Uncertain data streams, where data is incomplete, imprecise, and even misleading, have been observed in a variety of environments. Feeding uncertain data streams to existing stream systems can produce results of unknown quality, which is of paramount concern to monitoring applications. In this paper, we present the Claro system that supports uncertain data stream processing for data that is nat...

The flexibility of XML data model allows a more natural representation of uncertain data compared with the relational model. The top-k matching of a twig pattern against probabilistic XML data is essential. Some classical twig pattern algorithms can be adjusted to process the probabilistic XML. However, as far as finding answers of the top-k probabilities is concerned, the existing algorithms s...

Nowadays, streams of data can be continuously generated by sensors in various real-life applications such as environment surveillance. Partially due to the inherited limitation of the sensors, data in these streams can be uncertain. To discover useful knowledge in the form of frequent patterns from streams of uncertain data, a few algorithms have been developed. They mostly use the sliding wind...

Data streams are long, relatively unstructured sequences of characters that contain information such as electronic mail or a tape backup of various documents and reports created in an office. This paper deals with a conceptual framework, using relational algebra and relational databases, within which data streams may be queried. As information is extracted from the data stream, it is put into a...

Lazy learning concept is performing the k-nearest neighbor algorithm, Is used to classification and similarly to clustering of k-nearest neighbor algorithm both are based on Euclidean distance based algorithm. Lazy learning is more advantages for complex and dynamic learning on data streams. In this lazy learning process is consumes the high memory and low prediction Efficiency .this process is...