A comprehensive and systematic literature review on the big data management techniques in the internet of things

A Naghib, N Jafari Navimipour, M Hosseinzadeh… - Wireless …, 2023 - Springer
Abstract The Internet of Things (IoT) is a communication paradigm and a collection of
heterogeneous interconnected devices. It produces large-scale distributed, and diverse data …

CEDAR: A cluster-based energy-aware data aggregation routing protocol in the internet of things using capuchin search algorithm and fuzzy logic

M Mohseni, F Amirghafouri, B Pourghebleh - Peer-to-Peer Networking and …, 2023 - Springer
Over the last decade, the Internet of Things (IoT) has received much interest from the
research and industrial communities due to its fundamental role in altering the human …

{ROLLER}: Fast and efficient tensor compilation for deep learning

H Zhu, R Wu, Y Diao, S Ke, H Li, C Zhang… - … USENIX Symposium on …, 2022 - usenix.org
Despite recent advances in tensor compilers, it often costs hours to generate an efficient
kernel for an operator, a compute-intensive sub-task in a deep neural network (DNN), on …

[HTML][HTML] Development and innovation of enterprise knowledge management strategies using big data neural networks technology

Y Zhao, S Wen, T Zhou, W Liu, H Yu, H Xu - Journal of Innovation & …, 2022 - Elsevier
To strengthen the development of enterprises and optimize knowledge management
strategies, the current situation of enterprise knowledge management (EKM) is investigated …

Cost-based or learning-based? A hybrid query optimizer for query plan selection

X Yu, C Chai, G Li, J Liu - Proceedings of the VLDB Endowment, 2022 - dl.acm.org
Traditional cost-based optimizers are efficient and stable to generate optimal plans for
simple SQL queries, but they may not generate high-quality plans for complicated queries …

AStitch: enabling a new multi-dimensional optimization space for memory-intensive ML training and inference on modern SIMT architectures

Z Zheng, X Yang, P Zhao, G Long, K Zhu… - Proceedings of the 27th …, 2022 - dl.acm.org
This work reveals that memory-intensive computation is a rising performance-critical factor in
recent machine learning models. Due to a unique set of new challenges, existing ML …

ML‐DDoSnet: IoT intrusion detection based on denial‐of‐service attacks using machine learning methods and NSL‐KDD

M Esmaeili, SH Goki, BHK Masjidi… - Wireless …, 2022 - Wiley Online Library
The Internet of Things (IoT) is a complicated security feature in which datagrams are
protected by integrity, confidentiality, and authentication services. The network is protected …

An in-depth study of microservice call graph and runtime performance

S Luo, H Xu, C Lu, K Ye, G Xu, L Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Loosely-coupled and light-weight microservices running in containers are replacing
monolithic applications gradually. Understanding the characteristics of microservices is …

CompressDB: Enabling efficient compressed data direct processing for various databases

F Zhang, W Wan, C Zhang, J Zhai, Y Chai… - Proceedings of the 2022 …, 2022 - dl.acm.org
In modern data management systems, directly performing operations on compressed data
has been proven to be a big success facing big data problems. These systems have …

Goodcore: Data-effective and data-efficient machine learning through coreset selection over incomplete data

C Chai, J Liu, N Tang, J Fan, D Miao, J Wang… - Proceedings of the …, 2023 - dl.acm.org
Given a dataset with incomplete data (eg, missing values), training a machine learning
model over the incomplete data requires two steps. First, it requires a data-effective step that …