EXtensible Markup Language (XML) security has become a relevant research topic due to the widespread use of XML as the language for information interchange and document definition over the Web. In this context, developing an access control mechanism in terms of XML is an important step for Web information security. In this paper, we present the protection and administration facilities of Author-Image , a Java-based system for discretionary access control to XML documents. Relevant features of Author-Image are both a set-oriented and a document-oriented credential-based document protection, a differentiated protection of document/document type contents through the support of multi-granularity protection objects and positive/negative authorizations, and the support for different access control strategies. In this paper, we focus on the strategies we have developed for enforcing access control. Additionally, we provide a description of the environment we have developed to help the Security Officer in performing administrative activities related to both security policy and subject credential management.

Online partnerships depend on federations of not only user identities but also of user entitlements across organizational boundaries.

Spatial join is a fundamental operation for many spatial queries in Geographical Information Systems (GIS). Therefore, the query optimizer of a GIS needs to evaluate the selectivity of spatial joins, in order to find the best execution plan for a given query. This situation has made it necessary to find good and efficient estimators for spatial join selectivity. In particular, spatial join estimation with respect to sets of rectangles is necessary. Indeed, in GIS sets of rectangles are generated in order to produce a synthetic representation of real geometric values through the Minimum Bounding Rectangles (MBR).

Several methods for this estimation have been proposed in literature. One of the best methods is based on precalculated histograms, that describe the distribution of rectangles in the reference space using grid based data structures. The size of an histogram for a given dataset can be comparable to the size of the R-tree built on the same dataset [4].

In this paper we present a new technique for estimating spatial join selectivity considering sets of rectangles as datasets. In particular, we propose a technique that is independent of the distribution of the rectangles in the reference space and produces an auxiliary structure which is an order of magnitude smaller than the corresponding histogram. Indeed, the proposed technique is based on very few statistical parameters and on a unique grid shared by all datasets.

This paper defines a collection of metrics on manuscript reviewing and presents historical data for ACM Transactions on Database Systems and The VLDB Journal.

Data products (macrodata or tabular data and microdata or raw data records), are designed to inform public or business policy, and research or public information. Securing these products against unauthorized accesses has been a long-term goal of the database security research community and the government statistical agencies. Solutions to this problem require combining several techniques and mechanisms. Recent advances in data mining and machine learning algorithms have, however, increased the security risks one may incur when releasing data for mining from outside parties. Issues related to data mining and security have been recognized and investigated only recently.This paper, deals with the problem of limiting disclosure of sensitive rules. In particular, it is attempted to selectively hide some frequent itemsets from large databases with as little as possible impact on other, non-sensitive frequent itemsets. Frequent itemsets are sets of items that appear in the database ``frequently enough’’ and identifying them is usually the first step toward association/correlation rule or sequential pattern mining. Experimental results are presented along with some theoretical issues related to this problem.

Online databases continually generate Web content that users can only access through direct database queries.

Video is increasingly the medium of choice for a variety of communication channels, resulting primarily from increased levels of networked multimedia systems. One way to keep our heads above the video sea is to provide summaries in a more tractable format. Many existing approaches are limited to exploring important low-level feature related units for summarization. Unfortunately, the semantics, content and structure of the video do not correspond to low-level features directly, even with closed-captions, scene detection, and audio signal processing. The drawbacks of existing methods are the following: (1) instead of unfolding semantics and structures within the video, low-level units usually address only the details, and (2) any important unit selection strategy based on low-level features cannot be applied to general videos. Providing users with an overview of the video content at various levels of summarization is essential for more efficient database retrieval and browsing. In this paper, we present a hierarchical video content description and summarization strategy supported by a novel joint semantic and visual similarity strategy. To describe the video content efficiently and accurately, a video content description ontology is adopted. Various video processing techniques are then utilized to construct a semi-automatic video annotation framework. By integrating acquired content description data, a hierarchical video content structure is constructed with group merging and clustering. Finally, a four layer video summary with different granularities is assembled to assist users in unfolding the video content in a progressive way. Experiments on real-word videos have validated the effectiveness of the proposed approach.

To achieve more efficient video indexing and access, we introduce a video database management framework and strategies for video content structure and events mining. The video shot segmentation and representative frame selection strategy are first utilized to parse the continuous video stream into physical units. Video shot grouping, group merging, and scene clustering schemes are then proposed to organize the video shots into a hierarchical structure using clustered scenes, scenes, groups, and shots, in increasing granularity from top to bottom. Then, audio and video processing techniques are integrated to mine event information, such as dialog, presentation and clinical operation, from the detected scenes. Finally, the acquired video content structure and events are integrated to construct a scalable video skimming tool which can be used to visualize the video content hierarchy and event information for efficient access. Experimental results are also presented to evaluate the performance of the proposed framework and algorithms.