Peter Bailis
ABSTRACT
Data volumes are rising at an increasing rate, stressing the limits of human attention. Current techniques for prioritizing user attention in this fast data are characterized by either cumbersome, ad-hoc analysis pipelines comprised of a diverse set of analytics tools, or brittle, static rule-based engines. To address this gap, we have developed MacroBase, a fast data analytics engine that acts as a search engine over fast data streams. MacroBase provides a set of highly-optimized, modular operators for streaming feature transformation, classification, and explanation. Users can leverage these optimized operators to construct efficient pipelines tailored for their use case. In this demonstration, SIGMOD attendees will have the opportunity to interactively answer and refine queries using MacroBase and discover the potential benefits of an advanced engine for prioritizing attention in high-volume, real-world data streams.
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Digital Library
- P. Bailis, E. Gan, S. Madden, D. Narayanan, K. Rong, and S. Suri. MacroBase: Prioritizing Attention in Fast Data. In SIGMOD, 2017. Google ScholarDigital Library
- P. Bailis, E. Gan, K. Rong, and S. Suri. Prioritizing Attention in Fast Data: Principles and Promise. In CIDR, 2017.Google Scholar
- E. Gan and P. Bailis. Ic2: Indexed cutoffs for kernel density classification. In SIGMOD, 2017.Google Scholar
- K. Rong and P. Bailis. ASAP: Automatic Smoothing for Attention Prioritization in Streaming Time Series Visualization. 2017. arXiv:1703.00983.Google Scholar
Index Terms
- Demonstration: MacroBase, A Fast Data Analysis Engine
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