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.
30132329
Evolutionary Programming in a Distributed Packet Scheduling Architecture
17500038
Frequency hopping pattern detection in wireless ad hoc networks
Frequency hopping is a technique that wireless devices communicate in a way that the transmission frequencies are alternated in a pre-determined ordered hopping pattern known only to the sending and receiving devices. In this paper, we develop an algorithm called frequency hopping pattern detection to detect the frequency hopping pattern of a wireless ad hoc network. The algorithm is performed in three stages. Stage 1 locates the first frequency in the pattern; stage 2 detects the remaining frequencies; and stage 3 calculates the entire hopping pattern. All three stages work together and guarantee the capture of all frequencies used, the pattern in which they occur, and the length of transmission for each frequency. The algorithm is simulated in a VHDL environment. Simulation results verify the correctness of the algorithm.
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.
19457184
Efficient Queuing Scheme for Multicast Switches in Overlay Networks
1481206
QoS Control and Performance Analysis of a Gigabit Network
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.
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.
2139364
A spike sorting framework using nonparametric detection and incremental clustering
We introduce a statistical computing framework to address two important issues in spike sorting: flexible spike shape modeling and realtime spike clustering. In this framework, spikes are detected based on a nonparametric shape distribution; detected spikes are further grouped by an incremental clustering algorithm involving the second-order statistics-covariance matrix. We performed experiments on both simulated and real signals to study spike detection accuracy and cluster separation.
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.
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.