Hasso-Plattner-Institut
  
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 Extracting value (e.g., understanding the underlying structure and making predictions) from such data, before it is outdated, is a major concern.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

Topic Shifts in StackOverflow: Ask it like Socrates

Gruetze, Toni and Krestel, Ralf and Naumann, Felix
In Proceedings of the 21st International Conference on Applications of Natual Language to Information Systems (NLDB), volume 9612 pages 213–221, 6 2016 Springer.

DOI: 10.1007/978-3-319-41754-7_18

Abstract:

Community based question-and-answer (Q&A) sites rely on well posed and appropriately tagged questions. However, most platforms have only limited capabilities to support their users in finding the right tags. In this paper, we propose a temporal recommendation model to support users in tagging new questions and thus improve their acceptance in the community. To underline the necessity of temporal awareness of such a model, we first investigate the changes in tag usage and show different types of collective attention in StackOverflow, a community-driven Q&A website for computer programming topics. Furthermore, we examine the changes over time in the correlation between question terms and topics. Our results show that temporal awareness is indeed important for recommending tags in Q&A communities.

BibTeX file

@inproceedings{GruetzeStackOverflow2016,
author = { Gruetze, Toni and Krestel, Ralf and Naumann, Felix },
title = { Topic Shifts in StackOverflow: Ask it like Socrates },
journal = { Lecture Notes in Computer Science },
year = { 2016 },
volume = { 9612 },
pages = { 213--221 },
month = { 6 },
abstract = { Community based question-and-answer (Q&A) sites rely on well posed and appropriately tagged questions. However, most platforms have only limited capabilities to support their users in finding the right tags. In this paper, we propose a temporal recommendation model to support users in tagging new questions and thus improve their acceptance in the community. To underline the necessity of temporal awareness of such a model, we first investigate the changes in tag usage and show different types of collective attention in StackOverflow, a community-driven Q&A website for computer programming topics. Furthermore, we examine the changes over time in the correlation between question terms and topics. Our results show that temporal awareness is indeed important for recommending tags in Q&A communities. },
publisher = { Springer },
booktitle = { Proceedings of the 21st International Conference on Applications of Natual Language to Information Systems (NLDB) },
priority = { 0 }
}

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last change: Fri, 12 Aug 2016 17:30:26 +0200