A comprehensive and systematic literature review on the big data management techniques in the internet of things
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 …
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
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 …
research and industrial communities due to its fundamental role in altering the human …
{ROLLER}: Fast and efficient tensor compilation for deep learning
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 …
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 …
strategies, the current situation of enterprise knowledge management (EKM) is investigated …
Cost-based or learning-based? A hybrid query optimizer for query plan selection
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 …
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
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 …
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
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 …
protected by integrity, confidentiality, and authentication services. The network is protected …
An in-depth study of microservice call graph and runtime performance
Loosely-coupled and light-weight microservices running in containers are replacing
monolithic applications gradually. Understanding the characteristics of microservices is …
monolithic applications gradually. Understanding the characteristics of microservices is …
CompressDB: Enabling efficient compressed data direct processing for various databases
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 …
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
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 …
model over the incomplete data requires two steps. First, it requires a data-effective step that …