An analysis of expert systems for business decision making at different levels and in different roles

This paper begins by analysing decision making activities and information requirements at three organizational levels and the characteristics of expert systems (ESs) intended for the two different roles of supporting and replacing a decision maker. It goes on to review the evidence from many years of commercial use of ESs at different levels and in different roles, and to analyse the evidence obtained from a pilot experiment involving developing ESs to fulfil two different roles in the same domain. The research finds that ESs in a replacement role prove to be effective for operational and tactical decisions, but have limitations at the strategic level. ESs in a support role, as advisory systems, can help to make better decisions, but their effectiveness can only be fulfilled through their users. In the experiments, an expert advisory system did not save a user's time, contrary to the expectations of many of its users, but an ES in a replacement role did improve the efficiency of decision making. In addition, the knowledge bases of the ESs in the different roles need to be different. Finally, the practical implications of the experience gained from developing and testing two types of ESs are discussed.     

Fuzzy methods for case-based recommendation and decision support

The paper proposes two case-based methods for recommending decisions to users on the basis of information stored in a database. In both approaches, fuzzy sets and related (approximate) reasoning techniques are used for modeling user preferences and decision principles in a flexible manner. The first approach, case-based decision making, can principally be seen as a case-based counterpart to classical decision principles well-known from statistical decision theory. The second approach, called case-based elicitation, combines aspects from flexible querying of databases and case-based prediction. Roughly, imagine a user who aims at choosing an optimal alternative among a given set of options. The preferences with respect to these alternatives are formalized in terms of flexible constraints, the expression of which refers to cases stored in a database. As both types of decision support might provide useful tools for recommender systems, we also place the methods in a broader context and discuss the role of fuzzy set theory in some related fields.     

An AHP model of DSS effectiveness

Empirical studies have reported equivocal, or even dysfunctional, results from the use of decision support systems (DSS). Recent examples are the Davis, Kottemann, and Remus production planning experiments. According to the researchers, these experiments demonstrate that DSS what-if analysis creates an ‘illusion of control’ that causes users to overestimate its effectiveness. Such experimental findings are contrary to case-supported DSS theory. This paper examines the discrepancy. It first overviews the decision-making process, presents a generic DSS, identifies the theoretical role of the DSS in improving decision making, develops a multiple criteria model of DSS effectiveness, and gives a DSS for delivering the model to users. Illustrating with recent empirical investigations and the Davis, Kottemann, and Remus studies, the DSS-delivered model is used to reconcile the incongruity between the experimental findings and the case-supported theory. The paper concludes with a discussion of the article's implications for information systems research and practice.     

A decision support system for cooperative transportation planning: Design,implementation, and performance assessment

In this paper, we describe a decision support system for cooperative transportation planning in the German food industry where several manufacturing companies share their fleets to reduce transportation costs. Besides using vehicles of their fleets, there are different outsourcing options offered by logistics service providers, but these are much more expensive. The decision-making kernel of the decision support system is implemented as a multi-agent-system (MAS). The kernel provides a distributed hierarchical algorithm for cooperative transportation planning and an on-line data layer that contains all the information for decision making. We sketch the distributed hierarchical transportation planning algorithm and identity the required software agents. The MAS interacts via web services with a commercial tour planning system that persistently stores the resulting tour plans, orders, and customer data. Moreover, the tour planning system is used to offer graphical user interfaces to interact with the users. The data layer is updated by order and customer data from the ERP systems of the different manufacturing companies. We describe the architecture and the implementation of the MAS and the overall coupling framework. Furthermore, we discuss the simulation-based performance assessment of the resulting decision support system when the system is applied in a rolling horizon setting and present some computational results. The results demonstrate that the MAS approach is appropriate for the cooperative transportation planning domain.     

