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 Extracting value (e.g., understanding the underlying structure and making predictions) from such data, before it is outdated, is a major concern.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

Learning Temporal Tagging Behaviour

Toni Gruetze, Gary Yao, Ralf Krestel
In Proceedings of the 24th International Conference on World Wide Web Companion (WWW), pages 1333–1338, 5 2015 ACM.

DOI: 10.1145/2740908.2741701

Abstract:

Social networking services, such as Facebook, Google+, and Twitter are commonly used to share relevant Web documents with a peer group. By sharing a document with her peers, a user recommends the content for others and annotates it with a short description text. This short description yield many chances for text summarization and categorization. Because today’s social networking platforms are real-time media, the sharing behaviour is subject to many temporal effects, i.e., current events, breaking news, and trending topics. In this paper, we focus on time-dependent hashtag usage of the Twitter community to annotate shared Web-text documents. We introduce a framework for time-dependent hashtag recommendation models and introduce two content-based models. Finally, we evaluate the introduced models with respect to recommendation quality based on a Twitter-dataset consisting of links to Web documents that were aligned with hashtags.

BibTeX file

@inproceedings{Gruetze2015TempWeb,
author = { Toni Gruetze, Gary Yao, Ralf Krestel },
title = { Learning Temporal Tagging Behaviour },
year = { 2015 },
pages = { 1333--1338 },
month = { 5 },
abstract = { Social networking services, such as Facebook, Google+, and Twitter are commonly used to share relevant Web documents with a peer group. By sharing a document with her peers, a user recommends the content for others and annotates it with a short description text. This short description yield many chances for text summarization and categorization. Because today’s social networking platforms are real-time media, the sharing behaviour is subject to many temporal effects, i.e., current events, breaking news, and trending topics. In this paper, we focus on time-dependent hashtag usage of the Twitter community to annotate shared Web-text documents. We introduce a framework for time-dependent hashtag recommendation models and introduce two content-based models. Finally, we evaluate the introduced models with respect to recommendation quality based on a Twitter-dataset consisting of links to Web documents that were aligned with hashtags. },
publisher = { ACM },
booktitle = { Proceedings of the 24th International Conference on World Wide Web Companion (WWW) },
priority = { 0 }
}

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last change: Fri, 12 Aug 2016 17:29:48 +0200