The Stratosphere platform for big data analytics

By Alexander B. Alexandrov, Rico Bergmann, Stephan Ewen, Johann Christoph Freytag, Fabian Hueske, Arvid Heise, Odej Kao, M. Leich, Ulf Leser, Volker Markl and 8 more. The VLDB journal, 2014

505 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/059eb34d95c73e37dca8e35b0ac5a2fb0142f3ee

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Information Systems

Citations in the corpus

Social big data: Recent achievements and new challenges (2015, unknown): “• Stratosphere [34].”
Automating model search for large scale machine learning (2015, background): “There has been a recent proliferation of systems designed for low-level, ad-hoc distributed predictive analytics, e.g., Apache Spark [48], GraphLab [22], Stratosphere [8], but few provide tooling for searching over a large space of predictive models.”
SystemML: Declarative Machine Learning on Spark (2016, methodology): “…in industries such as manufacturing, mobile, automotive, and health care, and (3) the resulting need to run these custom ML algorithms on distributed, data-parallel systems such as MapReduce (MR) [14], Spark [41], or Flink [2] for scalability and performance on cost-eﬃcient commodity clusters.”
The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing (2015, background): “Most that provide event-timebased windowing either rely on ordering (SQLStream [27]), or have limited window triggering(3) semantics in event-time mode (Stratosphere/Flink [3, 6]).”
TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries (2015, background): “There has been a recent proliferation of systems designed for low-level, ad-hoc distributed predictive analytics, e.g., Apache Spark [52], GraphLab [27], Stratosphere [9], but none of these provide users with a declarative interface with which to specify predictive queries.”
SampleClean: Fast and Reliable Analytics on Dirty Data (2015, background): “5 ActiveClean: Machine Learning on Dirty Data [31] Analytics is moving beyond SQL, and the growing popularity of predictive models [1, 7, 17, 28] leads to additional challenges in managing dirty data.”
Probabilistic Demand Forecasting at Scale (2017, background): “Dataﬂows have proven themselves as a useful abstraction for general distributed, data-intensive computing [8, 1, 6].”
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models (2016, background): “Machine Learning on large and growing datasets is a key data management challenge with signiﬁcant interest in both industry and academia [1, 5, 10, 20].”
Spark SQL: Relational Data Processing in Spark (2015, background): “Stratosphere [2] also has a semi-structured model, but offers APIs in Scala and Java that let users easily call UDFs.”