xPAD: A platform for analytic data flows
Simitsis.pdf (2,903Mb) (Restricted access) Request copy
Què és aquest botó?
Aquest botó permet demanar una còpia d'un document restringit a l'autor. Es mostra quan:
- Disposem del correu electrònic de l'autor
- El document té una mida inferior a 20 Mb
- Es tracta d'un document d'accés restringit per decisió de l'autor o d'un document d'accés restringit per política de l'editorial
Document typeConference report
Rights accessRestricted access - publisher's policy
As enterprises become more automated, real-time, and data-driven, they need to integrate new data sources and specialized processing engines. The traditional business intelligence architecture of Extract-Transform-Load (ETL) flows, followed by querying, reporting, and analytic operations, is being generalized to analytic data flows that utilize a variety of data types and operations. These complicated flows are difficult to design, implement and maintain since they span a variety of systems. Additionally, new design requirements may be imposed such as design for fault-tolerance, freshness, maintainability, sampling, etc. To reduce development time and maintenance costs, automation is needed. We present xPAD, our platform to manage analytic data flows. xPAD enables flow design. We show how these designs can be optimized, not just for performance, but for other objectives as well. xPAD is engine-agnostic. We show how it can generate executable code for a number of execution engines. It can also import existing flows from other engines and optimize those flows. In that way, it can transform a flow written for one engine into an optimized flow for a different engine. In our demonstration, we will also use various example flows to show optimization for different objectives and comparison of flow execution on different engines.
CitationSimitsis, A.; Wilkinson, K.; Jovanovic, P. xPAD: A platform for analytic data flows. A: ACM SIGMOD Conference. "Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data". 2013, p. 1109-1112.
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder