Generating rules from data mining for collaboration moderator services

A Moderator is a knowledge based system that supports collaborative working by raising awareness of the priorities and requirements of other team members. However, the amount of advice a Moderator can provide is limited by the knowledge it contains on team members. The use of data mining techniques can contribute towards automating the process of knowledge acquisition for a Moderator and enable hidden data patterns and relationships to be discovered to facilitate the moderation process. A novel approach is presented, consisting of a knowledge discovery framework which provides a semi-automatic methodology to generate rules by inserting relationships discovered as a result of data mining into a generic template. To demonstrate the knowledge discovery framework methodology an application case is described. The application case acquires knowledge for a Moderator to make project partners aware of how to best formulate a proposal for a European research project by data mining summaries of successful past projects. Findings from the application case are presented.     

A review and framework of laser-based collaboration support

New technologies are emerging to enable and support physical, implicit and explicit collaborations. They are essential for dealing with increasingly complex systems in unstructured, dynamic environments. The purpose of this article is to review the role of laser technology in enabling better, more precise interactions and their control, and to identify opportunities and challenges in this area. While the most common applications of laser technology are found in medical and health care, manufacturing, and communication, other domains such as safety, quality assurance, agriculture, construction, entertainment, defense, transportation, and law enforcement also benefit from it. In spite of the rapid dissemination of this technology, its role in support of collaboration and discovery is still in its infancy. Research activities concerning new ways of using lasers as a collaboration supporting technology that may strengthen new areas have been relatively limited. Nevertheless, the translation to this domain of collaboration support has been recognized as vital for activities that demand increasingly more coordinated effort among interacting agents (e.g., humans, machines, particles) and digital, possibly also photonic agents. Recent advances in laser technology in a number of application domains are reviewed in this article, focusing primarily on lasers’ role for supporting different forms of precision interactions and collaboration. In addition, a framework with five collaboration support functions and five collaboration dimensions is defined for this review. The taxonomy framework is useful for enabling better understanding of the existing and emerging opportunities that laser-based technology offers for collaboration support, its advantages and several research gaps.     

A framework for fuzzy knowledge based control

Two major problems appear during the design of a framework. The first is related to synthesizing generic elements for a family of applications and connecting them to an integrated control flow. The second lies in the design of a powerful, modular, reliable architecture that is easy to (re)use and understand. The fact of including design patterns in the architecture of frameworks minimizes the second problem. Indeed, design patterns provide proven, flexible, well‐engineered design solutions at a higher abstraction level than classes. Their associated documentation records information from experienced object‐oriented designers about solutions to recurrent problems, about contexts in which the patterns are applicable, about forces involved and consequences related to their use. This paper presents a number of the benefits of integrating design patterns in the development of an object‐oriented framework related to fuzzy logic control. It also reports on an object‐oriented design for Fuzzy Knowledge Based Control (FKBC) that includes design patterns to facilitate the development, maintenance and documentation of the FKBC framework. Copyright © 2000 John Wiley & Sons, Ltd.     

A knowledge transfer framework to support rapid process modeling in aerosol jet printing

Aerosol jet printing (AJP) technology recently gained considerable attention in an electronic manufacturing industry due to its ability to fabricate parts with fine resolution and high flexibility. However, morphology control has been identified as the main limitation of AJP process, which drastically affects the electrical performance of printed components. Even though previous researches have made significant efforts in process modeling to improve the controllability of the the printed line morphology, the modeling process is still inefficient under modified operating conditions due to the repeated experiments. In this paper, a knowledge transfer framework is proposed for efficient modeling of the AJP process under varied operating conditions. The proposed framework consists of three critical steps for rapid process modeling of AJP. First, a sufficient source domain dataset at a certain operating condition is collected to develop a source model based on Gaussian process regression. Then, the representative experimental points are selected from the source domain to construct a target dataset under different operating conditions. Finally, classical knowledge transfer approaches are adopted to extract the built-in knowledge from the source model; thus, a new process model can be developed efficiently by the transferred knowledge and the representative dataset from the target domain. The validity of the proposed framework for the rapid process modeling of AJP is investigated by case study, and the limitations of the classical knowledge transfer approaches adopted in AJP are also analyzed systematically. The proposed framework is developed based on the principles of knowledge discovery, which is different from traditional process modeling approaches in AJP. Therefore, the modeling process is more systematic and cost-efficient, which will be helpful to improve the controllability of the line morphology. Additionally, due to its data-driven based characteristics, the proposed framework can be applied to other additive manufacturing technologies for process modeling researches.     

