Bilinear R-parity Violation in Rare Meson Decays

We discuss rare meson decays $K^ + \to \pi ^ - \ell ^ + \ell '^ +$ and $D^ + \to K^ - \ell ^ + \ell '^ +$ ($\ell ,\ell ' = e,\mu$) in a supersymmetric extension of the standard model with explicit breaking of R-parity by bilinear Yukawa couplings in the superpotential. Estimates of the branching ratios for these decays are given. We also compare our numerical results with analogous ones previously obtained for two other mechanisms of lepton number violation: exchange by massive Majorana neutrinos and trilinear R-parity violation.Comment: 5 pages, 1 figure; To appear in the Proceedings of the 13th Lomonosov Conference on Elementary Particle Physics, 23 -- 29 August, 2007, Moscow, Russi

Rare semileptonic meson decays in R-parity violating MSSM

We discuss rare meson decays $K^ + \to \pi ^ - \ell ^ + \ell '^ +$ and $D^ + \to K^ - \ell ^ + \ell '^ +$ ($\ell, \ell'=e, \mu$) in a supersymmetric extension of the Standard Model with R-parity violation. Estimates of the branching ratios for these decays are presented.Comment: 5 pages, 1 figure; title modified to better reflect the contents, a normalization error corrected for D-meson decays, modifying parts of Table 1; a reference and DESY Report number added; to appear in the Proceedings of the 12th. Lomonosov Conference on Elementary Particle Physics, Moscow State University, Moscow, Russia, 25-31 August 200

Heuristic Approaches for Generating Local Process Models through Log Projections

Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i.e. subsets of possible events are taken into account to create so-called local process models. Often such smaller models provide valuable insights into the behavior of the process, especially when no adequate and comprehensible single overall process model exists that is able to describe the traces of the process from start to end. The practical application of LPM discovery is however hindered by computational issues in the case of logs with many activities (problems may already occur when there are more than 17 unique activities). In this paper, we explore three heuristics to discover subsets of activities that lead to useful log projections with the goal of speeding up LPM discovery considerably while still finding high-quality LPMs. We found that a Markov clustering approach to create projection sets results in the largest improvement of execution time, with discovered LPMs still being better than with the use of randomly generated activity sets of the same size. Another heuristic, based on log entropy, yields a more moderate speedup, but enables the discovery of higher quality LPMs. The third heuristic, based on the relative information gain, shows unstable performance: for some data sets the speedup and LPM quality are higher than with the log entropy based method, while for other data sets there is no speedup at all.Comment: paper accepted and to appear in the proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM), special session on Process Mining, part of the Symposium Series on Computational Intelligence (SSCI

Log-based Evaluation of Label Splits for Process Models

Process mining techniques aim to extract insights in processes from event logs. One of the challenges in process mining is identifying interesting and meaningful event labels that contribute to a better understanding of the process. Our application area is mining data from smart homes for elderly, where the ultimate goal is to signal deviations from usual behavior and provide timely recommendations in order to extend the period of independent living. Extracting individual process models showing user behavior is an important instrument in achieving this goal. However, the interpretation of sensor data at an appropriate abstraction level is not straightforward. For example, a motion sensor in a bedroom can be triggered by tossing and turning in bed or by getting up. We try to derive the actual activity depending on the context (time, previous events, etc.). In this paper we introduce the notion of label refinements, which links more abstract event descriptions with their more refined counterparts. We present a statistical evaluation method to determine the usefulness of a label refinement for a given event log from a process perspective. Based on data from smart homes, we show how our statistical evaluation method for label refinements can be used in practice. Our method was able to select two label refinements out of a set of candidate label refinements that both had a positive effect on model precision.Comment: Paper accepted at the 20th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, to appear in Procedia Computer Scienc

Guided Interaction Exploration in Artifact-centric Process Models

Artifact-centric process models aim to describe complex processes as a collection of interacting artifacts. Recent development in process mining allow for the discovery of such models. However, the focus is often on the representation of the individual artifacts rather than their interactions. Based on event data we can automatically discover composite state machines representing artifact-centric processes. Moreover, we provide ways of visualizing and quantifying interactions among different artifacts. For example, we are able to highlight strongly correlated behaviours in different artifacts. The approach has been fully implemented as a ProM plug-in; the CSM Miner provides an interactive artifact-centric process discovery tool focussing on interactions. The approach has been evaluated using real life data sets, including the personal loan and overdraft process of a Dutch financial institution.Comment: 10 pages, 4 figures, to be published in proceedings of the 19th IEEE Conference on Business Informatics, CBI 201