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Quantitative Data Cleaning (QDC) is the use of statistical and other analytical techniques to detect, quantify, and correct data quality problems (or glitches). Current QDC approaches focus on addressing each category of data glitch individually. However, in real-world data, different types of data glitches co-occur in complex patterns. These patterns and interactions between glitches offer valuable...
Temporal data analysis in data warehouses and datastreaming systems often uses time decay to reduce the importance of older tuples, without eliminating their influence, on the results of the analysis. While exponential time decay is commonly used in practice, other decay functions (e.g. polynomial decay) are not, even though they have been identified as useful. We argue that this is because the usual...
Privacy protection is a major concern when microdata is released for ad hoc analyses. Anonymization schemes have to guarantee privacy goals, as well as preserve sufficient information to support reasonably accurate answers to ad hoc queries. In this paper, we focus on the case when the sensitive attributes are numerical (e.g., salary) for which (k,e)-anonymity was shown to be an appropriate privacy...
Information extraction from text databases is a useful paradigm to populate relational tables and unlock the considerable value hidden in plain-text documents. However, information extraction can be expensive, due to various complex text processing steps necessary in uncovering the hidden data. There are a large number of text databases available, and not every text database is necessarily relevant...
The overwhelming flow of information in many data stream applications forces many companies to outsource to a third-party the deployment of a data stream management system (DSMS) for performing desired computations. Remote computations intrinsically raise issues of trust, making query execution assurance on data streams a problem with practical implications. Consider a client observing the same data...
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