Analytical enrichment: A target to source approach for missing requirements in decision support systems

Operational Systems collect transactional data and support the execution of business processes in an organization. These systems are often the data source for Decision Support Systems (DSS), i.e. analytical systems designed to aid business users in the decision-making process. For this reason, several problems in Operational Systems, such as missing data requirements or data quality issues, can lead to unfulfilled analytical needs of the DSS and, consequently, have a negative effect on the Decision Making Process since relevant business queries may not be answered. The objective of this study is to understand the impact of the integration of DSS requirements in the design of operational systems. To achieve this objective, this dissertation uses a real use case DSS to identify the missing requirements and develop a DSS prototype to demonstrate the positive impact on the Decision-Making process when these requirements are fulfilled. Throughout this development, ways of dealing with the various types of missing requirements are going to be addressed. Additionally, a methodology to evaluate the missing requirements is suggested, along with a proposal to classify and understand the missing requirements and how they can be dealt with. Also, the evaluation method is applied, and the developed prototype is compared to the baseline system in order to measure the impact. Finally, the benefits of this integration are shown, as well as other factors that can also constrain the DSS requirements.Os sistemas operacionais recolhem dados transacionais e apoiam a execução de processos de negócio numa organização. Estes sistemas são frequentemente a fonte de dados para os Sistemas de Apoio à Decisão (SAD), ou seja, sistemas analíticos concebidos para auxiliar os utilizadores empresariais no processo de tomada de decisão. Por esta razão, vários problemas nos Sistemas Operacionais, tais como requisitos de dados em falta ou questões de qualidade de dados, podem levar a necessidades analíticas não satisfeitas do SAD e, consequentemente, ter um efeito negativo no Processo de Tomada de Decisão, uma vez que as questões de negócio relevantes podem não ser respondidas. O objetivo do presente estudo é compreender o impacto da integração dos requisitos do SAD na conceção dos sistemas operacionais. Para atingir este objetivo, esta dissertação utiliza um caso de estudo real de um SAD para identificar os requisitos em falta e desenvolver um SAD protótipo para demonstrar o impacto positivo no processo de Tomada de Decisão quando estes requisitos são cumpridos. Ao longo deste desenvolvimento, as formas de lidar com os vários tipos de requisitos em falta serão abordadas. É também proposto um método de avaliação para compreender e categorizar os requisitos em falta e a forma como podem ser tratados. Além disso, o método de avaliação é aplicado, e o protótipo desenvolvido é comparado com o sistema de base, no sentido de medir o impacto. Finalmente, são mostrados os benefícios desta integração, bem como outros fatores que também podem limitar os requisitos do SAD

Statistical investigation of trends and spatial distribution of Danish drinking water quality

L'approvigionamento idrico in Danimarca è interamente basato su risorse sotterranee egli unici trattamenti implementati prima della distribuzione sono aerazione, filtrazionee aggiustamento del pH. Diverse pratiche di trattamento all'acquedotto vanno ad influire sul prodotto finale. Il presente studio è incentrato sui cambiamenti nel tempo e sui livelli medi dei principali componenti inorganici, con particolare attenzione ai oro effetti su gusto, corrosione e salute dental

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Advancing MATTERS

The goal of this project is to improve the data integration and administration center for the MATTERS Dashboard for the Massachusetts High Technology Council (MHTC), a pro-technology advocacy and lobbyist organization. Our system is comprised of two parts: the data integration pipeline manager and an administration center. Talend Open Studio is used as a core enabler for integrating data from a variety of online web sources. The Administration Center allows for future MHTC administrators to easily upload and view economy, talent, and ranking data, thus further impacting policies created in Massachusetts

BUILDING DSS USING KNOWLEDGE DISCOVERY IN DATABASE APPLIED TO ADMISSION & REGISTRATION FUNCTIONS

This research investigates the practical issues surrounding the development and implementation of Decision Support Systems (DSS). The research describes the traditional development approaches analyzing their drawbacks and introduces a new DSS development methodology. The proposed DSS methodology is based upon four modules; needs' analysis, data warehouse (DW), knowledge discovery in database (KDD), and a DSS module. The proposed DSS methodology is applied to and evaluated using the admission and registration functions in Egyptian Universities. The research investigates the organizational requirements that are required to underpin these functions in Egyptian Universities. These requirements have been identified following an in-depth survey of the recruitment process in the Egyptian Universities. This survey employed a multi-part admission and registration DSS questionnaire (ARDSSQ) to identify the required data sources together with the likely users and their information needs. The questionnaire was sent to senior managers within the Egyptian Universities (both private and government) with responsibility for student recruitment, in particular admission and registration. Further, access to a large database has allowed the evaluation of the practical suitability of using a data warehouse structure and knowledge management tools within the decision making framework. 1600 students' records have been analyzed to explore the KDD process, and another 2000 records have been used to build and test the data mining techniques within the KDD process. Moreover, the research has analyzed the key characteristics of data warehouses and explored the advantages and disadvantages of such data structures. This evaluation has been used to build a data warehouse for the Egyptian Universities that handle their admission and registration related archival data. The decision makers' potential benefits of the data warehouse within the student recruitment process will be explored. The design of the proposed admission and registration DSS (ARDSS) will be developed and tested using Cool: Gen (5.0) CASE tools by Computer Associates (CA), connected to a MSSQL Server (6.5), in a Windows NT (4.0) environment. Crystal Reports (4.6) by Seagate will be used as a report generation tool. CLUST AN Graphics (5.0) by CLUST AN software will also be used as a clustering package. Finally, the contribution of this research is found in the following areas: A new DSS development methodology; The development and validation of a new research questionnaire (i.e. ARDSSQ); The development of the admission and registration data warehouse; The evaluation and use of cluster analysis proximities and techniques in the KDD process to find knowledge in the students' records; And the development of the ARDSS software that encompasses the advantages of the KDD and DW and submitting these advantages to the senior admission and registration managers in the Egyptian Universities. The ARDSS software could be adjusted for usage in different countries for the same purpose, it is also scalable to handle new decision situations and can be integrated with other systems

Bit-to-board analysis for IT decision making

Verhoef, C. [Promotor]Peters, R.J. [Copromotor