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 …
Harnessing scalable transactional stream processing for managing large language models [vision]
Cstream: Parallel data stream compression on multicore edge devices
In the burgeoning realm of Internet of Things (IoT) applications on edge devices, data stream
compression has become increasingly pertinent. The integration of added compression …
compression has become increasingly pertinent. The integration of added compression …
IoT Sensor Data Stream Compression with Hybrid Compression Algorithms
K Garikipati, T Muppala, AV Chowdary… - 2024 15th …, 2024 - ieeexplore.ieee.org
The recent world advancement in technology especially in the use of IoT devices has posed
some serious complications in terms of how effectively we can convey data, this is normally …
some serious complications in terms of how effectively we can convey data, this is normally …
Fast Parallel Recovery for Transactional Stream Processing on Multicores
Transactional stream processing engines (TSPEs) have gained increasing attention due to
their capability of processing real-time stream applications with transactional semantics …
their capability of processing real-time stream applications with transactional semantics …
MorphStream: Scalable Processing of Transactions over Streams on Multicores
Transactional Stream Processing Engines (TSPEs) form the backbone of modern stream
applications handling shared mutable states. Yet, the full potential of these systems …
applications handling shared mutable states. Yet, the full potential of these systems …
A hardware-conscious stateful stream compression framework for iot applications (vision)
Data stream compression has attracted vast interest in emerging IoT (Internet of Things)
applications. However, adopting stream compression on IoT applications is non-trivial due to …
applications. However, adopting stream compression on IoT applications is non-trivial due to …
Compressing network data with deep learning
D Gili Fernández De Romarategui - 2024 - upcommons.upc.edu
This thesis delves into the problem of compressing data generated by mobile networks,
which are formed by many antennas and IoT devices, that emit huge quantities of …
which are formed by many antennas and IoT devices, that emit huge quantities of …
[PDF][PDF] FreewayML: An Adaptive and Stable Streaming Learning Framework for Dynamic Data Streams
Streaming (machine) learning (SML) can capture dynamic changes in real-time data and
perform continuous updates. It has been widely applied in real-world scenarios such as …
perform continuous updates. It has been widely applied in real-world scenarios such as …