototype database model using a dimensional modeling technique and the graphic user interface tool for reports and analysis. The researchers have demonstrated their understanding on the subject matter and as a matter of fact, possible future work has been suggested from where we stopped. Keywords - Data Warehouse, Modeling, Simulation, Prototype and Decision Support Syste

A Business Intelligence Solution, based on a Big Data Architecture, for processing and analyzing the World Bank data

The rapid growth in data volume and complexity has needed the adoption of advanced technologies to extract valuable insights for decision-making. This project aims to address this need by developing a comprehensive framework that combines Big Data processing, analytics, and visualization techniques to enable effective analysis of World Bank data. The problem addressed in this study is the need for a scalable and efficient Business Intelligence solution that can handle the vast amounts of data generated by the World Bank. Therefore, a Big Data architecture is implemented on a real use case for the International Bank of Reconstruction and Development. The findings of this project demonstrate the effectiveness of the proposed solution. Through the integration of Apache Spark and Apache Hive, data is processed using Extract, Transform and Load techniques, allowing for efficient data preparation. The use of Apache Kylin enables the construction of a multidimensional model, facilitating fast and interactive queries on the data. Moreover, data visualization techniques are employed to create intuitive and informative visual representations of the analysed data. The key conclusions drawn from this project highlight the advantages of a Big Data-driven Business Intelligence solution in processing and analysing World Bank data. The implemented framework showcases improved scalability, performance, and flexibility compared to traditional approaches. In conclusion, this bachelor thesis presents a Business Intelligence solution based on a Big Data architecture for processing and analysing the World Bank data. The project findings emphasize the importance of scalable and efficient data processing techniques, multidimensional modelling, and data visualization for deriving valuable insights. The application of these techniques contributes to the field by demonstrating the potential of Big Data Business Intelligence solutions in addressing the challenges associated with large-scale data analysis

Easy designing steps of a local data warehouse for possible analytical data processing

Data warehouse (DW) are used in local or global level as per usages. Most of the DW was designed for online purposes targeting the multinational firms. Majority of local firms directly purchase such readymade DW applications for their usages. Customization, maintenance and enhancement are very costly for them. To provide fruitful e-services, the Government departments, academic Institutes, firms, Telemedicine firms etc. need a DW of themselves. Lack of electricity and internet facilities, especially in rural areas, does not motivate citizen to use the benefits of e-services. In this digital world, every local firm is interested in having their DW that may support strategic and decision making for the business. This study highlights the basic technical designing steps of a local DW. It gives several possible solutions that may arise during the design of the process of Extraction Transformation and Loading (ETL). It gives detail steps to develop the dimension table, fact table and loading data. Data analytics normally answers business questions and suggest future solutions

Dublin Smart City Data Integration, Analysis and Visualisation

Data is an important resource for any organisation, to understand the in-depth working and identifying the unseen trends with in the data. When this data is efficiently processed and analysed it helps the authorities to take appropriate decisions based on the derived insights and knowledge, through these decisions the service quality can be improved and enhance the customer experience. A massive growth in the data generation has been observed since two decades. The significant part of this generated data is generated from the dumb and smart sensors. If this raw data is processed in an efficient manner it could uplift the quality levels towards areas such as data mining, data analytics, business intelligence and data visualisation

Review of modern business intelligence and analytics in 2015: How to tame the big data in practice?: Case study - What kind of modern business intelligence and analytics strategy to choose?

The objective of this study was to find out the state of art architecture of modern business intelligence and analytics. Furthermore the status quo of business intelligence and analytics' architecture in an anonymous case company was examined. Based on these findings a future strategy was designed to guide the case company towards a better business intelligence and analytics environment. This objective was selected due to an increasing interest on big data topic. Thus the understanding on how to move on from traditional business intelligence practices to modern ones and what are the available options were seen as the key questions to be solved in order to gain competitive advantage for any company in near future. The study was conducted as a qualitative single-case study. The case study included two parts: an analytics maturity assessment, and an analysis of business intelligence and analytics' architecture. The survey included over 30 questions and was sent to 25 analysts and other individuals who were using a significant time to deal with or read financial reports like for example managers. The architecture analysis was conducted by gathering relevant information on high level. Furthermore a big picture was drawn to illustrate the architecture. The two parts combined were used to construct the actual current maturity level of business intelligence and analytics in the case company. Three theoretical frameworks were used: first framework regarding the architecture, second framework regarding the maturity level and third framework regarding reporting tools. The first higher level framework consisted of the modern data warehouse architecture and Hadoop solution from D'Antoni and Lopez (2014). The second framework included the analytics maturity assessment from the data warehouse institute (2015). Finally the third framework analyzed the advanced analytics tools from Sallam et al. (2015). The findings of this study suggest that modern business intelligence and analytics solution can include both data warehouse and Hadoop components. These two components are not mutually exclusive. Instead Hadoop is actually augmenting data warehouse to another level. This thesis shows how companies can evaluate their current maturity level and design a future strategy by benchmarking their own actions against the state of art solution. To keep up with the fast pace of development, research must be continuous. Therefore in future for example a study regarding a detailed path of implementing Hadoop would be a great addition to this field

To Calibrate & Validate an Agent-Based Simulation Model - An Application of the Combination Framework of BI solution & Multi-agent platform

National audienceIntegrated environmental modeling approaches, especially the agent-based modeling one, are increasingly used in large-scale decision support systems. A major consequence of this trend is the manipulation and generation of huge amount of data in simulations, which must be efficiently managed. Furthermore, calibration and validation are also challenges for Agent-Based Modelling and Simulation (ABMS) approaches when the model has to work with integrated systems involving high volumes of input/output data. In this paper, we propose a calibration and validation approach for an agent-based model, using a Combination Framework of Business intelligence solution and Multi-agent platform (CFBM). The CFBM is a logical framework dedicated to the management of the input and output data in simulations, as well as the corresponding empirical datasets in an integrated way. The calibration and validation of Brown Plant Hopper Prediction model are presented and used throughout the paper as a case study to illustrate the way CFBM manages the data used and generated during the life-cycle of simulation and validation