An authoring tool for decision support systems in context questions of ecological knowledge

Decision support systems (DSS) support business or organizational decision-making activities, which require the access to information that is internally stored in databases or data warehouses, and externally in the Web accessed by Information Retrieval (IR) or Question Answering (QA) systems. Graphical interfaces to query these sources of information ease to constrain dynamically query formulation based on user selections, but they present a lack of flexibility in query formulation, since the expressivity power is reduced to the user interface design. Natural language interfaces (NLI) are expected as the optimal solution. However, especially for non-expert users, a real natural communication is the most difficult to realize effectively. In this paper, we propose an NLI that improves the interaction between the user and the DSS by means of referencing previous questions or their answers (i.e. anaphora such as the pronoun reference in “What traits are affected by them?”), or by eliding parts of the question (i.e. ellipsis such as “And to glume colour?” after the question “Tell me the QTLs related to awn colour in wheat”). Moreover, in order to overcome one of the main problems of NLIs about the difficulty to adapt an NLI to a new domain, our proposal is based on ontologies that are obtained semi-automatically from a framework that allows the integration of internal and external, structured and unstructured information. Therefore, our proposal can interface with databases, data warehouses, QA and IR systems. Because of the high NL ambiguity of the resolution process, our proposal is presented as an authoring tool that helps the user to query efficiently in natural language. Finally, our proposal is tested on a DSS case scenario about Biotechnology and Agriculture, whose knowledge base is the CEREALAB database as internal structured data, and the Web (e.g. PubMed) as external unstructured information.

Intelligent Decision-Making System Frameworks for a Digital Plan of Work: A Theoretical Investigation for the Construction Industry

The construction industry is characterised by fragmentation and suffers from lack of collaboration, often adopting adversarial working practices to achieve deliverables. For the UK Government and construction industry, BIM is a game changer aiming to rectify this fragmentation and promote collaboration. However it has become clear that there is an essential need to have better controls and definitions of both data deliverables and data classification. Traditional methods and techniques for collating and inputting data have shown to be time consuming and provide little to improve or add value to the overall task of improving deliverables. Hence arose the need in the industry to develop a Digital Plan of Work (DPoW) toolkit that would aid the decision making process, providing the required control over the project workflows and data deliverables, and enabling better collaboration through transparency of need and delivery. The specification for the existing Digital Plan of Work (DPoW) was to be, an industry standard method of describing geometric, requirements and data deliveries at key stages of the project cycle, with the addition of a structured and standardised information classification system. However surveys and interviews conducted within this research indicate that the current DPoW resembles a digitised version of the pre-existing plans of work and does not push towards the data enriched decision-making abilities that advancements in technology now offer. A Digital Framework is not simply the digitisation of current or historic standard methods and procedures, it is a new intelligent driven digital system that uses new tools, processes, procedures and work flows to eradicate waste and increase efficiency. In addition to reporting on conducted surveys above, this research paper will present a theoretical investigation into usage of Intelligent Decision Support Systems within a digital plan of work framework. Furthermore this paper will present findings on the suitability to utilise advancements in intelligent decision-making system frameworks and Artificial Intelligence for a UK BIM Framework. This should form the foundations of decision-making for projects implemented at BIM level 2. The gap identified in this paper is that the current digital toolkit does not incorporate the intelligent characteristics available in other industries through advancements in technology and collation of vast amounts of data that a digital plan of work framework could have access to and begin to develop, learn and adapt for decision-making through the live interaction of project stakeholders.

Using decision support systems to facilitate the social process of knowledge management

The social processes involved in engaging small groups of 3-15 managers in their sharing, organising, acquiring, creating and using knowledge can be supported with software and facilitator assistance. This paper introduces three such systems that we have used as facilitators to support groups of managers in their social process of decision-making by managing knowledge during face-to-face meetings. The systems include Compendium, Group Explorer (with Decision Explorer) and V*I*S*A. We review these systems for group knowledge management where the aim is for better decision-making, and discuss the principles of deploying each in a group meeting. © 2006 Operational Research Society Ltd. All rights reserved.

Designing multiple user perspectives and functionality for clinical decision support systems

Clinical Decision Support Systems (CDSSs) need to disseminate expertise in formats that suit different end users and with functionality tuned to the context of assessment. This paper reports research into a method for designing and implementing knowledge structures that facilitate the required flexibility. A psychological model of expertise is represented using a series of formally specified and linked XML trees that capture increasing elements of the model, starting with hierarchical structuring, incorporating reasoning with uncertainty, and ending with delivering the final CDSS. The method was applied to the Galatean Risk and Safety Tool, GRiST, which is a web-based clinical decision support system (www.egrist.org) for assessing mental-health risks. Results of its clinical implementation demonstrate that the method can produce a system that is able to deliver expertise targetted and formatted for specific patient groups, different clinical disciplines, and alternative assessment settings. The approach may be useful for developing other real-world systems using human expertise and is currently being applied to a logistics domain. © 2013 Polish Information Processing Society.

