Vastream: A visual analytics system for fast data streams
© 2019 Association for Computing Machinery. All rights reserved. Processing high-volume, high-velocity data streams is an important big data problem in many sciences, engineering, and technology domains. There are many open-source distributed stream processing and cloud platforms that offer low-latency stream processing at scale, but the visualization and user-interaction components of these systems are limited to visualizing the outcome of stream processing results. Visual analysis represents a new form of analysis where the user has more control and interactive capabilities either to dynamically change the visualization, analytics or data management processes. VAStream provides an environment for big data stream processing along with interactive visualization capabilities. The system environment consists of hardware and software modules to optimize streaming data workflow (that includes data ingest, pre-processing, analytics, visualization, and collaboration components). The system environment is evaluated for two real-time streaming applications. The real-time event detection using social media streams uses text data arriving from sources such as Twitter to detect emerging events of interest. The real-time river sensor network analysis project uses unsupervised classification methods to classify sensor network streams arriving from the US river network to detect water quality problems. We discuss implementation details and provide performance comparison results of various individual stream processing operations for both stream processing applications.
ACM International Conference Proceeding Series
Katragadda, S., Gottumukkala, R., Venna, S., Lipari, N., Gaikwad, S., Pusala, M., Chen, J., Borst, C., Raghavan, V., & Bayoumi, M. (2019). Vastream: A visual analytics system for fast data streams. ACM International Conference Proceeding Series. Retrieved from https://ir.una.edu/geography_facpub/27