206703335
Minitrack: E-Government Information and Knowledge Management
The public sector realizes the strategic importance of methods, tools and concepts applied to the efficient and effective Information and Knowledge resource Management (IKM). Because governments and public administrations deal with vast amounts of information and knowledge, it is particularly important to leverage research agendas that advance tenets related to the management of these resources. For example, much of the work of public authorities and governments is related to the collection, processing, interpretation, and use of data, information and knowledge on citizens, businesses, governments themselves, the society, the market, the environment, laws, politics, etc.
1422012
The Effect of Organizational/Technological Factors and the Nature of Knowledge on Knowledge Sharing
This study investigates the dynamics of a knowledge sharing effort in New York State government that involved multiple organizations, divisions, and geographically separated offices in the development of the Multi-Purpose Access for Customer Relations & Operational Support System. Using a case study approach, we address the question of how multiple organizational and technological factors — distributed leadership, alignment of issues and incentives, coordination of a number and variety of groups, trust, technology, and implementation strategy — interact with the nature of knowledge to influence the knowledge sharing process. A major contribution of this study is that it uses a multi-dimensional view of knowledge, examining the interactive impact of the nature of knowledge with multiple organizational and technological factors in public sector knowledge management research.
31337744
Information Strategies to Support Full Information Product Pricing: the Role of Trust
In this paper, we report the importance of trust in the development and operation of distribution networks that attach non-price information to products to mitigate market dynamics introduced by information asymmetries. Often this non-price information is transmitted from producers to consumers through trusting networks or under certifiable labels such as Organic or Fair Trade. We are calling such networks Full Information Product Pricing (FIPP) Networks. This study is part of a larger project aimed at understanding the characteristics of FIPP networks. This initial exploration on four cases in Canada and Latin America indicated that trust, in the forms of institutional trust, calculative trust, and relational trust, plays key roles in FIPP operations and expansion. It is critical for building collaboration, coordinating network activities, and mitigating the risks associated with information asymmetry.
15093523
Divergent approaches and converging views: drawing sensible linkages between knowledge management and organizational learning
Knowledge management (KM) and organizational learning (OL) have developed in both divergent and convergent ways. In particular, these fields have relatively distinct intellectual traditions and conditions that gave rise to disciplines, as well as a certain level of disparity in research focus and view of knowledge. For example, KM focuses more on the content of knowledge and products of managing the knowledge, while OL emphasizes the process of meaning creation, decision making, and growth of learning capability. The two literatures also reveal, however, convergences with regard to the nature of knowledge and knowledge sharing in the organizational context. Thus the two fields have started to establish a consolidated view of knowledge, in which knowledge is related to practice and situated in the historical, social, and cultural context where it is created and acquired. In addition, both fields recognize the multi-level nature of knowledge and learning and are striving to bridge the gap between individual knowledge and collective memories.
5864048
The nature of knowledge and its influence on knowledge sharing practice: experiences from building the MACROS system
This study investigates how the interactive influences of the nature of knowledge and multiple organizational and technological factors - trust, leadership, incentives and issues, group size and variety, implementation strategy, and technology - facilitated and/or impeded the knowledge sharing processes. Using a case study approach, the research focuses on the modifying effects of four characteristics of knowledge- codifiability, context-embeddedness, practice-embeddedness, and dynamics- on the processes and outcomes of knowledge sharing in a case of building the multi-purpose access for customer relations & operational support (MACROS) system involving multiple organizations, divisions, and geographically separated offices. The case results suggest that modifying effects occurred along at least three dimensions- codifiability, context-embeddedness, and practice-embeddedness. The levels of codifiability appeared to dictate the implementation strategy; reduced context-embeddedness allowed for more effective group coordination; reduced context-embeddedness also enhanced trust; codified knowledge is more effective than uncodified knowledge in demonstrating concrete incentives; and technology interacts with context-embeddedness and practice-embeddedness. The results of this study have theoretical and practical implications for a larger set of problems encountered in sizable organizations. More specifically, even though the relevance of the nature of knowledge has been widely acknowledged by I. Nonaka, et. al. (2001), it is not always clear how it comes into play. This study conceptualized the nature of knowledge as a modifying variable, and the results provide a new and more comprehensive framework for investigating the relevance of the nature of knowledge in knowledge management research.
12908572
North American digital government working group: initial steps in the development of a comparative and transnational agenda
This poster describes the first year's progress of the North American Digital Government Working Group on Comparative and Transnational Digital Government. This group was formed in 2007, with the support of the NSF Digital Government Research Program and the home institutions of the members in Canada, Mexico, and the U. S., to advance e-government research across geographic and political boundaries in the region. During the first year of activities, the group members joined their expertise and interests in two face-to-face meetings. Several intellectual products emerged from these initial interactions. The group members will continue the discussion of its research agenda in Montreal, Canada as part of the activities of the 9th Annual International Conference on Digital Government Research.