Bioenergy decision support systems:worth the effort?

The purpose of this research is to explore the disparity between the existing model-orientated bioenergy decision support system (DSS) functions and what is desired by practitioners, in particular bioenergy project developers. This research has compiled the published bioenergy project development models, to highlight the characteristics emphasised by academics. When contrasted against a UK practitioner’s perspective through the administration of a Likert style questionnaire, it is clear that the general DSS issues still persist. Finally, the research suggests how this ’theory-practice’ divide could be addressed. The research contribute

Networks of Evolutionary Processors (NEP) as Decision Support Systems

This paper presents the application of Networks of Evolutionary Processors to Decision Support Systems, precisely Knowledge-Driven DSS. Symbolic information and rule-based behavior in Networks of Evolutionary Processors turn out to be a great tool to obtain decisions based on objects present in the network. The non-deterministic and massive parallel way of operation results in NP-problem solving in linear time. A working NEP example is shown.

Towards the Problems of an Evaluation of Data Uncertainty in Decision Support Systems

The question of forming aim-oriented description of an object domain of decision support process is outlined. Two main problems of an estimation and evaluation of data and knowledge uncertainty in decision support systems – straight and reverse, are formulated. Three conditions being the formalized criteria of aimoriented constructing of input, internal and output spaces of some decision support system are proposed. Definitions of appeared and hidden data uncertainties on some measuring scale are given.

Representing human expertise by the OWL web ontology language to support knowledge engineering in decision support systems

Clinical decision support systems (CDSSs) often base their knowledge and advice on human expertise. Knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. This paper reports on the development of an ontology specification for managing knowledge engineering in a CDSS for assessing and managing risks associated with mental-health problems. The Galatean Risk and Safety Tool, GRiST, represents mental-health expertise in the form of a psychological model of classification. The hierarchical structure was directly represented in the machine using an XML document. Functionality of the model and knowledge management were controlled using attributes in the XML nodes, with an accompanying paper manual for specifying how end-user tools should behave when interfacing with the XML. This paper explains the advantages of using the web-ontology language, OWL, as the specification, details some of the issues and problems encountered in translating the psychological model to OWL, and shows how OWL benefits knowledge engineering. The conclusions are that OWL can have an important role in managing complex knowledge domains for systems based on human expertise without impeding the end-users' understanding of the knowledge base. The generic classification model underpinning GRiST makes it applicable to many decision domains and the accompanying OWL specification facilitates its implementation.

Advances in decision support systems on the endeavour for supply chain optimality

Industry practitioners are seeking to create optimal logistics networks through more efficient decision-making leading to a shift of power from a centralized position to a more decentralized approach. This has led to researchers, exploring with vigor, the application of agent based modeling (ABM) in supply chains and more recently, its impact on decision-making. This paper investigates reasons for the shift to decentralized decision-making and the impact on supply chains. Effective decentralization of decision-making with ABM and hybrid modeling is investigated, observing the methods and potential of achieving optimality.

A dynamic knowledge-based decision support system to handle solids separation problems in activated sludge systems: development and validation

El sistema de fangs activats és el tractament biològic més àmpliament utilitzat arreu del món per la depuració d'aigües residuals. El seu funcionament depèn de la correcta operació tant del reactor biològic com del decantador secundari. Quan la fase de sedimentació no es realitza correctament, la biomassa no decantada s'escapa amb l'efluent causant un impacte sobre el medi receptor. Els problemes de separació de sòlids, són actualment una de les principals causes d'ineficiència en l'operació dels sistemes de fangs activats arreu del món. Inclouen: bulking filamentós, bulking viscós, escumes biològiques, creixement dispers, flòcul pin-point i desnitrificació incontrolada. L'origen dels problemes de separació generalment es troba en un desequilibri entre les principals comunitats de microorganismes implicades en la sedimentació de la biomassa: els bacteris formadors de flòcul i els bacteris filamentosos. Degut a aquest origen microbiològic, la seva identificació i control no és una tasca fàcil pels caps de planta. Els Sistemes de Suport a la Presa de Decisions basats en el coneixement (KBDSS) són un grup d'eines informàtiques caracteritzades per la seva capacitat de representar coneixement heurístic i tractar grans quantitats de dades. L'objectiu de la present tesi és el desenvolupament i validació d'un KBDSS específicament dissenyat per donar suport als caps de planta en el control dels problemes de separació de sòlids d'orígen microbiològic en els sistemes de fangs activats. Per aconseguir aquest objectiu principal, el KBDSS ha de presentar les següents característiques: (1) la implementació del sistema ha de ser viable i realista per garantir el seu correcte funcionament; (2) el raonament del sistema ha de ser dinàmic i evolutiu per adaptar-se a les necessitats del domini al qual es vol aplicar i (3) el raonament del sistema ha de ser intel·ligent. En primer lloc, a fi de garantir la viabilitat del sistema, s'ha realitzat un estudi a petita escala (Catalunya) que ha permès determinar tant les variables més utilitzades per a la diagnosi i monitorització dels problemes i els mètodes de control més viables, com la detecció de les principals limitacions que el sistema hauria de resoldre. Els resultats d'anteriors aplicacions han demostrat que la principal limitació en el desenvolupament de KBDSSs és l'estructura de la base de coneixement (KB), on es representa tot el coneixement adquirit sobre el domini, juntament amb els processos de raonament a seguir. En el nostre cas, tenint en compte la dinàmica del domini, aquestes limitacions es podrien veure incrementades si aquest disseny no fos òptim. En aquest sentit, s'ha proposat el Domino Model com a eina per dissenyar conceptualment el sistema. Finalment, segons el darrer objectiu referent al seguiment d'un raonament intel·ligent, l'ús d'un Sistema Expert (basat en coneixement expert) i l'ús d'un Sistema de Raonament Basat en Casos (basat en l'experiència) han estat integrats com els principals sistemes intel·ligents encarregats de dur a terme el raonament del KBDSS. Als capítols 5 i 6 respectivament, es presenten el desenvolupament del Sistema Expert dinàmic (ES) i del Sistema de Raonament Basat en Casos temporal, anomenat Sistema de Raonament Basat en Episodis (EBRS). A continuació, al capítol 7, es presenten detalls de la implementació del sistema global (KBDSS) en l'entorn G2. Seguidament, al capítol 8, es mostren els resultats obtinguts durant els 11 mesos de validació del sistema, on aspectes com la precisió, capacitat i utilitat del sistema han estat validats tant experimentalment (prèviament a la implementació) com a partir de la seva implementació real a l'EDAR de Girona. Finalment, al capítol 9 s'enumeren les principals conclusions derivades de la present tesi.