A Spatio-Temporal Model for the Manipulation of Lineage Metadata

Nowadays one of the most successful applications of GIS is the management of a land-use cadastre. A lot of corporate GIS databases are in development, they support the legal management and distribution of cadastral maps. However, the propagation of geographical updates toward cadastral databases is still a methodological and technical problem to address in the context of large applications with many different users. This paper proposes a model based on lineage metadata that supports the management of geographical changes in the context of a corporate cadastre application. Geographical and cadastral changes are identified from an analysis of the French cadastre which acts as a case study for the development of our model. The lineage metadata model is based on the application of a direct acyclic graph that permits the management of the evolution of geographical objects and the generation of historical queries. The proposed model is specified and validated with the O ₂ object-oriented database management system.     

Improving image mining through Geoprocessing

Geoprocessing Information Systems (GIS) deal with structured information concerned some geographical localization. So one uses three-dimensional image representation systems in a huge database, where it is possible to insert many data about some interest domain, say, agriculture, economics, industry, demographics and so on. Images are powerful information sources that can soundly support decision making processes. An image can be seen as a set of elements with spatial localization and color. To interpret an image includes deriving clusters and relations between such elements. This article proposes an integration of Geoprocessing and Image Mining to support image based decisions in several domains such as healthcare.     

COLLECT: COLLaborativE ConText-aware service oriented architecture for intelligent decision-making in the Internet of Things

Internet of Things (IoT) has radically transformed the world; currently, every device can be connected to the Internet and provide valuable information for decision-making. In spite of the fast evolution of technologies accompanying the grow of IoT, we are still faced with the challenge of providing a service oriented architecture, which facilitates the inclusion of data coming together from several IoT devices, data delivery among a system's agents, real-time data processing and service provision to users. Furthermore, context-aware data processing and architectures still pose a challenge, in spite of being key requirements in order to get stronger IoT architectures. To face this challenge, we propose a COLLaborative ConText Aware Service Oriented Architecture (COLLECT), which facilitates both the integration of IoT heterogeneous domain context data — through the use of a light message broker — and easy data delivery among several agents and collaborative participants in the system — making use of an enterprise service bus —. In addition, this architecture provides real-time data processing thanks to the use of a complex event processing engine as well as services and intelligent decision-making procedures to users according to the needs of the domain in question. As a result, COLLECT has a great impact on context-aware decentralized and collaborative reasoning for IoT, promoting context-aware intelligent decision making in such scope. Since context-awareness is key for a wide range of recommender and intelligent systems, the presented novel solution improves decision making in a large number of fields where such systems require to promptly process a variety of ubiquitous collaborative and context-aware data.     

Fuzzy cognitive map architectures for medical decision support systems

Medical decision support systems can provide assistance in crucial clinical judgments, particularly for inexperienced medical professionals. Fuzzy cognitive maps (FCMs) is a soft computing technique for modeling complex systems, which follows an approach similar to human reasoning and the human decision-making process. FCMs can successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause and effect relationships among the concepts to model the behavior of any system. Medical decision systems are complex systems that can be decomposed to non-related and related subsystems and elements, where many factors have to be taken into consideration that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall clinical decision with a different degree. Thus, FCMs are suitable for medical decision support systems and appropriate FCM architectures are proposed and developed as well as the corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.     

User support: revealing structure instead of surface

Abstract

The development of different help systems and the application of numerous approaches to user support have shown (a) that end-users may encounter insuperably complex use situations, and (b) that it is possible to assist users significantly by implementing computerized help systems. There are many approaches to the realization of user support, varying from the use of natural language to user modelling. However, the current help systems seem to focus on relatively technical data processing issues, ignoring the organizational context in which the use takes place. It is asserted in this paper that it is relevant for users to perceive the organizational context and that it is possible to reflect the context in a support system. Representing the context in a support system is made possible by introducing a context database. A context database is parallel to the actual database and contains information about task flows, task-connected information objects and the like. Therefore the analysis of work and information systems has to be based on related areas. The areas of inquiry are (a) tasks, (b) job design, (c) organization of work, (d) computer applications and (e) information media. The following kinds of mappings can be incorporated within the context database: [organizational unit Ol]-T_person PI in job]-[job task Tl]-[task-connected information Il]-[task-connected information 12]-[job task T2]-[person in job P2]-[organizational unit O2], This type of chain (or parts of it) can then be visualized as context support.     

