Automatic discovery of linear restraints among variables of a program
POPL '78 Proceedings of the 5th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
POPL '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
WCRE '01 Proceedings of the Eighth Working Conference on Reverse Engineering (WCRE'01)
Finding Structure in Unstructured Processes: The Case for Process Mining
ACSD '07 Proceedings of the Seventh International Conference on Application of Concurrency to System Design
Apron: A Library of Numerical Abstract Domains for Static Analysis
CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
Adaptive Learning from Evolving Data Streams
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
New Region-Based Algorithms for Deriving Bounded Petri Nets
IEEE Transactions on Computers
Fuzzy mining: adaptive process simplification based on multi-perspective metrics
BPM'07 Proceedings of the 5th international conference on Business process management
Process mining meets abstract interpretation
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Mining frequent closed trees in evolving data streams
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Mining frequent closed graphs on evolving data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Handling concept drift in process mining
CAiSE'11 Proceedings of the 23rd international conference on Advanced information systems engineering
ICATPN'05 Proceedings of the 26th international conference on Applications and Theory of Petri Nets
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
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Concept drift is an important concern for any data analysis scenario involving temporally ordered data. In the last decade Process mining arose as a discipline that uses the logs of information systems in order to mine, analyze and enhance the process dimension. There is very little work dealing with concept drift in process mining. In this paper we present the first online mechanism for detecting and managing concept drift, which is based on abstract interpretation and sequential sampling, together with recent learning techniques on data streams.