A team formation model based on knowledge and collaboration

Appointing a good leader to the position of team manager and having competent workers collaborate as team members is a key to success in business activities of an enterprising institution. The traditional methodologies of human resource management have defined the required abilities for team managers and team members, and evaluated those abilities of employees. However, it is difficult to consider those abilities systematically in practice. In addition, the current management paradigm undergoes rapid transitions into knowledge management. In step with these trends, this study presents a framework for analyzing the knowledge of the candidates for managers and team members for the new team, and proposes a genetic algorithm and social network measures for choosing a team manager and team members. A prototype was built for testing the feasibility of the model. The testing data are from an R&D institute’s human resource management department. The results show that our proposed approach is a quantitative and systematic method for selecting proper personnel for appropriate teams.     

A procedural framework for dynamic changes of human interactions in knowledge intensive services

The knowledge intensive service processes should be managed in a human-oriented way since humans who naturally undertake complex operations of an intellectual nature in the processes are the most valuable resources. The most fundamental nature of human work is collaborative and dynamic. Humans interact and communicate with each other to accomplish their jobs in the process. To help them to work together, a strong representation of the process should be provided to facilitate them to clearly understand who they should interact with and what activities need to be performed. For the clear representation, Human Interaction Management (HIM), which has been suggested to comprehensively support human work, adopts a role-based approach to process modeling. It, however, tends to hide elements of interactions although the collaborative human interaction is one of the most fundamental nature of human work. To remedy this problem, a state-driven modeling approach to human interactions was presented. It clearly visualizes the interactions so that humans can be guided through it. However, they do not just follow the previously defined sequence of activities, but continuously work out how they are going to proceed from now on according to the state of things they encounter throughout the life of the work. To fully support the dynamic nature of human work, human interactions should be flexibly managed. Therefore, this paper presents a framework for the flexible management of human interactions. The framework provides a capability to flexibly manage the interactions in a decentralized way by allowing interaction participants to dynamically change the involved interaction based on the continuous negotiation of how to achieve the ultimate goal of the interaction. It will be a basis for realization of decentralized management of human interactions in knowledge intensive service processes.     

A framework for a knowledge based cold spray repairing system

Journal of Intelligent Manufacturing - Restoring damaged components is a very promising and high-value project, which enable to save a lot of production time and cost, and thus has already...     

Co-Insights framework for collaborative decision support and tacit knowledge transfer

Abundant software tools use visual analytics (VA) techniques to support various decisions with the aim of boosting better insights. Large organizations, however, lose efficiency in selecting the right tools to support the persons who apply the tools to various decision tasks. Consequently, the creation and sharing of insights are far from optimal, leading consistently to sub-optimal decisions. In this work, the Co-Insights framework is introduced with automated collaboration support features to enable effective creation and sharing of distributed insights. A collaboration network (Co-Net) is established to model the collaborative decision making process in an organization. Two important features of the Co-Insights framework are developed: collaborative agent allocation analysis (CA3) for task–participant matching; and a robust mechanism for the recommendation of selected VA tools, by participant–tool matching. Thus, by better matching of tasks and tools with participants, the creation and sharing of insights are improved in any collaborative team for better decision making, accompanied with the tacit knowledge transfer to sustain the entire organization. To validate the effectiveness of these two main features, two experiments built on the Co-Net model are performed to test the newly developed algorithms. It has been found that CA3 significantly improves the matching scores by up to 35%, compared with conventional task–participant matching methods. The neural network based participant–tool matching mechanism yields robust results with 4% mismatches for 10% noise levels, and with 16% mismatches for 30% noise levels. Real case applications and implications are described, and further plans to extend this new framework are also outlined based on the reported experiments and evaluations.     