Hybrid visual-driven decision support system in video monitor manufacturing

An intelligent system that emulates human decision behaviour based on visual data acquisition is proposed. The approach is useful in applications where images are used to supply information to specialists who will choose suitable actions. An artificial neural classifier aids a fuzzy decision support system to deal with uncertainty and imprecision present in available information. Advantages of both techniques are exploited complementarily. As an example, this method was applied in automatic focus checking and adjustment in video monitor manufacturing. Copyright © 2005 IFAC.

Clinical decision support capabilities of commercially-available clinical information systems.

BACKGROUND: The most effective decision support systems are integrated with clinical information systems, such as inpatient and outpatient electronic health records (EHRs) and computerized provider order entry (CPOE) systems. Purpose The goal of this project was to describe and quantify the results of a study of decision support capabilities in Certification Commission for Health Information Technology (CCHIT) certified electronic health record systems. METHODS: The authors conducted a series of interviews with representatives of nine commercially available clinical information systems, evaluating their capabilities against 42 different clinical decision support features. RESULTS: Six of the nine reviewed systems offered all the applicable event-driven, action-oriented, real-time clinical decision support triggers required for initiating clinical decision support interventions. Five of the nine systems could access all the patient-specific data items identified as necessary. Six of the nine systems supported all the intervention types identified as necessary to allow clinical information systems to tailor their interventions based on the severity of the clinical situation and the user's workflow. Only one system supported all the offered choices identified as key to allowing physicians to take action directly from within the alert. Discussion The principal finding relates to system-by-system variability. The best system in our analysis had only a single missing feature (from 42 total) while the worst had eighteen.This dramatic variability in CDS capability among commercially available systems was unexpected and is a cause for concern. CONCLUSIONS: These findings have implications for four distinct constituencies: purchasers of clinical information systems, developers of clinical decision support, vendors of clinical information systems and certification bodies.

Engineering Agent Systems for Decision Support

This paper discusses how agent technology can be applied to the design of advanced Information Systems for Decision Support. In particular, it describes the different steps and models that are necessary to engineer Decision Support Systems based on a multiagent architecture. The approach is illustrated by a case study in the traffic management domain.

Environmental Decision Support: a Distributed Artificial Intelligence Approach

Decision making in any environmental domain is a complex and demanding activity, justifying the development of dedicated decision support systems. Every decision is confronted with a large variety and amount of constraints to satisfy as well as contradictory interests that must be sensibly accommodated. The first stage of a project evaluation is its submission to the relevant group of public (and private) agencies. The individual role of each agency is to verify, within its domain of competence, the fulfilment of the set of applicable regulations. The scope of the involved agencies is wide and ranges from evaluation abilities on the technical or economical domains to evaluation competences on the environmental or social areas. The second project evaluation stage involves the gathering of the recommendations of the individual agencies and their justified merge to produce the final conclusion. The incorporation and accommodation of the consulted agencies opinions is of extreme importance: opinions may not only differ, but can be interdependent, complementary, irreconcilable or, simply, independent. The definition of adequate methodologies to sensibly merge, whenever possible, the existing perspectives while preserving the overall legality of the system, will lead to the making of sound justified decisions. The proposed Environmental Decision Support System models the project evaluation activity and aims to assist developers in the selection of adequate locations for their projects, guaranteeing their compliance with the applicable regulations.