Comparison of the Decision Support systems and the Transaction Support System Development Methodologies

I: General Issues.- Modeling Knowledge: Model-based Decision Support and Soft Computations.- Benefits of Decision Support Using Soft Computing.- Evolving Connectionist-based Decision Support Systems.- An Agent-based Soft Computing Society with Application Applications in Financial Investment Planning.- A Rough Sets/Neoral Networks Approach to Knowledge Discovery for the Development of Decision Support Systems.- II: Applications and Implementations.- Decision Support Systems in Healthcare: Emerging Trends and Success Factors.- A View of Public and Private Sectors for Taiwan's BOT Project Financing Using Fuzzy Multi-Criteria Methods.- Relational Structures for the Analysis of Decision Information in an Electronic Market.- A Fuzzy Evaluation Model: A Case for Intermodal Terminals in Europe.- Application of Kemeny's Median for Group Decision Support.- An Internet-based Group Decision and Consensus Reaching Support System.- Limpio: A DSS for the Urban Waste Collection Problem.- A Decision Support System for Air Quality Control Based on Soft Computing Methods.- Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems.- Intelligent Information Systems for Crime Analysis.- Application of Fuzzy Decision Trees to Reservoir Recognition.- Introducing SACRA: A Decision Support System for the Construction of Cattle Diets.- Prediction of Parthenium Weed Dispersal Using Fuzzy Logic on GIS Spatial Image.

Distributed decision support and organizational connectivity: A case study

Changing systems for supporting farmers' decisions: problems, paradigms, and prospects

This paper aims to introduce the Decision Support System (DSS), which is an area informatics approach to area studies, and the research network complex based on DSS's application in Thailand. In particular, the paper presents and discusses current activities of the DSS research network at the national level in Thailand rather than being a theoretical and analytical study of the DSS mechanism or its cases. DSS has wide applications in Thailand, extending to various fields such as agricultural and natural resource management, economic and social management, historical and cultural preservation, and so on. DSS generally refers to a support system embedded into a computer system for providing intellectual resources with the appropriate numerical model necessary for decision making in production activities. Agricultural and natural resources have been the foundation of social and economic development in Thailand since the country's first national plan in the1960s. Decision making to maintain agricultural productivity as well as to protect natural resources requires well-integrated data sets. The Thailand Research Fund(TRF) established a DSS research and development network (TRF-DSS) in 2002 to help various research teams in Thailand develop DSS tools and components for addressing agricultural and natural resource management issues. Using a "systems"approach, the DSS framework allows researchers and users to identify and integrate key components as well as to define databases and model-base management systems. This paper focuses on the TRF-DSS research and development network, which consists of 12 universities, two line agencies in Thailand, and a line agency in Cambodia. During 2002–10, a total of 59 research projects were granted a budget allocation of 140.1 million baht, and a budget of 15.4 million baht was allocated to support activities of the network. In addition, 10 projects were funded to carry out post-project activities, with a total budget of 1.5 million baht. More than 20 DSS tools were developed and implemented by various users, ranging from policy makers to provincial and local government agencies engaged in short- and long-term planning and management. Most DSS tools were designed to allow the integration of biophysical and socioeconomic data as well as the decision support modules for alternatives evaluation and analysis. These approaches support the choices of dynamic simulation models as well as multi-criteria analyses for modelbase software development. They also allow users to evaluate various alternatives in agricultural and natural resource management. Networking is a powerful platform for DSS research and development and maybe applied to other types of research and development agenda in Thailand, such as area study projects. DSS tools contribute to an understanding of sustainability of production systems against a background of climate change, poverty reduction, and food security.

This commentary examines the historical importance of decision support to the information systems (IS) field from the viewpoint of four researchers whose work spans the several decades of decision support systems (DSS) research. Given this unique “generational” vantage point, we present the changes in and impact of DSS research as well as future considerations for decision support in the IS field. We argue that the DSS area has remained vital as technology has evolved and our understanding of decision-making processes has deepened. DSS work over the last several years has contributed both breadth and depth to decision-making research; the challenge now is to make sense of it all by placing it in an understandable context and by applying our analysis to the relevant issues looming in the future. One major outcome of this commentary is the identification of future trends in DSS research and what the users of these new DSS outlets can learn from the past. Trends include the increasing impact of social and mobile computing on DSS research, as well as knowledge management DSS and negotiation support systems that shift the focus to delivering more customer-centric and marketplace support.

Recently, very large-scale decision support systems (DSSs) have been developed, which tackle very complex problems, associated with very extensive and polymorphic information, which probably is geographically highly dispersed. The management, updating, modification and upgrading of the data and program core of such an information system is, as a rule, a very difficult task, which encompasses many hazards and risks. The purpose of the present work was (a) to list the more significant of these hazards and risks and (b) to introduce a new general methodology for designing decision support (DS) systems that are robust and circumvent these risks. The core of this new approach was the introduction of a meta-database, called teleological, on the base of which management, updating, modification, reduction, growth and upgrading of the system may be safely and efficiently achieved. The very same teleological meta-database can be used for the construction of a sound decision support system, incorporating elements of a previous one at a future stage.

