A Simulation Study of Managed Qualitative Thinking Support Systems (MQTSS)

Routine blood tests drive diagnosis, prognosis, and monitoring in traditional clinical decision support systems. As a routine diagnostic tool with standardized laboratory workflows, clinical blood analysis offers superior accessibility to a comprehensive assessment of physiological parameters. These parameters can be integrated and automated at scale, allowing for in-depth clinical inference and cost-effectiveness compared to other modalities such as imaging, genetic testing, or histopathology. Herein, we extensively review the analytical value of routine blood tests leveraged by artificial intelligence (AI), using the ICD-10 classification as a reference. A significant gap exists between standard disease-associated features and those selected by machine learning models. This suggests an amount of non-perceived information in traditional decision support systems that AI could leverage with improved performance metrics. Nonetheless, AI-derived support for clinical decisions must still be harmonized regarding external validation studies, regulatory approvals, and clinical deployment strategies. Still, as we discuss, the path is drawn for the future application of scalable artificial intelligence (AI) to enhance, extract, and classify patterns potentially correlated with pathological states with restricted limitations in terms of bias and representativeness.

It is shown that, although many aspects of a traditional decision support system (TDSS) and an organizational decision support system (ODSS) are similar, there are important differences that must be understood if the ODSS is to be successful. Differences in purposes lead to differences in design, differences in managing the development effort, differences in implementation, and differences in maintenance. In fact, some aspects of building an ODSS are more similar to building a traditional information system than to building a TDSS. The suggested approach to building an ODSS combines principles and good practice for building both types of systems. The points made are illustrated with examples from the ODSS that are currently being implemented by the US Air Force. The system, called the Enlisted Force Management System, is being used to help members of the Air Staff in the Pentagon make decisions related to their enlisted personnel. >

Business organisations are increasing their influence and presence throughout the world as domestic economies become oversaturated. Organisations increasingly turn to international commerce to discover new markets. Globalisation requires that firms adapt their management and decision-making practices to recognise localised cultural, political, legal, social, and security requirements. In response, organisations are revamping their decision processes, tailoring them as cultures evolve, political assumptions and realities change, laws are enacted, social issues come to the forefront, and security requirements mature. Traditional decision support systems (DSS) are incapable of readily incorporating localised variables into decision-making processes and must be enhanced. Geographic Information Systems (GIS), have the potential to enhance traditional management decision-making (DSS) tools by adapting them for use in global environments. This paper proposes the melding of GIS and DSS toolsets to create a new framework, hereinafter referred to as Geographically-Enhanced Decision Support Systems (GEDSS), for enhanced management and decision-making for businesses.

Adopting Artificial Intelligence in a Decision Support System – Learnings from Comment Moderation Systems

Traditional decision support systems (DSS) and executive information systems (EIS) gather and present information from several sources for business purposes. It is an information technology to help the knowledge worker (executive, manager, analyst) make faster and better decisions. So far, these data were stored statically and persistently in a database, typically in a data warehouse. Data warehouses collect masses of operational data, allowing analysts to extract information by issuing decision support queries on the otherwise discarded data. In a typical scenario, an organization stores a detailed record of its operations in a database, which is then analyzed to improve efficiency, detect sales opportunities, and so on. Performing complex analysis on these data is an essential component of these organizations’ businesses. Chaudhuri and Dayal (1997) present an excellent survey on decision-making and online analytical processing (OLAP) technologies for traditional database systems. ?n many applications however, it may not be possible to process queries within a database management system (DBMS). These applications involve data items that arrive online from multiple sources in a continuous, rapid and time-varying fashion (Babcock et. al., 2002). These data may or may not be stored in a database. As a result, a new class of data-intensive applications has recently attracted a lot of attention: applications in which the data is modeled not as persistent relations but rather as transient data streams. Examples include financial applications (streams of transactions or ticks), network monitoring (stream of packets), security, telecommunication data management (stream of calls or call packets), web applications (clickstreams), manufacturing, wireless sensor networks (measurements), RFID data, and others. In data streams we usually have “continuous” queries (Terry et. al., 1992; Babu & Widom, 2002) rather than “one-time.” The answer to a continuous query is produced over time, reflecting the stream data seen so far. Answers may be stored and updated as new data arrives or may be produced as data streams themselves. Continuous queries can be used for monitoring, alerting, security, personalization, etc. Data streams can be either transactional (i.e., log interactions between entities, such as credit card purchases, web clickstreams, phone calls), or measurement (i.e., monitor evolution of entity states, such as physical phenomena, road traffic, temperature, network). How to best model, express and evaluate complex queries over data streams is an open and difficult problem. This involves data modeling, rich querying capabilities to support real-time decision support and mining, and novel evaluation and optimization processing techniques. In addition, the kind of decision support over data streams is quite different from “traditional” decision-making: decisions are “tactical” rather than “strategic.” Research on data streams is currently among the most active areas in database research community. Flexible and efficient stream querying will be a crucial component of any future data management and decision support system (Abiteboul et al., 2005).