19316206
Image Computing: Fractional Spectra and Circular Moments via FrFT
In image computing, feature extraction plays a key part for image pattern classification. In this article we adopt discrete Fractional Fourier Transform (FrFT) for fractional feature extraction. Firstly, a criterion is proposed to determine the FrFT order for an image class so that it may be optimally discriminated from other classes in the FrFT domain, and the transformed features are called fractional spectra. Secondly, four types of fractional moments respectively called circular center, circular range, circular skewness, and circular kurtosis are defined and computed from the FrFT results of an image with different FrFT orders. The extracted image features are then classified with a previously proposed nonlinear classifier called Kernel-based Nonlinear Representor (KNR). And face recognition experiments are taken for illustrative examples.
17951154
A component-oriented software toolkit for patient-specific finite element model generation
A component-oriented software system, i.BioMech (image-based biomechanical modeling) is proposed for generation of patient-specific finite element model. It applies a systematic software engineering approach to patient/subject-specific meshing and assignment of material properties. The prototype program is based on the component object model (COM), which enables ease of combination of existing mesh generation algorithms and material property assignment schemes, and incorporation of new ones. It also facilitates utilization by other programming languages or platforms. Data input comprises a series of medical images captured from the patient. The output is a patient-specific finite element model for computational analysis using commercially available finite element software. The prototype software system provides a framework to compare the different finite element mesh generation methods as well as schemes for material property assignment. Our focus is on patient/subject-specific modeling of the human vertebrae.
30018886
Federation Based Solution for Peer-to-Peer Network Management
Recently, Peer-to-Peer (P2P) technology has become one of the hottest topics in the research area of Internet. With a variety of P2P applications, especially those applications sharing large-size file resources among a large scale of user community, P2P brought us an even more exciting Internet life. However, P2P also made lots of troubles to network managers because of consuming too much network bandwidth sometimes. Lacking of effective management solution, some ISPs plan to block all P2P services on their network boundaries. In this paper, we propose a federation-based solution for peer-to-peer network management. By setting up a P2P federation, ISPs and P2P service providers can work together for P2P network management. From P2P federation service, ISPs can get P2P nodes information of their own network to make some control to those that disturb normal network services. At the same time, service providers of P2P may get routing information of specific ISPs to optimize the routing of P2P network itself by joining in federation. Under such scenario, it will save much cost for ISPs to detect and control P2P traffic and give up the idea of killing P2P services. Save and help to the development of P2P are the most important target of this solution.
44524631
Evaluating the Disaster Defense Ability of Information Systems
Disaster prevention and recovery is an important branch of security informatics. People need to investigate the disaster prevention and recovery capacity of information systems in order to make them more robust. In this paper we propose a framework to evaluate the disaster defense ability of information systems. In the research a hierarchy of criterions is built up which covers both the disaster prevention ability and the disaster recovery ability. And a fuzzy assessment method is designed to fit the evaluating process. We also develop a software tool based on the framework to assist the information security evaluators.
16883818
Application of Immune Classifier Based on Increment of Diversity in the Model Species Genomes Identification
The model species genomes project has played a very important role in the research of human genomes. By comparing and identifying the genomes' information in the different biological evolution stages with the model species genomes, it is favorable to understand deeply the genomes' structure and function of the advanced species, especially the human being, and to reveal the life's essential law. In the paper, the DNA sequences of three model species including C. elegans, S. cerevisiae and A.thaliana are divided into three types: intron, exon, intergenic DNA at first. The bases of each sequence are given number (1, 2, hellip, N) base by base from the beginning. According to three phases of the codons, we can extract three subsequences which numbers are respectively 3n+1, 3n+2 and 3n+3 (n=0, 1, 2, ...,N/3-1). Calculating respectively the probability of four bases in three subsequence we can attain 12 parameters which are regarded as the state parameters of the diversity sources. Each sequence of intron, exon and intergenic DNA is expressed by a 12-dimension eigenvector. All of these sequences' 12-dimension samples are divided into the training sample set and the testing sample set. Then, according to the immune network theory, a immune classifier based on the diversity increment is constructed, which considers the reciprocal of the diversity increment as affinity function and makes the immune network evolve in the direction of identifying the antigens by submitting unceasingly the antigens one by one, which is a record of the training sample set. Finally, the classifier has high performance and its prediction accuracy is up to 85% by test.