or overruns, based on the analysis of event logs. We define a set of time-related process risk indicators, i.e., patterns observable in event logs that highlight the likelihood of an overrun, and then show how instances of these patterns can be identified automatically using statistical principles

on partial orders. We also discover episode rules to predict behavior and discover correlated behaviors in processes. We have developed a ProM plug-in that exploits efficient algorithms for the discovery of frequent episodes and episode rules. Experimental results based on real-life event logs demonstrate

Frequent failures are becoming a serious concern to the community of high-end computing, especially when the applications and the underlying systems rapidly grow in size and complexity. In order to develop effective fault-tolerant strategies, there is a critical need to predict failure events

that the first-fund dummy loses significance in these models, indicating that it is a poor proxy for experience. Since the log aggregate investment amount proxy has the largest economic effect, we will use it in all subsequent models to proxy for the parent firm's experience. Our results are generally

preserving necessary failure patterns for failure analysis; (3) causality-related filtering to com-bine correlated events for filtering through apriori associ-ation rule mining. We demonstrate the effectiveness of our preprocessing method by using real failure logs collected from the Cray XT4 at ORNL

bubble model for general and arbitrary risk-aversion within the general stochastic discount factor theory. We suggest guidelines for using log-periodicity and explain how to develop and interpret statistical tests of log-periodicity. We discuss the issue of prediction based on our results

for process improvement, auditing, and fraud detection. Traces in such event logs can be classified as desirable or undesirable (e.g., faulty or fraudulent behavior). In this paper, we present a comprehensive framework for discovering signatures that can be used to explain or predict the class of seen

on huge data sets. A number of products are available in the market.A number of algorithms have been proposed so far for mining frequent patterns. The data in these logs if used properly can prove useful in improving system performance and generating various reports on the usage of data. This information

state error exceeds a specified threshold. In this scheme, a subsystem is able to make broadcast decisions using its locally sampled data. It can also locally predict the maximal allowable number of successive data dropouts (MANSD) and the state-based deadlines for transmission delays. Moreover

and to detect these trends, it is possible to predict failures and reconfigure systems before catastrophic events can occur. The volume of data present m an event log makes real-time hand analysis for the purposes of prediction infeasible. Automated means of analysis must be used, and methods of reducing