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Closed-loop Big Data Analysis with Visualization and Scalable Computing
حلقه بسته تحلیل داده های بزرگ با بصری سازی و محاسبات مقیاس پذیر-2017 Many scientific investigations require data-intensive research where big data are collected and analyzed.
To get big insights from big data, we need to first develop our initial hypotheses from the data and
then test and validate our hypotheses about the data. Visualization is often considered a good means
to suggest hypotheses from a given dataset. Computational algorithms, coupled with scalable computing,
can perform hypothesis testing with big data. Furthermore, interactive visual interfaces can allow domain
experts to directly interact with data and participate in the loop to refine their research questions and
redirect their research directions. In this paper we discuss a framework that integrates information
visualization, scalable computing, and user interfaces to explore large-scale multi-modal data streams.
Discovering new knowledge from the data requires the means to exploratively analyze datasets of this
scale—allowing us to freely “wander” around the data, and make discoveries by combining bottom-up
pattern discovery and top-down human knowledge to leverage the power of the human perceptual
system. We start with a novel interactive temporal data mining method that allows us to discover
reliable sequential patterns and precise timing information of multivariate time series. We then proceed
to a parallelized solution that can fulfill the task of extracting reliable patterns from large-scale time
series using iterative MapReduce tasks. Our work exploits visual-based information technologies to allow
scientists to interactively explore, visualize and make sense of their data. For example, the parallel mining
algorithm running on HPC is accessible to users through asynchronous web service. In this way, scientists
can compare the intermediate data to extract and propose new rounds of analysis for more scientifically
meaningful and statistically reliable patterns, and therefore statistical computing and visualization can
bootstrap each another. Furthermore, visual interfaces in the framework allows scientists to directly
participate in the loop and can redirect the analysis direction. All these combine to reveal an effective
and efficient way to perform closed-loop big data analysis with visualization and scalable computing
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