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

Overview of methodologies for building ontologies

A few research groups are now proposing a series of steps and methodologies for developing ontologies. However, mainly due to the fact that Ontological Engineering is still a relatively immature discipline, each work group employs its own methodology. Our goal is to present the most representative methodologies used in ontology development and to perform an analysis of such methodologies against the same framework of reference. So, the goal of this paper is not to provide new insights about methodologies, but to put it all in one place and help people to select which methodology to use

BUSINESS INTELLIGENT AGENTS FOR ENTERPRISE APPLICATION

Fierce competition in a market increasingly crowded and frequent changes in consumer requirements are the main forces that will cause companies to change their current organization and management. One solution is to move to open architectures and virtual type, which requires addressing business methods and technologies using distributed multi-agent systems. Intelligent agents are one of the most important areas of artificial intelligence that deals with the development of hardware and software systems able to reason, learn to recognize natural language, speak, make decisions, to recognize objects in the working environment etc. Thus in this paper, we presented some aspects of smart business, intelligent agents, intelligent systems, intelligent systems models, and I especially emphasized their role in managing business processes, which have become highly complex systems that are in a permanent change to meet the requirements of timely decision making. The purpose of this paper is to prove that there is no business without using the integration Business Process Management, Web Services and intelligent agents.business intelligence, intelligent agents, intelligent systems, management, enterprise, web services

Requirements modelling and formal analysis using graph operations

The increasing complexity of enterprise systems requires a more advanced analysis of the representation of services expected than is currently possible. Consequently, the specification stage, which could be facilitated by formal verification, becomes very important to the system life-cycle. This paper presents a formal modelling approach, which may be used in order to better represent the reality of the system and to verify the awaited or existing system’s properties, taking into account the environmental characteristics. For that, we firstly propose a formalization process based upon properties specification, and secondly we use Conceptual Graphs operations to develop reasoning mechanisms of verifying requirements statements. The graphic visualization of these reasoning enables us to correctly capture the system specifications by making it easier to determine if desired properties hold. It is applied to the field of Enterprise modelling

Coordination approaches and systems - part I : a strategic perspective

This is the first part of a two-part paper presenting a fundamental review and summary of research of design coordination and cooperation technologies. The theme of this review is aimed at the research conducted within the decision management aspect of design coordination. The focus is therefore on the strategies involved in making decisions and how these strategies are used to satisfy design requirements. The paper reviews research within collaborative and coordinated design, project and workflow management, and, task and organization models. The research reviewed has attempted to identify fundamental coordination mechanisms from different domains, however it is concluded that domain independent mechanisms need to be augmented with domain specific mechanisms to facilitate coordination. Part II is a review of design coordination from an operational perspective

Incorporating the knowledge management cycle in e-business

In e-business, knowledge can be extracted from the recorded information by intelligent data analysis and then utilised in the business transaction. E-knowledge is a foundation for e-business. E-business can be supported by an intelligent information system that provides intelligent business process support and advanced support of the e-knowledge management cycle. Knowledge is stored as knowledge models that can be updated in the e-knowledge management cycle. As illustrated in examples, the e-knowledge cycle aids in the business decision taking, production management, and costs management

Hi, how can I help you?: Automating enterprise IT support help desks

Question answering is one of the primary challenges of natural language understanding. In realizing such a system, providing complex long answers to questions is a challenging task as opposed to factoid answering as the former needs context disambiguation. The different methods explored in the literature can be broadly classified into three categories namely: 1) classification based, 2) knowledge graph based and 3) retrieval based. Individually, none of them address the need of an enterprise wide assistance system for an IT support and maintenance domain. In this domain the variance of answers is large ranging from factoid to structured operating procedures; the knowledge is present across heterogeneous data sources like application specific documentation, ticket management systems and any single technique for a general purpose assistance is unable to scale for such a landscape. To address this, we have built a cognitive platform with capabilities adopted for this domain. Further, we have built a general purpose question answering system leveraging the platform that can be instantiated for multiple products, technologies in the support domain. The system uses a novel hybrid answering model that orchestrates across a deep learning classifier, a knowledge graph based context disambiguation module and a sophisticated bag-of-words search system. This orchestration performs context switching for a provided question and also does a smooth hand-off of the question to a human expert if none of the automated techniques can provide a confident answer. This system has been deployed across 675 internal enterprise IT support and maintenance projects.Comment: To appear in IAAI 201

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

The Global Artificial Intelligence Revolution Challenges Patent Eligibility Laws

This Article examines patent eligibility jurisprudence of artificial intelligence in the United States, Europe, France, Japan, and Singapore. It identifies de facto requirements of patent-eligible artificial intelligence. It also examines the adaptability of patent eligibility jurisprudence to adapt with the growth of artificial intelligence