Distributed Decision Support System Research Articles

Crowd Evacuation Simulation Based on WebGIS

In order to improve the efficiency of people evacuation from hazardous regions and reduce the ill effects of disasters, a distributed decision support system for large scale crowd evacuation is proposed. The system is supported by the technology of Web Geographic Information System (WebGIS). Basically, by analyzing the system framework we may acquire a novel approach to establish a distributed large-scale crowd evacuation platform for emergency management. Research results show that the proposed system is useful for devising evacuation plans in advance as well as real-time management under disaster.

An agent-based distributed collaborative decision support system

The agent-based technology has emerged as a new paradigm for conceptualizing, designing, and implementing software systems. It has been considered as an important approach for developing industrial distributed systems. In this paper, we propose a Multi-agent view of a distributed collaborative decision support systems. These systems refer to a group of logically decentralized participants facilitator and decision-makers that cooperate to achieve objectives that are typically beyond the capabilities of any individual participant, and where the facilitator acts as a manager of the overall collaborative decision making activities that requires among those participant collaborating on decision making The main aims of the proposed multi-agent distributed collaborative decision support system are a distributed work support by enabling the flow of information and interoperation among distributed decision makers, and an efficient management of the information circulated in a distributed environment by providing content related aid. Specifically, agents were used to support a geographically dispersed collaborative decision making to solve jointly a problem and make a decision. In such systems, intelligent agents offer tremendous potential in supporting all stages of collaborative decision process such as idea generation, alternatives organizing and evaluation that would allow the group members focus on solutions that were found to be significant. They enhance overall system performance, in particular along such dimensions as computational efficiency, reliability, extensibility, responsiveness, maintainability reuse, and flexibility. In this way, we take advantage of the capacities of both the decision-maker and the machine. We apply the multi-agent system paradigm to decision support in a global contingency management.

Automatic Composition of Heterogeneous Models Based on Semantic Web Services

As an important function of a distributed decision support system, model composition aims to aggregate model functions to solve complex decision problems. Most existing methods on model composition only apply to the models which have the same type of input and output data so that they can be linked together directly. Those methods are inadequate for the heterogeneous models, since a heterogeneous model may have different types of input and output data that are represented in either qualitative or quantitative manner. This paper aims to address the problem of heterogeneous model composition by employing the techniques based on semantic web services and artificial intelligence planning. In this paper, the heterogeneous model composition problem is converted to the problem of planning in nondeterministic domains under partial observability. An automatic composition method is presented to generate the composite model based on the planning as model checking technique. The experiment results are also presented in this paper to show the feasibility and capability of our approach in dealing with the complex problems involving heterogeneous models.

Distributed Privacy-Preserving Decision Support System for Highly Imbalanced Clinical Data

When a medical practitioner encounters a patient with rare symptoms that translates to rare occurrences in the local database, it is quite valuable to draw conclusions collectively from such occurrences in other hospitals. However, for such rare conditions, there will be a huge imbalance in classes among the relevant base population. Due to regulations and privacy concerns, collecting data from other hospitals will be problematic. Consequently, distributed decision support systems that can use just the statistics of data from multiple hospitals are valuable. We present a system that can collectively build a distributed classification model dynamically without the need of patient data from each site in the case of imbalanced data. The system uses a voting ensemble of experts for the decision model. The imbalance condition and number of experts can be determined by the system. Since only statistics of the data and no raw data are required by the system, patient privacy issues are addressed. We demonstrate the outlined principles using the Nationwide Inpatient Sample (NIS) database. Results of experiments conducted on 7,810,762 patients from 1050 hospitals show improvement of 13.68% to 24.46% in balanced prediction accuracy using our model over the baseline model, illustrating the effectiveness of the proposed methodology.

