Decision Support Architecture Research Articles

Profit analytics in disruption risk for electrical energy supply network considering cost-oriented big data

Electrical energy consumption varies in different markets. Several different types of generators are used to supply electricity for consumers. The balance between supply and demand leads to prevent lack of energy. Nonetheless, with the growing number of markets and consumers, larger amount of data is generated making the analysis harder. Thus, decision support architecture for analytical purposes is significant. Large amount of data, recently called Big Data, is one of the significant sources of gaining and analyzing information as a decision support for electrical energy markets (EEMs). Market policymakers are emphasizing the impact of analytical approaches for business strategy setting in power supply and consumption to mitigate the risk of power failure and disruptions. In this article, the EEM influenced by big data of supply and demand and disruption is investigated to achieve power business continuity. A comprehensive architecture for EEM process is proposed. Supply and demand cost analysis is performed based on disruptions for an EEM. A pricing-based profit scenario optimization in a dynamic supply network having multiple power states is worked out. Numerical experiment is performed to show the effectiveness of the proposed paradigm based on data management.

Use of Domain Engineering in Hyperautomation Applied to Decision Making in Government

This article presents the domain engineering process carried out to obtain the requirements for the implementation of an Artificial Intelligence (AI) compliance framework aimed at the public sector. Owing to the current competitive and fast economy, which generates huge demand for increasingly efficient, reliable, and transparent intelligent systems, decision-support architectures should also be developed under strong restrictions of cost and time. Such a context requires adequate structures, processes, and technologies for coping with the complexity of building such intelligent systems. Currently, many public organizations have adopted applications for process automation, with the aim of refraining from repetitive work and producing more efficient results. However, what is not so often observed is the development of intelligent engines to support complex public decision-making. Possible explanations are the plethora of available data sources and the number of legal norms to be abided by. Moreover, it is important to highlight the need to incorporate transparency, auditability, reusability, and flexibility into such systems. Thus, they can be safely utilized in various analogous situations, reducing the need to develop new applications from scratch. An architecture suitable for supporting public decision-making with so many features and increasingly unstructured data, as well as abundant regulation, needs well-crafted formal specifications. This article aims to analyze three existing frameworks and carry out domain engineering studies in three cases to produce some guidance for future public applications and services based on AI. Next, we provide a conceptual preliminary architectural definition for the public sector. The proposed architecture targets were identified in the three cases studied, namely, frequent tasks of process mining requirements, detection of anomalies, and extraction of rules and public policies for helping public servants. All these aim at expedient AI development for public decision-making.

Decision support architectures for the recovery of interdependent infrastructure with large‐scale damage and socially vulnerable populations

AbstractInfrastructure damage has household‐level consequences after a major disaster. Losses are experienced due to factors such as unavailable services and impaired mobility. Socially vulnerable residents, in particular, have few resources with which to adapt. Decision support tools for making justifiable, transparent, repeatable decisions that center the needs of users during recovery are currently nonexistent. In part, this is because infrastructure recovery is a complex process, often involving the coordination of multiple entities. The recovery problem can be rendered more tractable by applying tools suitable for modeling complex systems and processes. System theoretic process analysis (STPA) can be used for goalsetting in a complex, dynamic system such as community civil infrastructure. STPA is used here to devise a decision support tool architecture suitable for coordinated multiagency recovery efforts. The example application is a long‐term recovery process with widespread infrastructure damage, population displacement, and other disruptions in system use due to a major disaster. In the example, losses and hazards are defined to reflect recovery challenges commonly faced by vulnerable populations experiencing partial or total displacement. This extension of STPA then reverses these hazards, starting with the most hazardous system states and progressing sequentially to less hazardous states until recovery is complete.

A Web-Based Decision Support System for Aircraft Dispatch and Maintenance

Aircraft dispatch involves determining the optimal dispatch option when an aircraft experiences an unexpected failure. Currently, maintenance technicians at the apron have limited access to support information and finding the right information in extensive maintenance manuals is a time-consuming task, often leading to technically induced delays. This paper introduces a novel web-based prototype decision support system to aid technicians during aircraft dispatch decision-making and subsequent maintenance execution. A system architecture for real-time dispatch decision support is established and implemented. The developed system is evaluated through a case study in an operational environment by licensed maintenance technicians. The system fully automates information retrieval from multiple data sources, performs alternative identification and evaluation for a given fault message, and provides the technician with on-site access to relevant information, including the related maintenance tasks. The case study indicates a potential time saving of up to 98% per dispatch decision. Moreover, it enables digitalization of the—currently mostly paper-based—dispatch decision process, thereby reducing logistics and paper waste. The prototype is the first to provide operational decision support in the aircraft maintenance domain and addresses the lack of correlation between theory and practice often found in decision support systems research by providing a representative case study. The developed custom parser for SGML-based documents enables efficient identification and extraction of relevant information, vastly contributing to the overall reduction of the decision time.

