Prof. Dr. h.c. Hasso Plattner

Projects Overview

Traditional databases are separated into ones for current data from the day-to-day business processes and ones for reporting and analytics. For fast moving businesses, moving data from one silo to another is cumbersome and takes too much time. As a result, the new data arriving in the reporting system is already old by the time it is loaded. HYRISE proposes a new way to solve this problem: It analyzes the query input and reorganizes the stored data in different dimensions.  In detail, HYRISE partitions the layout of the underlying tables in a vertical and horizontal manner depending on the input to this layout management component. The workload is specified as a set of queries and weights and is processed by calculating the layout dependent costs for those queries. Based on our cost-model we can now calculate the best set of partitions for this input workload. This optimization allows great speed improvements compared to traditional storage models. Read More.

Contact: Markus Dreseler, Jan KossmannMartin Boissier, Stefan Klauck, Dr. Michael Perscheid,  Prof. Dr. h.c. Hasso Plattner

Research Area: In-Memory Data Management on Modern Hardware

Data-Driven Causal Inference

The emergence of the Internet of Things (IoT) allows for a comprehensive analysis of industrial manufacturing processes. While domain experts within the company have enough expertise to identify the most common relationships, they will require support in the context of both, an increasing amount of observational data and the complexity of large systems of observed features. This gap can be closed by machine learning algorithms of causal inference that derive the underlying causal relationships between the observed features. Read More.

Contact: Johannes Huegle, Christopher HagedornDr. Rainer Schlosser, Dr. Michael Perscheid

Research Area: Data-Driven Decision Support

Modern e-commerce platforms pose both opportunities as well as hurdles for merchants. While merchants can observe markets at any point in time and automatically reprice their products, they also have to compete simultaneously with dozens of competitors.

Our platform enables analyses of how a strategy's performance is affected by customer behavior, price adjustment frequencies, the competitors' strategies, and the exit/entry of competitors.We compared traditional rule-based strategies with simple data-driven strategies. We find that data-driven merchants are superior to rule-based approaches as soon as a sufficiently large data set has been gathered. Read More.

Contact: Dr. Rainer Schlosser, Martin Boissier

Project Archive

Find a list of previous projects here.