Our group includes PostDocs, PhD students, and student assistants, and is headed by Prof. Felix Naumann. If you are interested in joining our team, please contact Felix Naumann.

For bachelor students we offer German lectures on database systems in addition with paper- or project-oriented seminars. Within a one-year bachelor project students finalize their studies in cooperation with external partners. For master students we offer courses on information integration, data profiling, search engines and information retrieval enhanced by specialized seminars, master projects and advised master theses.

Most of our research is conducted in the context of larger research projects, in collaboration across students, across groups, and across universities. We strive to make available most of our data sets and source code.

Sebastian Schmidl

Ph.D. student at the chair for Infomation Systems at Hasso Plattner Institute for Digital Engineering. I am in the distributed computing research group, where we investigate computationally complex problems and how they can be solved in distributed environments.

Contact Information

Hasso-Plattner-Institut für Digital Engineering gGmbH
Prof.-Dr.-Helmert-Str. 2-3
D-14482 Potsdam

Office: F-2.04

Phone: +49 331 5509 4977
Email: Sebastian Schmidl

Research Interests

Distributed computing
Scalable and reactive systems
Data engineering

Teaching

Bachelor Projects:

UltraMine - Scalable Analytics on Time Series Data (2020/2021)

Lectures:

Guest Lecture about distributed discovery of Order Dependencies for the Data Profiling course (2020/2021)

Publications

Weise, J., Schmidl, S., Papenbrock, T.: Optimized Theta-Join Processing. In: Sattler, K.-U., Herschel, M., and Lehner, W. (eds.) Proceedings of the Conference on Database Systems for Business, Technology, and Web (BTW). pp. 59-78. Gesellschaft für Informatik, Bonn (2021).
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Further Information
Editor(s)Sattler, Kai-Uwe and Herschel, Melanie and Lehner, Wolfgang
URNhttp://dx.doi.org/10.18420/btw2021-03
AbstractThe Theta-Join is a powerful operation to connect tuples of different relational tables based on arbitrary conditions. The operation is a fundamental requirement for many data-driven use cases, such as data cleaning, consistency checking, and hypothesis testing. However, processing theta-joins without equality predicates is an expensive operation, because basically all database management systems (DBMSs) translate theta-joins into a Cartesian product with a post-filter for non-matching tuple pairs. This seems to be necessary, because most join optimization techniques, such as indexing, hashing, bloom-filters, or sorting, do not work for theta-joins with combinations of inequality predicates based on <, <=, !=, >=, >. In this paper, we therefore study and evaluate optimization approaches for the efficient execution of theta-joins. More specifically, we propose a theta-join algorithm that exploits the high selectivity of theta-joins to prune most join candidates early; the algorithm also parallelizes and distributes the processing (over CPU cores and compute nodes, respectively) for scalable query processing. The algorithm is baked into our distributed in-memory database system prototype A2DB. Our evaluation on various real-world and synthetic datasets shows that A2DB significantly outperforms existing single-machine DBMSs including PostgreSQL and distributed data processing systems, such as Apache SparkSQL, in processing highly selective theta-join queries.
Schmidl, S., Schneider, F., Papenbrock, T.: An Actor Database System for Akka.Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW) - Workshopband. pp. 225-234 (2019).
[ Download ] [ Details ]

Further Information
URNhttp://dx.doi.org/10.18420/btw2019-ws-23
AbstractSystem architectures for data-centric applications are commonly comprised of two tiers: An application tier and a data tier. The fact that these tiers do not typically share a common format for data is referred to as object-relational impedance mismatch. To mitigate this, we develop an actor database system that enables the implementation of application logic into the data storage runtime. The actor model also allows for easy distribution of both data and computation across multiple nodes in a cluster. More specifically, we propose the concept of domain actors that provide a type-safe, SQL-like interface to develop the actors of our database system and the concept of Functors to build queries retrieving data contained in multiple actor instances. Our experiments demonstrate the feasibility of encapsulating data into domain actors by evaluating their memory overhead and performance. We also discuss how our proposed actor database system framework solves some of the challenges that arise from the design of distributed databases such as data partitioning, failure handling, and concurrent query processing.

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Sebastian Schmidl

Research Interests

Links

Teaching

Publications

Become a student at HPI

Notes on the Coronavirus

Statements on HPI

Events

30.01.2020 | Inaugural Lecture: Prof. Dr. Katharina Hölzle

12.12.2019 | Inauguration Lecture of Prof. Dr. Falk Uebernickel

21.11.2019 | HPI-Mount Sinai Digital Health Forum 2019

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