ConKMeL: a contextual knowledge management framework to support multimedia e-Learning

As the latest stage of learning and training evolution, e-Learning is supposed to provide intelligent functionalities not only in processing multi-media education resources but also in supporting context-sensitive pedagogical education processes. Towards providing an integrated solution for intelligent multimedia e-Learning, this paper presents a context-aware knowledge management framework named ConKMeL. Proposed framework features a semantic context-based approach for representing and integrating information and knowledge in e-Learning. Requirement analysis in university e-Learning environments shows that knowledge communications are usually in a hybrid mode across different conceptual levels. Based on the fact, a multi-layer contextual knowledge representation model called KG (knowledge graph) is presented. Corresponding key issues in development such as context-based knowledge retrieval and logical knowledge interpretation are discussed. On the application side, a scenario-based learning case study is shown to demonstrate the concepts and techniques developed in the ConKMeL framework.

A peer-to-peer collaboration framework for multi-sensor data fusion

A peer-to-peer collaboration framework for multi-sensor data fusion in resource-rich radar networks is presented. In this high data volume real-time application, data from multiple radars are combined to improve the accuracy of radar scans (e.g., correct for attenuation) and to provide a composite view of the area covered by the radars. Data fusion process is subject to two constraints: (1) the accuracy requirement of the final fused results, which may be different at different end nodes, and (2) the real-time requirements of the application. The accuracy requirement is achieved by dynamically selecting the appropriate set of data to exchange among the multiple radar nodes. A mechanism for selecting a dataset based on current application-specific needs is presented. We also present a dynamic peer-selection algorithm, Best Peer Selection (BPS), that chooses a set of peers based on their computation and communication capabilities to minimize the data processing time per integration algorithm. Simulation-based results show that BPS can deliver a significant performance improvement, even when the peers have high variability in available network and computation resources.     

A methodology for knowledge based decision support for facilities layout planning

The area of facilities layout planning contains a mix of specialized models in addition to a requirement for human experience and subjective judgments in the decision process. In this paper, we recognize that the task of choosing a suitable facilities layout planning model for a given problem is a non-trivial one, due to the unique requirements of individual situations, and the varying nature of the available models. To facilitate the selection of an appropriate model for facilities layout problems, we propose a methodology for the design of a knowledge based Decision Support System (DSS) which is calibrated with heuristics elicited from experienced layout planners. The rationale and structure of the model are discussed, as are its mode of usage and future research issues.     

A knowledge based decision support system for computer performance management

Computer systems managers make decisions about hardware and software selection, performance evaluation, capacity planning, and other resource variables on the basis of factual data, accounting data, subjective judgements, and assumptions about the resource consumption of the jobs being run. The importance of computer resource planning calls for effective support methods. A Knowledge-Based DSS (KBDSS) will be able to assist managers in making these policy decisions by utilizing knowledge of the existing configuration and its capabilities, the organizational computing environment, available external resources, and their suppliers. Combining procedural and declarative methods, such a KBDSS may provide early warning of possible bottlenecks, forecast growth of hardware usage, and employ knowledge based inferencing to suggest suitable remedial actions to the systems manager. This paper presents a KBDSS for supporting computer resource planning decisions using a procedural/declarative framework, and illustrates the system's usage aspects.     

User-centric knowledge representations based on ontology for AEC design collaboration

Fostering collaboration in the AEC (Architecture/Engineering/Construction) field is difficult, due to the differing educational and disciplinary backgrounds of the participants. Current approaches to managing such collaboration in the AEC industry often fail to overcome the disciplinary differences among the participants, resulting in cost overruns, missed schedules, and diminished satisfaction of the clients or society. Their failure is due to the lack of understanding of the nature of multi-disciplinary design and the lack of tools that can support them. The primary objective of this research is to establish a suitable model for machine-mediated collaboration. In contrast to the monolithic model, which is insensitive to changes, we propose to develop a distributed and flexible model, where each domain of expertise retains its own data in the form most appropriate for its needs, and where ontology-based, intelligent filters translate neutral design data into domain-specific ones. The filtered data appear semantically-rich to the participant, even when it was generated by another participant. To verify the feasibility of the proposed filter-based communication model, we developed and tested a prototype. The result of the prototype test demonstrates that the proposed model can enable designers from different disciplines participating in an AEC project to better understand the dynamic process of design and achieve a high level of shared understanding.     

