Moving Beyond Traditional Decision Support Systems

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.

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.

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.

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. >

The continued growth in the volume of available domain and technical data has been facilitated by a corresponding advancement in and communication technology. This information overload can result in inefficient use of time and resources as well as the creation of recommended courses of action that are overruled by the decision makers' judgment and experience. In order to address these problems, multiple knowledge sources and an inference process capable of mirroring the human thought processes (especially judgment and experience) must be available at the right time to the persons or groups needing the knowledge for decision making. Such a concept can be referred to as MANAGED QUALITATIVE THINKING SUPPORT SYSTEMS (MQTSS). Traditional decision support systems (DSS) rely upon decision maker or staff expertise to render knowledge in support of decision making. If the decision maker or staff has insufficient domain or technical expertise to utilize the DSS's embedded models, interpret results, or implement the recommendations, knowledge delivery may be compromised or rendered ineffective. MQTSS can alleviate these support problems and improve knowledge delivery for decision making by reducing knowledge search times, streamlining decision-making tasks, reducing decision time, and promoting appropriate qualitative thinking,. The MQTSS approach theoretically can enhance the decision making process and decision outcomes. This paper attempts to replicate an earlier study by this author to test the theory. First, the MQTSS approach is presented. Next, an system is created to deliver the technology to management. Finally, a simulation experiment is reported that compares the effectiveness of support rendered by a traditional decision support system and the created MQTSS system. The paper closes with conclusions and implications for systems research.

In recent years, applications aimed at exploring and analyzing spatial data have emerged, powered by the increasing need of software that integrates Geographic Information Systems (GIS) and On-Line Analytical Processing (OLAP). These applications have been called SOLAP (Spatial OLAP). In previous work, the authors have introduced Piet, a system based on a formal data model that integrates in a single framework GIS, OLAP (On-Line Analytical Processing), and Moving Object data. Real-world problems are inherently spatio-temporal. Thus, in this paper we present a data model that extends Piet, allowing tracking the history of spatial data in the GIS layers. We present a formal study of the two typical ways of introducing time into Piet: timestamping the thematic layers in the GIS, and timestamping the spatial objects in each layer. We denote these strategies snapshot-based and timestamp-based representations, respectively, following well-known terminology borrowed from temporal databases. We present and discuss the formal model for both alternatives. Based on the timestamp-based representation, we introduce a formal First-Order spatio-temporal query language, which we denote $\mathcal{L}_t,$ able to express spatio-temporal queries over GIS, OLAP, and trajectory data. Finally, we discuss implementation issues, the update operators that must be supported by the model, and sketch a temporal extension to Piet-QL, the SQL-like query language that supports Piet.

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

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

This study focuses on using techniques such as digital terrain model (DTM), geographic information system (GIS), and decision support system (DSS) for forestland ecosystem classification and suitability analysis of the Liukuei Experimental Forest of Taiwan Forestry Research Institute. The content includes the delineation of ecosystem units using DTM, the development of a hierarchical ecosystem classification system using GIS and multivariate statistical analysis, and the establishment of a forestland classification DSS with an application on site selection of a Taiwanese native species-Taiwania (Taiwania cryptomerioides). The results indicate that DTM is a fast, easy, feasible, and automatic approach for delineating ecosystem units of different spatial scales. The developed hierarchical ecosystem classification is a satisfactory scheme for Liukuei's forestland classification because the developed scheme coincides with the terrain characteristics along a continuum. The established DSS can effectively and feasibly analyze forestland classification under different spatial scales. Meanwhile, the system can easily perform site selection for Taiwania. From the results, it is concluded that techniques such as DTM, GIS, and DSS are useful for forest managers in the reasonable planning of forestland classification and management practice. In addition, the ecosystem approaches obtained from the Experimental Forest can be extended to island-wide forestland classification in Taiwan.

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).

The incredible progress witnessed in geographic information and pervasive systems equipped with positioning technologies have motivated the evolving of classic data towards mobility or trajectory data resulting from moving objects' displacements and activities. Provided trajectory data have to be extracted, transformed and loaded into a data warehouse for analysis and/or mining purposes; however, this later, qualified as traditional, is poorly suited to handle spatio-temporal data features and to exploit them, efficiently, for decision making tasks related to mobility issues. Because of this mismatch, we propose a bottom-up approach which offers the possibility to model and analyse the trajectories of moving object activities in order to improve decision making tasks by extracting pertinent knowledge and guaranteeing the coherence of provided analysis results at the lowest cost and time consuming. We illustrate our approach through a creamery trajectory decision support system.

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.