Due to the inaccuracy and noisy, uncertainty is inherent in time series streams, and increases the complexity of streams clustering. For the continuous arriving and massive data size, efficient data storage is a crucial task for clustering uncertain data streams. With hash-compressed structure, an extended uncertain sketch and update strategy are proposed to store uncertain data streams. And ba...

Uncertainty is inherent in data streams, and present new challenges to data streams mining. For continuous arriving and large size of data streams, modeling sequences of uncertain time series data streams require significantly more space. Therefore, it is important to construct compressed representation for storing uncertain time series data. Based on granules, sequential sketches are created t...

Uncertainty in data values is pervasive in all real-world environments and have received a lot of attention in the literature. Over the last decade or so, several extensions to the relational model have been proposed to address the issue of how data uncertainty can be modeled in a comprehensive manner. This chapter provides a summary of the major extensions. We discuss the strengths and weaknes...

There has been much interest in answering top-k queries on probabilistic data in various applications such as market analysis, personalised services, and decision making. In probabilistic relational databases, the most common problem in answering top-k queries (ranking queries) is selecting the top-k result based on scores and top-k probabilities. In this paper, we firstly propose novel answers...

We propose a framework for representing conditioned probabilistic relational data. In this framework the existence of tuples in possible worlds is determined by Boolean expressions composed from elementary events. The probability of a possible world is computed from the probabilities associated with these elementary events. In addition, a set of global constraints conditions the database. Condi...

Some challenges in frequent pattern mining from data streams are the drift of data distribution and the computational efficiency. In this work an additional challenge is considered: data streams describe complex objects modeled by multiple database relations. A multi-relational data mining algorithm is proposed to efficiently discover approximate relational frequent patterns over a sliding time...