Results 161 to 170 of about 26,646,267 (236)
Some of the next articles are maybe not open access.
Online ensemble learning algorithm for imbalanced data stream
Applied Soft Computing, 2021In many practical applications, due to the inability to collect complete training data sets at one time, the adaptability of the classifier is poor. Online ensemble learning can better solve this problem. However, most of the data streams are imbalanced.
Hongle Du +4 more
semanticscholar +1 more source
Spatial data streaming or streaming spatial data
Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application, 2010Have you ever counted the number of times the word "streaming" has occurred in a geospatial oriented conference proceedings over the past few years? Have you ever monitored the growth of the geospatial research and industrial community? Have you ever noticed that geospatial researchers are living the luxury of an era where real-time data is streamed at
Balan Sethu Raman, Mohamed Ali
openaire +1 more source
An Adaptive Authenticated Data Structure With Privacy-Preserving for Big Data Stream in Cloud
IEEE Transactions on Information Forensics and Security, 2020With the rapid development of 5G network, big data and IoT, data in many environments is often continuously and dynamically generated with high growth rates, just like stream.
Yi Sun +3 more
semanticscholar +1 more source
Real-time event detection from the Twitter data stream using the TwitterNews+ Framework
Information Processing & Management, 2019Detecting events in real-time from the Twitter data stream has gained substantial attention in recent years from researchers around the world. Different event detection approaches have been proposed as a result of these research efforts. One of the major
M. Hasan, M. Orgun, Rolf Schwitter
semanticscholar +1 more source
Streaming Random Patches for Evolving Data Stream Classification
Industrial Conference on Data Mining, 2019Ensemble methods are a popular choice for learning from evolving data streams. This popularity is due to (i) the ability to simulate simple, yet, successful ensemble learning strategies, such as bagging and random forests; (ii) the possibility of ...
Heitor Murilo Gomes, J. Read, A. Bifet
semanticscholar +1 more source
WIREs Computational Statistics, 2010
AbstractAs the ability to collect data continues to outstrip the ability to process and analyze it, the age‐old paradigm of store‐and‐process is becoming untenable. Finding one or two interesting items in the midst of many possible signals depends on context which often changes over time.
openaire +1 more source
AbstractAs the ability to collect data continues to outstrip the ability to process and analyze it, the age‐old paradigm of store‐and‐process is becoming untenable. Finding one or two interesting items in the midst of many possible signals depends on context which often changes over time.
openaire +1 more source
A survey on data preprocessing for data stream mining: Current status and future directions
Neurocomputing, 2017Data preprocessing and reduction have become essential techniques in current knowledge discovery scenarios, dominated by increasingly large datasets. These methods aim at reducing the complexity inherent to real-world datasets, so that they can be easily
S. Ramírez-Gallego +4 more
semanticscholar +1 more source
Decentralized self-adaptation for elastic Data Stream Processing
Future generations computer systems, 2018Data Stream Processing (DSP) applications are widely used to develop new pervasive services, which require to seamlessly process huge amounts of data in a near real-time fashion.
V. Cardellini +3 more
semanticscholar +1 more source