Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Sebastian Kruse

former member

Research Interests

  • Data profiling
  • Distributed systems
  • Map/Reduce frameworks
  • Query optimization
  • Cross-platform/polyglot data processing

Projects

Teaching

Master's Theses

  • Estimating Metadata of Query Results using Histograms (Cathleen Ramson, 2014)
  • Quicker Ways of Doing Fewer Things: Improved Index Structures and Algorithms for Data Profiling (Jakob Zwiener, 2015)
  • Methods of Denial Constraint Discovery (Tobias Bleifuß, 2016)
  • Optimizing Cross-Platform Iterations on 
    the Rheem Platform (Jonas Kemper, ongoing)

Seminars

Master Projects

Bachelor Projects

Guest Lectures

Professional Activities

Talks

Publications

RHEEM: Enabling Cross-Platform Data Processing - May The Big Data Be With You! -

Agrawal, Divy; Chawla, Sanjay; Kaoudi, Zoi; Kruse, Sebastian; Quiané-Ruiz, Jorge Arnulfo; Contreras-Rojas, Bertty; Elmagarmid, Ahmed; Idris, Yasser; Lucas, Ji; Mansour, Essam; Ouzzani, Mourad; Papotti, Paolo; Tang, Nan; Thirumuruganathan, Saravanan; Troudi, Anis in Proceedings of the VLDB Endowment (PVLDB) 2018 .

Solving business problems increasingly requires going beyond the limits of a single data processing platform (platform for short), such as Hadoop or a DBMS. As a result, organizations typically perform tedious and costly tasks to juggle their code and data across different platforms. Addressing this pain and achieving automatic cross-platform data processing is quite challenging: finding the most efficient platform for a given task requires quite good expertise for all the available platforms. We present Rheem, a general-purpose cross-platform data processing system that decouples applications from the underlying platforms. It not only determines the best platform to run an incoming task, but also splits the task into subtasks and assigns each subtask to a specific platform to minimize the overall cost (e.g., runtime or monetary cost). It features (i) a robust interface to easily compose data analytic tasks; (ii) a novel cost-based optimizer able to find the most efficient platform in almost all cases; and (iii) an executor to efficiently orchestrate tasks over different platforms. As a result, it allows users to focus on the business logic of their applications rather than on the mechanics of how to compose and execute them. Using different real-world applications with Rheem, we demonstrate how cross-platform data processing can accelerate performance by more than one order of magnitude compared to single-platform data processing.
[ DOI ]
RHEEM: Enabling Cross-Pla... - Download
Further Information
Tags isg  myown  rheem