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.
16854565
ProM 4.0: Comprehensive Support for Real Process Analysis
This tool paper describes the functionality of ProM. Version 4.0 of ProM has been released at the end of 2006 and this version reflects recent achievements in process mining. Process mining techniques attempt to extract non-trivial and useful information from so-called "event logs". One element of process mining is control-flow discovery, i.e., automatically constructing a process model (e.g., a Petri net) describing the causal dependencies between activities. Control-flow discovery is an interesting and practically relevant challenge for Petri-net researchers and ProM provides an excellent platform for this. For example, the theory of regions, genetic algorithms, free-choice-net properties, etc. can be exploited to derive Petri nets based on example behavior. However, as we will show in this paper, the functionality of ProM 4.0 is not limited to control-flow discovery. ProM 4.0 also allows for the discovery of other perspectives (e.g., data and resources) and supports related techniques such as conformance checking, model extension, model transformation, verification, etc. This makes ProM a versatile tool for process analysis which is not restricted to model analysis but also includes log-based analysis.
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.
13965034
Performance analysis of large multicast switches with multicast virtual output queues
In multicast switches, the accommodation of multicast traffic in multiple queues per input buffer reduces the throughput degradation caused by head-of-line (HOL) blocking. Such an arrangement, called multicast virtual output queuing (MC-VOQ), is very promising in theory but can only be implemented in practice with heavy approximation. Complete avoidance of the HOL blocking problem would in fact require a distinct queue for each fanout set possible, leading to an exponential growth of the number of queues needed with the switch size. If only a limited number of queues can be used per input buffer, criteria must be identified for setting the number of queues, for associating the fanout sets with the individual queues, and for scheduling the transmission of packets out of the queues. This paper presents an analytical model for the investigation of saturation throughput and packet delay in MC-VOQ multicast switches. The model relies on the assumption of Poisson-distributed uniform input traffic and random queuing and scheduling policies. Extensive simulation experiments validate the results of the analysis for large switch sizes.
5987381
Algorithm performance contest
This contest involved the running and evaluation of computer vision and pattern recognition techniques on different data sets with known groundwidth. The contest included three areas; binary shape recognition, symbol recognition and image flow estimation. A package was made available for each area. Each package contained either real images with manual groundtruth or programs to generate data sets of ideal as well as noisy images with known groundtruth. They also contained programs to evaluate the results of an algorithm according to the given groundtruth. These evaluation criteria included the generation of confusion matrices, computation of the misdetection and false alarm rates and other performance measures suitable for the problems. The paper summarizes the data generation for each area and experimental results for a total of six participating algorithms.
40520726
Simulation and Performance Analysis of MPLS 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.