Modelling clinical experience data as an evidence for patient-oriented decision support

BackgroundEvidence-based Clinical Decision Support Systems (CDSSs) usually obtain clinical evidences from randomized controlled trials based on coarse-grained groups. Individuals who are beyond the scope of the original trials cannot be accurately and objectively supported. Also, patients’ opinions and preferences towards the health care delivered to them have rarely been considered. In this regards, we propose to use clinical experience data as an evidence to support patient-oriented decision-making.MethodsThe experience data of similar patients from social networks as subjective evidence and the argumentation rules derived from clinical guidelines as objective evidence are combined to support decision making together. They are integrated into a comprehensive decision support architecture. The patient reviews are crawled from social networks and sentimentally analyzed to become structured data which are mapped to the Clinical Sentiment Ontology (CSO). This is used to build a Patient Experience Knowledge Base (PEKB) that can complement the original clinical guidelines. An Experience Inference Engine (EIE) is developed to match similar experience cases from both patient preference features and patient conditions and ultimately, comprehensive clinical recommendations are generated.ResultsA prototype system is designed and implemented to show the feasibility of the decision support architecture. The system allows patients and domain experts to easily explore various choices and trade-offs via modifying attribute values to select the most appropriate decisions.ConclusionsThe integrated decision support architecture built is generic to solving a wide range of clinical problems. This will lead to better-informed clinical decisions and ultimately improved patient care.

EHR-Independent Predictive Decision Support Architecture Based on OMOP.

Background The increasing availability of molecular and clinical data of cancer patients combined with novel machine learning techniques has the potential to enhance clinical decision support, example, for assessing a patient's relapse risk. While these prediction models often produce promising results, a deployment in clinical settings is rarely pursued. Objectives In this study, we demonstrate how prediction tools can be integrated generically into a clinical setting and provide an exemplary use case for predicting relapse risk in melanoma patients. Methods To make the decision support architecture independent of the electronic health record (EHR) and transferable to different hospital environments, it was based on the widely used Observational Medical Outcomes Partnership (OMOP) common data model (CDM) rather than on a proprietary EHR data structure. The usability of our exemplary implementation was evaluated by means of conducting user interviews including the thinking-aloud protocol and the system usability scale (SUS) questionnaire. Results An extract-transform-load process was developed to extract relevant clinical and molecular data from their original sources and map them to OMOP. Further, the OMOP WebAPI was adapted to retrieve all data for a single patient and transfer them into the decision support Web application for enabling physicians to easily consult the prediction service including monitoring of transferred data. The evaluation of the application resulted in a SUS score of 86.7. Conclusion This work proposes an EHR-independent means of integrating prediction models for deployment in clinical settings, utilizing the OMOP CDM. The usability evaluation revealed that the application is generally suitable for routine use while also illustrating small aspects for improvement.

An application of AHP for parameter importance evaluation for farm machinery selection

The farm machinery selection is a complex decision making task. It involves a variety of variables associated with farming which include social and economic aspects of the decision maker. The aim of this work is to develop decision support architecture for tractor selection process. Different parameters for selecting tractor were identified; their importance was studied by literature survey and evaluated with AHP. The study concludes that the parameter operation type receives highest importance in tractor selection process. Other parameters like, service, type of business, brand and cost are also important in tractor selection process.

A New Architecture for Intelligent Clinical Decision Support for Intensive Medicine

Abstract Real-time and intelligent decision support systems are of most importance to supply intensive care professionals with important information in useful time. The work presented hereby shows an architectural overview of the communication system with bedside devices such as vital sign monitors. Intelligent Decision Support System for Intensive Medicine (ICDS4IM) goal is to ensure information quality and availability to Intensive Medicine professionals to take supported decisions in a mutable environment where complex and unpredictable events are a common state. Therefore, this work focus on Health Information Systems, Interoperability and Information Diffusion and Archive. Moreover, communication standards and the usage of a new technology such as containerization are discussed.

