219,391 research outputs found
Distributed Online Big Data Classification Using Context Information
Distributed, online data mining systems have emerged as a result of
applications requiring analysis of large amounts of correlated and
high-dimensional data produced by multiple distributed data sources. We propose
a distributed online data classification framework where data is gathered by
distributed data sources and processed by a heterogeneous set of distributed
learners which learn online, at run-time, how to classify the different data
streams either by using their locally available classification functions or by
helping each other by classifying each other's data. Importantly, since the
data is gathered at different locations, sending the data to another learner to
process incurs additional costs such as delays, and hence this will be only
beneficial if the benefits obtained from a better classification will exceed
the costs. We model the problem of joint classification by the distributed and
heterogeneous learners from multiple data sources as a distributed contextual
bandit problem where each data is characterized by a specific context. We
develop a distributed online learning algorithm for which we can prove
sublinear regret. Compared to prior work in distributed online data mining, our
work is the first to provide analytic regret results characterizing the
performance of the proposed algorithm
Unleashing the Power of Hashtags in Tweet Analytics with Distributed Framework on Apache Storm
Twitter is a popular social network platform where users can interact and
post texts of up to 280 characters called tweets. Hashtags, hyperlinked words
in tweets, have increasingly become crucial for tweet retrieval and search.
Using hashtags for tweet topic classification is a challenging problem because
of context dependent among words, slangs, abbreviation and emoticons in a short
tweet along with evolving use of hashtags. Since Twitter generates millions of
tweets daily, tweet analytics is a fundamental problem of Big data stream that
often requires a real-time Distributed processing. This paper proposes a
distributed online approach to tweet topic classification with hashtags. Being
implemented on Apache Storm, a distributed real time framework, our approach
incrementally identifies and updates a set of strong predictors in the Na\"ive
Bayes model for classifying each incoming tweet instance. Preliminary
experiments show promising results with up to 97% accuracy and 37% increase in
throughput on eight processors.Comment: IEEE International Conference on Big Data 201
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