Scalable Progressive Analytics on Big Data in the Cloud - Chandramouli, Badrish; Goldstein, Jonathan; Quamar, Abdul (2014)
Analytics over the increasing quantity of data stored in the Cloud has become very expensive, particularly due to the pay-as-you-go Cloud computation model. Data scientists typically manually extract samples of increasing data size (progressive samples) using domain-specific sampling strategies for exploratory querying. This provides them with user-control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. We propose a new progressive analytics system based on a progress model called Prism that (1) allows users to communicate progressive samples to the system; (2) allows efficient and deterministic query processing over samples; and (3) provides repeatable semantics and provenance to data scientists. We show that one can realize this model for atemporal relational queries using an unmodified temporal streaming engine, by re-interpreting temporal event fields to denote progress. Based on Prism, we build Now! - a progressive data-parallel computation framework for Windows Azure, where progress is understood as a first-class citizen in the framework. Now! works with "progress-aware reducers'" - in particular, it works with streaming engines to support progressive SQL over big data. Extensive experiments on Windows Azure with real and synthetic workloads validate the scalability and benefits of Now! and its optimizations, over current solutions for progressive analytics.
International Conference on Very Large Databases (PVLDB Vol. 6, Issue. 14)