Integrating Knowledge Modelling in Business Process Management

In this paper we present a new approach for integrating Business Process Management and Knowledge Management. We focus on the modelling of weakly-structured knowledge-intensive business processes. We develop a framework for modelling this type of processes that explicitly considers knowledge-related tasks and knowledge objects and present a workflow tool that is an implementation of our theoretical meta-model. As an example, we sketch one case study, the process for granting full old age pension as it is performed in the Greek Social Security Institution. Finally we briefly describe some related approaches and compare them to our work and draw the main conclusions and further research directions

Supporting knowledge-intensive work in public administration processes.

Knowledge management efforts focus much on the strategic applications of knowledge-related initiatives and not so much on their implications at the level of concrete business processes. On the other hand, business process management efforts have not concentrated on leveraging knowledge. In this paper we attempt to fill that gap by developing a tool for proactive, context-sensitive delivery of knowledge. We focus on the modelling of knowledge-intensive business processes and we develop a framework for modelling this type of processes that explicitly considers knowledge-related tasks and knowledge objects. We present a tool that is an implementation of our theoretical meta-model and realises proactive, context-sensitive delivery of knowledge, integrated with the workflow enactment. As an example, we sketch one case study, the process for granting full old-age pension as it is performed in the Greek Social Security Institution, discussing the benefits derived from applying our tool. Finally, we draw the main conclusions of our work and discuss further research directions

Knowledge Modelling in Weakly-Structured

In this paper we present a new approach for integrating knowledge management and business process management. We focus on the modelling of weakly-structured knowledge-intensive business processes. We develop a framework for modelling this type of processes that explicitly considers knowledge-related tasks and knowledge objects and present a work#ow tool that is an implementation of our theoretical meta-model. As an example, we sketch one case study, the process for granting full old age pension as it is performed in the Greek Social Security Institution. Finally we brie#y describe some related approaches and compare them to our work and draw the main conclusions and further research directions

On the Optimization of a Probabilistic Data Aggregation Framework for Energy Efficiency in Wireless Sensor Networks

Among the key aspects of the Internet of Things (IoT) is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs) have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting—both in terms of data and energy—data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios

Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach

Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) systems have emerged recently as a flexible and dynamic computing environment, providing task offloading service to the users. In order for such a paradigm to be viable, the operator of a UAV-mounted MEC server should enjoy some form of profit by offering its computing capabilities to the end users. To deal with this issue in this paper, we apply a usage-based pricing policy for allowing the exploitation of the servers’ computing resources. The proposed pricing mechanism implicitly introduces a more social behavior to the users with respect to competing for the UAV-mounted MEC servers’ computation resources. In order to properly model the users’ risk-aware behavior within the overall data offloading decision-making process the principles of Prospect Theory are adopted, while the exploitation of the available computation resources is considered based on the theory of the Tragedy of the Commons. Initially, the user’s prospect-theoretic utility function is formulated by quantifying the user’s risk seeking and loss aversion behavior, while taking into account the pricing mechanism. Accordingly, the users’ pricing and risk-aware data offloading problem is formulated as a distributed maximization problem of each user’s expected prospect-theoretic utility function and addressed as a non-cooperative game among the users. The existence of a Pure Nash Equilibrium (PNE) for the formulated non-cooperative game is shown based on the theory of submodular games. An iterative and distributed algorithm is introduced which converges to the PNE, following the learning rule of the best response dynamics. The performance evaluation of the proposed approach is achieved via modeling and simulation, and detailed numerical results are presented highlighting its key operation features and benefits

Managing Knowledge in Weakly-Structured Administrative Processes

IT support for knowledge workers in their daily work can take many different guises: groupware systems and information access and retrieval tools support knowledge processes, while workflow management covers the support for rigidly structured processes. However, what is missing so far is an environment that integrates the business process and knowledge management aspects of weakly-structured knowledge work and actively supports the worker in using and adding to knowledge resources. This paper aims to present a new approach to support weakly-structured knowledge-intensive business processes. As an example we sketch a case study from the Greek public sector

Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market

Software Defined Networks (SDN) and Mobile Edge Computing (MEC), capable of dynamically managing and satisfying the end-users computing demands, have emerged as key enabling technologies of 5G networks. In this paper, the joint problem of MEC server selection by the end-users and their optimal data offloading, as well as the optimal price setting by the MEC servers is studied in a multiple MEC servers and multiple end-users environment. The flexibility and programmability offered by the SDN technology enables the realistic implementation of the proposed framework. Initially, an SDN controller executes a reinforcement learning framework based on the theory of stochastic learning automata towards enabling the end-users to select a MEC server to offload their data. The discount offered by the MEC server, its congestion and its penetration in terms of serving end-users’ computing tasks, and its announced pricing for its computing services are considered in the overall MEC selection process. To determine the end-users’ data offloading portion to the selected MEC server, a non-cooperative game among the end-users of each server is formulated and the existence and uniqueness of the corresponding Nash Equilibrium is shown. An optimization problem of maximizing the MEC servers’ profit is formulated and solved to determine the MEC servers’ optimal pricing with respect to their offered computing services and the received offloaded data. To realize the proposed framework, an iterative and low-complexity algorithm is introduced and designed. The performance of the proposed approach was evaluated through modeling and simulation under several scenarios, with both homogeneous and heterogeneous end-users