Machine learning techniques underlying Big Data analytics have the potential to benefit data intensive communities in e.g., bioinformatics and neuroscience domain sciences.
Today’s innovative advances in these domain communities are
increasingly built upon multi-disciplinary knowledge discovery
and cross-domain collaborations. Consequently, shortened time
to knowledge discovery is a challenge when investigating new
methods, developing new tools, or integrating datasets. The
challenge for a domain scientist particularly lies in the actions
to obtain guidance through query of massive information from
diverse text corpus comprising of a wide-ranging set of topics.
In this paper, we propose a novel “domain-specific topic model”
(DSTM) that can drive conversational agents for users to
discover latent knowledge patterns about relationships among
research topics, tools and datasets from exemplar scientific
domains. The goal of DSTM is to perform data mining to
obtain meaningful guidance via a chatbot for domain scientists
to choose the relevant tools or datasets pertinent to solving a
computational and data intensive research problem at hand.
Our DSTM is a Bayesian hierarchical model that extends the
Latent Dirichlet Allocation (LDA) model and uses a Markov
chain Monte Carlo algorithm to infer latent patterns within
a specific domain in an unsupervised manner. We apply our
DSTM to large collections of data from bioinformatics and
neuroscience domains that include hundreds of papers from
reputed journal archives, hundreds of tools and datasets.
Through evaluation experiments with a perplexity metric, we
show that our model has better generalization performance
within a domain for discovering highly specific latent topics.
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Multi-way Time Series Join on Multi-length Patterns
This paper introduces a new pattern mining task that considers aligning or joining a set of time series based on an arbitrary number of subsequences (i.e., patterns) with arbitrary lengths. Joining multiple time series along common patterns can be pivotal in clustering and summarizing large time series datasets. An exact algorithm to join hundreds of time series based on multi-length patterns is impractical due to the high computational costs. This paper proposes a fast algorithm named MultiPAL to join multiple time series at interactive speed to summarize large time series datasets. The algorithm exploits Matrix Profiles of the individual time series to enable a greedy search over possible joins. The algorithm is orders of magnitude faster than the exact solution and can utilize hundreds of Matrix Profiles. We evaluate our algorithm for sequential mining on data from various real-world domains, including power management and bioacoustics monitoring.
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- Award ID(s):
- 1757207
- NSF-PAR ID:
- 10315780
- Date Published:
- Journal Name:
- 2021 IEEE International Conference on Data Mining (ICDM)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICDM51629.2021.00054
