Nowadays computer scientists are faced with fast growing and permanently evolving data, which are represented as observations made sequentially in time. A common problem in the data mining community is the recognition of recurring patterns within temporal databases or streaming data. This dissertation proposal aims at developing and investigating efficient methods for the recognition of context...

Predefined pattern detection from time series is an interesting and challenging task. In order to reduce its computational cost and increase effectiveness, a number of time series representation methods and similarity measures have been proposed. Most of the existing methods focus on full sequence matching, that is, sequences with clearly defined beginnings and endings, where all data points co...

MOTIVATION Following the advent of microarray technology in recent years, the challenge for biologists is to identify genes of interest from the thousands of genetic expression levels measured in each microarray experiment. In many cases the aim is to identify pattern in the data series generated by successive microarray measurements. RESULTS Here we introduce a new method of detecting patter...

Based on the algorithm for pattern matching in a character string, a pattern description language (PDL) is developed. The compilation of a regular expression, that conforms to the PDL, creates a nondeterministic pattern matching machine (PMM) that can be used as a searching device for detecting sequential patterns or functional (statistical) relationships in multidimensional data. As an example...

Knowledge extraction from gene expression data has been one of the main challenges in the bioinformatics field during the last few years. In this context, a particular kind of data, data retrieved in a temporal basis (also known as time series), provide information about the way a gene can be expressed during time. This work presents an exhaustive analysis of last proposals in this area, partic...

We introduce FuncICA, a new independent component analysis method for pattern discovery in inherently functional data, such as time series data. We show how applying the dual of temporal ICA to temporal data, and likewise applying the dual of spatiotemporal ICA to spatiotemporal data, enables independent component regularization not afforded by the primal forms applied to their original domains...

In this paper, a technique for the automatic detection of any recurrent pattern in ECG time series is introduced. The wavelet transform is used to obtain a multiresolution representation of some example patterns for signal structure extraction. Neural Networks are trained with the wavelet transformed templates providing an efficient detector even for temporally varying patterns within the compl...

In this paper, we introduce SPIRIT (Streaming Pattern dIscoveRy in multIple Timeseries). Given n numerical data streams, all of whose values we observe at each time tick t, SPIRIT can incrementally find correlations and hidden variables, which summarise the key trends in the entire stream collection. It can do this quickly, with no buffering of stream values and without comparing pairs of strea...

In this paper, two types of patterns, convex and concave patterns, are introduced to characterize finite sequences of a given time series. It is shown with an example that the pattern-based characterization of time series is promising for the purposes of subsequence matching. Statistical properties of the patterns are examined for the historical Dow Jones Index as well.