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Predicting deadline transgressions using event logs

by Anastasiia Pika, Colin J. Fidge, Arthur H. M. Ter Hofstede, Moe T. Wynn - In Proc. of BPM Workshop 2012, volume 132 of LNBIP , 2013
"... Abstract. Effective risk management is crucial for any organisation. One of its key steps is risk identification, but few tools exist to support this process. Here we present a method for the automatic discovery of a particular type of process-related risk, the danger of deadline transgressions or o ..."
Abstract - Cited by 8 (3 self) - Add to MetaCart
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

Discovery of Frequent Episodes in Event Logs

by Maikel Leemans
"... Abstract. Lion’s share of process mining research focuses on the discov-ery of end-to-end process models describing the characteristic behavior of observed cases. The notion of a process instance (i.e., the case) plays an important role in process mining. Pattern mining techniques (such as frequent ..."
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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

Failure Prediction in IBM BlueGene/L Event Logs

by Yinglung Liang, Yanyong Zhang
"... 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. To th ..."
Abstract - Cited by 13 (0 self) - Add to MetaCart
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

Whom You Know Matters: Venture Capital Networks and Investment Performance,

by Yael Hochberg , Alexander Ljungqvist , Yang Lu , Steve Drucker , Jan Eberly , Eric Green , Yaniv Grinstein , Josh Lerner , Laura Lindsey , Max Maksimovic , Roni Michaely , Maureen O'hara , Ludo Phalippou Mitch Petersen , Jesper Sorensen , Per Strömberg Morten Sorensen , Yael Hochberg , Johnson - Journal of Finance , 2007
"... Abstract Many financial markets are characterized by strong relationships and networks, rather than arm's-length, spot-market transactions. We examine the performance consequences of this organizational choice in the context of relationships established when VCs syndicate portfolio company inv ..."
Abstract - Cited by 138 (8 self) - Add to MetaCart
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

System Log Pre-processing to Improve Failure Prediction

by Ziming Zheng, Zhiling Lan, Byung H. Park, Al Geist - In Proc. of DSN , 2009
"... Log preprocessing, a process applied on the raw log be-fore applying a predictive method, is of paramount impor-tance to failure prediction and diagnosis. While existing fil-tering methods have demonstrated good compression rate, they fail to preserve important failure patterns that are cru-cial for ..."
Abstract - Cited by 15 (4 self) - Add to MetaCart
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

Significance of log-periodic precursors to financial crashes

by Didier Sornette, Anders Johansen , 2001
"... We clarify the status of log-periodicity associated with speculative bubbles preceding financial crashes. In particular, we address Feigenbaum’s [2001] criticism and show how it can be rebuked. Feigenbaum’s main result is as follows: “the hypothesis that the log-periodic component is present in the ..."
Abstract - Cited by 49 (19 self) - Add to MetaCart
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

Discovering Signature Patterns from Event Logs

by R. P. Jagadeesh, Chandra Bose
"... Abstract—More and more information about processes is recorded in the form of so-called “event logs”. High-tech systems such as X-ray machines and high-end copiers provide their manufacturers and services organizations with detailed event data. Larger organizations record relevant business events fo ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
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

Extraction and Analysis of User Profile from Event Logs

by Anjali Jachak, Anuj Sharma
"... This paper discusses the analysis of data from log files and presents novel methods and ideas of analyzing these log files. Huge amount of data is generated on daily basis in every IT or non-IT organization. This data is stored in logs. These logs contain data which can prove valuable. These log fil ..."
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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

Event-triggering in distributed networked control systems

by Xiaofeng Wang, Michael D. Lemmon
"... Abstract—This paper examines event-triggered data transmis-sion in distributed networked control systems with packet loss and transmission delays. We propose a distributed event-triggering scheme, where a subsystem broadcasts its state information to its neighbors only when the subsystem’s local sta ..."
Abstract - Cited by 57 (7 self) - Add to MetaCart
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

Trend Analysis and Modeling of Uni/MultiProcessor Event Logs

by Jeffery P. Hansen, Jeffery P. Hansen, Copyright Jeffery, Paul Hansen , 1988
"... This research has been funded by the Boeing Corporation under conlract 1-41819. Event logs provide an abundance of information about the health of a computing system. Previous studies have shown that definite trends precede many failures and crashes. By designing a system to monitor the event log an ..."
Abstract - Cited by 6 (0 self) - Add to MetaCart
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
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