A study on factor analysis to support knowledge based decisions for a smart class

A smart class provides an environment that enables collaboration, sharing, and participation between teachers and students. Thanks to the great attention paid to the smart class idea, with a view to providing effective and efficient learning for students, many state-of-the-art technologies have been applied to the field of education. However, simple infrastructure construction and the introduction of state-of-the-art technology have many limitations in obtaining the desired effects of a smart class. This study aims to discover the important elements that allow a smart class to achieve positive effects in education and to support the design and application of a smart class based on the derived elements. In the study, an integrated teaching and learning assistance system was applied to a smart class. A smart class environment was constructed that was applied and test operated in an elementary school for 4 weeks. Through the test operation of the smart class, the important elements of an effective smart class were determined to be system playfulness, perceived usefulness, perceived ease of use, and attitude toward class.     

基于XML数据库服务系统的框架

针对分布式异构数据库的跨数据库联合检索和自动发布问题,构建基于XML和Web Services技术的数据库服务系统。该系统框架由元数据应用管理、数据库自动发布和跨数据库联合检索模块构成。参考科学数据库核心元数据标准,开发元数据管理工具,并设计基于元数据的数据库在线自动发布及导航目录服务系统。该系统以工程化学数据库为案例并得到应用,实现其多个数据库的资源共享与数据集成服务。     

A formal framework to represent spatial knowledge

Visual representations are an essential element in human–computer interaction and can be conceived as a collection of graphical objects arranged in a two-dimensional space. It is quite natural to model visual representations through the qualitative relationships holding between their objects, and therefore, qualitative spatial relations are a fundamental way of representing spatial knowledge. To this aim, in this paper we present a framework of qualitative spatial relations providing a general, domain-independent approach to specify visual representations.     

一种基于兴趣度知识的推荐系统框架

随着Web Mining技术的应用.基于Web Mining技术的推荐系统得到了迅速发展.本文就此系统作了一些改进,并提出了工作框架RESIK.     

A scalable WebRTC-based framework for remote video collaboration applications

Multimedia Tools and Applications - Remote video collaboration is common nowadays in conferencing, telehealth and remote teaching applications. To support these low-latency and interactive use...     

A framework for automated construction of resource space based on background knowledge

Resource Space Model is a kind of data model which can effectively and flexibly manage the digital resources in cyber-physical system from multidimensional and hierarchical perspectives. This paper focuses on constructing resource space automatically. We propose a framework that organizes a set of digital resources according to different semantic dimensions combining human background knowledge in WordNet and Wikipedia. The construction process includes four steps: extracting candidate keywords, building semantic graphs, detecting semantic communities and generating resource space. An unsupervised statistical language topic model (i.e., Latent Dirichlet Allocation) is applied to extract candidate keywords of the facets. To better interpret meanings of the facets found by LDA, we map the keywords to Wikipedia concepts, calculate word relatedness using WordNet’s noun synsets and construct corresponding semantic graphs. Moreover, semantic communities are identified by GN algorithm. After extracting candidate axes based on Wikipedia concept hierarchy, the final axes of resource space are sorted and picked out through three different ranking strategies. The experimental results demonstrate that the proposed framework can organize resources automatically and effectively.     

A KDD framework to support database audit

Understanding data semantics from real-life databases is considered following an audit perspective: it must help experts to analyse what properties actually hold in the data and support the comparison with desired properties. This is a typical problem of knowledge discovery in databases (KDD) and it is specified within the framework of Mannila and Toivonen where data mining consists in querying theories e.g., the theories of approximate inclusion dependencies. This formalization enables us to identify an important subtask to support database audit as well as a generic algorithm. Next, we consider the DREAM relational database reverse engineering method and DREAM heuristics are revisited within this new setting. This revised version was published online in July 2006 with corrections to the Cover Date.