Distributed data stream processing and edge computing: A survey on resource elasticity and future directions

MD De Assuncao, A da Silva Veith, R Buyya - Journal of Network and …, 2018‏ - Elsevier
Under several emerging application scenarios, such as in smart cities, operational
monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous …

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 …

Assessing the impact of big data on firm innovation performance: Big data is not always better data

M Ghasemaghaei, G Calic - Journal of business research, 2020‏ - Elsevier
In this study, we explore the impacts of big data's main characteristics (ie, volume, variety,
and velocity) on innovation performance (ie, innovation efficacy and efficiency), which …

Does big data enhance firm innovation competency? The mediating role of data-driven insights

M Ghasemaghaei, G Calic - Journal of Business Research, 2019‏ - Elsevier
Grounded in gestalt insight learning theory and organizational learning theory, we collected
data from 280 middle and top-level managers to investigate the impact of each big data …

Samza: stateful scalable stream processing at LinkedIn

SA Noghabi, K Paramasivam, Y Pan… - Proceedings of the …, 2017‏ - dl.acm.org
Distributed stream processing systems need to support stateful processing, recover quickly
from failures to resume such processing, and reprocess an entire data stream quickly. We …

Deep learning in big data analytics: a comparative study

B Jan, H Farman, M Khan, M Imran, IU Islam… - Computers & Electrical …, 2019‏ - Elsevier
Deep learning methods are extensively applied to various fields of science and engineering
such as speech recognition, image classifications, and learning methods in language …

Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

CLP Chen, CY Zhang - Information sciences, 2014‏ - Elsevier
It is already true that Big Data has drawn huge attention from researchers in information
sciences, policy and decision makers in governments and enterprises. As the speed of …

A serverless real-time data analytics platform for edge computing

S Nastic, T Rausch, O Scekic, S Dustdar… - IEEE Internet …, 2017‏ - ieeexplore.ieee.org
Contemporary solutions for cloud-supported, edge-data analytics mostly apply analytics
techniques in a rigid bottom-up approach, regardless of the data's origin. Typically, data are …

[ספר][B] Principles of distributed database systems

MT Özsu, P Valduriez - 1999‏ - Springer
The first edition of this book appeared in 1991 when the technology was new and there were
not too many products. In the Preface to the first edition, we had quoted Michael Stonebraker …

Complex event recognition in the big data era: a survey

N Giatrakos, E Alevizos, A Artikis, A Deligiannakis… - The VLDB Journal, 2020‏ - Springer
The concept of event processing is established as a generic computational paradigm in
various application fields. Events report on state changes of a system and its environment …