Add to ORKGInternational audienceSequences of events describing the behavior and actions of agents or systems can be collected in several domains. An episode is a collection of events that occur in a given partial order. By performing a recognition of recurrent episodes in several sequences and comparing them, it is possible to determine a pattern common to all the sequences. In this paper, we propose an approach to recognize episodes that are common in a set of event sequences. The method described is applied to the automotive domain for learning diagnosis procedures
In this thesis we present a solution to the problem of identification of significant sets of episode...
Frequent episode discovery is a popular framework for mining data available as a long sequence of ev...
In this paper, we aim to tackle the problem of recognising temporal sequences in the context of a mu...
This paper is concerned with the framework of frequent episode discovery in event sequences. A new t...
Lion's share of process mining research focuses on the discovery of end-to-end process models descri...
Abstract. Lion’s share of process mining research focuses on the discov-ery of end-to-end process mo...
Discovering patterns in temporal data is an important task in Data Mining. A successful method for t...
International audienceChronicle recognition is an efficient and robust method for fault diagnosis. T...
Industrial network communication is highly deterministic as result of availability requirement of co...
In this paper, we model multi-agent events in terms of a temporally varying sequence of sub-events, ...
Abstract. Fluents are logical descriptions of situations that persist, and composite uents are stati...
In this paper, we model multi-agent events in terms of a temporally varying sequence of sub-events, ...
AbstractIn this paper, we model multi-agent events in terms of a temporally varying sequence of sub-...
In this paper we consider the process of discovering frequent episodes in event sequences. The most ...
Frequent episode discovery is a popular framework for pattern discovery from sequential data. It has...
In this thesis we present a solution to the problem of identification of significant sets of episode...
Frequent episode discovery is a popular framework for mining data available as a long sequence of ev...
In this paper, we aim to tackle the problem of recognising temporal sequences in the context of a mu...
This paper is concerned with the framework of frequent episode discovery in event sequences. A new t...
Lion's share of process mining research focuses on the discovery of end-to-end process models descri...
Abstract. Lion’s share of process mining research focuses on the discov-ery of end-to-end process mo...
Discovering patterns in temporal data is an important task in Data Mining. A successful method for t...
International audienceChronicle recognition is an efficient and robust method for fault diagnosis. T...
Industrial network communication is highly deterministic as result of availability requirement of co...
In this paper, we model multi-agent events in terms of a temporally varying sequence of sub-events, ...
Abstract. Fluents are logical descriptions of situations that persist, and composite uents are stati...
In this paper, we model multi-agent events in terms of a temporally varying sequence of sub-events, ...
AbstractIn this paper, we model multi-agent events in terms of a temporally varying sequence of sub-...
In this paper we consider the process of discovering frequent episodes in event sequences. The most ...
Frequent episode discovery is a popular framework for pattern discovery from sequential data. It has...
In this thesis we present a solution to the problem of identification of significant sets of episode...
Frequent episode discovery is a popular framework for mining data available as a long sequence of ev...
In this paper, we aim to tackle the problem of recognising temporal sequences in the context of a mu...