This paper conceptualizes speech prosody data mining and its potential application in data-driven phonology/phonetics research. We first conceptualize Speech Prosody Mining (SPM) in a time-series data mining framework. Specifically, we propose using efficient symbolic representations for speech prosody time-series similarity computation. We experiment with both symbolic and numeric representati...

Knowledge Discovery in time series usually requires symbolic time series. Many discretization methods that convert numeric time series to symbolic time series ignore the temporal order of values. This often leads to symbols that do not correspond to states of the process generating the time series. We propose a new method for meaningful unsupervised discretization of numeric time series called ...

This paper introduces a symbolic time series representation using monotonic sub-sequences and bottom up segmentation. The representation minimizes the square error between the segments and their monotonic approximations. The representation can robustly classify the direction of a segment and is scale invariant with respect to the time and value dimensions. This paper describes two experiments. ...

The analysis of symbolic dynamics applied to physiological time series is able to retrieve information about dynamical properties of the underlying system that cannot be gained with standard methods like e.g. spectral analysis. Different approaches for the transformation of the original time series to the symbolic time series have been proposed. Yet the differences between the approaches are un...

In order to extract and to visualize qualitative information from a highdimensional time series, we apply ideas from symbolic dynamics. Counting certain ordinal patterns in the given series, we obtain a series of matrices whose entries are symbol frequencies. This matrix series is explored by simple methods from nominal statistics and information theory. The method is applied to detect and to v...

Among the many dimensionality reduction methods for timeseries data, Symbolic Aggregate approXimation (SAX) is perhaps the most popular due to its simplicity and uniqueness. With SAX, time-series data can be represented as string sequences which enables the utilization of methods found in text mining and bioinformatics to enhance data mining tasks. We propose an application of L-tuples to impro...

Taking an example from statistics, we show how symbolic summation can be used to find generalizations of binomial identities that involve infinite series. In such generalizations, the infinite series are replaced by truncated versions.

IS adoption is inherently a social practice shaped by the immediate social context but capital power does not have a stronger presence in ANT and sociomateriality. The paper describes Bourdieu’s practice theory and suggests that by this lens we can better understand IS adoption (e.g., implementation of MRP II/ERP/ERP II) as symbolic capital changing power relations in an adopted organization. S...