35,977 research outputs found
Handling Concept Drift for Predictions in Business Process Mining
Predictive services nowadays play an important role across all business
sectors. However, deployed machine learning models are challenged by changing
data streams over time which is described as concept drift. Prediction quality
of models can be largely influenced by this phenomenon. Therefore, concept
drift is usually handled by retraining of the model. However, current research
lacks a recommendation which data should be selected for the retraining of the
machine learning model. Therefore, we systematically analyze different data
selection strategies in this work. Subsequently, we instantiate our findings on
a use case in process mining which is strongly affected by concept drift. We
can show that we can improve accuracy from 0.5400 to 0.7010 with concept drift
handling. Furthermore, we depict the effects of the different data selection
strategies
A log mining approach for process monitoring in SCADA
SCADA (Supervisory Control and Data Acquisition) systems are used for controlling and monitoring industrial processes. We propose a methodology to systematically identify potential process-related threats in SCADA. Process-related threats take place when an attacker gains user access rights and performs actions, which look legitimate, but which are intended to disrupt the SCADA process. To detect such threats, we propose a semi-automated approach of log processing. We conduct experiments on a real-life water treatment facility. A preliminary case study suggests that our approach is effective in detecting anomalous events that might alter the regular process workflow
Relational Algebra for In-Database Process Mining
The execution logs that are used for process mining in practice are often
obtained by querying an operational database and storing the result in a flat
file. Consequently, the data processing power of the database system cannot be
used anymore for this information, leading to constrained flexibility in the
definition of mining patterns and limited execution performance in mining large
logs. Enabling process mining directly on a database - instead of via
intermediate storage in a flat file - therefore provides additional flexibility
and efficiency. To help facilitate this ideal of in-database process mining,
this paper formally defines a database operator that extracts the 'directly
follows' relation from an operational database. This operator can both be used
to do in-database process mining and to flexibly evaluate process mining
related queries, such as: "which employee most frequently changes the 'amount'
attribute of a case from one task to the next". We define the operator using
the well-known relational algebra that forms the formal underpinning of
relational databases. We formally prove equivalence properties of the operator
that are useful for query optimization and present time-complexity properties
of the operator. By doing so this paper formally defines the necessary
relational algebraic elements of a 'directly follows' operator, which are
required for implementation of such an operator in a DBMS
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