The correlation network, typically formed by computing pairwise correlations between variables, has recently become a competitive paradigm to discover insights in various application domains, such as climate prediction, financial marketing, and bioinformatics. In this study, we adopt this paradigm to detect cheating behavior hidden in business distribution channels, where falsified big deals are often made by collusive partners to obtain lower product prices—a behavior deemed to be extremely harmful to the sale ecosystem. To this end, we assume that abnormal deals are likely to occur between two partners if their purchase-volume sequences have a strong negative correlation. This seemingly intuitive rule, however, imposes several research challenges. First, existing correlation measures are usually symmetric and thus cannot distinguish the different roles of partners in cheating. Second, the tick-to-tick correspondence between two sequences might be violated due to the possible delay of purchase behavior, which should also be captured by correlation measures. Finally, the fact that any pair of sequences could be correlated may result in a number of false-positive cheating pairs, which need to be corrected in a systematic manner. To address these issues, we propose a correlation network analysis framework for cheating detection. In the framework, we adopt an asymmetric correlation measure to distinguish the two roles, namely, cheating seller and cheating buyer , in a cheating alliance. Dynamic Time Warping is employed to address the time offset between two sequences in computing the correlation. We further propose two graph-cut methods to convert the correlation network into a bipartite graph to rank cheating partners, which simultaneously helps to remove false-positive correlation pairs. Based on a 4-year real-world channel dataset from a worldwide IT company, we demonstrate the effectiveness of the proposed method in comparison to competitive baseline methods.

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.

Although pricing communication services has received attention, there has been little work where quality-of-service (QoS) guarantees are incorporated into ex ante pricing of communication links. In recent years, however, data networks providing services with QoS guarantees have become quite popular. We study optimal pricing for communication links provided over packet-switching networks with explicit QoS guarantees in terms of an expected per-packet delay. We first develop a pricing scheme for multiple classes of service when the delay guarantees are fixed and exogenously determined and analyze this scheme with one and two classes. Subsequently, we extend this analysis to the case where a provider can choose her own QoS parameters. We also examine long-run pricing and capacity planning for one and two classes of service. Finally, we consider other QoS guarantees such as jitter, packet-loss ratio and maximum delay.

Browser and proxy-server caching are effective and relatively inexpensive methods of improving Web performance. Most existing research considers caching to occur independently at the browser and the proxy server. When the browser and the proxy-server cache independently, documents may get duplicated across the two levels. This paper analyzes the impact of document duplication on the performance of several browser-proxy caching policies. We first derive an exact expression and an accurate approximation for the delay under a joint browser-proxy caching policy in which no duplication is permitted. This policy is compared to a base or benchmark policy in which caching occurs independently at the two levels, and hence, duplication of documents is freely permitted. We next propose a more general caching policy in which a controlled amount of duplication is permitted. This policy is analyzed and an exact expression and an approximate expression for performance are derived. Finally, a simulation study is performed to confirm the accuracy of the theoretical results and extend these results for situations that are difficult to analyze mathematically.

In telecommunication industry, as a superior, but compatible, technology replaces old technology, consumers gradually abandon old service/device and adopt new counterpart that has more features and better performance. This process usually generates multiple externalities within the new technology adopters as well as the old technology users. While the increase in subscription of the new service encourages more consumers to join in, the decline in performance due to increasing network load deters the adoption process. In the mean while, the diminishing population of the old technology users alleviates the congestion of the old network, hence further discourages the growth of the new services. These concurrent externalities ultimately determine the market growth dynamics. We consider the case that the new service is downward compatible and model the new service adoption process with the two-side externalities. We also propose optimal pricing strategies such that the profit of the service provider is maximized for given other constraints. The results enable decision-makers to determine the optimal marketing strategies in many practical uses such as the deployment of Third Generation (3G) networks and upgrades of networking infrastructures.

The explosive growth in online shopping has provided online retailers impressive opportunities to extend revenue and profit. However, retailers may lose considerable online business from slow response times at electronic shopping sites. Although increasing server capacity may improve response time, the resources needed to do so are clearly not free. In this study, we propose a scheme that can improve a server's performance with no more than its current capacity. This scheme is based on priority processing, where the priority assigned to a customer depends on the potential revenue that is likely to be generated from the customer. The results for single-period analysis show that the benefit from priority processing increases as the server becomes more heavily utilized. We have also modeled a multiperiod version of the problem, where the demand in a period depends on the quality of service (QoS) that buyers receive in the previous period. In the multiperiod problem, both the server capacity and the processing policy in each period are determined optimally. Here, it may be optimal for the retailer to sacrifice profit and increase QoS in initial periods in order to increase demand (and revenue) in later periods. Thus, the processing policy of the server evolves from an emphasis on QoS in initial periods, to one on profit in subsequent periods.

This research evaluates and analyzes existing mechanisms of online reputation systems based on cooperative feedbacks of past transaction information. We find existing popular feedback systems do not provide sustained incentives for sellers to behave honestly over time. We propose a new design of reputation system based on exponential smoothing. This mechanism is shown to be more robust compared to the existing systems. We relax the assumption of a static product value and have implemented a two-level exponential smoothing policy to process and aggregate reputation information. The simulation results show that the policy can serve as a sustained incentive mechanism for the seller.

The importance of software maintenance in managing the life-cycle costs of a system cannot be overemphasized. Beyond a point, however, it is better to replace a system rather than maintain it. We derive model and operating policy that reduces the sum of maintenance and replacement costs in the useful life of a software system. The main goal is to compare uniform (occurring at fixed time intervals) versus flexible (occurring at varying, planned time intervals) polices for maintenance and replacement. The model draws from the empirical works of earlier researchers to consider 1) inclusion of user requests for maintenance, 2) scale economies in software maintenance, 3) efficiencies derived from replacing old software technology with new software technology, and 4) the impact of software reuse on replacement and maintenance. Results from our model show that the traditional practice of maintaining or replacing a software system at uniform time intervals may not be optimal. We also find that an increase in software reuse leads to more frequent replacement, but the number of maintenance activities is not significantly impacted.