SOA Information Engineering System Based on Knowledge Grid Applications in Smart Grids

Smart grids have the characteristics of being observable,controllable,adaptive,self-healing,embedded independent processing,and real-time analysis.With the development of smart grids,constructing a grid to cover global,unified information systems,which should be adapted to fulf ill the requirements of the characteristics,is essential.This paper presents an service-oriented architecture(SOA)for smart grid information-engineering systems based on knowledge grid,which could form as a service-oriented architecture through business,technology and management;it would extract potentially valuable information from the massive amount of information on the generation side,the grid side,and the electricity side,then share the useful information to improve availability,security and stability.

Data Mining Ontology Development for High User Usability

This paper mainly introduces the development and implementation of the user centered data mining service ontology on Universal Knowledge Grid （UKG）. UKG is an ontology-based grid architecture model to build large-scale distributed knowledge discovery system on the grid. The data mining ontology services are the main service offering by UKG. It can meet the user requirements of knowledge discovery in different domains and different hierarchies and make the system exoteric, extensible and high usable. A data min- ing solution for money laundering is introduced.

Knowledge Map： Mathematical Model and Dynamic Behaviors

Knowledge representation and reasoning is a key issue of the Knowledge Grid. This paper proposes a Knowledge Map (KM) model for representing and reasoning causal knowledge as an overlay in the Knowledge Grid. It extends Fuzzy Cognitive, Maps (FCMs) to represent and reason not only simple cause-effect relations, but also time-delay causal relations, conditional probabilistic causal relations and sequential relations. The mathematical model and dynamic behaviors of KM are presented. Experiments show that, under certain conditions, the dynamic behaviors of KM can translate between different states. Knowing this condition, experts can control or modify the constructed KM while its dynamic behaviors do not accord with their expectation. Simulations and applications show that KM is more powerful and natural than FCM in emulating real world.

Framework for Grid Manufacturing

With the development of networked manufacturing, it is more and more imminent to solve prob-lems caused by inherent limitations of network technology, such as heterogeneity, collaboration collision, and decentralized control. This paper presents a framework for grid manufacturing, which neatly combines grid technology with the infrastructure of advanced manufacturing technology. The paper studies grid-oriented knowledge description and acquisition, and constructs a distributed knowledge grid model. The pa-per also deals with the protocol of node description in collaborative design, and describes a distributed col-laborative design model. The protocol and node technology leads to a collaborative production model for grid manufacturing. The framework for grid manufacturing offers an effective and feasible solution for the problems of networked manufacturing. The grid manufacturing will become an advanced distributed manu-facturing model and promote the development of advanced manufacturing technologies.

A Heuristic Algorithm for Task Scheduling Based on Mean Load on Grid

Efficient task scheduling is critical to achieving high performance on grid computing environment. The task scheduling on grid is studied as optimization problem in this paper. A heuristic task scheduling algorithm satisfying resources load balancing on grid environment is presented. The algorithm schedules tasks by employing mean load based on task predictive execution time as heuristic information to obtain an initial scheduling strategy. Then an optimal scheduling strategy is achieved by selecting two machines satisfying condition to change their loads via reassigning their tasks under the heuristic of their mean load. Methods of selecting machines and tasks are given in this paper to increase the throughput of the system and reduce the total waiting time. The efficiency of the algorithm is analyzed and the performance of the proposed algorithm is evaluated via extensive simulation experiments. Experimental results show that the heuristic algorithm performs significantly to ensure high load balancing and achieve an optimal scheduling strategy almost all the time. Furthermore, results show that our algorithm is high efficient in terms of time complexity.