As actuarial analysis and evaluation is a time-consuming, complex and error-prone process, it can be improved or enhanced considerably by automated reasoning. Efforts to reduce the inaccuracy and incorrectness of analyses and to enhance the confidence levels of actuarial analysis have led to the development of an intelligent decision support system called ActuaExpert, which assists, not replaces, actuaries. ActuaExpert assumes the role of a hypothetical actuary capable of assessing risks, creating policies that minimize risk and its financial impacts on companies, and maximizing the profits for insurance companies. It has a knowledge base containing the expertise of statistics, finance, and business, and a case base of past episodes and consequences of decisions. By combining knowledge-based problem solving with case-based reasoning, ActuaExpert demonstrates forms of intelligent behavior not yet observed in traditional decision support systems and expert systems.

It is well known that security analysis is a time-consuming and error-prone process. However, it can be improved or enhanced considerably by automated reasoning. Efforts to reduce the inaccuracy and incorrectness of analyses and to enhance the confidence levels of stock selection have led to the development of an intelligent decision support system called TradeExpert, which assists, not replaces, portfolio managers. TradeExpert assumes the role of a hypothetical securities analyst capable of analyzing stocks, calling market turns, and making recommendations. It has a knowledge base of stock trading expertise, and a case base of past episodes and consequences of decisions. By combining knowledge-based problem solving with case-based reasoning and fuzzy inference, TradeExpert demonstrates forms of intelligent behavior not yet observed in traditional decision support systems and expert systems. The novelty of this research lies in its application to analogical reasoning, fuzzy reasoning, and knowledge-based decision making.

With the rapid development of digital economy, the market environment in which enterprises are located is increasingly complex, and data has become one of the most important strategic resources of enterprises. Facing the increasing accumulation of multi-source heterogeneous data, the traditional decision support system (DSS) adopted by enterprises has obvious shortcomings in data integration ability, system flexibility and user interaction experience, and it is difficult to meet the needs of enterprises for efficient, accurate and real-time decision-making. In this regard, this paper will comprehensively sort out the application feasibility of business intelligence technology (BI) in this field, and put forward a design scheme of enterprise-level business intelligence analysis system, aiming at helping enterprises extract valuable information from massive data and supporting management to make scientific decisions. Practice has proved that the whole system based on business intelligence technology can effectively integrate all kinds of data resources inside and outside the enterprise, and provide comprehensive, accurate and timely information support for the enterprise through in-depth data analysis and mining, help the enterprise understand the market and customer needs, optimize business processes, improve decision-making level, and then provide theoretical support and practical guidance for the digital transformation of the enterprise.

As causality becomes non-linear, data-driven decision making becomes challenging because traditional Decision Support Systems (DSSs) do not support these environments. Existing analytical methods struggle in complex environments where variables that interact with each other as well as themselves. DEAR is a new framework that supports complex decision making. This framework performs change detection, evaluates causal relationships and interactions, assesses the risk of known interventions, and recommends action using a guided exploration strategy that blends historically successful solutions with possible alternatives.

