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.

Emerging database applications require the use of new indexing structures beyond B-trees and R-trees. Examples are the k-D tree, the trie, the quadtree, and their variants. They are often proposed as supporting structures in data mining, GIS, and CAD/CAM applications. A common feature of all these indexes is that they recursively divide the space into partitions. A new extensible index structure, termed SP-GiST is presented that supports this class of data structures, mainly the class of space partitioning unbalanced trees. Simple method implementations are provided that demonstrate how SP-GiST can behave as a k-D tree, a trie, a quadtree, or any of their variants. Issues related to clustering tree nodes into pages as well as concurrency control for SP-GiST are addressed. A dynamic minimum-height clustering technique is applied to minimize disk accesses and to make using such trees in database systems possible and efficient. A prototype implementation of SP-GiST is presented as well as performance studies of the various SP-GiST’‘s tuning parameters.

The emergence of extensible index structures, e.g., GiST (generalized search tree) [J.M. Hellerstein et al. (1995)] and SP-GiST (space-partitioning generalized search tree) [W. G Aref et al., (2001)], calls for a set of extensible algorithms to support different operations (e.g., insertion, deletion, and search). Extensible bulk operations (e.g., bulk loading and bulk insertion) are of the same importance and need to be supported in these index engines. In this paper, we propose two extensible buffer-based algorithms for bulk operations in the class of space-partitioning trees; a class of hierarchical data structures that recursively decompose the space into disjoint partitions. The main idea of these algorithms is to build an in-memory tree of the target space-partitioning index. Then, data items are recursively partitioned into disk-based buffers using the in-memory tree. Although the second algorithm is designed for bulk insertion, it can be used in bulk loading as well. The proposed extensible algorithms are implemented inside SP-GiST; a framework for supporting the class of space-partitioning trees. Both algorithms have I/O bound O(NH/B), where N is the number of data items to be bulk loaded/inserted, B is the number of tree nodes that can fit in one disk page, H is the tree height in terms of pages after applying a clustering algorithm. Experimental results are provided to show the scalability and applicability of the proposed algorithms for the class of space-partitioning trees. A comparison of the two proposed algorithms shows that the first algorithm performs better in case of bulk loading. However the second algorithm is more general and can be used for efficient bulk insertion.

The linear quadtree is a spatial access method that is built by decomposing the spatial objects in a database into quadtree blocks and storing these quadtree blocks in a B-tree. The linear quadtree is very useful for geographic information systems because it provides good query performance while using existing B-tree implementations. An algorithm and a cost model are presented for processing window queries in linear quadtrees. The algorithm can handle query windows of any shape in the general case of spatial databases with overlapping objects. The algorithm recursively decomposes the space into quadtree blocks, and uses the quadtree blocks overlapping the query window to search the B-tree. The cost model estimates the I/O cost of processing window queries using the algorithm. The cost model is also based on a recursive decomposition of the space, and it uses very simple parameters that can easily be maintained in the database catalog. Experiments with real and synthetic data sets verify the accuracy of the cost model.

In our project, we are adopting a new approach for handling video data. We view the video as a well-defined data type with its own description, parameters and applicable methods. The system is based on PREDATOR, an open-source object-relational DBMS. PREDATOR uses Shore as the underlying storage manager. Supporting video operations (storing, searching-by-content and streaming) and new query types (query-by-example and multi-feature similarity searching) requires major changes in many of the traditional system components. More specifically, the storage and buffer manager has to deal with huge volumes of data with real-time constraints. Query processing has to consider the video methods and operators in generating, optimizing and executing the query plans

Real-world video-based applications require database technology that is capable of storing digital video in the form of video databases and providing content-based video search and retrieval. Methods for handling traditional data storage, query, search, retrieval, and presentation cannot be extended to provide this functionality. The VDBMS research initiative is motivated by the requirements of video-based applications to search and retrieve portions of video data based on content and by the need for testbed facilities to facilitate research in the area of video database management. In this paper we describe the VDBMS video database research platform, a system that supports comprehensive and efficient database management for digital video. Our fundamental concept is to provide a full range of functionality for video as a well-defined abstract database data type, with its own description, parameters, and applicable methods. Research problems that are addressed by VDBMS to support the handling of video data include MPEG7 standard multimedia content representation, algorithms for image-based shot detection, image processing techniques for extracting low-level visual features, a high-dimensional indexing technique to access the high-dimensional feature vectors extracted by image preprocessing, multimedia query processing and optimization, new query operators, real-time stream management, a search-based buffer management policy, and an access control model for selective, content-based access to streaming video. VDBMS also provides an environment for testing the correctness and scope of new video processing techniques, measuring the performance of algorithms in a standardized way, and comparing the performance of different implementations of an algorithm or component. We are currently developing video component wrappers with well-defined interfaces to facilitate the modification or replacement of video processing components. The ultimate goal of the VDBMS project is a flexible, extensible framework that can be used by the research community for developing, testing, and benchmarking video database technologies.

Real-world video-based applications require database technology that is capable of storing digital video in the form of video databases and providing content-based video search and retrieval. Methods for handling traditional data storage, query, search, retrieval, and presentation cannot be extended to provide this functionality. The VDBMS research initiative is motivated by the requirements of video-based applications to search and retrieve portions of video data based on content and by the need for testbed facilities to facilitate research in the area of video database management. In this paper we describe the VDBMS video database research platform, a system that supports comprehensive and efficient database management for digital video. Our fundamental concept is to provide a full range of functionality for video as a well-defined abstract database data type, with its own description, parameters, and applicable methods. Research problems that are addressed by VDBMS to support the handling of video data include MPEG7 standard multimedia content representation, algorithms for image-based shot detection, image processing techniques for extracting low-level visual features, a high-dimensional indexing technique to access the high-dimensional feature vectors extracted by image preprocessing, multimedia query processing and optimization, new query operators, real-time stream management, a search-based buffer management policy, and an access control model for selective, content-based access to streaming video. VDBMS also provides an environment for testing the correctness and scope of new video processing techniques, measuring the performance of algorithms in a standardized way, and comparing the performance of different implementations of an algorithm or component. We are currently developing video component wrappers with well-defined interfaces to facilitate the modification or replacement of video processing components. The ultimate goal of the VDBMS project is a flexible, extensible framework that can be used by the research community for developing, testing, and benchmarking video database technologies.

In many image processing applications, edge detection is a useful method for obtaining a simplified image that preserves the domain geometric structures and spatial relationships among variant image components. For providing automatic edge detection, two problems should be solved: one is feature extraction for calculating the edge strength, another is feature classification for automatic edge detection. For solving these two problems, we propose an improved automatic edge detection technique. Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem. A fast entropic thresholding technique is also developed for solving the feature classification problem. Experimental results have confirmed that this proposed edge detector can provide more reasonable results as compared with the traditional isotropic edge operators, and its calculation cost has been reduced as compared with the complex edge detectors. Good balance between the calculation cost and the edge detection accuracy is achieved.