Jixue Liu

Strong functional dependencies and their application to normal forms in XML

In this article, we address the problem of how to extend the definition of functional dependencies (FDs) in incomplete relations to XML documents (called XFDs) using the well-known strong satisfaction approach.We propose a syntactic definition of strong XFD satisfaction in an XML document and then justify it by showing that, similar to the case in relational databases, for the case of simple paths, keys in XML are a special case of XFDs. We also propose a normal form for XML documents based on our definition of XFDs and provide a formal justification for it by proving that it is a necessary and sufficient condition for the elimination of redundancy in an XML document.

Discover Dependencies from Data—A Review

Functional and inclusion dependency discovery is important to knowledge discovery, database semantics analysis, database design, and data quality assessment. Motivated by the importance of dependency discovery, this paper reviews the methods for functional dependency, conditional functional dependency, approximate functional dependency, and inclusion dependency discovery in relational databases and a method for discovering XML functional dependencies.

Constraint Preserving Transformation from Relational Schema to XML Schema

XML has become the standard for publishing and exchanging data on the Web. However, most business data is managed and will remain to be managed by relational database management systems. As such, there is an increasing need to efficiently and accurately publish relational data as XML documents for Internet-based applications. One way to publish relational data is to provide virtual XML documents for relational data via an XML schema which is transformed from the underlying relational database schema such that users can access the relational database through the XML schema. In this paper, we discuss issues in transforming a relational database schema into the corresponding XML schema. We aim to preserve all integrity constraints defined in a relational database schema, to achieve high level of nesting and to avoid introducing data redundancy in the transformed XML schema. In the paper, we first propose a basic transformation algorithm which introduces no data redundancy, then we improve the algorithm by exploring further nesting of the transformed XML schema.

On the equivalence between FDs in XML and FDs in relations

With the growing use of the eXtensible Markup Language (XML) in database technology as a format for the permanent storage of data, the topic functional dependencies in XML (XFDs) has assumed increased importance because of its central role in database design. Recently, two different approaches have been proposed for defining an XFD. The first uses the concept of a ‘tree tuple’, whereas the second uses the concept of a ‘closest node’. In general, the two approaches are not comparable, but are comparable when a Document Type Definition is present and there is no missing information in the XML document. The first contribution of this article shows that when the two XFD definitions are comparable, the definitions are equivalent, and so there is essentially a common definition of an XFD in complete XML documents. The second contribution is to provide justification for the definition of a ‘closest node’ XFD. We show that if a complete flat relation is mapped to an XML document by an arbitrary sequence of nest operations, the XML document satisfies a ‘closest node’ XFD if and only if the relation satisfies the corresponding functional dependency. The class of XML documents generated in this fashion is a subset of the class of XML documents for which the two definitions of XFDs coincide. Hence ‘tree tuple’ and ‘closest node’ XFDs both capture the semantics of FDs when a complete relation is mapped to an XML document via arbitrary nesting.

Effective Pruning for the Discovery of Conditional Functional Dependencies

Conditional functional dependencies (CFDs) have been proposed as a new type of semantic rules extended from traditional functional dependencies. They have shown great potential for detecting and repairing inconsistent data. Constant CFDs are 100% confidence association rules. The theoretical search space for the minimal set of CFDs is the set of minimal generators and their closures in data. This search space has been used in the currently most efficient constant CFD discovery algorithm. In this paper, we propose pruning criteria to further prune the theoretic search space, and design a fast algorithm for constant CFD discovery. We evaluate the proposed algorithm on a number of media to large real-world data sets. The proposed algorithm is faster than the currently most efficient constant CFD discovery algorithm, and has linear time performance in the size of a data set.

Local XML functional dependencies

Keys and functional dependencies play a fundamental role in relational databases where they are used in integrity enforcement and in database design. Similarly, these constraints will play a fundamental role in XML and recently keys and functional dependencies in XML have been defined. In this paper we extend the previous definition of functional dependencies in XML to local functional dependencies in XML. Local functional dependencies (LFDs) are functional dependencies which hold only in a certain part of an XML document and not in the whole document. We also define, and prove correct, axioms for reasoning about the implication of LFDs in XML. Finally, we examine the relationship between LFDs and keys and show that the recently introduced concept of a relative key is a special case of a LFD.

An improvement of symbolic aggregate approximation distance measure for time series

Abstract Symbolic Aggregate approXimation (SAX) as a major symbolic representation has been widely used in many time series data mining applications. However, because a symbol is mapped from the average value of a segment, the SAX ignores important information in a segment, namely the trend of the value change in the segment. Such a miss may cause a wrong classification in some cases, since the SAX representation cannot distinguish different time series with similar average values but different trends. In this paper, we firstly design a measure to compute the distance of trends using the starting and the ending points of segments. Then we propose a modified distance measure by integrating the SAX distance with a weighted trend distance. We show that our distance measure has a tighter lower bound to the Euclidean distance than that of the original SAX. The experimental results on diverse time series data sets demonstrate that our proposed representation significantly outperforms the original SAX representation and an improved SAX representation for classification.

Mining Causal Association Rules

Discovering causal relationships is the ultimate goal of many scientific explorations. Causal relationships can be identified with controlled experiments, but such experiments are often very expensive and sometimes impossible to conduct. On the other hand, the collection of observational data has increased dramatically in recent decades. Therefore it is desirable to find causal relationships from the data directly. Significant progress has been made in the field of discovering causal relationships using the Causal Bayesian Network (CBN) theory. The applications of CBNs, however, are greatly limited due to the high computational complexity. In another direction, association rule mining has been shown to be an efficient data mining means for relationship discovery. However, although causal relationships imply associations, the reverse does not always hold. In this paper we study how to use an efficient association mining approach to discover potential causal rules in observational data. We make use of the idea of retrospective cohort studies, a widely used approach in medical and social research, to detect causal association rules. In comparison with the constraint-based methods within the CBN paradigm, the proposed approach is faster and is capable of finding a cause consisting of combined variables.

Information based data anonymization for classification utility

Anonymization is a practical approach to protect privacy in data. The major objective of privacy preserving data publishing is to protect private information in data whereas data is still useful for some intended applications, such as building classification models. In this paper, we argue that data generalization in anonymization should be determined by the classification capability of data rather than the privacy requirement. We make use of mutual information for measuring classification capability for generalization, and propose two k-anonymity algorithms to produce anonymized tables for building accurate classification models. The algorithms generalize attributes to maximize the classification capability, and then suppress values by a privacy requirement k (IACk) or distributional constraints (IACc). Experimental results show that algorithm IACk supports more accurate classification models and is faster than a benchmark utility-aware data anonymization algorithm.

Functional Dependencies, from Relational to XML

The flexibility of XML allows the same data to be represented in many different ways. Some representations may be better than others in that they require less storage or have less redundancy. In this paper we define functional dependencies in XML (XFDs) and investigate their effect on the design of XML documents. We then define two subtypes of XFDs, namely partial and transitive XFDs, which cause the same problems in XML document design as the corresponding types of FDs in relations. We further show that the removal of such types of XFDs can lead to a better document design. On the basis of this, we define the concept of upward XFDs and analyze its use in maximizing the nesting levels in XML documents without introducing redundancy. We further propose guidelines to nesting elements in XML documents.