in There is no doubt that the availability of cheap and powerful computers has increased the interest in ML applications. With spatially correlated data, the maximization of the likelihood requires considerable computer power mainly for two numerical operations: matrix inversion and maximization in multidimensional

Consider the problem of monitoring tens of thousands of time series data streams in an online fashion and making decisions based on them. In addition to single stream statistics such as average and standard deviation, we also want to find high correlations among all pairs of streams. A stock market

In this paper, we address the problem of cor-relation clustering in the dynamic data stream model. The stream consists of updates to the edge weights of a graph on n nodes and the goal is to find a node-partition such that the end-points of negative-weight edges are typically in different clusters

In this paper, we introduce SPIRIT (Streaming Pattern dIscoveRy in multIple Timeseries) . Given n numerical data streams, all of whose values we observe at each time tick t, SPIRIT can incrementally find correlations and hidden variables, which summarise the key trends in the entire stream

not necessarily scale up, let alone operate on semi-infinite streams. Thus, we introduce the dynamic tensor analysis (DTA) method, and its variants. DTA provides a compact summary for high-order and high-dimensional data, and it also reveals the hidden correlations. Algorithmically, we designed DTA very carefully

A major problem in detecting events in streams of data is that the data can be imprecise (e.g. RFID data). However, current state-of-the-art event detection systems such as Cayuga [14], SASE [46] or SnoopIB[1], assume the data is precise. Noise in the data can be captured using techniques

In many applications, classifiers need to be built based on multiple related data streams. For example, stock streams and news streams are related, where the classification patterns may involve features from both streams. Thus instead of mining on a single isolated stream, we need to examine

prototype Data Stream Management System and a thorough performance evaluation is presented.

In many applications such as IP network management, data arrives in streams, and queries over those streams need to be processed online using limited storage.

, all the news articles published by dierent agen-cies in dierent languages tend to cover the same event for a certain period, exhibiting a correlated bursty topic pattern in all the news article streams. In general, mining corre-lated bursty topic patterns from coordinated text streams can reveal