Experimental and simulated time series are necessarily discretized in time. However, many real and artificial systems are more naturally modeled as continuous-time systems. This paper reviews the major techniques employed to estimate a continuous vector field from a finite discrete time series. We compare the performance of various methods on experimental and artificial time series and explore ...

Semi-continuous data arise in many applications where naturally-continuous data become contaminated by the data generating mechanism. The resulting data contain several values that are “too frequent”, and in that sense are a hybrid between discrete and continuous data. The main problem is that standard statistical methods, which are geared towards continuous or discrete data, cannot be applied ...

Understanding the performance and dynamic behavior of workflow is crucial in being able to modify, maintain, and improve it. A particularly difficult aspect of measuring the performance of workflows is the dissemination of event data and its transformation into business metrics. In this paper we introduce an architecture that supports a continuous integration of event data from various source s...

How to derive meaningful information from continuous data is the central topic of this work. When age data about 10 000 or 100 000 people is given, how can we represent the data in a way that is understandable to the analyst and also reveals signi cant metainformation about the people included in the datasets? In this work I am reviewing di erent clustering algorithms and proposing the most sui...