Energy aware knowledge extraction from Petri nets supporting decision-making in service-oriented automation

This paper introduces an approach to decision support systems in service-oriented automation control systems, which considers the knowledge extracted from the Petri nets models used to describe and execute the process behavior. Such solution optimizes the decision-making taking into account multi-criteria, namely productive parameters and also energy parameters. In fact, being manufacturing processes typically energy-intensive, this allows contributing for a clean and saving environment (i.e. a better and efficient use of energy). The preliminary experimental results, using a real laboratorial case study, demonstrate the applicability of the knowledge extracted from the Petri nets models to support real-time decision-making systems in service-oriented automation systems, considering some energy efficiency criteria

A Data Centric Privacy Preserved Mining Model for Business Intelligence Applications

In present day competitive scenario, the techniques such as data warehouse and on-line analytical process (OLAP) have become a very significant approach for decision support in data centric applications and industries. In fact the decision support mechanism puts certain moderately varied needs on database technology as compared to OLAP based applications. Data centric decision support schemes (DSS) like data warehouse might play a significant role in extracting details from various areas and for standardizing data throughout the organization to achieve a singular way of detail presentation. Such efficient data presentation facilitates information for decision making in business intelligence (BI) applications in various industrial services. In order to enhance the effectiveness and robust computation in BI applications, the optimization in data mining and its processing is must. On the other hand, being in a multiuser scenario, the security of data on warehouse is also a critical issue, which is not explored till date. In this paper a data centric and service oriented privacy preserved model for BI applications has been proposed. The optimization in data mining has been accomplished by means of C5.0 classification algorithm and comparative study has been done with C4.5 algorithm. The implementation of enhanced C5.0 algorithm with BI model would provide much better performance with privacy preservation facility for Business Intelligence applications

Applications of agent architectures to decision support in distributed simulation and training systems

This work develops the approach and presents the results of a new model for applying intelligent agents to complex distributed interactive simulation for command and control. In the framework of tactical command, control communications, computers and intelligence (C4I), software agents provide a novel approach for efficient decision support and distributed interactive mission training. An agent-based architecture for decision support is designed, implemented and is applied in a distributed interactive simulation to significantly enhance the command and control training during simulated exercises. The architecture is based on monitoring, evaluation, and advice agents, which cooperate to provide alternatives to the dec ision-maker in a time and resource constrained environment. The architecture is implemented and tested within the context of an AWACS Weapons Director trainer tool. The foundation of the work required a wide range of preliminary research topics to be covered, including real-time systems, resource allocation, agent-based computing, decision support systems, and distributed interactive simulations. The major contribution of our work is the construction of a multi-agent architecture and its application to an operational decision support system for command and control interactive simulation. The architectural design for the multi-agent system was drafted in the first stage of the work. In the next stage rules of engagement, objective and cost functions were determined in the AWACS (Airforce command and control) decision support domain. Finally, the multi-agent architecture was implemented and evaluated inside a distributed interactive simulation test-bed for AWACS Vv\u27Ds. The evaluation process combined individual and team use of the decision support system to improve the performance results of WD trainees. The decision support system is designed and implemented a distributed architecture for performance-oriented management of software agents. The approach provides new agent interaction protocols and utilizes agent performance monitoring and remote synchronization mechanisms. This multi-agent architecture enables direct and indirect agent communication as well as dynamic hierarchical agent coordination. Inter-agent communications use predefined interfaces, protocols, and open channels with specified ontology and semantics. Services can be requested and responses with results received over such communication modes. Both traditional (functional) parameters and nonfunctional (e.g. QoS, deadline, etc.) requirements and captured in service requests

A service-driven approach to assist water management during extreme events

Water shortages and flooding have caused large property losses and endangered human lives in many areas. Rapid and informed response is needed to ensure effective water management, including reliable and immediate data synthesis, near-real-time forecasting, and model-based decision support for water operations. A structure to rapidly process heterogeneous information and models needed for near-real-time water management is critical for decision makers. This dissertation develops a service-driven approach to decision support in water management, focusing on case studies related to drought and flooding. For flood management, real-time reservoir management is a critical component of decision support. Estimating and predicting reservoir inflows is particularly essential for water managers, given that flood conditions change rapidly. We propose a data-driven framework for real-time reservoir inflow prediction, using a service-oriented approach, that enables ease of access through a Web browser. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics-based model that estimates errors in the flow predictions from a physics-based model. The performances of these two models are compared for short-term prediction. In addition, both the statistical and hybrid models have been published as Web services through Microsoft’s Azure Machine Learning (AzureML) service, and are accessible through a browser-based Web application. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short-term forecasts. However, for longer-term prediction (2 hours or more), the hybrid model fits the observations more closely than the purely statistical or physics-based prediction models alone. The second case study focuses on droughts, developing methods to better manage significant imbalances between water supply and demand. A service-driven approach is used to couple river modeling services with optimization services for determining optimal water allocation strategies under daily drought scenarios in a permit system. An accurate and computationally efficient meta-model approach is then developed to relieve the computational burden of the simulation-optimization model. This work uses a drought event in the Upper Guadalupe River Basin, Texas, in April 2015 as a case study to illustrate the benefits of the approach. Weather and water demand uncertainty are considered through scenario-based optimization. The results have demonstrated that the simulation-optimization model services can easily be coupled using DataWolf workflow tool and AzureML service, providing improved water allocation strategies relative to the current approach. The scenario analysis shows that the permit grouping system, which organizes water right permit holders into groups rather than considers each water user individually, is an easy and manageable approach for water allocation. In addition, the adaptive meta-model approach is efficient to relieve the computational burden in simulation-optimization model, thereby enabling large-scale real-time Web services for decision support

