An Adaptive Hierarchical Data Structure Searching Data Archives
Wavelets, Data structures, Information storage and retrieval systems, and Data mining
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The rapid increase in generating and collecting data with high resolution and recording frequency due to the availability and affordability of measurement technology is resulting in huge data archives that are growing exponentially in size. This explosive growth in accumulated data has generated an urgent need for new techniques and tools that can automatically support engineers and data analysts in transforming the enormous amounts of data into useful information and knowledge. In this book, we introduce a novel method for analyzing multidimensional data sets and discovering index vectors by ...
The rapid increase in generating and collecting data
with high resolution and recording frequency due to
the availability and affordability of measurement
technology is resulting in huge data archives that
are growing exponentially in size. This explosive
growth in accumulated data has generated an urgent
need for new techniques and tools that can
automatically support engineers and data analysts in
transforming the enormous amounts of data into
useful information and knowledge. In this book, we
introduce a novel method for analyzing
multidimensional data sets and discovering index
vectors by the use of discontinuity detection. Jumps
in data are detected using wavelet coefficients that
are calculated dynamically for every level of
wavelet coefficients. The discovered index vectors
of the analyzed channels (raw data) are related in a
dynamic alignment of tree-like structure. Channels
are in relation according to a number of variables
that are set adaptively such as the allowable
average
alignment difference between two discontinuity
events. This method is implemented and tested
against real automotive emission test data.
with high resolution and recording frequency due to
the availability and affordability of measurement
technology is resulting in huge data archives that
are growing exponentially in size. This explosive
growth in accumulated data has generated an urgent
need for new techniques and tools that can
automatically support engineers and data analysts in
transforming the enormous amounts of data into
useful information and knowledge. In this book, we
introduce a novel method for analyzing
multidimensional data sets and discovering index
vectors by the use of discontinuity detection. Jumps
in data are detected using wavelet coefficients that
are calculated dynamically for every level of
wavelet coefficients. The discovered index vectors
of the analyzed channels (raw data) are related in a
dynamic alignment of tree-like structure. Channels
are in relation according to a number of variables
that are set adaptively such as the allowable
average
alignment difference between two discontinuity
events. This method is implemented and tested
against real automotive emission test data.
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