54 citations · 66 across the 5 of their papers we have counts for
5 papers
Lightweight Asynchronous Snapshots for Distributed Dataflows
Paris Carbone, Gyula Fóra, Stephan Ewen +2
Distributed stateful stream processing enables the deployment and execution of large scale continuous computations in the cloud, targeting both low latency and high throughput. One…
Enabling Operator Reordering in Data Flow Programs Through Static Code Analysis
Fabian Hueske, Aljoscha Krettek, Kostas Tzoumas
In many massively parallel data management platforms, programs are represented as small imperative pieces of code connected in a data flow. This popular abstraction makes it hard t…
Spinning Fast Iterative Data Flows
Stephan Ewen, Kostas Tzoumas, Moritz Kaufmann +1
Parallel dataflow systems are a central part of most analytic pipelines for big data. The iterative nature of many analysis and machine learning algorithms, however, is still a cha…
Opening the Black Boxes in Data Flow Optimization
Fabian Hueske, Mathias Peters, Matthias Sax +4
Many systems for big data analytics employ a data flow abstraction to define parallel data processing tasks. In this setting, custom operations expressed as user-defined functions…
Implementing Performance Competitive Logical Recovery
David Lomet, Kostas Tzoumas, Michael Zwilling
New hardware platforms, e.g. cloud, multi-core, etc., have led to a reconsideration of database system architecture. Our Deuteronomy project separates transactional functionality f…