Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Scalable approximate query processing with the DBO engine
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
Dremel: interactive analysis of web-scale datasets
Proceedings of the VLDB Endowment
Communications of the ACM
Trust me, i'm partially right: incremental visualization lets analysts explore large datasets faster
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Temporal Analytics on Big Data for Web Advertising
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Processing a trillion cells per mouse click
Proceedings of the VLDB Endowment
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Exploratory analysis on big data requires us to rethink data management across the entire stack -- from the underlying data processing techniques to the user experience. We demonstrate Stat! -- a visualization and analytics environment that allows users to rapidly experiment with exploratory queries over big data. Data scientists can use Stat! to quickly refine to the correct query, while getting immediate feedback after processing a fraction of the data. Stat! can work with multiple processing engines in the backend; in this demo, we use Stat! with the Microsoft StreamInsight streaming engine. StreamInsight is used to generate incremental early results to queries and refine these results as more data is processed. Stat! allows data scientists to explore data, dynamically compose multiple queries to generate streams of partial results, and display partial results in both textual and visual form.