An IIoT-based architecture for decision support in the aeronautic industry

The Industry 4.0 movement is driving innovation in manufacturing through the application of digital technologies, leading to solid performance improvements. In this context, this paper introduces a real-time analytical framework based on predictive, simulation and optimization technologies applied to decision support in manufacturing systems, enabled by an underlying reference implementation of an open Industrial Internet of Things (IIoT) platform. This architecture integrates critical equipment, manufacturing and corporate systems through a Unified IIoT Cloud Platform. A real case study on the aeronautic industry demonstrates the proposal feasibility of this architecture to enhance productivity, predict equipment failures and bring agility to react to unexpected events. In this case study, the monitoring tool displays the current status of the critical resources and the predictive tool calculates a probability of failure. When this probability reaches a certain threshold, the simulation tool is triggered to evaluate the impact of the disruption in the system’s productivity. Results from the tools are displayed online through an alert system so that each stakeholder is informed timely and in a contextualized way.

Lift Cycle-Based Decision Support Solution Plan for Pipeline Integrity Management

1. Abstract With the development of industrial big data, the Internet of things, cloud computing, and artificial intelligence, the pipeline operation management mode has changed and thus shows a gradual development from the digital pipeline toward the intelligent pipeline. The construction of a decision support technology platform can effectively solve the issue of data accumulation, the problems of various currently used systems, and the separation between data acquisition and application. This is necessary to achieve safe, efficient, and sustainable development of oil and gas pipelines. This paper comprehensively analyzes the construction of digital pipeline in both domestic and foreign pipeline businesses as well as the situation of data acquisition and utilization. Furthermore, the characteristics and difficulties in the development of decision support technology and establishment of data standards and databases of the life cycle data of pipelines are discussed. The integrity management decision support architecture infrastructure for the life cycle of pipelines was proposed, including construction period control, operation period control, and decision support. The paper introduces an integrity decision support platform for pipelines on the basis of Geographic Information System (GIS), and constructs an integrity management decision support platform combining pipeline construction, operation, and maintenance. This platform can be applied to 1) data acquisition during the construction period, the visual management of the construction quality, and the handover of the digital database; 2) online data acquisition in the operation period of the pipeline, high consequence area, management and risk assessment, integrity assessment, corrosion protection potential control, risk control in regional upgrading areas, and Unmanned Aerial Vehicle (UAV) inspection; 3) decision support for the integrity management of the pipeline, such as big data modeling, emergency decision support, risk identification for big data of welding seams, disaster monitoring, early-warning on the basis of Internet of Things (IOT), real-time monitoring of pipeline leakage, training for maintaining remote equipment, visual inspection for remote faults and hidden problems, and mobile applications. <span style=font-size:11.0pt; line-height:107%;font-family: times= new= roman,serif;mso-fareast-font-family:= calibri;mso-fareast-theme-font:minor-latin;mso-ansi-language:en-us;mso-fareast-language:= en-us;mso-bidi-language:ar-sa=>Keywords: <span style=font-size:10.0pt; line-height:107%;font-family: times= new= roman,serif;mso-fareast-font-family:= calibri;mso-fareast-theme-font:minor-latin;mso-ansi-language:en-us;mso-fareast-language:= en-us;mso-bidi-language:ar-sa=> <span style=font-size:10.0pt; line-height:107%;font-family: times= new= roman,serif;mso-fareast-font-family:= calibri;mso-fareast-theme-font:minor-latin;mso-ansi-language:en-us;mso-fareast-language:= en-us;mso-bidi-language:ar-sa=>Database; Decision Support; GIS Platform; Life Cycle; Pipeline Integrity Management

Service oriented architecture for clinical decision support: a systematic review and future directions.

The use of a service-oriented architecture (SOA) has been identified as a promising approach for improving health care by facilitating reliable clinical decision support (CDS). A review of the literature through October 2013 identified 44 articles on this topic. The review suggests that SOA related technologies such as Business Process Model and Notation (BPMN) and Service Component Architecture (SCA) have not been generally adopted to impact health IT systems' performance for better care solutions. Additionally, technologies such as Enterprise Service Bus (ESB) and architectural approaches like Service Choreography have not been generally exploited among researchers and developers. Based on the experience of other industries and our observation of the evolution of SOA, we found that the greater use of these approaches have the potential to significantly impact SOA implementations for CDS.

A proposed clinical decision support architecture capable of supporting whole genome sequence information.

Whole genome sequence (WGS) information may soon be widely available to help clinicians personalize the care and treatment of patients. However, considerable barriers exist, which may hinder the effective utilization of WGS information in a routine clinical care setting. Clinical decision support (CDS) offers a potential solution to overcome such barriers and to facilitate the effective use of WGS information in the clinic. However, genomic information is complex and will require significant considerations when developing CDS capabilities. As such, this manuscript lays out a conceptual framework for a CDS architecture designed to deliver WGS-guided CDS within the clinical workflow. To handle the complexity and breadth of WGS information, the proposed CDS framework leverages service-oriented capabilities and orchestrates the interaction of several independently-managed components. These independently-managed components include the genome variant knowledge base, the genome database, the CDS knowledge base, a CDS controller and the electronic health record (EHR). A key design feature is that genome data can be stored separately from the EHR. This paper describes in detail: (1) each component of the architecture; (2) the interaction of the components; and (3) how the architecture attempts to overcome the challenges associated with WGS information. We believe that service-oriented CDS capabilities will be essential to using WGS information for personalized medicine.

