41,251 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
LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances
Predicting the completion time of business process instances would be a very
helpful aid when managing processes under service level agreement constraints.
The ability to know in advance the trend of running process instances would
allow business managers to react in time, in order to prevent delays or
undesirable situations. However, making such accurate forecasts is not easy:
many factors may influence the required time to complete a process instance. In
this paper, we propose an approach based on deep Recurrent Neural Networks
(specifically LSTMs) that is able to exploit arbitrary information associated
to single events, in order to produce an as-accurate-as-possible prediction of
the completion time of running instances. Experiments on real-world datasets
confirm the quality of our proposal.Comment: Article accepted for publication in 2017 IEEE Symposium on Deep
Learning (IEEE DL'17) @ SSC
Scalable discovery of hybrid process models in a cloud computing environment
Process descriptions are used to create products and deliver services. To lead better processes and services, the first step
is to learn a process model. Process discovery is such a technique which can automatically extract process models from event logs.
Although various discovery techniques have been proposed, they focus on either constructing formal models which are very powerful
but complex, or creating informal models which are intuitive but lack semantics. In this work, we introduce a novel method that returns
hybrid process models to bridge this gap. Moreover, to cope with today’s big event logs, we propose an efficient method, called f-HMD,
aims at scalable hybrid model discovery in a cloud computing environment. We present the detailed implementation of our approach
over the Spark framework, and our experimental results demonstrate that the proposed method is efficient and scalabl
Change Mining in Adaptive Process Management Systems
The wide-spread adoption of process-aware information systems has resulted in a bulk of computerized information about real-world processes. This data can be utilized for process performance analysis as well as for process improvement. In this context process mining offers promising perspectives. So far, existing mining techniques have been applied to operational processes, i.e., knowledge is extracted from execution logs (process discovery), or execution logs are compared with some a-priori process model (conformance checking). However, execution logs only constitute one kind of data gathered during process enactment. In particular, adaptive processes provide additional information about process changes (e.g., ad-hoc changes of single process instances) which can be used to enable organizational learning. In this paper we present an approach for mining change logs in adaptive process management systems. The change process discovered through process mining provides an aggregated overview of all changes that happened so far. This, in turn, can serve as basis for all kinds of process improvement actions, e.g., it may trigger process redesign or better control mechanisms
PKind: A parallel k-induction based model checker
PKind is a novel parallel k-induction-based model checker of invariant
properties for finite- or infinite-state Lustre programs. Its architecture,
which is strictly message-based, is designed to minimize synchronization delays
and easily accommodate the incorporation of incremental invariant generators to
enhance basic k-induction. We describe PKind's functionality and main features,
and present experimental evidence that PKind significantly speeds up the
verification of safety properties and, due to incremental invariant generation,
also considerably increases the number of provable ones.Comment: In Proceedings PDMC 2011, arXiv:1111.006
Analysis of Software Binaries for Reengineering-Driven Product Line Architecture\^aAn Industrial Case Study
This paper describes a method for the recovering of software architectures
from a set of similar (but unrelated) software products in binary form. One
intention is to drive refactoring into software product lines and combine
architecture recovery with run time binary analysis and existing clustering
methods. Using our runtime binary analysis, we create graphs that capture the
dependencies between different software parts. These are clustered into smaller
component graphs, that group software parts with high interactions into larger
entities. The component graphs serve as a basis for further software product
line work. In this paper, we concentrate on the analysis part of the method and
the graph clustering. We apply the graph clustering method to a real
application in the context of automation / robot configuration software tools.Comment: In Proceedings FMSPLE 2015, arXiv:1504.0301
Dreaming of atmospheres
Here we introduce the RobERt (Robotic Exoplanet Recognition) algorithm for
the classification of exoplanetary emission spectra. Spectral retrievals of
exoplanetary atmospheres frequently requires the preselection of
molecular/atomic opacities to be defined by the user. In the era of
open-source, automated and self-sufficient retrieval algorithms, manual input
should be avoided. User dependent input could, in worst case scenarios, lead to
incomplete models and biases in the retrieval. The RobERt algorithm is based on
deep belief neural (DBN) networks trained to accurately recognise molecular
signatures for a wide range of planets, atmospheric thermal profiles and
compositions. Reconstructions of the learned features, also referred to as
`dreams' of the network, indicate good convergence and an accurate
representation of molecular features in the DBN. Using these deep neural
networks, we work towards retrieval algorithms that themselves understand the
nature of the observed spectra, are able to learn from current and past data
and make sensible qualitative preselections of atmospheric opacities to be used
for the quantitative stage of the retrieval process.Comment: ApJ accepte
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