Information Discovery and Analysis Systems (IDAS) are designed to correlate multiple sources of data and use data mining techniques to identify potential significant events. Application domains for IDAS are numerous and include the emerging area of homeland security.Developing test cases for an IDAS requires background data sets into which hypothetical future scenarios can be overlaid. The IDAS can then be measured in terms of false positive and false negative error rates. Obtaining the test data sets can be an obstacle due to both privacy issues and also the time and cost associated with collecting a diverse set of data sources.In this paper, we give an overview of the design and architecture of an IDAS Data Set Generator (IDSG) that enables a fast and comprehensive test of an IDAS. The IDSG generates data using statistical and rule-based algorithms and also semantic graphs that represent interdependencies between attributes. A credit card transaction application is used to illustrate the approach.

We study the delay characteristics of voice packets that share a transmission medium with bursty data traffic. In order to model the data traffic accurately, we used real data traffic traces upon which we superimposed simulated voice sources generating real time traffic at a constant rate. Our simulation environment allows us to easily develop and compare different adaptive algorithms for the play-out delay of voice streams. We motivate and compare the performance of three algorithms based on exponential smoothing techniques. We test the algorithms in the context of a shared 10 Mbps Ethernet LAN, using the CSMA/CD protocol.

Neural networks appear well suited to applications in the control of communications systems for two reasons: adaptivity and high speed. This paper describes application of neural networks to two problems, admission control and switch control, which exploit the adaptivity and speed property, respectively. The admission control problem is the selective admission of a set of calls from a number of inhomogeneous call classes, which may have widely differing characteristics as to their rate and variability of traffic, onto a network. It is usually unknown in advance which combinations of calls can be simultaneously accepted so as to ensure satisfactory performance. The approach adopted is that key network performance parameters are observed while carrying various combinations of calls, and their relationship is learned by a neural network structure. The network model chosen has the ability to interpolate or extrapolate from the past results and the ability to adapt to new and changing conditions. The switch control problem is the service policy used by a switch controller in transmitting packets. In a crossbar switch with input queueing, significant loss of throughput can occur when head-of-line service order is employed. A solution can be based on an algorithm which maximizes throughput. However since this solution is typically required in less than one microsecond, software implementation policy is infeasible. We will carry out an analysis of the benefits of such a policy, describe some existing proposed schemes for its implementation, and propose a further scheme that provides this submicrosecond optimization.

TUNEX, an expert system developed for performance tuning of the UNIX operating system, is described. TUNEX was developed on UNIX system V. It uses the properties, commands and utilities of this version. The tuning activities it is concerned with include: (1) adjusting operating system tunable parameters, such as number of disk buffers; (2) running maintenance routines, i.e. reorganizing file systems; (3) developing operation rules, such as off-peak hour runs of backups; and (4) modifying hardware, buying an additional disk drive. The structure of TUNEX is presented and performance analysis modules which provide quantitative information to this tool are briefly described. The overhead in the resource usage introduced by the performance monitoring and tuning tool itself is discussed; the author points to the areas in which additional resources are required by TUNEX.>