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Nebulastream: Complex analytics beyond the cloud
The arising Internet of Things (IoT) will require significant changes to current stream
processing engines (SPEs) to enable large-scale IoT applications. In this paper, we present …
processing engines (SPEs) to enable large-scale IoT applications. In this paper, we present …
[HTML][HTML] Validity constraints for data analysis workflows
Porting a scientific data analysis workflow (DAW) to a cluster infrastructure, a new software
stack, or even only a new dataset with some notably different properties is often challenging …
stack, or even only a new dataset with some notably different properties is often challenging …
Distributed transactions on serverless stateful functions
Serverless computing is currently the fastest-growing cloud services segment. The most
prominent serverless offering is Function-as-a-Service (FaaS), where users write functions …
prominent serverless offering is Function-as-a-Service (FaaS), where users write functions …
On the semantics of distributed reactive programming: the cost of consistency
The reactive programming paradigm aims to simplify the development of reactive systems. It
provides abstractions to define time-changing values that are automatically updated by the …
provides abstractions to define time-changing values that are automatically updated by the …
Morphstream: Adaptive scheduling for scalable transactional stream processing on multicores
Transactional stream processing engines (TSPEs) differ significantly in their designs, but all
rely on non-adaptive scheduling strategies for processing concurrent state transactions …
rely on non-adaptive scheduling strategies for processing concurrent state transactions …
A survey on transactional stream processing
Transactional stream processing (TSP) strives to create a cohesive model that merges the
advantages of both transactional and stream-oriented guarantees. Over the past decade …
advantages of both transactional and stream-oriented guarantees. Over the past decade …
Towards concurrent stateful stream processing on multicore processors
Recent data stream processing systems (DSPSs) can achieve excellent performance when
processing large volumes of data under tight latency constraints. However, they sacrifice …
processing large volumes of data under tight latency constraints. However, they sacrifice …
TSpoon: Transactions on a stream processor
Stream processing systems are increasingly becoming a core element in the data
processing stack of many large companies, where they complement data management …
processing stack of many large companies, where they complement data management …
[HTML][HTML] Transactions across serverless functions leveraging stateful dataflows
Serverless computing is currently the fastest-growing cloud services segment. The most
prominent serverless offering is Function-as-a-Service (FaaS), where users write functions …
prominent serverless offering is Function-as-a-Service (FaaS), where users write functions …
S-QUERY: opening the black box of internal stream processor state
Distributed streaming dataflow systems have evolved into scalable and fault-tolerant
production-grade systems. Their applicability has departed from the mere analysis of …
production-grade systems. Their applicability has departed from the mere analysis of …