11 citations · 54 across the 10 of their papers we have counts for
10 papers
Towards a Multimodal Stream Processing System
Uélison Jean Lopes dos Santos, Alessandro Ferri, Szilard Nistor +3
In this paper, we present a vision for a new generation of multimodal streaming systems that embed MLLMs as first-class operators, enabling real-time query processing across multip…
PDSP-Bench: A Benchmarking System for Parallel and Distributed Stream Processing
Pratyush Agnihotri, Boris Koldehofe, Roman Heinrich +2
The paper introduces PDSP-Bench, a novel benchmarking system designed for a systematic understanding of performance of parallel stream processing in a distributed environment. Such…
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks
Roman Heinrich, Manisha Luthra, Johannes Wehrstein +2
Traditionally, query optimizers rely on cost models to choose the best execution plan from several candidates, making precise cost estimates critical for efficient query execution.…
COSTREAM: Learned Cost Models for Operator Placement in Edge-Cloud Environments
Roman Heinrich, Carsten Binnig, Harald Kornmayer +1
In this work, we present COSTREAM, a novel learned cost model for Distributed Stream Processing Systems that provides accurate predictions of the execution costs of a streaming que…
No One Size (PPM) Fits All: Towards Privacy in Stream Processing Systems
Mikhail Fomichev, Manisha Luthra, Maik Benndorf +1
Stream processing systems (SPSs) have been designed to process data streams in real-time, allowing organizations to analyze and act upon data on-the-fly, as it is generated. Howeve…
Zero-Shot Cost Models for Distributed Stream Processing
Roman Heinrich, Manisha Luthra, Harald Kornmayer +1
This paper proposes a learned cost estimation model for Distributed Stream Processing Systems (DSPS) with an aim to provide accurate cost predictions of executing queries. A major…
TCEP: Transitions in Operator Placement to Adapt to Dynamic Network Environments
Manisha Luthra, Boris Koldehofe, Niels Danger +3
Distributed Complex Event Processing (DCEP) is a commonly used paradigm to detect and act on situational changes of many applications, including the Internet of Things (IoT). DCEP…
INetCEP: In-Network Complex Event Processing for Information-Centric Networking
Manisha Luthra, Boris Koldehofe, Jonas Höchst +4
Emerging network architectures like Information-centric Networking (ICN) offer simplicity in the data plane by addressing named data. Such flexibility opens up the possibility to m…
Efficient Complex Event Processing in Information-centric Networking at the Edge
Manisha Luthra, Johannes Pfannmüller, Boris Koldehofe +4
Information-centric Networking (ICN) is an emerging Internet architecture that offers promising features, such as in-network caching and named data addressing, to support the edge…
Operator as a Service: Stateful Serverless Complex Event Processing
Manisha Luthra, Sebastian Hennig, Kamran Razavi +2
Complex Event Processing (CEP) is a powerful paradigm for scalable data management that is employed in many real-world scenarios such as detecting credit card fraud in banks. The s…