Distributed decision support system for airport ground handling management using WSN and MAS

The economic situation in the air transportation industry claims for new business models supported by accurate management processes, which need constant feedback of the real status of the environment. The objective of this work is to achieve an updated/real decision support systems (DSS) to allocate resources in an airport even when disturbances occur by combining artificial intelligent techniques with visibility technologies. This work proposes the combined use of Multi-agent systems (MAS) along with Wireless Sensor Networks (WSN) to provide the required information on the status of the resources and the environment. The MAS is based on a double layer of decision-taking levels and on a Markov reward function whereas the WSN is based on a Zigbee network of Radio Frequency Identification (RFID) readers with active tags as end nodes, which are carried by the physical resources. The proposed distributed DSS has been tested at Ciudad Real Central Airport in Spain.

A Critical Analysis on Efficacy of Clinical Decision Support Systems in Health Care Domain

Clinical Practice guidelines strongly relies on evidence based medical literature. In Health care domain decision support systems are playing a competent role in diagnosis and treatment from multiple diseases. Among different Hospitals all over the world the Information technology domain emphasis key roles in improvement of patient health care to great extent. The Concept of Data Mining (DM) and Decision Support systems (DSS) in medical domain provides an efficient mechanism to extract the multiple records of patient treatment diagnostics from previously stored records in Data base (DB) or Data Ware House (DWH) and compare these guidelines to perform strong analysis that results in efficient decision making. Along the previously mentioned techniques the era of Telemedicine has also being developed that results in generation of multiple techniques in diagnosis of multiple diseases and health improvement using Mobile Health care systems specially worth full for the rural areas where latest medical facilities are unavailable at the point of need. The required information in Database or in Data Ware House might be the historical data of patient or the health based summery of different patients in diverse stages. Now these days the emergence of distributed decision support systems in health care domain covers the health care treatment procedures in more comprehensive manner including surgical procedures and radiological treatment. In this paper we are going to analyze the multiple health care diagnosis procedures and treatment techniques using various decision support systems designed and implemented by various researchers all over the world and compare the effectiveness and efficiency of each decision support system in health care domain. Our research study is also helpful for physicians and health care practioners in analyzing multiple scenarios related to interesting pattern recognitions and intelligent decision making.

A Distributed Scenario-Based Decision Support System for Robust Decision-Making in Complex Situations

Decision-making in emergency management is a challenging task as the consequences of decisions are considerable, the threatened systems are complex and information is often uncertain. This paper presents a distributed system facilitating better-informed decision-making in strategic emergency management. The construction of scenarios provides a rationale for collecting, organising, and processing information. The set of scenarios captures the uncertainty of the situation and its developments. The relevance of scenarios is ensured by gearing the scenario construction to assessing alternatives, thus avoiding time-consuming processing of irrelevant information. The scenarios are constructed in a distributed setting allowing for a flexible adaptation of reasoning (principles and processes) to both the problem at hand and the information available. This approach ensures that each decision can be founded on a coherent set of scenarios. The theoretical framework is demonstrated in a distributed decision support system by orchestrating experts into workflows tailored to each specific decision.

A Web-accessible distributed data warehouse for brain tumour diagnosis

AbstractCurrently, biological databases (DBs) are a common tool to complement the research of a wide range of biomedical disciplines, but there are only a few specialized medical DBs for human brain tumour magnetic resonance spectroscopy (MRS) data; they typically store a limited range of biological data (i.e. clinical information, magnetic resonance imaging and MRS data) and are not offered as open-source Structured Query Language relational DB schemas. We present a novel approach to biological DBs: a distributed Web-accessible DB for storing and managing clinical and biomedical data related to brain tumours from different clinical centres. This tool is designed for multi-platform systems with dissimilar DB management systems. Being the main data repository of the HealthAgents (HA) project, it uses multi-agent technology and allows the centres to share data and obtain diagnosis classifications from other centres distributed around the world in a reliable way.The HA project aims to create an agent-based distributed decision support system (DSS) to assist doctors to provide a brain tumour diagnosis and prognosis. The HA DB enables the DSS to totally integrate with its Graphical User Interface to perform classifications with the stored data and visualize the results using the HA distributed agents framework. This new feature converts the system presented in the first application in the world to combine a storage and management tool for brain tumour data and a complete Web-based DSS to obtain automatic diagnosis.

