Runtime adaptation of data stream processing systems: The state of the art

V Cardellini, F Lo Presti, M Nardelli… - ACM Computing …, 2022 - dl.acm.org
Data stream processing (DSP) has emerged over the years as the reference paradigm for
the analysis of continuous and fast information flows, which often have to be processed with …

Recent advancements in event processing

M Dayarathna, S Perera - ACM Computing Surveys (CSUR), 2018 - dl.acm.org
Event processing (EP) is a data processing technology that conducts online processing of
event information. In this survey, we summarize the latest cutting-edge work done on EP …

Towards a computing continuum: Enabling edge-to-cloud integration for data-driven workflows

D Balouek-Thomert, EG Renart… - … Journal of High …, 2019 - journals.sagepub.com
Dramatic changes in the technology landscape marked by increasing scales and
pervasiveness of compute and data have resulted in the proliferation of edge applications …

[BOOK][B] Real-time linked dataspaces: Enabling data ecosystems for intelligent systems

E Curry - 2020 - library.oapen.org
This open access book explores the dataspace paradigm as a best-effort approach to data
management within data ecosystems. It establishes the theoretical foundations and …

Self‐adaptation on parallel stream processing: A systematic review

A Vogel, D Griebler, M Danelutto… - Concurrency and …, 2022 - Wiley Online Library
A recurrent challenge in real‐world applications is autonomous management of the
executions at run‐time. In this vein, stream processing is a class of applications that compute …

SLA management for big data analytical applications in clouds: A taxonomy study

X Zeng, S Garg, M Barika, AY Zomaya, L Wang… - ACM Computing …, 2020 - dl.acm.org
Recent years have witnessed the booming of big data analytical applications (BDAAs). This
trend provides unrivaled opportunities to reveal the latent patterns and correlations …

Overflow: Multi-site aware big data management for scientific workflows on clouds

R Tudoran, A Costan, G Antoniu - IEEE transactions on cloud …, 2015 - ieeexplore.ieee.org
The global deployment of cloud datacenters is enabling large scale scientific workflows to
improve performance and deliver fast responses. This unprecedented geographical …

An intelligent parallel distributed streaming framework for near real-time science sensors and high-resolution medical images

S Shivadekar, J Mangalagiri, P Nguyen… - … conference on parallel …, 2021 - dl.acm.org
Our goals are to address challenges such as latency, scalability, throughput and
heterogeneous data sources of streaming analytics and deep learning pipelines in science …

Enactment of adaptation in data stream processing with latency implications—a systematic literature review

C Qin, H Eichelberger, K Schmid - Information and Software Technology, 2019 - Elsevier
Context Stream processing is a popular paradigm to continuously process huge amounts of
data. Runtime adaptation plays a significant role in supporting the optimization of data …

A dynamically scalable cloud data infrastructure for sensor networks

T Li, K Keahey, K Wang, D Zhao, I Raicu - Proceedings of the 6th …, 2015 - dl.acm.org
As small, specialized sensor devices become more ubiquitous, reliable, and cheap,
increasingly more domain sciences are creating" instruments at large"-dynamic, often self …