Product development decisions represent challenging processes with various stakeholders from different disciplines and with different perspectives involved. Decisions can be highly complex due to the high amount of influencing factors that have to be considered simultaneously. Among other methods and tools, decision support systems (DSS) were developed to support decision-makers in handling complex decisions. One main disadvantage of DSS is the little adaptation to and integration of decision-maker needs and characteristics. Thus, current trends focus on the development of human- and decision-maker-centered DSS. This paper aims in providing a multilevel stepwise procedure for the modeling of decision-makers in order to enable a more effective decision support. The decision-maker modeling procedure is based on findings from literature research and results from research projects performed in cooperation with industry partners. The procedure serves as a guideline for generating diverse decision-maker models for different applications and for integrating human cognitive thinking procedures within the design and functionality of decision support methodologies and systems. The procedure represents an important design element of the human-centered decision support methodology currently being developed at the Institute of Product Development of the Technische Universität München (Germany).

A statistical method for improving the accuracy of predictive mathematical models used in drilling operations is proposed. The scheme can be applied in drilling automation, for example for improving accuracy in automated control systems such as managed pressure drilling (MPD), and for more reliable real-time monitoring and decision support systems. The proposed method is applied to a simulated case of MPD. Hydraulic model predictions of BHP are continuously compared to PWD readings. A short sequence with varying pump rate is run to train the statistics, which are collected at each pump rate, and refined considering also the pump rate derivative. This is visualized as a 3D surface giving a clear illustration of the error signature of the simulated case. The estimated error is added to the set point of the choke control system, demonstrating a clear improvement of BHP control. We proposed a new statistical method that uses real-time sensor data to perform automatic fingerprinting of the predictive performance of a given model. The principle is simple but powerful and can be applied to many cases. It can be combined with smart visualization tools in decision support systems, helping to build confidence in the users, or even utilized to adjust the initial prediction for improved accuracy in control systems. The tool is independent of the internal calculations in the mathematical model, and it can therefore be utilized in combination with any model.

ObjectiveThe study investigates users’ tendency to access decision support (DS) systems as a function of the correlation between the DS information and the information users already have, the ongoing interaction with such systems, and the effect of correlated information on subjective trust.BackgroundPrevious research has shown inconclusive findings regarding whether people prefer information that correlates with information they already have. Some studies conclude that individuals recognize the value of noncorrelated information, given its unique content, while others suggest that users favor correlated information as it aligns with existing evidence. The impact of the level of correlation on performance, subjective trust, and the decision to use DS remains unclear.MethodIn an experiment (N = 481), participants made classification decisions based on available information. They could also purchase additional DS with different degrees of correlation with the available information.ResultsParticipants tended to purchase information more often when the DS was not correlated with the available information. Correlated information reduced performance, and the effect of correlation on subjective trust and performance depended on DS sensitivity.ConclusionAdditional information may not improve performance when it is correlated with available information (i.e., it is redundant). Hence, the benefits of additional information and DS depend on the information the system and the operator use.ApplicationIt is essential to analyze the correlations between information sources and design the available information to allow optimal task performance and possibly minimize redundancy (e.g., by locating sensors in different positions to capture independent data).

Decision support systems have always had a goal of supporting decision-makers. Over time, DSS have taken many forms, or many forms of computer-based support have been considered in the context of DSS, depending on one’s particular perspective. Regardless, there have been decision support systems (DSS), expert systems, executive information systems, group DSS (GDSS), group support systems (GSS), collaborative systems (or computer-supported collaborative work (CSCW) environments), knowledge-based systems, and inquiring systems, all of which are described elsewhere in this encyclopedia. The progression of decision support system types that have emerged follows to some degree the increasing complexity of the problems being addressed. Some of the early DSS involved single decision-makers utilizing spreadsheet models to solve problems. Such an approach would be inadequate in addressing complex problems because one aspect of problem complexity is that multiple stakeholders typically exist. Baldwin (1993) examined the need for supporting multiple views and provides the only attempt found in the information systems literature to operationalize the concept of a perspective. In his work, a view is defined as a set of beliefs that partially describe a general subject of discourse. He identified three major components of a view: the belief or notion to convey, a language to represent the notion, and a subject of discourse. He further described notions as comprising aspects and a vantage point. Aspects are the characteristics or attributes of a subject or situation that a particular notion emphasizes. A vantage point is described by the level of detail (i.e., overview or detailed analysis). Assuming the subject of discourse can be identified with the notion, Baldwin described how differences in views may occur via differences in the notion, the language, or both.