A generic and extensible automatic classification framework applied to brain tumour diagnosis in HealthAgents

AbstractNew biomedical technologies enable the diagnosis of brain tumours by using non-invasive methods. HealthAgents is a European Union-funded research project that aims to build an agent-based distributed decision support system (dDSS) for the diagnosis of brain tumours. This is achieved using the latest biomedical knowledge, information and communication technologies and pattern recognition (PR) techniques. As part of the PR development of HealthAgents, an independent and automatic classification framework (CF) has been developed. This framework has been integrated with the HealthAgents dDSS using the HealthAgents agent platform. The system offers (1) the functionality to search for distributed classifiers to solve specific questions; (2) automatic classification of new cases; (3) instant deployment of new validated classifiers; and (4) the ability to rank a set of classifiers according to their performance and suitability for the case in hand. The CF enables both the deployment of new classifiers using the provided Extensible Markup Language1 classifier specification, and the inclusion of new PR techniques that make the system extensible. These features may enable the rapid integration of PR laboratory results into industrial or research applications, such as the HealthAgents dDSS. Two classification nodes have been deployed and they currently offer classification services by means of dedicated servers connected to the HealthAgents agent platform: one node being located at the Katholieke Universiteit Leuven, Belgium and the other at the Universidad Politécnica de Valencia, Spain. These classification nodes share the current set of brain tumour classifiers that have been trained from in vivo magnetic resonance spectroscopy data. The combination of the CF with a distributed agent system constitutes the basis of the brain tumour dDSS developed in HealthAgents.

The HealthAgents ontology: knowledge representation in a distributed decision support system for brain tumours

AbstractIn this paper we present our experience of representing the knowledge behind HealthAgents (HA), a distributed decision support system for brain tumour diagnosis. Our initial motivation came from the distributed nature of the information involved in the system and has been enriched by clinicians’ requirements and data access restrictions. We present in detail the steps we have taken towards building our ontology starting from knowledge acquisition to data access and reasoning. We motivate our representational choices and show our results using domain examples used by clinical partners in HA.

Cooperative planning in express carrier networks — An empirical study on the effectiveness of a real-time Decision Support System

For small transportation firms cooperation in a carrier network is a proper mean to overcome the inefficiencies from deadheading. To be successful, such a network has to secure two different but equally important aspects: the partners have to be aware of specific consolidation potentials through order exchange which is an optimization and communication problem, and, the partners have to experience incentives to contribute actively to the network which is the problem of finding a fair cost/profit allocation schema for order exchanges. In this paper we discuss the experience with the development of a Decision Support System for a specific express carrier network. We illustrate how the consolidation potentials in such a network with autonomously planning carriers can be exploited and cost effectiveness can be improved substantially through the use of a suitable distributed Decision Support System if the two success factors awareness and fairness are addressed properly.

Reseach on Distributed Material Requirements Planning Based on Distributed Decision Support Systems

Distributed Material Requirements Planning is widely used in manufacture enterprise. The decentralized decisions of purchasing and warehousing can be coordinated by Distributed Decision Support Systems. Distributed Decision Support Systems can improve flexibility to make right decision instantly.

A Framework for Situated Distributed Decision Support

The increasing complexity of business environment makes centralized decision making inadequate in many cases. With the distributed computing paradigm gaining more support and attention investigation of new models for distributed decision support systems is an important direction of research. This paper looks to expand the recently introduced model for situated DSS to address distributed and coordinated decision support. To demonstrate the applicability of this framework, a prototypical application for lead time management has been implemented. Simulation experiments have been conducted to investigate the impacts of real-time information and information coordination on decision performance. Overall, the results support our expectation that both information delay and information coordination have significant impacts on decision performance.

Distributed Decision Support System for the Safety of Coalmine

Distributed Decision Support System for the Safety of Coalmine

Multi-Criteria Spatial Decision Support System DECERNS

Land-use planning and environmental management often requires an implementation of both geoyspatial information analysis and value-driven criteria within the decision-making process. DECERNS (Decision Evaluation in Complex Risk Network Systems) is a web-based distributed decision support system for multicriteria analysis of a wide range of spatially-explicit land management alternatives. It integrates mainly basic and some advanced GIS functions and implements several Multi-Criteria Decision Analysis (MCDA) methods and tools. DECERNS can also be integrated with a model server containing generic and site specific models for in-depth analysis of project and environmental risks as well as other decision criteria under consideration. This paper provides an overview of the modeling approaches as well as methods and tools used in DECERNS. Application of the DECERNS WebSDSS (Web-based Spatial Decision Support System) for a housing site selection case study is presented.

