Peer-to-peer (P2P) networks are social networks for pooling network and information resources and are considered superior conduits for distributed computing and data management. In this paper, we utilize the theories of social networks and economic incentives to investigate the formation of P2P networks with rational participating agents (active peers). The paper proposes a framework for multilevel formation dynamics, including an individual level (content-sharing decision and group selection) and a group level (membership admission, splitting, and interconnection). It is found that if the network size (the number of peer nodes) is sufficiently large, the stable (self-selected equilibrium) free-riding ratio could be nonzero, contrary to the common belief that everybody should free ride. The efficient (welfare-maximizing) free-riding ratio is not necessarily zero; that is, a certain degree of free riding is beneficial and should be tolerated. The sharing level in a network increases (decreases) with the download (upload) capacities of its peer nodes. In addition, the heterogeneity of content availability and upload capacity discourages sharing activities. Although the sharing level of a stable group is typically lower than that of an efficient group, the self-formed network may have a larger or smaller group size than what is efficient, depending on the structure of the group admission decision process. It is also observed that self-organized interconnections among groups lead to network inefficiency because the network may be over- or underlinked. To recover the efficiency loss during the formation process, we propose internal transfer mechanisms to force stable networks to become efficient.

The notion of a data warehouse for integrating operational data into a single repository is rapidly becoming popular in modern organizations. An important issue in this context is how often one should synchronize the data warehouse to reflect the changes in the constituent operational data sources. If the synchronization is performed very frequently, the associated cost might be quite high, although the data warehouse would only have a small amount of stale data. On the other hand, if the data warehouse is synchronized infrequently, it might result in costly errors in business decisions arising from the stale data. This paper examines the trade-off between the synchronization and staleness costs and derives the optimal synchronization frequency.

In modern organizations, decision makers must often be able to quickly access information from diverse sources in order to make timely decisions. A critical problem facing many such organizations is the inability to easily reconcile the information contained in heterogeneous data sources. To overcome this limitation, an organization must resolve several types of heterogeneity problems that may exist across different sources. We examine one such problem called the entity heterogeneity problem, which arises when the same real-world entity type is represented using different identifiers in different applications. A decision-theoretic model to resolve the problem is proposed. Our model uses a distance measure to express the similarity between two entity instances. We have implemented the model and tested it on real-world data. The results indicate that the model performs quite well in terms of its ability to predict whether two entity instances should be matched or not. The model is shown to be computationally efficient. It also scales well to large relations from the perspective of the accuracy of prediction. Overall, the test results imply that this is certainly a viable approach in practical situations.

We report the results of an empirical study comparing user comprehension of object oriented (OO) and process oriented (PO) models. The fundamental difference is that while OO models tend to focus on structure, PO models tend to emphasize behaviour or processes. Proponents of the OO modeling approach argue that it lends itself naturally to the way humans think. However, evidence from research in cognitive psychology and human factors suggests that human problem solving is innately procedural. Given these conflicting viewpoints, we investigate empirically if OO models are in fact easier to understand than PO models. But, as suggested by the theory of cognitive fit, model comprehension may be influenced by task-specific characteristics. We therefore compare OO and PO models based on whether the comprehension activity involves: 1) only structural aspects, 2) only behavioral aspects, or 3) a combination of structural and behavioral aspects. We measure comprehension through subjects' responses to questions designed along these three dimensions. Results show that for most of the simple questions, no significant difference was observed insofar as model comprehension is concerned. For most of the complex questions, however, the PO model was found to be easier to understand than the OO model. In addition to describing the process and the outcomes of the experiments, we present the experimental method employed as a viable approach for conducting research into various phenomena related to the efficacy of alternative systems analysis and design methods. We also identify areas where future research is necessary, along with a recommendation of appropriate research methods for empirical examination.

A model is developed that assigns primary and secondary (backup) concentrator coverage to each terminal site. The objective is to minimize communications costs as well as costs for setting up and operating the concentrators subject to capacity constraints. A relaxation of the problem is studied, and an effective solution procedure that makes the use of this relaxation is developed. Experimental results over a wide range of problem structures show that this solution procedure is very effective. It is also found to be significantly faster than a state-of-the-art commercial integer programming package.>