Uncertain data streams are increasingly common in real-world deployments and monitoring applications require the evaluation of complex queries on such streams. In this paper, we consider complex queries involving conditioning (e.g., selections and group by’s) and aggregation operations on uncertain data streams. To characterize the uncertainty of answers to these queries, one generally has to c...

Current research on data stream classification mainly focuses on certain data, in which precise and definite value is usually assumed. However, data with uncertainty is quite natural in real-world application due to various causes, including imprecise measurement, repeated sampling and network errors. In this paper, we focus on uncertain data stream classification. Based on CVFDT and DTU, we pr...

Most existing stream clustering algorithms adopt the online component and offline component. The disadvantage of two-phase algorithms is that they can not generate the final clusters online and the accurate clustering results need to be got through the offline analysis. Furthermore, the clustering algorithms for uncertain data streams are incompetent to find clusters of arbitrary shapes accordi...

Discovering Probabilistic Frequent Itemsets (PFI) in uncertain data is very challenging since algorithms designed for deterministic data are not applicable in this context. The problem is even more difficult for uncertain data streams where massive frequent updates need be taken into account while respecting data stream constraints. In this paper, we propose FMU (Fast Mining of Uncertain data s...

This paper proposes a novel naı̈ve Bayesian classifier in categorical uncertain data streams. Uncertainty in categorical data is usually represented by vector valued discrete pdf, which has to be carefully handled to guarantee the underlying performance in data mining applications. In this paper, we map the probabilistic attribute to deterministic points in the Euclidean space and design a dista...