A large number of noise are usually carried in the original RFID data and need to be cleaned up before further processing. Outlier detection is an effective method for RFID data cleaning. In this paper, a point probability data model was proposed to describe the uncertain RFID data streams. The wavelet density threshold was incorporated in this method to adaptively detect the outliers in the sl...

With the evolution of technology, the ways to acquire data and the applications of data are more diverse. As data volume continuously grows, the data quality may not be high as usual. The data can be defected, imprecise or inaccurate due to the process of data acquiring. Recently, the skyline query is widely used in data analysis to derive the results that meets more than one specific condition...

Data processing in distributed environments is often across heterogeneous systems, bearing the need to exchange provenance information, such as, how and when data was generated, combined, recombined, and processed. Distributed systems involve multiple participants and data sources which can produce unreliable, erroneous data. Besides, there maybe exists oceans amount of data to deal with, e.g.,...

Most existing traditional grid-based clustering algorithms for uncertain data streams that used the fixed meshing method have the disadvantage of low clustering accuracy. In view of above deficiencies, this paper proposes a novel algorithm APDG-CUStream, Adjustable Probability Density Grid-based Clustering for Uncertain Data Streams, which adopts the online component and offline component. In o...

Lahar is a warehousing system for Markovian streams—a common class of uncertain data streams produced via inference on probabilistic models. Example Markovian streams include text inferred from speech, location streams inferred from GPS or RFID readings, and human activity streams inferred from sensor data. Lahar supports OLAP-style queries on Markovian stream archives by leveraging novel appro...

Recently, some approaches of finding probabilistic skylines on uncertain data have been proposed. In these approaches, a data object is composed of instances, each associated with a probability. The probabilistic skyline is then defined as a set of non-dominated objects with probabilities exceeding or equaling a given threshold. In many applications, data are generated as a form of continuous d...

With the rapid development of data collection methods and their practical applications, the management of uncertain data streams has drawn wide attention in both academia and industry. System capacity planning and Quality of service (QoS) metrics are two very important problems for data stream management systems (DSMSs) to process streams e±ciently due to unpredictable input characteristics and...

In this chapter, we will study the clustering problem for uncertain data. When information about the data uncertainty is available, it can be leveraged in order to improve the quality of the underlying results. We will provide a survey of the different algorithms for clustering uncertain data. This includes recent algorithms for clustering static data sets, as well as the algorithms for cluster...