Enhancing management decision making in global enterprises using GIS principles

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.

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.

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.

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. >

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).

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.

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.

This article presents an approach to problems of emergency management on motorways using a Decision Support System (DSS). The advantages of this approach in comparison with conventional operational methods are acceptable data management costs provided by spatial data already stored in a Geographical Information System (GIS), generation of new data using various spatial functions as well as transparency for all emergency services. DSS deploys GIS in conjunction with other decision models thus becoming a powerful tool for the coordination of all participants in a decision-making process during emergency situations giving them a more cooperative surrounding. For the purpose of more efficient emergency management on motorways, the main idea is to provide an organisational support by combining GIS with decision models in order to provide an effective spatial DSS concept.

Social security of the state is the ability to guarantee and provide its citizens with safe and decent living standards. Study of the impact of the war on the state of national security, in particular human social security, will help to identify weaknesses in the state policy implementation in the political, economic and social areas. The impact of Russia's full-scale war against our country on the social security of Ukrainians requires substantiation of new, more effective mechanisms and tools for strengthening human social security in times of war. Under these circumstances, there is a need to apply new technologies to manage the socio-economic processes of the state for analysis and decision-making, and to reflect the current situation. These technologies will make it possible to predict scenarios and offer managers at all levels the best ways to achieve a balanced result. We need technologies that can effectively combine space and time with large amounts of special data in the form of attribute information about objects in the operational environment, background information about regions, climatic conditions, socio-economic conditions and processes in the region, etc. The need for prompt decision-making based on real indicators of anthropogenic, social, and environmental nature, taking into account the factors of space and time in a particular region or area of study, necessitates the involvement of specialized software platforms – geographic information systems that combine analytical capabilities with geospatial analysis. Today, a large number of general-purpose geographic information systems have been developed, but despite the variety of such software products, there is still a need to create specialized geographic information documents and databases that allow obtaining up-to-date geographic information data, the use of which leads to a real reduction in the time required to solve specialized tasks of information management systems. The analysis and decision support system in the environment of existing geographic information platforms will allow to take into account the influence of external factors, analyze the possibility of development, expansion, and monitor the state of the general environment when assessing the current state of input data, using information on the spatial distribution of the necessary resources, information on the competitive environment, etc. With the help of spatial and surface analysis modules, a GIS user can analyze spatially compatible objects and their interrelationships, taking into account the dynamics of their development. Currently, the world leader among geographic information systems is the ArcGIS products. It is a complete system that allows you to collect, organize, manage, analyze, exchange and distribute geographic data. Additionally, ArcGIS, depending on the type and specification of the platform, has other specialized modules that provide a wide range of opportunities for working with geodata and their visual display, namely: creation, management, visualization, analysis and exchange of geographic information. In addition, there are many compatible applications and extensions that integrate into the GIS platform and are focused on solving highly specialized tasks, such as assessing the state of the environment (water, soil, vegetation) or forecasting the development of natural phenomena and man-made emergencies. The article analyzes and substantiates the approaches to the use of a geographic information system as an effective tool for studying the impact of a full-scale war on human social security, improving the efficiency, completeness and quality of information and spatial support for management processes. The modern and full-featured ArcGIS system is considered as a platform that allows not only to form a geographic information system of any scale and purpose, but also to form on its basis an effective system of socio-economic monitoring, which is combined with decision support systems. The article presents an algorithm for studying the impact of a full-scale war on human social security using the ArcGIS system as an effective decision support tool with the use of spatially specialized databases.

Geographic information systems (GISs) as a technology have been studied and reported extensively and, not unexpectedly, in the field of geography. The various ways of capturing spatial data, arranging attribute data into appropriate database structures, and making the resulting large data sets efficient to store and query have been extensively researched and reported (Densham, 1991). However, the geographic research community has only recently noted the need to study how GISs are used as decision tools, especially with regard to how such decision making might be related to a decision maker’s cognitive style (Mennecke, Crossland, et al., 2000). As an example, the University Consortium for Geographic Information Science called for research examining how geographic knowledge is acquired through different media and by users with different levels of experience and training (University Consortium for Geographic Information Science, 1996). Researchers in the fields of decision sciences and information systems have more recently begun to make contributions in the area of decision making with GISs. When a GIS is employed as a decision support system, in these studies the resultant system is often referred to as a spatial decision support system, or SDSS (see Crossland, 1992; Crossland, Perkins, et al., 1995; Mennecke et al., 2000). A geographic information system in its simplest form is a marriage of accurately scaled digital maps with a database. The digital maps comprise spatially referenced details such as natural elements (lakes, rivers, topographic elevation contours, etc.), manmade objects (buildings, roads, pipelines, etc.), and political boundaries (city limits, state and county lines, international boundaries, etc.). These natural elements are typically referenced, with varying degrees of precision, to latitude/longitude coordinates on the earth’s surface. It must be noted here that the degree of precision and, more importantly, differences in degrees of precision for the various elements are the subjects of much research and user consternation in applications of GISs to solving problems. The database, in turn, catalogs information about the various spatial elements (e.g., the names of rivers, names of buildings, building owner, operator of a pipeline, etc.). These descriptive entries in the database are often referred to as attributes of the various spatial elements. A GIS may be paired with the global positioning system (GPS), from which real-time, satellite-derived location information may be derived, as provided by an appropriate GPS receiver.

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

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.

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.

ABSTRACT: Watershed management decision making is a complex process. Cooperation and communication among federal, state, and local stakeholders is required while balancing biophysical and socioeconomic concerns. The public is taking part in environmental decisions, and the need for technology transfer from public agencies to stakeholders is increasing. Information technology has had a profound influence on watershed management over the past decade. Advances in data acquisition through remote sensing, data utilization through geographic information systems (GIS), and data sharing through the Internet have provided watershed managers access to more information for management decisions. In the future, applications incorporating hydrologic simulation models, GIS, and decision support systems will be deployed through the Internet. In addition to challenges in making complex modeling technology available to diverse audiences, new information technology issues, such as interoperability, Internet access, and security, are introduced when GIS, simulation models, and decision support systems are integrated in an Internet environment. This paper presents a review of current use of information technology in watershed management decision making and a discussion of issues created when developing Internet based, integrated watershed management decision support systems. A prototype spatial decision support system (SDSS) for rangeland watershed management was developed using web services, which are components that communicate using text based messages, thus eliminating proprietary protocols. This new framework provides an extensible, accessible, and interoperable approach for SDSS.