Unpacking Online Streams

Online Clustering of Data Streams

We consider the problem of clustering data streams. A data stream can roughly be thought of as a transient, continuously increasing sequence of time-stamped data. In order to maintain an up-to-date clustering structure, it is necessary to analyze the incoming data in an online manner, tolerating but a constant time delay. For this purpose, we develop an efficient online version of the classical...

Online Spectral Clustering on Network Streams

Graph is an extremely useful representation of a wide variety of practical systems in data analysis. Recently, with the fast accumulation of stream data from various type of networks, significant research interests have arisen on spectral clustering for network streams (or evolving networks). Compared with the general spectral clustering problem, the data analysis of this new type of problems m...

Online classification of nonstationary data streams

Mark Last Ben-Gurion University of the Negev Department of Information Systems Engineering Beer-Sheva 84105, Israel Email: [email protected] Abstract Most classification methods are based on the assumption that the data conforms to a stationary distribution. However, the real-world data is usually collected over certain periods of time, ranging from seconds to years, and ignoring possible...

Online Machine Learning in Big Data Streams

The area of online machine learning in big data streams covers algorithms that are (1) distributed and (2) work from data streams with only a limited possibility to store past data. The first requirement mostly concerns software architectures and efficient algorithms. The second one also imposes nontrivial theoretical restrictions on the modeling methods: In the data stream model, older data is...

Online Algorithms for Caching Multimedia Streams