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EIDSS 2017 : Exploring Intelligent Decision Support Systems: Current State and New Trends


When N/A
Where N/A
Submission Deadline Jul 1, 2017
Notification Due Jul 15, 2017
Final Version Due Sep 30, 2017
Categories    computer science   data mining   information systems   semantic web

Call For Papers

Decision Support Systems (DSS) are gaining an increased popularity in various domains, including business, engineering, the military, and medicine. They are especially valuable in situations in which the amount of available information is prohibitive for the intuition of an unaided human decision maker and in which precision and optimality are of importance. Decision support systems can aid human cognitive deficiencies by integrating various sources of information, providing intelligent access to relevant knowledge, and aiding the process of structuring decisions. They can also support choice among well-defined alternatives and build on formal approaches, such as the methods of engineering economics, operations research, statistics, and decision theory. They can also employ artificial intelligence methods to address heuristically problems that are intractable by formal techniques. Proper application of decision-making tools increases productivity, efficiency, and effectiveness and gives many businesses a comparative advantage over their competitors, allowing them to make optimal choices for technological processes and their parameters, planning business operations, logistics, or investments. These systems allow individuals and organizations to deal with unstructured or semi-structured decision problems that demand extensive experience and expert knowledge. However, the increasing complexity of the problems and the continuous growing of information and knowledge that should be mastered by responsible people in organizations underline the need for DSSs driven by advanced and modern technologies. From this perspective, the field of DSS is expanding to use new technologies such as Social Media, Semantic Web, Linked Data, Big Data, and Machine Learning. These technologies are converging to provide integrated support for individuals and organizations to make more rational decisions.
The objective of this book will be to explore the latest results of research, development and applications of DSSs in different fields. This book proposal will consider the following domains, but will not be limited: aerospace, agriculture, banking, business services, e-commerce, eHealth, e-government, finances, supply chain and others.
This book aims to disseminate innovative and high-quality research regarding the foundations, methods, methodologies, models, tools, and techniques for designing, developing, implementing, and evaluating advanced DSSs in different fields.
Topic Coverage
Topics of interest in this book include, but are not restricted to:
• Analytics for decision-making
• Application of Knowledge-Based Methods
• Big Data mining to support decision-making
• Business Intelligence
• Collaborative decision making
• Data mining
• Decision-making and Internet of Things
• Decision-making models
• Decision-making through machine learning
• DSS foundations and development
• Experts systems
• Fuzzy logic
• Geographic information system for decision-making
• Knowledge Acquisition & Representation
• Knowledge-based Decision Support Systems
• Industrial applications and case-studies
• Impact of DSS in industrial performance
• Linked Data to support decision-making
• Multicriteria DSS
• Ontological engineering
• Operational research and management science
• Probabilistic Graphical Models
• Semantic knowledge bases for decision making
• Social Media use in decision-making
• Web-based and mobile DSS
• Web 2.0 and 3.0 based knowledge artefacts for decision making
Target audience
The target audience includes researchers, practitioners and (Masters/PhD) students. Therefore, papers need to address both scientific and practical implications of the research.
Type of contributions and length
• Case studies: In-depth reports of DSS implementations to support decision-making in an organization or business.
• Full research papers: Both quantitative and qualitative contributions that study a particular aspect of Decision Support Systems. Only completed research will be considered, meaning that research in progress will not be considered to be included in the book.
• Conceptual papers: Contributions that synthesize existing studies.
This type of contributions are typically 15 to 20 pages in length (excluding references) when applying the Springer formatting instructions. Contributions should be original and not be submitted elsewhere.
Submission Guidelines and Other Considerations
Chapters submitted must not have been previously published or be under consideration for publication in other journals, books, though they may represent significant extensions of prior work. All submitted chapters will undergo a rigorous peer-review process (with at least two reviewers) that will consider programmatic relevance, scientific quality, significance, originality, style and clarity.
The acceptance process will focus on chapters that address relevant contributions in the form of theoretical and experimental research and case studies applying new perspectives for developing Decision Support Systems. Before submitting a chapter proposal, authors must carefully read over the Springer’s Author Guidelines.
Authors should submit their complete chapter via email: , and the subject of the email should be: “Chapter Submission: Exploring Intelligent DSS Current State and New Trends” according to the following timeline.
Important Dates
• Submission deadline: July 1st, 2017
• First selection of submissions: July 15th, 2017
• Completion of first‐round reviews: September 15th, 2017
• Revised chapters: September 30st, 2017
• Target of the second (last) round of reviews: October, 15th, 2017
• Publication (tentative): Spring 2018
Rafael Valencia-García, Universidad de Murcia, Spain (
Mario Andrés Paredes-Valverde, Universidad de Murcia, Spain (
María del Pilar Salas-Zárate, Universidad de Murcia, Spain (
Giner Alor-Hernández, Instituto Tecnológico de Orizaba, México ( )

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