ON KNOWLEDGE REPRESENTATION FOR INFERENCES IN CAST METHOD BANKS

This article explains why aspects of knowledge representation must be considered in the context of computer aided systems theory (CAST). CAST method banks support human experts during the process of problem solving. They should be understood as decision support systems, as assistants of their human expert users. One key to making this approach work is the communication between the expert and the system. The assistant should provide systematical and goal-directive information about the current problem state for the human expert. Another, even more important requirement is the assistant's knowledge about all available methods at a certain problem-solving state and their expected impact on the further problem-solving process. Knowledge representation denotes how the problem domain is represented within the support system and how it is used. We investigate different forms of knowledge representations and summarize criteria for the applicability of different forms of knowledge representations in CAST systems.     

User support: revealing structure instead of surface

The development of different help systems and the application of numerous approaches to user support have shown (a) that end-users may encounter insuperably complex use situations, and (b) that it is possible to assist users significantly by implementing computerized help systems. There are many approaches to the realization of user support, varying from the use of natural language to user modelling. However, the current help systems seem to focus on relatively technical data processing issues, ignoring the organizational context in which the use takes place. It is asserted in this paper that it is relevant for users to perceive the organizational context and that it is possible to reflect the context in a support system. Representing the context in a support system is made possible by introducing a context database. A context database is parallel to the actual database and contains information about task flows, task-connected information objects and the like. Therefore the analysis of work and information systems has to be based on related areas. The areas of inquiry are (a) tasks, (b) job design, (c) organization of work, (d) computer applications and (e) information media. The following kinds of mappings can be incorporated within the context database: [organizational unit Ol]-T_person PI in job]-[job task Tl]-[task-connected information Il]-[task-connected information 12]-[job task T2]-[person in job P2]-[organizational unit O2], This type of chain (or parts of it) can then be visualized as context support.     

Navigational behaviors in hypermedia documents in music

Abstract

Hypermedia is defined as an interactive multimedia application where discrete information units of various formats can be accessed in nonlinear ways. This kind of application does differ from traditional information delivery systems in allowing the user considerable freedom in making navigational decisions; users are able to determine, at least to some extent, the media-form of presentation and the order of material presented. However given this freedom, it is possible in some situations that learners might stray from the teacher's instructional objective and become overly engaged in unrelated browsing activities. This study investigates navigational behaviors made by college undergraduate music appreciation students who used a hypermedia program as a supplement to traditional instruction. Results indicated that students accessed these materials in strikingly different ways. While some users read the screens in linear fashion not viewing any multimedia nodes, others made many nonlinear jumps and investigated multimedia nodes often. Although most students did access the musical nodes, most did not listen to the musical examples in their entirety. Overall lesson length varied considerably. Results also indicated that students acted in dynamic fashions, making many navigational actions that were not related to the established instructional objective.     

Rationality in Information Systems Support to Decision Making

Management information systems serve business organizations by providing information for decision making. Various types of systems serve different types of decision contexts. The philosophic basis of information system support is discussed. The rational (or normative) philosophy is widely used, and appears in business theory in the form of agency theory and transaction cost analysis. While this approach has been valuable in some contexts, there are other contexts where the rational approach has limited in utility for real business decision making. Decision makers need to consider subjective factors to enable them to cope with the high levels of uncertainty, incomplete understanding, and imperfect data typical of dynamic open systems. There are alternative philosophies upon which to base decision-making that are appropriate for specific decision contexts. Churchman identified empirical, multi-perspective frameworks, dialectic, and cause-and-effect inquiring systems as alternatives to the rational (normative) system. A number of information system tools, such as decision support systems, expert systems, and group support systems can be supported by models based on philosophies other than rational models. A more empirically based philosophy, with decision-makers balancing hypothesis generation and observations of performance, is often more appropriate. The relationship between Churchman's inquiring systems and information system types are discussed.     

