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Andreas Solti

dblp:35/9961 · also Andreas Rogge-Solti · DBLP profile ↗
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12ranked-venue papers
5as first author
1since 2021 · last 2021
0000-0002-0537-6598ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 10 · 3 first-author · 1 since 2021Databases, data management, data science and information retrieval · 4 · 1 first-authorArtificial intelligence and machine learning · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Empirical software engineering · 50% Program analysis · 50%
Databases, data mining, and information retrieval
1 paper
Data mining · 100%
Theoretical computer science
1 paper
Automata and formal languages · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining
process mining
0.512021
Mining Batch Activation Rules from Event Logs · IEEE Trans. Serv. Comput. 2021
Empirical software engineering
process mining
0.412020
Monotone Precision and Recall Measures for Comparing Executions and Specifications of Dynamic Systems · ACM Trans. Softw. Eng. Methodol. 2020
Program analysis
specification mining
0.412020
Monotone Precision and Recall Measures for Comparing Executions and Specifications of Dynamic Systems · ACM Trans. Softw. Eng. Methodol. 2020

Methods — techniques the papers use, named apart from their topics

monotonicity analysis · 0.9behavioural quotients · 0.9inductive rule mining · 0.5
YearPublicationVenuePosition
2021 Mining Batch Activation Rules from Event Logs
abstract
Batch processing refers to an organization of work in which cases are synchronized such that they can be processed as a group. Prior research has studied batch processing mainly from a deductive angle, trying to identify optimal rules for composing batches. As a consequence, we lack methodological support to investigate according to which rules human resources build batches in work settings where batching rules are not strictly enforced. In this paper, we address this research gap by developing a technique to inductively mine batch activation rules from process execution data. The obtained batch activation rules can be used for various purposes, including to explicate the real-life batching behavior of human resources; to determine the compliance between the prescribed and actual batching behavior; or to investigate the influence of alternative batching behavior on service levels. The evaluation of our technique using both synthetic and real-world data demonstrates its effectiveness. With this work we complement prescriptive research on batch processing with a descriptive technique that is empirically grounded in process execution data.
Niels Martin, Andreas Solti, Jan Mendling, Benoît Depaire, An Caris
IEEE Trans. Serv. Comput.2
2020 Monotone Precision and Recall Measures for Comparing Executions and Specifications of Dynamic Systems
abstract
The behavioural comparison of systems is an important concern of software engineering research. For example, the areas of specification discovery and specification mining are concerned with measuring the consistency between a collection of execution traces and a program specification. This problem is also tackled in process mining with the help of measures that describe the quality of a process specification automatically discovered from execution logs. Though various measures have been proposed, it was recently demonstrated that they neither fulfil essential properties, such as monotonicity , nor can they handle infinite behaviour. In this article, we address this research problem by introducing a new framework for the definition of behavioural quotients. We prove that corresponding quotients guarantee desired properties that existing measures have failed to support. We demonstrate the application of the quotients for capturing precision and recall measures between a collection of recorded executions and a system specification. We use a prototypical implementation of these measures to contrast their monotonic assessment with measures that have been defined in prior research.
Artem Polyvyanyy, Andreas Solti, Matthias Weidlich 0001, Claudio Di Ciccio, Jan Mendling
ACM Trans. Softw. Eng. Methodol.2
2018 Misplaced product detection using sensor data without planograms
Andreas Solti, Manuel Raffel, Giovanni Romagnoli, Jan Mendling
Decis. Support Syst.1
2017 Discovery of Fuzzy DMN Decision Models from Event Logs
Ekaterina Bazhenova, Stephan Haarmann, Sven Ihde, Andreas Solti, Mathias Weske
CAiSE4
2016 In Log and Model We Trust? A Generalized Conformance Checking Framework
Andreas Solti, Arik Senderovich, Matthias Weidlich 0001, Jan Mendling, Avigdor Gal
BPM1
2016 Efficient and Customisable Declarative Process Mining with SQL
Stefan Schönig, Andreas Solti, Cristina Cabanillas, Stefan Jablonski, Jan Mendling
CAiSE2
2016 The ROAD from Sensor Data to Process Instances via Interaction Mining
Arik Senderovich, Andreas Solti, Avigdor Gal, Jan Mendling, Avishai Mandelbaum
CAiSE2
2015 Mining Project-Oriented Business Processes
Saimir Bala, Cristina Cabanillas, Jan Mendling, Andreas Solti, Axel Polleres
BPM4
2015 Data-Driven Performance Analysis of Scheduled Processes
Arik Senderovich, Andreas Solti, Avigdor Gal, Jan Mendling, Avishai Mandelbaum, Sarah Kadish, Craig A. Bunnell
BPM2
2015 Prediction of business process durations using non-Markovian stochastic Petri nets
Andreas Solti, Mathias Weske
Inf. Syst.1
2014 Temporal Anomaly Detection in Business Processes
Andreas Solti, Gjergji Kasneci
BPM1
2013 Prediction of Remaining Service Execution Time Using Stochastic Petri Nets with Arbitrary Firing Delays
Andreas Solti, Mathias Weske
ICSOC1