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Microarrays are high-throughput technologies whose data are known to be noisy. In this work, we propose a graph-based method which first identifies the extent to which a single microarray experiment is noisy and then applies an error function to clean individual expression levels. These two steps are unified within a framework based on a graph representation of a separate data set from some repository. We demonstrate the utility of our method by comparing our results against statistical methods by applying both techniques to simulated microarray data. Our results are encouraging and indicate one potential use of microarray data from past experiments.

Application of information technologies (IT) in the field of audit is worth studying. Chinese data-oriented IT audit is researched in this paper. Firstly, the conditions of IT audit used in domestic and overseas are analyzed. Then, the main steps of data-oriented IT audit used in China are presented, including: data acquisition, data cleaning and data processing. And data processing method based on outlier detection and visualization data processing methods are proposed. Then, the popular data-oriented IT audit software developed in China are given. Finally, data-oriented IT audit used in Chinese online audit are introduced, and the differences of data acquisition and data processing methods between online audit and single computer audit are analyzed.

Researchers develop data extractor which could effectively extract data from web sources, tabulate them, and used it for further processing. However, not all data are correctly extracted, they may either missed certain valuable information or contain additional unnecessary information. In the case of unnecessary information, researchers use a cleaning method to remove them such that the data extracted are free of errors. Removing these data is important as unnecessary information may affect the accuracy of subsequent extractor tools, hence may eventually prevent the tool from performing its task efficiently. In this research proposal, we embark on a data cleaning tool to clean data using ontology tools, which could effectively clean data based on their semantics. Experimental results show that our tool is highly efficient in data cleaning and is able to outperform existing state of the art tools.

XML, the eXtensible Markup Language, is fast evolving into the new standard for data representation and exchange on the WWW. This has resulted in a growing number of data cleaning techniques to locate "dirty" data (artifacts). In this paper, we present DEEPDETECT – an extensible system that detects attribute outliers and duplicates in XML documents. Attribute outlier detection finds objects that contain deviating values with respect to a relevant group of objects. This entails utilizing the correlation among element values in a given XML document. Duplicate detection in XML requires the identification of subtrees that correspond to real world objects. Our system architecture enables sharing of common operations that prepare XML data for the various artifact detection techniques. DEEPDETECT also provides an intuitive visual interface for the user to specify various parameters for preprocessing and detection, as well as to view results.