In recent years, thanks to the ICT systems, there has been a continued growth and an increasing accumulation of the available information. Managing information has become one of the key activities in any sector. Indeed, information management emerged as the core and the basis of manufacturing enterprise. The research activity mainly focused on the development of a Knowledge Based Engineering (KBE) methodology for supporting a manufacturing company, in particular railway manufacturers, in reusing company knowledge. A Decision Support System (DSS) has been developed with the scope of identifying products already designed that fully or partly fit what required by new bids. The DSS has been built within a PLM software and part of the research has concentrated on comparing the PLM suites available in the market searching for the best tool able to act the role of a centralized management dashboard for knowledge reuse. The proposed methodology involves different business units and several expertise, emphasizing the importance of a collaborative environment and the need of a methodology for design review sessions. Contents of this thesis retrace these considerations: the second chapter reports a technology scouting on the available PLM tools; the third and the fourth chapters deal with a KBE methodology and the development of a DSS; the fifth one proposes the development of an immersive virtual reality environment aimed at deploying review sessions by design and manufacturing teams with interdisciplinary skills; at last, in the sixth chapter, it is reported a Discrete Event Simulation application as example of a procedural KBE application.

We sought to develop a computerized clinical decision support for clinicians treating patients with type 2 diabetes mellitus (T2DM). We designed, developed, and tested a computer-assisted decision support (CADS) system using statistical analyses of self-monitoring of blood glucose data, laboratory data, medical and medication history, and individualized hemoglobin A1c goals. A rule-based expert system generated recommendations for changes in therapy and accompanying explanations. A clinical decision support system (CADS) was developed that considers 9 classes of medications and 69 regimens with combinations of up to 4 therapeutic agents. The preferred sequences of regimens can be customized. The program is integrated with a "comprehensive diabetes management system," electronic medical record systems, and a method for uploading data from memory glucose meters via telephone without use of a computer or the Internet. The software provides a report to the clinician regarding the overall quality of glycemic control and identifies problems, e.g., hypoglycemia, hyperglycemia, glycemic variability, and insufficient data. The program can recommend continuation of current therapy, adjustment of dosages of current medications, or change of regimen and can provide explanations for its recommendations. If the user rejects the recommendations, the program will recommend alternative approaches. The CADS also provides access to Food and Drug Administration-approved prescribing information, guidelines from professional organizations, and selections from the general medical literature. The system has been extensively tested with real and synthetic data and is ready for evaluation in multicenter clinical trials. A clinical decision support system to assist with the management of patients with T2DM was designed, developed, tested, and found to perform well.

Renal dosing clinical decision support (CDS) systems have demonstrated clinical effectiveness and potential benefits for patient outcomes. However, the high override rates consistently reported are problematic and undesirable. To understand providers' use patterns of renal dosing CDS, we investigated the override reasons obtained from primary care practices affiliated with two teaching hospitals. We selected a stratified random sample of 300 alerts and reviewed electronic medical records. Appropriateness criteria and an inter-rater reliability process were used. We found that two thirds of alerts were overridden inappropriately, and this proportion was similar for frequent over-riders as compared to the remainder of physicians. These findings imply that strategies are needed to convince providers to accept more clinically appropriate suggestions, though they need to be broadly targeted.

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Integrated pest management (IPM) prioritizes non-chemical alternatives for managing pests and/or diseases, reserving chemicals as last solution, when economically and environmentally justified. The holistic consideration of the agroecosystem makes decision-making in crop protection more complex, as it requires selecting the most appropriate action at the right time. Decision support systems (DSS) for IPM help manage the risks of pests and diseases affecting crops by predicting potential threats and suggesting suitable control measures. However, their adoption remains modest among farmers, despite demonstrated benefits. An anonymous online survey was conducted among farmers, advisors, and other agricultural stakeholders to investigate factors influencing DSS uptake, or lack thereof. The survey distinguished between DSS users and non-users, enabling targeted questions on different aspects of DSS use and adoption. In total, 78 respondents were classified as DSS users and 29 as DSS non-users . Data were analyzed through clustering, followed by statistical validation of clusters and induction of a classification tree, to identify the most influential features shaping cluster formation among both DSS users and DSS non-users . The analysis identified four clusters in the DSS users’ dataset and two clusters in the DSS non-users’ dataset. For DSS users , DSS output reliability and usefulness were identified as the most influential features. Membership in a farmers’ network and DSS ease of use also contributed to cluster separation. For DSS non-users , DSS availability for different cropping systems was the most important attribute determining cluster separation. The results indicate that farmer involvement in DSS development and calibration may enhance trust and, therefore, adoption. Moreover, the need for user-friendly systems providing simple and clear information was highlighted. Although the lack of DSS tailored to specific cropping systems emerged as the main barrier, limited awareness and concerns about costs were also important obstacles. The applied methodological approach presents a reproducible and robust AI-based framework, built on open-source tools, to analyze stakeholders’ perceptions of IPM DSS and extract knowledge from complex survey data. This study provides valuable insights to facilitate DSS adoption for more sustainable crop protection and underscores the importance of both DSS technical performance and end-user needs.