Personalized Decision Support Systems

Decision support systems (DSS) are computerized systems that assist humans to make decisions. Early versions were designed for executives, but over time DSSs were designed for workers at any level in the organization (Keen & Morton, 1978; Rockart, 1979). Due to increasing costs in providing benefits and services, organizations are forcing workers and consumers to take increasing responsibility for insurance, health care, and financial planning decisions. Extreme events, such as terrorism, pandemics, and natural disasters will swamp the capacity of governmental agencies to serve their citizenry. Individuals in affected communities must turn to local agencies or ad hoc groups for assistance. Personal decision support systems (PDSS), consisting of databases, model-based expertise, and intelligent interfaces, along with wireless communications, Internet resources, and personal computing, provide sufficient resources to assist informed individuals and groups in solving problems. This article reviews the typical components of a DSS and the different types of systems that have evolved. The article poses three types of problems facing individuals, including routine problem solving, immediate survival needs, and long-term evolutionary growth. Personal decision support issues of acquiring information, processing information, and dissemination are outlined. Future trends and research opportunities are discussed.

[1]  J. Rockart Chief executives define their own data needs. , 1979, Harvard business review.

[2]  Minh Q. Huynh,et al.  Effective Use of Information Systems/Technologies in the Mergers and Acquisitions Environment: A Resource-Based Theory Perspective , 2010, Int. J. Intell. Inf. Technol..

[3]  Ralph H. Sprague,et al.  Invited Article: A Framework for the Development of Decisoin Support Systems , 1980, MIS Q..

[4]  V. Sugumaran The Inaugural Issue of the International Journal of Intelligent Information Technologies , 2005 .

[5]  Ankush Mittal,et al.  Bayesian Network Technologies: Applications and Graphical Models , 2007 .

[6]  Luigi Portinale,et al.  Applications of Bayesian Networks in Reliability Analysis , 2007 .

[7]  Roy Gelbard,et al.  Handling Fuzzy Similarity for Data Classification , 2009, Encyclopedia of Artificial Intelligence.

[8]  Torsten Priebe,et al.  A Context-Based Approach for Supporting Knowledge Work with Semantic Portals , 2005, Int. J. Semantic Web Inf. Syst..

[9]  Vijayan Sugumaran Intelligent Information Technologies: Concepts, Methodologies, Tools and Applications , 2007 .

[10]  Shishir K. Shandilya,et al.  Opinion Mining and Information Retrieval: Techniques for E-Commerce , 2011 .

[11]  Alejandro Pazos Sierra,et al.  Encyclopedia of Artificial Intelligence , 2008 .

[12]  Ralph H. Sprague,et al.  A Framework for the Development of Decision Support Systems , 1993 .

[13]  Henry Lieberman,et al.  Out of context: Computer systems that adapt to, and learn from, context , 2000, IBM Syst. J..

[14]  Fulvio Mastrogiovanni,et al.  Proactive Assistance in Ecologies of Physically Embedded Intelligent Systems: A Constraint-Based Approach , 2011 .