Support for real-time decision making in mobile financial applications

Mobile users making real-time decisions based on current information need confidence that their context has been taken into consideration in producing the system’s recommendations. This chapter reviews current use of mobile technologies for context-aware real-time decision support. Specifically, it describes a framework for assessing the impact of mobility in decision making. The framework uses dynamic context model of data quality to represent uncertainties in the mobile decision-making environment. This framework can be used for developing visual interactive displays for communicating to the user relevant changes in data quality when working in mobile environments. As an illustration, this chapter proposes a real-time decision support procedure for on-the-spot assistance to the mobile consumer when choosing the best payment option to efficiently manage their budget. The proposed procedure is based on multi-attribute decision analysis, scenario reasoning, and a quality of data framework. The feasibility of the approach is demonstrated with a mobile decision-support system prototype implementation. This article is part of the “Handbook on Decision Support Systems” edited by Frada Burstein and Clyde W. Holsapple (2008) Springer.     

Finding an application-appropriate model for XML data warehouses

Decision support systems help the decision making process with the use of OLAP (On-Line Analytical Processing) and data warehouses. These systems allow the analysis of corporate data. As OLAP and data warehousing evolve, more and more complex data is being used. XML (Extensible Markup Language) is a flexible text format allowing the interchange and the representation of complex data. Finding an appropriate model for an XML data warehouse tends to become complicated as more and more solutions appear. Hence, in this survey paper we present an overview of the different proposals that use XML within data warehousing technology. These proposals range from using XML data sources for regular warehouses to those using full XML warehousing solutions. Some researches merely focus on document storage facilities while others present adaptations of XML technology for OLAP. Even though there are a growing number of researches on the subject, many issues still remain unsolved.     

FACTORS AFFECTING THE COMPARATIVE ADVANTAGES OF SEQUENTIAL AND BATCHED DECISION MAKING

Whether to batch-process data or to randomly update files in real-time is a common design choice faced by information system analysts. A related (but different) problem is whether or not decisions should be batch processed. Though this latter problem is undoubtedly of greater managerial concern it has received less attention. This paper discusses considerations relevant to the optimal batching and timing of decisions and illustrates these by examples. A simple illustrative mathematical model of the problem is developed and optimized. This topic is of interest to MIS designers since their systems may include automated decision making and always support the decision making process.     

Hesitant information sets and application in group decision making

The recent information set theory provides a useful mechanism to represent an agent’s perceived information values. However, often a decision-maker (DM) considers multiple evaluations for the same information source value. To this end, we extend the recent information set as hesitant information set (HIS). It gives the multiple perceived information values, corresponding to an information source value. In the context of multi-attribute decision making, HIS represents a set of different possible subjective utilities that an agent may perceive as an evaluation of an alternative-attribute pair. The basic operations, and properties of HIS are investigated. A few information measures based on HIS are presented. Besides many illustrative examples, a real application in group multi attribute decision making problem is included.     

Complementary case-based reasoning and competitive fuzzy cognitive maps for advanced medical decisions

This paper presents a new hybrid modeling methodology suitable for complex decision making processes. It extends previous work on competitive fuzzy cognitive maps for medical decision support systems by complementing them with case based reasoning methods. The synergy of these methodologies is accomplished by a new proposed algorithm that leads to more dependable advanced medical decision support systems that are suitable to handle situations where the decisions are not clearly distinct. The methodology developed here is applied successfully to model and test two decision support systems, one a differential diagnosis problem from the speech pathology area for the diagnosis of language impairments and the other for decision making choices in external beam radiation therapy.