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KV-match: A Subsequence Matching Approach Supporting Normalization and Time Warping [Extended Version]
The volume of time series data has exploded due to the popularity of new
applications, such as data center management and IoT. Subsequence matching is a
fundamental task in mining time series data. All index-based approaches only
consider raw subsequence matching (RSM) and do not support subsequence
normalization. UCR Suite can deal with normalized subsequence match problem
(NSM), but it needs to scan full time series. In this paper, we propose a novel
problem, named constrained normalized subsequence matching problem (cNSM),
which adds some constraints to NSM problem. The cNSM problem provides a knob to
flexibly control the degree of offset shifting and amplitude scaling, which
enables users to build the index to process the query. We propose a new index
structure, KV-index, and the matching algorithm, KV-match. With a single index,
our approach can support both RSM and cNSM problems under either ED or DTW
distance. KV-index is a key-value structure, which can be easily implemented on
local files or HBase tables. To support the query of arbitrary lengths, we
extend KV-match to KV-match, which utilizes multiple varied-length
indexes to process the query. We conduct extensive experiments on synthetic and
real-world datasets. The results verify the effectiveness and efficiency of our
approach.Comment: 13 page
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