Nowadays, managers in organizations usually have to confront increasingly complicated decision environments and dynamically changed decision demands. Traditional Decision Support Systems (DSSs) sometimes neglect that both requirements and objectives of decisions are indeterminate, and thus do not support flexible decision making. In order to meet dynamically changed contexts, DSS generators are introduced to build specific DSSs. However, DSS generators cost greatly but only bring about limited extensions. This paper reconstructs traditional DSS architecture using Service Oriented approaches. It describes the hierarchical model, conceptual model and implementation model for the framework of Service-based DSS, or SBDSS. In addition, it introduces the layered DSS adaptability model and presents the approach to high adaptabilities with context sharing and the assembly model of SCA and Spring Framework. Finally a prototype based on SBDSS is also given to show its feasibility.

As businesses grow across continents in terms of reach, scale, and diversity in products, the complexity of management problems and efforts to maximize the profit in corporations have increased significantly. To assist managers and employees at all levels in decision making, corporate modeling is essential to provide decision support information. Corporate modeling within business organizations has moved from standalone database applications to integrated and flexible enterprise-wide information systems. Computer modeling and planning tools such as decision support systems (DSS) are widely used by corporate management to select alternatives based on key business performance measures. Enterprise resource planning (ERP) systems have also been introduced as a new form of corporate modeling that does the same thing as traditional DSS, but they are not confined to the boundaries of business applications. This article reviews the various types of corporate models along with development and applications of advanced DSS and ERP systems. Keywords: corporate modeling; customer-centric enterprise information systems; decision support systems; enterprise resource planning; evolutionary multiagent systems

A SDSS combines database storage technologies, geographic information systems (GIS) and decision modeling into tools which can be used to address a wide variety of decision support areas (Eklund, Kirkby, and Pollitt, 1996). Recently, various emerging technologies in computer hardware and software such as speedy microprocessors, gigabit network connections, fast internet mapping servers along with Web-based technologies like extensible markup language (XML), Web services, etc provide promising opportunities to take the traditional spatial decision support systems one step further to provide easy-to-use, round-the-clock access to spatial data and decision support over the Web. Traditional DSS and Web-based spatial DSS can be further improved by integrating expert knowledge and utilizing intelligent software components (such as expert systems and intelligent agents) to emulate the human intelligence and decision making. These kinds of decision support systems are classified as intelligent decision support systems. The objective of this chapter is to discuss the development of an intelligent web-based spatial decision support system and demonstrate it with a case study for planning snow removal operations.

The growing volume of textual data presents genuine, modern day challenges that traditional decision support systems, focused on quantitative data processing, are unable to address. The costs of competitive intelligence, customer experience metrics, and manufacturing controls are escalating as organizations are buried in piles of open-ended responses, news articles and documents. The emerging field of text mining is capable of transforming natural language into actionable results, acquiring new insight and managing information overload.

Impact of GDSS: Opening the black box

Integrating the concept of mobility into the professional and organizational realm offers the possibility of reducing geographical disparities related to organization services. The advances made in technology, geographic information systems and pervasive systems equipped with global positioning (GPS) technologies have been able to bring about an evolution from classic data approaches towards the modeling of trajectory data resulting from moving activities of moving objects. As such, trajectory data needs first to be loaded into a Data Warehouse for analysis purposes. However, the traditional approaches used are poorly suited to handle spatio-temporal data features and also the decision making tasks related to mobility issues. Because of this mismatch, the authors propose to move beyond traditional approaches and propose a repository that is able to analyse trajectories of moving objects. Improving decision making and extracting pertinent knowledge with reduced costs and time expended are the main goals of this revised analysis approach. Thus, the authors propose an approach in which they employ the Bottom-up approach to modeling a Decision Support System which is designed to support Trajectory Data. As an example to illustrate this approach, the authors use a creamery and dairy milk mobile cistern application to demonstrate the effectiveness of their approach.