Agile Data Fusion and Knowledge Base Architecture for Critical Decision Support

This paper describes the architecture and deployment of a software platform for information fusion, knowledge hosting and critical decision support. The work has been carried out under the TRIDEC project (www.tridec-online.eu), focusing on geo-information fusion and collaborative decision making. Four technologies underpin the architecture: 1) A message oriented middleware, for distributed communications; 2) A leveraged hybrid storage solution, for efficient storage of heterogeneous datasets and semantic knowledge; 3) A generic data fusion container, for dynamic algorithms control; and 4) A single conceptual model and schema, as systems’ semantic meta-model. Deployment for industrial drilling operations is described. Agility is manifested with the ability to integrate data sources from a proprietary domain, dynamically discover new datasets and configure and task fusion algorithms to operate on them, aided by efficient information storage. The platform empowers decision support by enabling dynamic discovery of information and control of the fusion process across geo-distributed locations.

Decision support for hospital bed management using adaptable individual length of stay estimations and shared resources

BackgroundElective patient admission and assignment planning is an important task of the strategic and operational management of a hospital and early on became a central topic of clinical operations research. The management of hospital beds is an important subtask. Various approaches have been proposed, involving the computation of efficient assignments with regard to the patients’ condition, the necessity of the treatment, and the patients’ preferences. However, these approaches are mostly based on static, unadaptable estimates of the length of stay and, thus, do not take into account the uncertainty of the patient’s recovery. Furthermore, the effect of aggregated bed capacities have not been investigated in this context. Computer supported bed management, combining an adaptable length of stay estimation with the treatment of shared resources (aggregated bed capacities) has not yet been sufficiently investigated. The aim of our work is: 1) to define a cost function for patient admission taking into account adaptable length of stay estimations and aggregated resources, 2) to define a mathematical program formally modeling the assignment problem and an architecture for decision support, 3) to investigate four algorithmic methodologies addressing the assignment problem and one base-line approach, and 4) to evaluate these methodologies w.r.t. cost outcome, performance, and dismissal ratio.MethodsThe expected free ward capacity is calculated based on individual length of stay estimates, introducing Bernoulli distributed random variables for the ward occupation states and approximating the probability densities. The assignment problem is represented as a binary integer program. Four strategies for solving the problem are applied and compared: an exact approach, using the mixed integer programming solver SCIP; and three heuristic strategies, namely the longest expected processing time, the shortest expected processing time, and random choice. A baseline approach serves to compare these optimization strategies with a simple model of the status quo. All the approaches are evaluated by a realistic discrete event simulation: the outcomes are the ratio of successful assignments and dismissals, the computation time, and the model’s cost factors.ResultsA discrete event simulation of 226,000 cases shows a reduction of the dismissal rate compared to the baseline by more than 30 percentage points (from a mean dismissal ratio of 74.7% to 40.06% comparing the status quo with the optimization strategies). Each of the optimization strategies leads to an improved assignment. The exact approach has only a marginal advantage over the heuristic strategies in the model’s cost factors (≤3%). Moreover,this marginal advantage was only achieved at the price of a computational time fifty times that of the heuristic models (an average computing time of 141 s using the exact method, vs. 2.6 s for the heuristic strategy).ConclusionsIn terms of its performance and the quality of its solution, the heuristic strategy RAND is the preferred method for bed assignment in the case of shared resources. Future research is needed to investigate whether an equally marked improvement can be achieved in a large scale clinical application study, ideally one comprising all the departments involved in admission and assignment planning.

A collaborative food safety service agent architecture with alerts and trust

With the recent advances in Internet and mobile technologies, there are increasing demands for ubiquitous access to food safety information for service integration and gathering first hand information. However, due to disparate food trading information among different food suppliers throughout the food supply chain such as food importers, food wholesalers, food retailers, it is still difficult for citizens to use them effectively during their marketplace shopping. To overcome this problem, we propose a Collaborative Food Safety Agent System (CFSAS) based on a scalable, flexible, and intelligent Multi-Agent Information System (MAIS) architecture for proactive aids and trust-based decision support on food purchasing to citizens. We formulate our MAIS architecture for CFSAS further with agent clusters based on a case study of the Center for Food Safety (CFS) in Hong Kong. Agent clusters may comprise several types of agents to achieve the goals involved in the major processes of a food safety mechanism. We show how agents help citizens better plan, understand, and specify their preferences collaboratively with the CFSAS. We further illustrate how this can be implemented with Web service technologies to integrate disparate food information resources along the food supply chain.

