Start Time and Duration Distribution Estimation in Semi-Structured Processes

Semi-structured processes are business workflows, where the execution of the workflow is not completely controlled by a workflow engine, i.e., an implementation of a formal workflow model. Examples are workflows where actors potentially have interaction with customers reporting the result of the interaction in a process aware information system. Building a performance model for resource management in these processes is difficult since the required information is only partially recorded. In this paper we propose a systematic approach for the creation of an event log that is suitable for available process mining tools. This event log is created by an incrementally cleansing of data. The proposed approach is evaluated in an experiment

Informational and Causal Architecture of Continuous-time Renewal and Hidden Semi-Markov Processes

We introduce the minimal maximally predictive models ({\epsilon}-machines) of processes generated by certain hidden semi-Markov models. Their causal states are either hybrid discrete-continuous or continuous random variables and causal-state transitions are described by partial differential equations. Closed-form expressions are given for statistical complexities, excess entropies, and differential information anatomy rates. We present a complete analysis of the {\epsilon}-machines of continuous-time renewal processes and, then, extend this to processes generated by unifilar hidden semi-Markov models and semi-Markov models. Our information-theoretic analysis leads to new expressions for the entropy rate and the rates of related information measures for these very general continuous-time process classes.Comment: 16 pages, 7 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/ctrp.ht

Search method for long-duration gravitational-wave transients from neutron stars

We introduce a search method for a new class of gravitational-wave signals, namely long-duration O(hours - weeks) transients from spinning neutron stars. We discuss the astrophysical motivation from glitch relaxation models and we derive a rough estimate for the maximal expected signal strength based on the superfluid excess rotational energy. The transient signal model considered here extends the traditional class of infinite-duration continuous-wave signals by a finite start-time and duration. We derive a multi-detector Bayes factor for these signals in Gaussian noise using \F-statistic amplitude priors, which simplifies the detection statistic and allows for an efficient implementation. We consider both a fully coherent statistic, which is computationally limited to directed searches for known pulsars, and a cheaper semi-coherent variant, suitable for wide parameter-space searches for transients from unknown neutron stars. We have tested our method by Monte-Carlo simulation, and we find that it outperforms orthodox maximum-likelihood approaches both in sensitivity and in parameter-estimation quality.Comment: 20 pages, 9 figures; submitted to PR

Bayesian Agglomerative Clustering with Coalescents

We introduce a new Bayesian model for hierarchical clustering based on a prior over trees called Kingman's coalescent. We develop novel greedy and sequential Monte Carlo inferences which operate in a bottom-up agglomerative fashion. We show experimentally the superiority of our algorithms over others, and demonstrate our approach in document clustering and phylolinguistics.Comment: NIPS 200

Estimating the Processing Time of Process Instances in Semi-Structured Processes - A Case Study

Performance analysis of Web applications are rather difficult since people can perform parts of an activity outside the application or get interrupted while performing an activity in the system. The lack of a performance model makes it hard to plan resources or get a better understanding of the way available resources are used. In this paper an approach for determining a performance model for semi-structured processes is applied to a case study

Software cost estimation

The paper gives an overview of the state of the art of software cost estimation (SCE). The main questions to be answered in the paper are: (1) What are the reasons for overruns of budgets and planned durations? (2) What are the prerequisites for estimating? (3) How can software development effort be estimated? (4) What can software project management expect from SCE models, how accurate are estimations which are made using these kind of models, and what are the pros and cons of cost estimation models

Spatial patterns in thunderstorm rainfall events and their coupling with watershed hydrological response

Weather radar systems provide detailed information on spatial rainfall patterns known to play a significant role in runoff generation processes. In the current study, we present an innovative approach to exploit spatial rainfall information of air mass thunderstorms and link it with a watershed hydrological model. Observed radar data are decomposed into sets of rain cells conceptualized as circular Gaussian elements and the associated rain cell parameters, namely, location, maximal intensity and decay factor, are input into a hydrological model. Rain cells were retrieved from radar data for several thunderstorms over southern Arizona. Spatial characteristics of the resulting rain fields were evaluated using data from a dense rain gauge network. For an extreme case study in a semi-arid watershed, rain cells were derived and fed as input into a hydrological model to compute runoff response. A major factor in this event was found to be a single intense rain cell (out of the five cells decomposed from the storm). The path of this cell near watershed tributaries and toward the outlet enhanced generation of high flow. Furthermore, sensitivity analysis to cell characteristics indicated that peak discharge could be a factor of two higher if the cell was initiated just a few kilometers aside. © 2005 Elsevier Ltd. All rights reserved