Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited...
Philipp Kranen, Ira Assent, Corinna Baldauf, Thoma...
A real-time AI system in the real world needs to monitor an immense volume of data. To do this, the system must filter out much of the incoming data. However, it must remain re ...
Streaming applications, such as environment monitoring and vehicle location tracking require handling high volumes of continuously arriving data and sudden fluctuations in these vo...
Kyungmin Cho, SungJae Jo, Hyukjae Jang, Su Myeon K...
alization is a combination of abstract generated forms and found imagery. The frequency, amplitude, and percentage differences between samples of incoming data are mapped to forms ...
We present a parallel version of BIRCH with the objective of enhancing the scalability without compromising on the quality of clustering. The incoming data is distributed in a cyc...
We consider storage in an extremely large-scale distributed computer system designed for stream processing applications. In such systems, incoming data and intermediate results ma...
Kirsten Hildrum, Fred Douglis, Joel L. Wolf, Phili...
Recent years have witnessed an increasing number of studies in stream mining, which aim at building an accurate model for continuously arriving data. Somehow most existing work ma...