Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Stratosphere

Stratosphere is a joint DFG project conducted by the Technische Universität Berlin, Humboldt Universität Berlin, and the Hasso-Plattner-Institut. It explores how the elasticity of clouds can be exploited for processing analytic queries massively in parallel. Unlike most traditional DBMS, Stratosphere inherently supports text-based and semi-structured data.

Official Project Site

The sub-projects at HPI focus on data quality improvements of linked open data, efficient and scalable data profiling, and knowledge discoevry.

Data Cleansing

We defined the declarative data cleansing language Meteor, implement the underlying basic operations, and develop cost estimations for the operations. Furthermore, we provide test data sets and example queries to evaluate the efficiency and effectivity of the data cleansing process.

Data Profiling

Detecting dependencies in the evergrowing amounts of data has a high computational complexity. One way to cope with this complexity is to distribute the computational work among multiple interconnected computers. However, most existing data profiling algorithms are not designed for parallel execution on computer clusters but rather to run on a single machine. Therefore, we research distributed modifications of existing algorithms as well as new algorithms that can be efficiently executed on computer clusters and that scale out on the number of the cluster nodes.

Knowledge Discovery

Driven by applications such as social media analytics, Web search, advertising, recommendation, mobile sensoring, genomic sequencing, astronomical observations, etc., the need for scalable learning, mining, and knowledge discovery methods is steadily growing. Often the challenge is to automatically process and analyze TBs of evolving data. Extracting value (e.g., understanding the underlying structure and making predictions) from such data, before it is outdated, is a major concern. Therefore, the goal is to enable the scalability of such applications based on Stratosphere.

Please contact Felix Naumann, Toni Grütze (Knowledge Discovery on Stratosphere), or Sebastian Kruse (Data Profiling on Stratosphere) for further questions.

Former members

Publications

The Stratosphere Platform for Big Data Analytics

Alexander Alexandrov, Rico Bergmann, Stephan Ewen, Johann-Christoph Freytag, Fabian Hueske, Arvid Heise, Odej Kao, Marcus Leich, Ulf Leser, Volker Markl, Felix Naumann, Mathias Peters, Astrid Rheinländer, Matthias J. Sax, Sebastian Schelter, Mareike Höger, Kostas Tzoumas, Daniel Warneke
The VLDB Journal, vol. 23(6):939-964 2014

Abstract:

We present Stratosphere, an open-source software stack for parallel data analysis. Stratosphere brings together a unique set of features that allow the expressive, easy, and efficient programming of analytical applications at very large scale. Stratosphere’s features include “in situ” data processing, a declarative query language, treatment of user-defined functions as first-class citizens, automatic program parallelization and optimization, support for iterative programs, and a scalable and efficient execution engine. Stratosphere covers a variety of “Big Data” use cases, such as data warehousing, information extraction and integration, data cleansing, graph analysis, and statistical analysis applications. In this paper, we present the overall system architecture design decisions, introduce Stratosphere through example queries, and then dive into the internal workings of the system’s components that relate to extensibility, programming model, optimization, and query execution. We experimentally compare Stratosphere against popular open-source alternatives, and we conclude with a research outlook for the next years.

BibTeX file

@article{Alexander2014a,
author = { Alexander Alexandrov, Rico Bergmann, Stephan Ewen, Johann-Christoph Freytag, Fabian Hueske, Arvid Heise, Odej Kao, Marcus Leich, Ulf Leser, Volker Markl, Felix Naumann, Mathias Peters, Astrid Rheinländer, Matthias J. Sax, Sebastian Schelter, Mareike Höger, Kostas Tzoumas, Daniel Warneke },
title = { The Stratosphere Platform for Big Data Analytics },
journal = { The VLDB Journal },
year = { 2014 },
volume = { 23 },
number = { 6 },
pages = { 939-964 },
month = { 0 },
abstract = { We present Stratosphere, an open-source software stack for parallel data analysis. Stratosphere brings together a unique set of features that allow the expressive, easy, and efficient programming of analytical applications at very large scale. Stratosphere’s features include “in situ” data processing, a declarative query language, treatment of user-defined functions as first-class citizens, automatic program parallelization and optimization, support for iterative programs, and a scalable and efficient execution engine. Stratosphere covers a variety of “Big Data” use cases, such as data warehousing, information extraction and integration, data cleansing, graph analysis, and statistical analysis applications. In this paper, we present the overall system architecture design decisions, introduce Stratosphere through example queries, and then dive into the internal workings of the system’s components that relate to extensibility, programming model, optimization, and query execution. We experimentally compare Stratosphere against popular open-source alternatives, and we conclude with a research outlook for the next years. },
priority = { 0 }
}

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last change: Mon, 18 May 2015 09:01:45 +0200