More and more public data sets which contain information about individuals are published in recent years. The urgency to reduce the risk of privacy disclosure from such data sets makes the approaches of privacy protection for data publishing widely employed. There are two popular models for privacy protection: k-anonymity and l-diversity. k-anonymity focuses on reducing the probability of identifying a particular person, which requires that each equivalence class (a set of records with the same identifier attributes) contains at least k records. l-diversity concentrates on reducing the inference from released sensitive attributes. It requires that each equivalence class has at least l “well-represented” sensitive attribute values. In this study, we view the privacy protection problem in a brand new perspective. We proposed a new model, Unique Distinct l-SR diversity based on the sensitivity of private information. Also, we presented two performance measures to evaluate how much sensitive information can be inferred from an equivalence class. l-SR diversity algorithm was implemented to achieve Unique Distinct l-SR diversity. We tested l-SR diversity on one benchmark data set and two synthetic data sets, and compared it with other l-diversity algorithms. The results show that our algorithm achieved better performance on minimizing inference of sensitive information and reached the comparable generalization data quality compared with other data publishing algorithms.

Abstract We investigate a method designed to improve the accuracy of process mining in scenarios where the identification of task labels for log events is uncertain. Such situations are prevalent in business processes where events consist of communications between people, such as email messages. We examine how the accuracy of an independent task identifier, such as a classification or clustering engine, can be improved by examining the currently mined process model. First, a classification scheme based on identifying the keywords in each message is presented to provide an initial labeling. We then demonstrate how these labels can be refined by considering the likelihood that the event represents a particular task as obtained via an analysis of the current representation of the process model. This process is then repeated a number of times until the model is sufficiently refined. Results show that both keyword classification and the current process model analysis can be significantly effective on their own, and when combined have the potential to correct virtually all errors when noise is low (less than 20%), and can reduce the error rate by about 85% when noise is in the 30–40% range. Copyright © 2010 Crown in the right of Canada.

The computation of data cubes is one of the most expensive operations in on-line analytical processing (OLAP). To improve efficiency, an iceberg cube represents only the cells whose aggregate values are above a given threshold (minimum support). Top-down and bottom-up approaches are used to compute the iceberg cube for a data set, but both have performance limitations. In this paper, a new algorithm, called Multi-Tree Cubing (MTC), is proposed for computing an iceberg cube. The Multi-Tree Cubing algorithm is an integrated top-down and bottom-up approach. Overall control is handled in a top-down manner, so MTC features shared computation. By processing the orderings in the opposite order from the Top-Down Computation algorithm, the MTC algorithm is able to prune attributes. The Bottom Up Computation (BUC) algorithm and its variations also perform pruning by relying on the processing of intermediate partitions. The MTC algorithm, however, prunes without processing such partitions. The MTC algorithm is based on a specialized type of prefix tree data structure, called an Attribute–Partition tree (AP-tree), consisting of attribute and partition nodes. The AP-tree facilitates fast, in-memory sorting and APRIORI-like pruning. We report on five series of experiments, which confirm that MTC is consistently as fast or faster than BUC, while finding the same iceberg cubes.

Peculiar data are objects that are relatively few in number and significantly different from the other objects in a data set. In this paper, we propose the PDD framework for detecting multiple categories of peculiar data. This framework provides an extensible set of perspectives for viewing data, currently including viewing data as a set of records, attributes, frequencies, intervals, sequences, or sequences of changes. By using these six views of the data, multiple categories of peculiar data can be detected to reveal different aspects of the data. For each view, the framework provides an extensible set of peculiarity measures to detect outliers and other kinds of peculiar data. The PDD framework has been implemented for Oracle and Access. Experiments are reported for data sets concerning Regina weather and NHL hockey.

We describe a method for spatio-temporal data mining based on expected distribution domain generalization (ExGen) graphs. Using familiar calendar and geographical concepts, such as workdays, weeks, climatic regions, and countries, spatio-temporal data can be aggregated into summaries in many ways. We automatically search for a summary with a distribution that is anomalous, i.e., far from user expectations. We repeatedly ranked possible summaries according to current expectations, and then allow the user to adjust these expectations.

A case selection algorithm selects representative cases from a large data set for future case-based reasoning tasks. This paper proposes the ESRS algorithm, based on extended similarity-based rough set theory, which selects a reasonable number of the representative cases while maintaining satisfactory classification accuracy. It also can handle noise and inconsistent data. Experimental results on synthetic and real sets of cases showed that its predictive accuracy is similar to that of well-known machine learning systems on standard data sets, while it has the advantage of being applicable to any data set where a similarity function can be defined.