19457184
Efficient Queuing Scheme for Multicast Switches in Overlay Networks
15229138
Frequency Hopping Pattern Recognition Algorithms for Wireless Sensor Networks
Frequency Hopping Spread Spectrum (FHSS) as a communications technique has also been considered as one of security mechanisms to provide a good level of security against eavesdroppers in wireless sensor networking environment. We claim that the version presently used in wireless sensor communications does not yield a high level of security. In this paper, we develop algorithms to break the FHSS hopping patterns. Simulations are performed to verify the algorithms. Results suggest that wireless sensor networking is not secure in its current form.
15211181
Optimally Quantized and Smoothed Histograms
We propose an approach using optimal quantization and smoothing to generate adaptive histograms for multi-class one dimensional data. The discretization of data is optimal in maximizing a quantizer performance measure, defined by a combination of average log likelihood, entropy and correct classification probability. The optimal partition is found by dynamic programming. The density of each bin is obtained by a smoothing technique that can be considered a generalized k nearest neighbor density estimation algorithm. However, our smoothing approach is much more efficient. Experimental results demonstrated the effectiveness of the optimally quantized and smoothed histograms. Even though obtaining one takes about quadratic time in sample size, an optimal histogram is much more efficient to use than typical kernel methods. Therefore, optimal histograms are more suitable in applications with massive data set.
15123491
Business process mining: An industrial application
Contemporary information systems (e.g., WfM, ERP, CRM, SCM, and B2B systems) record business events in so-called event logs. Business process mining takes these logs to discover process, control, data, organizational, and social structures. Although many researchers are developing new and more powerful process mining techniques and software vendors are incorporating these in their software, few of the more advanced process mining techniques have been tested on real-life processes. This paper describes the application of process mining in one of the provincial offices of the Dutch National Public Works Department, responsible for the construction and maintenance of the road and water infrastructure. Using a variety of process mining techniques, we analyzed the processing of invoices sent by the various subcontractors and suppliers from three different perspectives: (1) the process perspective, (2) the organizational perspective, and (3) the case perspective. For this purpose, we used some of the tools developed in the context of the ProM framework. The goal of this paper is to demonstrate the applicability of process mining in general and our algorithms and tools in particular.
6715244
Discovering Social Networks from Event Logs
Process mining techniques allow for the discovery of knowledge based on so-called “event logs”, i.e., a log recording the execution of activities in some business process. Many information systems provide such logs, e.g., most WFM, ERP, CRM, SCM, and B2B systems record transactions in a systematic way. Process mining techniques typically focus on performance and control-flow issues. However, event logs typically also log the performer, e.g., the person initiating or completing some activity. This paper focuses on mining social networks using this information. For example, it is possible to build a social network based on the hand-over of work from one performer to the next. By combining concepts from workflow management and social network analysis, it is possible to discover and analyze social networks. This paper defines metrics, presents a tool, and applies these to a real event log within the setting of a large Dutch organization.
13467606
Mining Social Networks: Uncovering Interaction Patterns in Business Processes
Increasingly information systems log historic information in a systematic way. Workflow management systems, but also ERP, CRM, SCM, and B2B systems often provide a so-called “event log”, i.e., a log recording the execution of activities. Unfortunately, the information in these event logs is rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs. This paper focuses on the mining social networks. This is possible because event logs typically record information about the users executing the activities recorded in the log. To do this we combine concepts from workflow management and social network analysis. This paper introduces the approach, defines metrics, and presents a tool to mine social networks from event logs.