  In todays digital era, data are everywhere from Internet of Things to health
care or financial applications. This leads to potentially unbounded
ever-growing Big data streams and it needs to be utilized effectively. Data
normalization is an important preprocessing technique for data analytics. It
helps prevent mismodeling and reduce the complexity inherent in the data
especially for data integrated from multiple sources and contexts.
Normalization of Big Data stream is challenging because of evolving
inconsistencies, time and memory constraints, and non-availability of whole
data beforehand. This paper proposes a distributed approach to adaptive
normalization for Big data stream. Using sliding windows of fixed size, it
provides a simple mechanism to adapt the statistics for normalizing changing
data in each window. Implemented on Apache Storm, a distributed real-time
stream data framework, our approach exploits distributed data processing for
efficient normalization. Unlike other existing adaptive approaches that
normalize data for a specific use (e.g., classification), ours does not.
Moreover, our adaptive mechanism allows flexible controls, via user-specified
thresholds, for normalization tradeoffs between time and precision. The paper
illustrates our proposed approach along with a few other techniques and
experiments on both synthesized and real-world data. The normalized data
obtained from our proposed approach, on 160,000 instances of data stream,
improves over the baseline by 89% with 0.0041 root-mean-square error compared
with the actual data.
