Ph.D. Student and Research Associate at Technical University Berlin mentored by Volker Markl, Tilmann Rabl, and Steffen Zeuch.

My research interests deal with designing and developing distributed data processing engines using modern hardware for scalable data management and machine learning. Currently, I am investigating distributed protocols for dealing with large distributed state in distributed stream processing engines.

Before joining TU Berlin and DFKI, I received my MSc degree from University of Salerno in collaboration with the Distributed and Parallel Systems Group, Innsbruck under supervision of Radu Prodan and Vittorio Scarano. I developed my Master's thesis while partecipating to an Erasmus+ program.

Bonaventura Del Monte
delmonte [at] tu [dash] berlin [dot] de
+49 30 31422784
TU Berlin
Einsteinufer 17
10587 Berlin
Germany
TU Berlin EN 741

Publications

SIGMOD'20 Paper Slides
Rhino: Efficient Management of Very Large Distributed State for Stream Processing Engines. Bonaventura Del Monte, Steffen Zeuch, Tilmann Rabl, Volker Markl, 2020 ACM SIGMOD International Conference on Management of Data, Portland, USA, 2020.
CIDR'20 Paper
The NebulaStream Platform: Data and Application Management for the Internet of Things. Steffen Zeuch, Ankit Chaudhary, Bonaventura Del Monte, Haralampos Gavriilidis, Dimitrios Giouroukis, Philipp M. Grulich, Sebastian Bress, Jonas Traub, Volker Markl, 10th Biennial Conference on Innovative Data Systems, Amsterdam, Netherlands, 2020.
VLDB'19 Paper
Analyzing efficient stream processing on modern hardware. Steffen Zeuch, Bonaventura Del Monte, Jeyhun Karimov, Clemens Lutz, Manuel Renz, Jonas Traub, Sebastian Breß, Tilmann Rabl, and Volker Markl, in Proceedings of VLDB 2019. International Conference on Very Large Data Bases (VLDB-2019), Los Angeles, USA, 2019.
BTW'19 Paper
On-the-fly Reconfiguration of Query Plans for Stateful Stream Processing Engines. Adrian Bartnik, Bonaventura Del Monte, Tilmann Rabl and Volker Markl, in Tagungsband Datenbanksysteme für Business, Technologie und Web (BTW 2019), Rostock, Germany, 2019.
Phd@VLDB'17 Paper
Efficient Migration of Very Large Distributed State for Scalable Stream Processing. Bonaventura Del Monte, in Proceedings of the VLDB 2017 PhD Workshop. International Conference on Very Large Data Bases (VLDB-2017), located at co-located with the 43rd International Conference on Very Large Databases (VLDB 2017), August 28, München, Germany, 2017.
EDBT'17 Paper
PROTEUS: Scalable Online Machine Learning for Predictive Analytics and Real-Time Interactive Visualization. Bonaventura Del Monte, Jeyhun Karimov, Alireza Rezaei Mahdiraji, Tilmann Rabl, Volker Markl, in Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference (EDBT/ICDT 2017). International Workshop on Big Data Management in European Projects (EuroPro), 1st, located at Joint Conference EDBT/ICDT, March 21, Venice, Italy, 2017.
ICGHPC'16 Paper
A scalable GPU-enabled framework for training deep neural networks. Bonaventura Del Monte, Radu Prodan, in the 2nd IEEE Int. Conf. on Green High Performance Computing (ICGHPC'16), Nagercoil, India, February 26-27, 2016.

Research Projects

I am currently a recipient of a Software Campus award and the principal investigator of the Rhino Project in collaboration with Huawei Technologies LTD.

I was involved with the following EU-funded projects:


Service

  • VLDB 2021, external reviewer
  • IEEE Transactions on Parallel and Distributed Systems 2020, external reviewer
  • Conference on Innovative Data Systems Research 2019, external reviewer
  • VLDB 2019, external reviewer
  • ACM SIGMOD 2019, external reviewer
  • ACM SIGMOD 2018, external reviewer

Teaching

  • Big Data Analytics Project, SS 2020, TU Berlin
  • Big Data Analytics Project, WS 2019-2020, TU Berlin
  • Big Data Analytics Project, SS 2019, TU Berlin
  • Big Data Analytics Project, WS 2018-2019, TU Berlin
  • Information Management Seminar, SS 2018, TU Berlin
  • Big Data Analytics Project, WS 2017-2018, TU Berlin
  • Big Data Analytics Project, SS 2017, TU Berlin
  • Information Management Seminar, SS 2017, TU Berlin

Mentored Master's Theses

  • Thao Ha, Spatial Analytics using the Apache Flink Platform, defended Aug 2017
  • Adrian Bartnik, Runtime Modifications of a Flink Jobgraph, defended Apr 2018
  • Dominik Schröck, Building an optimizer for Dynamic Rescaling of Distributed Stream Processing Engines, defended Jul 2018
  • Oleksandr Chumak, Efficient State Management for Large Streaming Machine Learning Moedels in Apache Flink, defended Feb 2019
  • Tobias Münch, Exploring On-Demand Optimizations for Stateful Streaming Queries, defended Jun 2019
  • Joan Tiffany Ong Lopez, State Migration in Apache Spark, defended Aug 2019
  • Venkata Subbarao Chunduri, Stream Processing for High-Speed Networks, defended March 2020