Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Method of Domain Specific Code Generation Based on Knowledge Graph for Quantitative Trading

Part 1: EIS concepts, Theory and MethodsInternational audienceQuantitative methods have been adopted by more and more individual investors for investment activities. Many third party platforms have been developed to help users complete the process of backtesting, which fills the gap between the trading strategy code and the trading strategy model. However, using a quantitative platform for backtesting has a high threshold for users who do not have programming experience. There is still a huge gap between the description and the code of trading strategy. Code generation allows developers to focus more on business related design and implementation, thereby increasing the efficiency of software development. The import of domain knowledge can improve the accuracy of requirement parsing to improve the quality of constructed code model. The general knowledge base is often incomplete in terms of domain specific terms and relationships, and the construction of domain knowledge graphs requires more domain related data. In this paper, encyclopedia data and the financial report data are used to extract domain terms and relations. And then a domain knowledge graph for quantitative trading is constructed to realize the automatic generation of quantitative trading strategy code

Development of a Decision Support System to Facilitate Multi-criteria Decision Making during Humanitarian Operation within a Project Life Cycle

Part III: Sustainable ServicesInternational audienceThe use of decision support systems is an important part of supply chain management. Quick and adequate decision making is sometimes difficult to achieve. Three issues arise: how to gather relevant data and use past experiences, how to make the decision when many criteria have to be taken into account and how can we ensure that the decision making process is quick. Those three issues are currently faced by many companies and some solutions have already been proposed in the literature. Yet, in some cases, it is difficult, if not possible to apply those solutions. Humanitarian organizations, for example, have difficulties to build on past experiences. Quick decision making in this sector is vital. The purpose of this paper is to design and develop decision-making tool to support the performance of humanitarian logistics. A case study at the French Red Cross will validate this proposal

CTI (computer telephony integration)

SIGLEAvailable from British Library Document Supply Centre-DSC:9309.5555(Feb 1998) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Learning from Errors: Error-based Exercises in Domain Modelling Pedagogy

Part 7: Teaching ChallengesInternational audienceConceptual modelling remains a challenging topic for educators, as it concerns ill-defined problems and requires substantial amount of practice for reaching even the initial level of proficiency. Year after year, novice modellers tend to make similar errors when learning to design models and some of those errors become persistent even at the higher level of proficiency. Are these errors the unavoidable “necessary evil” or there is a possibility to address them at the very early stage of a modeller’s education? In this work, we examine a novel approach to teaching conceptual modelling by identifying the most frequent errors in students’ models and introducing error-based step-by-step exercises in the framework of a Small Private Online Course for university students