Evaluating e-Government services from a citizens' prespective: A reference process model

Evaluating and optimizing e-government services is imperative for governments especially due to the capacity of e-services to transform public administrations and assist the interactions of governments with citizens, businesses and other government agencies. Existing widely applied evaluation approaches neglect to incorporate citizens’ satisfaction measures. Several citizen satisfaction models and indicators have been suggested in academia; however a reference process model that can assist practitioners to apply these performance measures is missing. In this paper we draw upon the evaluation approach proposed by the EU funded project CEES and propose a reference process model that captures re-usable practices for e-government evaluation from a citizens’ perspective. The novelty of the proposed approach is that using DEA for evaluating the e-services the assessment results in suggestions for strategic improvement of the e-services.EU FP7 Marie Curie People Project “CEES - Citizen oriented Evaluation of E-Government Systems” (reference IAPP-2008-230658

A decision support system for fostering smart energy efficient districts

The role of ICT is becoming prominent in tackling some of the urban societal challenges such as energy wastage and increasing carbon emissions. In this context, the concept of DAREED aims to deliver an integrated decision support system (DSS) to drive energy efficiency and low carbon activities at both a building and district level. The main aim of this paper is to present the technical concept of the Best Practices recommendation component of the DAREED system. This component seeks to compare and identify existing best practices to recommend practical actions to various stakeholders (e.g. building managers, citizens) in order to improve energy performance considering the global needs of a building. This paper also discusses the context of the three field trial sites (based in UK, Spain and Italy) in which the DAREED platform along with the best practices tool is to be tested and validated.This work evolved in the context of the project DAREED (Decision support Advisor for innovative business models and useR engagement for smart Energy Efficient Districts), www.dareed.eu, a project cofunded by the EC within FP7, Grant agreement no: 609082

Knowledge Representation Concepts for Automated SLA Management

Outsourcing of complex IT infrastructure to IT service providers has increased substantially during the past years. IT service providers must be able to fulfil their service-quality commitments based upon predefined Service Level Agreements (SLAs) with the service customer. They need to manage, execute and maintain thousands of SLAs for different customers and different types of services, which needs new levels of flexibility and automation not available with the current technology. The complexity of contractual logic in SLAs requires new forms of knowledge representation to automatically draw inferences and execute contractual agreements. A logic-based approach provides several advantages including automated rule chaining allowing for compact knowledge representation as well as flexibility to adapt to rapidly changing business requirements. We suggest adequate logical formalisms for representation and enforcement of SLA rules and describe a proof-of-concept implementation. The article describes selected formalisms of the ContractLog KR and their adequacy for automated SLA management and presents results of experiments to demonstrate flexibility and scalability of the approach.Comment: Paschke, A. and Bichler, M.: Knowledge Representation Concepts for Automated SLA Management, Int. Journal of Decision Support Systems (DSS), submitted 19th March 200

United Nations Development Assistance Framework for Kenya

The United Nations Development Assistance Framework (2014-2018) for Kenya is an expression of the UN's commitment to support the Kenyan people in their self-articulated development aspirations. This UNDAF has been developed according to the principles of UN Delivering as One (DaO), aimed at ensuring Government ownership, demonstrated through UNDAF's full alignment to Government priorities and planning cycles, as well as internal coherence among UN agencies and programmes operating in Kenya. The UNDAF narrative includes five recommended sections: Introduction and Country Context, UNDAF Results, Resource Estimates, Implementation Arrangements, and Monitoring and Evaluation as well as a Results and Resources Annex. Developed under the leadership of the Government, the UNDAF reflects the efforts of all UN agencies working in Kenya and is shaped by the five UNDG programming principles: Human Rights-based approach, gender equality, environmental sustainability, capacity development, and results based management. The UNDAF working groups have developed a truly broad-based Results Framework, in collaboration with Civil Society, donors and other partners. The UNDAF has four Strategic Results Areas: 1) Transformational Governance encompassing Policy and Institutional Frameworks; Democratic Participation and Human Rights; Devolution and Accountability; and Evidence-based Decision-making, 2) Human Capital Development comprised of Education and Learning; Health, including Water, Sanitation and Hygiene (WASH), Environmental Preservation, Food Availability and Nutrition; Multi-sectoral HIV and AIDS Response; and Social Protection, 3) Inclusive and Sustainable Economic Growth, with Improving the Business Environment; Strengthening Productive Sectors and Trade; and Promoting Job Creation, Skills Development and Improved Working Conditions, and 4) Environmental Sustainability, Land Management and Human Security including Policy and Legal Framework Development; and Peace, Community Security and Resilience. The UNDAF Results Areas are aligned with the three Pillars (Political, Social and Economic) of the Government's Vision 2030 transformational agenda