AbstractÐDiscovering sequential patterns is an important problem in data mining with a host of application domains including medicine, telecommunications, and the World Wide Web. Conventional sequential pattern mining systems provide users with only a very restricted mechanism (based on minimum support) for specifying patterns of interest. As a consequence, the pattern mining process is typically characterized by lack of focus and users often end up paying inordinate computational costs just to be inundated with an overwhelming number of useless results. In this paper, we propose the use of Regular Expressions (REs) as a flexible constraint specification tool that enables user-controlled focus to be incorporated into the pattern mining process. We develop a family of novel algorithms (termed SPIRITÐSequential Pattern mIning with Regular expressIon consTraints) for mining frequent sequential patterns that also satisfy user-specified RE constraints. The main distinguishing factor among the proposed schemes is the degree to which the RE constraints are enforced to prune the search space of patterns during computation. Our solutions provide valuable insights into the trade-offs that arise when constraints that do not subscribe to nice properties (like antimonotonicity) are integrated into the mining process. A quantitative exploration of these trade-offs is conducted through an extensive experimental study on synthetic and real-life data sets. The experimental results clearly validate the effectiveness of our approach, showing that speedups of more than an order of magnitude are possible when RE constraints are pushed deep inside the mining process. Our experimentation with real-life data also illustrates the versatility of REs as a user-level tool for focusing on interesting patterns.

The prevalent use of XML highlights the need for a generic, flexible access-control mechanism for XML documents that supports efficient and secure query access, without revealing sensitive information to unauthorized users. This paper introduces a novel paradigm for specifying XML security constraints and investigates the enforcement of such constraints during XML query evaluation. Our approach is based on the novel concept of security views, which provide for each user group (a) an XML view consisting of all and only the information that the users are authorized to access, and (b) a view DTD that the XML view conforms to. Security views effectively protect sensitive data from access and potential inferences by unauthorized users, and provide authorized users with necessary schema information to facilitate effective query formulation and optimization. We propose an efficient algorithm for deriving security view definitions from security policies (defined on the original document DTD) for different user groups. We also develop novel algorithms for XPath query rewriting and optimization such that queries over security views can be efficiently answered without materializing the views. Our algorithms transform a query over a security view to an equivalent query over the original document, and effectively prune query nodes by exploiting the structural properties of the document DTD in conjunction with approximate XPath containment tests. Our work is the first to study a flexible, DTD-based access-control model for XML and its implications on the XML query-execution engine. Furthermore, it is among the first efforts for query rewriting and optimization in the presence of general DTDs for a rich class of XPath queries. An empirical study based on real-life DTDs verifies the effectiveness of our approach.

We propose a novel index structure, termed XTrie

Recent years have witnessed an increasing interest in designing algorithms for querying and analyzing streaming data (i.e., data that is seen only once in a fixed order) with only limited memory. Providing (perhaps approximate) answers to queries over such continuous data streams is a crucial requirement for many application environments; examples include large telecom and IP network installations where performance data from different parts of the network needs to be continuously collected and analyzed.In this paper, we consider the problem of approximately answering general aggregate SQL queries over continuous data streams with limited memory. Our method relies on randomizing techniques that compute small "sketch" summaries of the streams that can then be used to provide approximate answers to aggregate queries with provable guarantees on the approximation error. We also demonstrate how existing statistical information on the base data (e.g., histograms) can be used in the proposed framework to improve the quality of the approximation provided by our algorithms. The key idea is to intelligently partition the domain of the underlying attribute(s) and, thus, decompose the sketching problem in a way that provably tightens our guarantees. Results of our experimental study with real-life as well as synthetic data streams indicate that sketches provide significantly more accurate answers compared to histograms for aggregate queries. This is especially true when our domain partitioning methods are employed to further boost the accuracy of the final estimates.

No abstract

No abstract

No abstract

Recent work has demonstrated the effectiveness of the wavelet decomposition in reducing large amounts of data to compact sets of wavelet coefficients (termed "wavelet synopses") that can be used to provide fast and reasonably accurate approximate query answers. A major shortcoming of these existing wavelet techniques is that the quality of the approximate answers they provide varies widely, even for identical queries on nearly identical values in distinct parts of the data. As a result, users have no way of knowing whether a particular approximate answer is highlyaccurate or off by many orders of magnitude. In this article, we introduce Probabilistic Wavelet Synopses, the first wavelet-based data reduction technique optimized for guaranteed accuracy of individual approximate answers. Whereas previous approaches rely on deterministic thresholding for selecting the wavelet coefficients to include in the synopsis, our technique is based on a novel, probabilistic thresholding scheme that assigns each coefficient a probability of being included based on its importance to the reconstruction of individual data values, and then flips coins to select the synopsis. We show how our scheme avoids the above pitfalls of deterministic thresholding, providing unbiased, highly accurate answers for individual data values in a data vector. We propose several novel optimization algorithms for tuning our probabilistic thresholding scheme to minimize desired error metrics. Experimental results on real-world and synthetic data sets evaluate these algorithms, and demonstrate the effectiveness of our probabilistic wavelet synopses in providing fast, highly accurate answers with improved quality guarantees.