Hasso-Plattner-Institut20 Jahre HPI
Hasso-Plattner-Institut20 Jahre HPI
  
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Current Projects

The current period is Fall 2019 and started with the HPI Future SOC Lab Day on November 12, 2019. The end is the next HPI Future SOC Lab Day on April 21, 2020. 

19 research projects are using the Lab's IT infrastructure. If you would like to receive more information about one or more projects, please contact us.

Germany

An Energy-Aware Runtime System for Heterogeneous Clusters

Abstract

We are planning to evaluate our Albatross runtime system (DOI: 10.1145/3217189.3217193) for energy-efficient and economic processing on heterogeneous compute infrastructures in the HPI Future SOC Lab in cooperation with the OSM Group (HPI).

 

Researchers

Principle Investigator: Prof. Dr.-Ing. habil Wolfgang Schröder-Preikschat || Contact Author: Dr.-Ing. Timo Hönig || Friedrich-Alexander-Universität Erlangen-Nürnberg

Towards production-ready tools for self-driving data management with DRL

Abstract

According to researchers deep reinforcement learning (DRL) is poised to revolutionize how autonomous systems are built. However there are several challenges for making DRL tools production-ready. In our project extension we seek to further develop 2 data management solutions and study improvements to existing practices for DRL in computer systems.

 

Researchers

Principle Investigator: Prof. Dr.rer.nat.hab Gunter Saake || Contact Author: Gabriel Campero Durand || University of Magdeburg

Machine Learning to scale telemedical interventions for cardiovascular diseases

Abstract

Cardiovascular diseases are the leading cause of death globally. Telemedicine interventions were shown to reduce the percentage of days lost due to unplanned cardiovascular hospital admissions and all-cause mortality. This project will train an AI-System that could help to scale such telemedical interventions by preprocessing and prioritizing.

 

Researchers

Principle Investigator: Prof. Dr. Andreas Polze || Contact Author: Jossekin Beilharz || Hasso Plattner Institute

Measurement-Based Software Performance Engineering for Microservices and Multi-Core Systems

Abstract

DevOps, microservices, and multi-core systems are gaining considerable attraction in research and practice. We would use the requested HPI Future SOC Lab resources to investigate the experimental evaluation of our activities on DevOps-oriented "Load Testing for Microservices" and "Software Performance Engineering for Multi-Core Systems". As included in the project description: During the past HPI Future SOC Lab periods, we have particularly used (dedicated) root access to two servers, namely 192.168.42.14 (896 GB RAM, 80 cores, Ubuntu) and 192.168.42.15 (32 GB RAM, 24 cores, Ubuntu). Dedicated access has been given to us due to our expected high resource demands. We would like to get access to these or comparable resources also in the next period.

 

Researchers

Principle Investigator: Dr.-Ing. Andre van Hoorn || Contact Author: Dr.-Ing. Andre van Hoorn || University of Stuttgart

Behaviour-based authentication: feature engineering based on large user profiles

Abstract

Our project contributes to the field of behaviour-based authentication. In the last years we collected a great amount of walking sequences of several people. As this dataset is too large to be processed on a normal machine, we hope to evaluate and improve our authentication model by the support of additional resources.

 

Researchers

Principle Investigator: Prof. Dr. Christoph Meinel || Contact Author: Vera Weidmann || neXenio GmbH

How efficient is the use of In Memory Database in Business Intelligence and BigData Reporting ?

Abstract

This work examines and analyzes the use of In-Memory Database in Business Intelligence and BigData reporting for Blockchain based data.

 

Researchers

Principle Investigator: Prof. Dr. Marc Jansen || Contact Author: Abdessatar Ben Ifa || Ruhr West University of Applied Sciences

Exploring Game-Theoretic Formation of Realistic Networks

Abstract

We have developed an agent-based game-theoretic model which promises a good explanation of the structure of real world networks. Previous large-scale experiments revealed that our model fails to produce networks with non-constant diameter. This project aims at simulating variations of the model that allow a more flexible diameter.

 

Researchers

Principle Investigator: Prof. Dr. Tobias Friedrich || Contact Author: Dr. Pascal Lenzner || Hasso Plattner Institute

Integrating Hardware Accelerators in Virtualized Environments

Abstract

In this project, we study mechanisms for integrating hardware accelerators in virtual machines and cloud infrastructures. Exemplary workloads include In-Memory Databases, scientific computation and multimedia applications. This project is a continuation of preceding projects conducted in the Spring and Fall Periods of 2018.

 

Researchers

Principle Investigator: Prof. Dr. Andreas Polze || Contact Author: Max Plauth || Hasso Plattner Institute

Improving Test Suite Generation by Testing Google Play's Top 1000 Apps IV

Abstract

Automated generation of test suites tackles the complexity of testing apps. The selection of the generation method is typically done in a trial-and-error fashion due to unknown characteristics of the apps. Analyzing the fitness landscapes of Google Play's top 1000 apps, we want to understand the characteristics and improve the generation method.

