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This paper provides the first comprehensive evaluation and analysis of modern (deep‐learning‐based) unsupervised anomaly detection methods for chemical process data. We focus on the Tennessee Eastman process dataset, a standard litmus test to benchmark anomaly detection methods for nearly three decades. Our extensive study will facilitate choosing appropriate anomaly detection methods in industrial...
For supporting interpretation, assessment and application of data mining models, explanation-aware methods are crucial. This paper presents an approach for explanation-aware feature selection and assessment using symbolic abstractions of time series. For that, we utilize the symbolic approximate aggregation (SAX) method for data abstraction to be implemented into data mining models. We investigate...
With the advent of cloud computing and its model for IT services based on the Internet and big data centers, the interest of industries into XaaS ("Anything as a Service") paradigm is increasing. Business intelligence and knowledge discovery services are typical services that companies tend to externalize on the cloud, due to their data intensive nature and the algorithms complexity. What...
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