9,649 research outputs found
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
On generation of time-based label refinements
Process mining is a research field focused on the analysis of event data with
the aim of extracting insights in processes. Applying process mining techniques
on data from smart home environments has the potential to provide valuable
insights in (un)healthy habits and to contribute to ambient assisted living
solutions. Finding the right event labels to enable application of process
mining techniques is however far from trivial, as simply using the triggering
sensor as the label for sensor events results in uninformative models that
allow for too much behavior (overgeneralizing). Refinements of sensor level
event labels suggested by domain experts have shown to enable discovery of more
precise and insightful process models. However, there exist no automated
approach to generate refinements of event labels in the context of process
mining. In this paper we propose a framework for automated generation of label
refinements based on the time attribute of events. We show on a case study with
real life smart home event data that behaviorally more specific, and therefore
more insightful, process models can be found by using automatically generated
refined labels in process discovery.Comment: Accepted at CS&P workshop 2016 Overlap in preliminaries with
arXiv:1606.0725
Visual analysis of sensor logs in smart spaces: Activities vs. situations
Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. Our research is focused on developing a visual analysis pipeline (service) that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The basic assumption is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed pipeline is employed to automatically extract models to be reused for ambient intelligence. In this paper, we present an user evaluation aimed at demonstrating the effectiveness of the approach, by comparing it wrt. a relevant state-of-the-art visual tool, namely SITUVIS
Matter-Antimatter Asymmetry - Aspects at Low Energy
The apparent dominance of matter over antimatter in our universe is an
obvious and puzzling fact which cannot be adequately explained in present
physical frameworks that assume matter-antimatter symmetry at the big bang.
However, our present knowledge of starting conditions and of known sources of
CP violation are both insufficient to explain the observed asymmetry. Therefore
ongoing research on matter-antimatter differences is strongly motivated as well
as attempts to identify viable new mechanisms that could create the present
asymmetry. Here we concentrate on possible precision experiments at low
energies towards a resolution of this puzzle.Comment: 6 pages, 1 figure; accepted for publication in Annalen der Physik
(2015
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