 

Researchers

Principle Investigator: Dr. Thomas Vogel || Contact Author: Dr. Thomas Vogel || Humboldt-Universität zu Berlin

Europe

WEEVIL- Fourth (WEEVILF)

Abstract

The WEEVILF project is designed as the extension of the previous one (WEEVILT), which was developed using the RX600S5-1 server from the HPI Future SOC Lab. This document details the main aims, scope and schedule development for the WEEVILF project. This project is a continuation of previous one, that is, this submission is a renewal for accessing HPI Future SOC Lab infrastructure.

 

Researchers

Principle Investigator: Prof. Dr. Carlos Juiz || Contact Author: Belen Bermejo || Universitat de les Illes Balears

Voice Biomarkers for Endocrinology and Neurology

Abstract

Voice biomarkers (instantaneous, cost-effective, nonintrusive) are an attractive computational alternative to the traditional medical tests. They are still making it to become accepted in clinical practice. We are at the phase of the data collection to prove or disprove a recent claim that glucose level is detectable from voice.

 

Researchers

Principle Investigator: Prof Lars Lundberg || Contact Author: Assit. Prof. Julia Sidorova || Blekinge Institute of Technology

Deep Representation Learning on Large Attributed Graphs

Abstract

Real-world graphs are often associated with a rich set of attributes (e.g., text, image) that can significantly influence the interactions, such as in political campaigns. This project wants to develop a Deep Learning unsupervised model able to generate a representation that encodes both the relational structure and node attributes in these graphs.

 

Researchers

Principle Investigator: Prof. Vincenzina Messina || Contact Author: PhD Debora Nozza || University of Milano-Bicocca

Benchmarking Java on Ethernet Cluster

Abstract

The aim of this project is to check the scalability of parallel, network intensive microbenchmarks and application written in Java, using the PCJ library, HPC Challenge 2014 award-winning Java library for high-performance parallel computing, on the 1000 Core Cluster - with high performance Ethernet interfaces.

 

Researchers

Principle Investigator: Dr. Marek Nowicki || Contact Author: Dr. Marek Nowicki || Nicolaus Copernicus University in Toruń

Worldwide

Efficient learning using deep learning in wireless environment

Abstract

This project focuses on how to customize deep learning (DL) for wireless communication by end-to-end learning. We will use DL methods for channel state information (CSI) feedback in massive MU-MIMO system to improve compression efficiency. Autoencoder, generative adversarial network and recurrent neural network are applied to this specific domain.

 

Researchers

Principle Investigator: Associate Prof. Huaming Wu || Contact Author: Associate Prof. Huaming Wu || Tianjin University, China

The usage of State-Of-Art Neural Networks in the classification problems

Abstract

Nowadays, the musical genre can be very ample and in some cases, creates discussions about what a person would consider it as only one particular genre. This kind of classification makes machine learning algorithms struggle to analyse it correctly. However, some new technologies like Generative Algorithm Networks(GANs) or XDeepFM could be manipulat

 

Researchers

Principle Investigator: Dr. Leandro Santos Coelho || Contact Author: Msc. Joao Sauer || Universidade Federal do Paraná, Brazil

Comparative of techniques for Musical Genre Classification

Abstract

The objective of this project is to make a deep comparative between different techniques for similarity measures, focusing in the Musical Genre Classification.

 

Researchers 

Principle Investigator: Joao Sauer || Contact Author: Joao Sauer || Universidade Federal do Paraná

CitySensing

Abstract

Big mobility and IoT data processing and analytics in Smart Cities. The planned activities are focused on research and development of methods, tools and software systems for efficient and effective processing, analysis and mining of Big mobility data collected leveraging mobile crowd sensing and Internet of Things paradigms in Smart Cities.

 

Researchers

Principle Investigator: Prof. Dragan Stojanovic || Contact Author: Prof. Dragan Stojanovic || University of Nis, Serbia

Designing practical algorithms through overfitting

Abstract

Since 2017, the teams around Prof. Tobias Friedrich (HPI, Chair for Algorithm Engineering) and Dr Markus Wagner (University of Adelaide, Australia) have explored the concept of automated algorithm configuration to design new search operators. This project builds upon the existing work and will push it toward real-world, interconnected problems.

 

Researchers

Principle Investigator: Dr. Markus Wagner || Contact Author: Dr. Markus Wagner || University of Adelaide, Australia

Towards an Intelligent Intrusion Detection System using Machine Learning and Deep Learning

Abstract

Nowadays, Cyberspace has become a warzone. Every piece of information stored or communicated using a network connected device is vulnerable to malware attacks. It is pertinent to develop techniques to protect information from malware attacks which are becoming more and more sophisticated with each passing day. Intrusion Detection Systems (IDS) prev

 

Researchers

Principle Investigator: Dr. Kuljit Kaur Chahal || Contact Author: Dr. Kuljit Kaur Chahal || Guru Nanak Dev University, India