Phil Weber

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There are many process mining algorithms and representations, making it difficult to choose which algorithm to use or compare results. Process mining is essentially a machine learning task, but little work as been done on systematically analysing algorithms to understand their fundamental properties, such as how much data is needed for confidence in mining.(More)
The para-linguistic information in a speech signal includes clues to the geographical and social background of the speaker. This paper is concerned with recognition of the 14 regional accents of British English. For Accent Identification (AID), acoustic methods exploit differences between the distributions of sounds, while phonotactic approaches exploit the(More)
Process mining uses event logs to learn and reason about business process models. Existing algorithms for mining the control-flow of processes in general do not take into account the probabilistic nature of the underlying process, which affects the behaviour of algorithms and the amount of data needed for confidence in mining. We contribute a first step(More)
This paper describes the use by nurses of a semi-automatic, natural language oriented, encoder help tool currently in use in the medical sector. The use of a standard language in the daily activities of nurses is not acceptable therefore the use of an encoder will link nursing data collection with a classification system.
This paper presents a fault detection method based on a classical transfer function parameter estimation algorithm in the discrete time domain. Non persistently exciting inputs plant an important problem for the convergence of the estimator. Here, the forgetting factor is adapted on-line in order to improve the convergence. Redundant discrete time transfer(More)
Noise is a challenge for process mining algorithms, but there is no standard definition of noise nor accepted way to quantify it. This means it is not possible to mine with confidence from event logs which may not record the underlying process correctly. We discuss one way of thinking about noise in process mining. We consider mining from a `noisy log' as(More)
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