Data and Knowledge Management in Designing Healthcare Information Systems

evolution in information and communication technologies has transformed the information management structure in almost all the organizations. It has had a great impact on healthcare organizations by improving health services and management by integrating technology with the knowledge management infrastructure. The healthcare industry is a knowledge-based community and is connected to hospitals, physicians, patients, laboratories, pharmaceuticals, clinics, pharmacies, and customers for sharing knowledge. A knowledge-based healthcare industry can improve the quality of care and service given to its people and also reduces the administrative cost. The objective of this research is to present and describe the knowledge management capabilities, the technical infrastructure, and the decision support architecture for such a healthcare management system. It envisions a healthcare knowledge management system (HKMS) that would help to integrate important components, disseminate knowledge to the respective users and to store historical data in a database. This will immensely help the managers and developers to identify their IT needs and to plan for and develop the technical infrastructure of the health care management system for their organizations.

Implementation of workflow engine technology to deliver basic clinical decision support functionality

BackgroundWorkflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic.ResultsWe present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture.ConclusionsWe describe an implementation of a free workflow technology software suite (available at http://code.google.com/p/healthflow) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that workflow engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of workflow engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform.

A service oriented architecture for decision support systems in environmental crisis management

Efficient management of natural disasters impose great research challenges to the current environmental crisis management systems in terms of both architecture and services. This is mainly due to the fact that a large amount of geospatial content is usually distributed, non-compliant to standards, and needs to be transmitted under a QoS guaranteed framework to support effective decision making either in case of an emergency or in advance planning. Incorporating real time capabilities in Web services, both in terms of dynamic configuration and service selection, is an open research agenda. The things get worst in geospatial context due to the huge amount of data transmitted from distributed sensors under heterogeneous platforms, making the need of synchronization an important issue. In this paper, we propose a flexible service oriented architecture for planning and decision support in environmental crisis management. The suggested architecture uses real time geospatial data sets and 3D presentation tools, integrated with added-value services, such as simulation models for assisting decision making in case of emergency. The proposed architectural framework goes beyond integration and presentation of static spatial data, to include real time middleware that is responsible for selecting the most appropriate method of the available geospatial content and service in order to satisfy the QoS requirements of users and/or application. A case study of a complete, real world implementation of the suggested framework dealing with forest fire crisis management system is also presented.

Accuracy and performance evaluation of a clinical decision support system for laboratory result alerts

This study was designed to evaluate the accuracy and performance of a clinical decision support (CDS) architecture consisting of a knowledge engine and data interface adapter (DIA), which were developed as critical components for a clinical decision support system (CDSS). Accuracy and performance are the most essential quality attributes. CDSSs are becoming increasingly important in efforts to increase quality control in hospitals, but if a CDSS adds a new burden to the legacy system, the manager is unlikely to adopt it in the hospital information system. A laboratory alerting system was selected for evaluation, because of complex rules and the large amount of the data interface. Evaluation cases (N = 323,455) were captured from a tertiary care and teaching hospital over a 3-month period. We were able to confirm the accuracy, stability, and feasibility of the CDS application architecture and the knowledge engine, but we identified areas of the DIA that require improvement in order to obtain satisfactory performance.

A Decision Support Architecture for Maritime Operations Exploiting Multiple METOC Centres and Uncertainty

Maritime operations are affected significantly by meteorological and oceanographic (METOC) conditions. The availability of multiple METOC centres able to deliver more accurate forecasts in the near future (2/3 days) is a promising resource toward better operational planning. However, how a specific maritime operation can benefit from such forecasts is not straightforward. A decision support architecture is required to combine METOC forecasts and human knowledge and select the best action from a set of pre-defined actions for the maritime operation. This paper describes a decision support architecture developed at the NATO Undersea Research Centre that is an improvement over a previous version in two ways: i) it exploits forecasts coming from more than one METOC centre, and ii) it exploits uncertainty associated with METOC forecasts. The former allows for the exploitation of different abilities of METOC centres at different conditions. The latter allows for the propagation of input uncertainty on output products (the risks related to each action), thus allowing operators to assess if risks related to different actions are statistically different. Both features increase the robustness of the previous decision support architecture. The effectiveness of the new architecture is demonstrated on an underwater glider surfacing experiment carried out on data collected during a cruise in the Adriatic Sea in 2006.