Decision support system in tactical air traffic flow management for air traffic flow controllers

A distributed decision support system for tactical air traffic flow management is developed for the First Integrated Center of Air Defense and Air Traffic Control (CINDACTA I) in Brasilia. The paper specifies the role of CINDACTA I, looking at the problems of air traffic flow management in Brazil and describing an initial evaluation of the decision support system. The decision process involves a meta-level control approach and reinforcement-learning algorithms that allow air traffic flow controllers and supervisors to acquire knowledge and get assistance to enhance their decision-making. The paper also develops simulations involving egalitarian and prioritization distribution of flight flows for the Sao Paulo Terminal Area.

Decision makers support system. Adapting the assistance to the decision maker's behaviour

Situations of distributed decision making are characterized by the existence of different points of view between decision makers (DM's), and by different temporal dynamics within the system, which reinforces importance of studies on information data flows between DM's, and is the basis of classical Distributed Decision Support System (DDSS) approaches. We propose not to limit the analysis of the situation (and consequently the role of the DDSS) to information flows, but to integrate cognitive modeling of each DM, and to use these latter to better analyze relevant information, in order to control it, within the flows exchanged. Our approach relies on an application framework concerning decision making in Maritime Surveillance.

A novel steganographic algorithm using animations as cover

Information security is gaining widespread importance in distributed decision support systems [T.S. Raghu, and H. Chen, Cyberinfrastructure for homeland security: Advances in information sharing, data mining, and collaboration systems. Decision Support Systems 43 (2007) 1321–1323]. Conventional steganographic models available in the literature on information hiding use the knowledge of cover-object and stego-object to extract embedded data. Embedding algorithms have been predominantly used in steganography for hiding data whereas encoding algorithms have been used for data compression. This research study proposes a modified model for steganographic system that incorporates encoding algorithms based upon the information theoretic model documented in Cachin [C. Cachin, An Information-Theoretic Model for Steganography, Information and Computation 192 (1) (2004) 41–56.]. The differences between the conventional model and the proposed model are analyzed in this study. We further implement the proposed model by designing a novel steganographic algorithm that utilizes animations as cover-objects. The proposed approach is then compared with an encoding algorithm for gif animations and a modified embedding algorithm with animations as the cover. Gifshuffle is a steganographic software that hides data in gif animations. Digital Invisible Ink Toolkit (DIIT) is a software that uses embedding algorithm to conceal data into images. DIIT is remodeled to hide data within animated images in this study. Experiments reveal that the encoding algorithms are suitable for information hiding using animations. The study finds that the proposed encoding framework with animations provides a practical framework for analysis and has a better security performance than Gifshuffle and DIIT. Although the proposed model achieves more hiding rate, Gifshuffle is computationally faster than other techniques.

Design of a high speed logic engine for distributed decision support systems

The paper provides a new approach to designing a massively parallel logic engine for automated reasoning in a distributed decision support system. The design includes extension of classical linear resolution of first order logic clauses by multi-reso

HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis

We present an agent-based distributed decision support system for the diagnosis and prognosis of brain tumors developed by the HealthAgents project. HealthAgents is a European Union funded research project, which aims to enhance the classification of brain tumors using such a decision support system based on intelligent agents to securely connect a network of clinical centers. The HealthAgents system is implementing novel pattern recognition discrimination methods, in order to analyze in vivo Magnetic Resonance Spectroscopy (MRS) and ex vivo/in vitro High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HR-MAS) and DNA micro-array data. HealthAgents intends not only to apply forefront agent technology to the biomedical field, but also develop the HealthAgents network, a globally distributed information and knowledge repository for brain tumor diagnosis and prognosis.