NebulaStream -
Data Management for the Internet of Things
NebulaStream is the first general purpose, end-to-end data management system for the IoT. It provides an out-of-the box experience with rich data processing functionalities and a high ease-of-use.

NebulaStream is a joint research project between the DIMA group at TU Berlin and the DFKI IAM group.

Features

NebulaStream combines and innovates state-of-the-art research from various research fields such as cloud, fog, and sensor networks to enable upcoming IoT applications over a unified sensor-fog-cloud environment.

Thousands of Queries

NebulaStream supports the execution of thousands of queries over millions of sensors.

Dynamic Topologies

NebulaStream detects and reacts to changes in the underlying topology without impacting query processing.

Heterogeneous Workloads

NebulaStream supports rich workloads beyond classical stream processing (e.g., geo-spatial analytics, complex-event processing, and machine learning).

Efficient Resource Utilization

NebulaStream compiles queries into highly efficient code, which increases hardware utilization and reduces energy consumption significantly.

Efficient State Management

NebulaStream supports rich stateful operations that take event ordering into account and provide different levels of guarantee.

Heterogeneous Device Support

NebulaStream supports a wide range of devices including different architectures (e.g., ARM, x86) and accelerators (e.g., GPUs, TPUs).

Publications


Project Overview

The NebulaStream Platform: Data and Application Management for the Internet of Things
CIDR 2020 | Steffen Zeuch, Ankit Chaudhary, Bonaventura Del Monte, Haralampos Gavriilidis, Dimitrios Giouroukis, Philipp M. Grulich, Sebastian Bress, Jonas Traub, Volker Markl Paper
NebulaStream: Complex Analytics Beyond the Cloud
VLIoT 2020 | Steffen Zeuch, Eleni Tzirita Zacharatou, Shuhao Zhang, Xenofon Chatziliadis, Ankit Chaudhary, Bonaventura Del Monte, Dimitrios Giouroukis, Philipp M. Grulich, Ariane Ziehn, Volker Markl

System Publications

A Survey of Adaptive Sampling and Filtering Algorithms for the Internet of Things
DEBS 2020 | Dimitrios Giouroukis, Alexander Dadian, Jonas Traub, Steffen Zeuch, Volker Markl Preprint
Rhino: Efficient Management of Very Large Distributed State for Stream Processing Engines
SIGMOD 2020 | Bonaventura Del Monte, Steffen Zeuch, Tilmann Rabl, Volker Markl Preprint
Grizzly: Efficient Stream Processing Through Adaptive Query Compilation
SIGMOD 2020 | Philipp M. Grulich, Sebastian Breß, Steffen Zeuch, Jonas Traub, Janis von Bleichert, Zongxiong Chen, Tilmann Rabl, Volker Markl Paper
Scaling a Public Transport Monitoring System to Internet of Things Infrastructures
EDBT 2020 | Haralampos Gavriilidis, Adrian Michalke, Laura Mons, Steffen Zeuch, Volker Markl Paper
Governor: Operator Placement for a Unified Fog-Cloud Environment
EDBT 2020 | Ankit Chaudhary, Steffen Zeuch, Volker Markl Paper
Disco: Efficient Distributed Window Aggregation
EDBT 2020 | Lawrence Benson, Philipp M. Grulich, Steffen Zeuch, Volker Markl, Tilmann Rabl Paper
SENSE: Scalable Data Acquisition from Distributed Sensors with Guaranteed Time Coherence
Preprint 2019 | Jonas Traub, Julius Hülsmann, Sebastian Breß, Tilmann Rabl, Volker Markl Preprint
Analyzing Efficient Stream Processing on Modern Hardware
VLDB 2019 | Steffen Zeuch, Sebastian Breß, Tilmann Rabl, Bonaventura Del Monte, Jeyhun Karimov, Clemens Lutz, Manuel Renz, Jonas Traub, Volker Markl Paper
Efficient Window Aggregation with General Stream Slicing
EDBT 2019 | Jonas Traub, Philipp Grulich, Alejandro Rodríguez Cuéllar, Sebastian Breß, Asterios Katsifodimos, Tilmann Rabl, Volker Markl Paper
Resense: Transparent Record and Replay of Sensor Data in the Internet of Things
EDBT 2019 | Dimitrios Giouroukis, Julius Hülsmann, Janis von Bleichert, Morgan Geldenhuys, Tim Stullich, Felipe Gutierrez, Jonas Traub, Kaustubh Beedkar, Volker Markl Paper
Generating Reproducible Out-Of-Order Data Streams
DEBS 2019 | Philipp M. Grulich, Jonas Traub, Sebastian Breß, Asterios Katsifodimos, Tilmann Rabl, Volker Markl Paper
Optimized On-Demand Data Streaming from Sensor Nodes
SoCC 2017 | Jonas Traub, Sebastian Breß, Tilmann Rabl, Asterios Katsifodimos, Volker Markl Paper

Team


Student Assistants:

Adrian Michalke, Dwi Prasetyo Adi Nugroho, Elena Ouro, Johannes Russ, Laura Mons, Zongxiong Chen

Contact Us & Join the Team

Join

We offer a variety of research positions, for PostDocs, PhD Students, Student Assistants and Bachelor/Master theses.

Topics include all aspects of the IoT: query compilation, query optimization, query processing, query languages, distributed data processing, complex-event processing, machine learning, signal processing, sensor networks, fog computing, temporal-spatial query processing, transactional data processing, modern hardware and many more.

Contact Us

Database Systems and Information Management (DIMA) Group Technische Universität Berlin
Sek. E-N 7, Room E-N 728
Einsteinufer 17
10587 Berlin
Germany
+49 30 314 23555 nebulastream(at)dima.tu-berlin.de