An intelligent annotation-based video data model called Smart VideoText is introduced. It utilizes the conceptual graph knowledge representation formalism to capture the semantic associations among the concepts described in text annotations of video data. The aim is to achieve more effective query, retrieval, and browsing capabilities based on the semantic content of video data. Finally, a generic and modular video database architecture based on the Smart VideoText data model is described.

A new quality of service (QoS) aware disk scheduling algorithm is presented. It is applicable in environments where data requests arrive with different QoS requirements such as real-time deadline, and user priority. Previous work on disk scheduling has focused on optimizing the seek times and/or meeting the real-time deadlines. A unified framework for QoS disk scheduling is presented that scales with the number of scheduling parameters. The general idea is based on modeling the disk scheduler requests as points in the multi-dimensional space, where each of the dimensions represents one of the parameters (e.g., one dimension represents the request deadline, another represents the disk cylinder number and a third dimension represents the priority of the request, etc.). Then the disk scheduling problem reduces to the problem of finding a linear order to traverse these multi-dimensional points. Space-filling curves are adopted to define a linear order for sorting and scheduling objects that lie in the multi-dimensional space. This generalizes the one-dimensional disk scheduling algorithms (e.g., EDF SATF, FIFO). Several techniques are presented to show how a QoS-aware disk scheduler deals with the progressive arrival of requests over time. Simulation experiments are presented to show a comparison of the alternative techniques and to demonstrate the scalability of the proposed QoS-aware disk scheduling algorithm over other traditional approaches.

For the past two decades, fractals (e.g., the Hilbert and Peano space-filling curves) have been considered the natural method for providing a locality-preserving mapping. The idea behind a locality-preserving mapping is to map points that are nearby in the multidimensional space into points that are nearby in the one-dimensional space. We argue against the use of fractals in locality-preserving mapping algorithms, and present examples with experimental evidence to show why fractals produce poor locality-preserving mappings. In addition, we propose an optimal locality-preserving mapping algorithm, termed the spectral locality-preserving mapping algorithm (Spectral LPM, for short), that makes use of the spectrum of the multidimensional space. We give a mathematical proof for the optimality of Spectral LPM, and also demonstrate its practical use.

This demo presents Casper; a framework in which users entertain anonymous location-based services. Casper consists of two main components; the location anonymizer that blurs the users exact location into cloaked spatial regions and the privacy-aware query processor that is responsible on providing location-based services based on the cloaked spatial regions. While the location anonymizer is implemented as a stand alone application, the privacy-aware query processor is embedded into PLACE; a research prototype for location-based database servers.

Spatio-temporal data streams that are generated from mobile stream sources (e.g., mobile sensors) experience similar environmental conditions that result in distinct phenomena. Several research efforts are dedicated to detect and track various phenomena inside a data stream management system (DSMS). In this paper, we use the detected phenomena to reduce the demand on the DSMS resources. The main idea is to let the query processor observe the input data streams at the phenomena level. Then, each incoming continuous query is directed only to those phenomena that participate in the query answer. Two levels of indexing are employed, a phenomenon index and a query index. The phenomenon index provides a fine resolution view of the input streams that participate in a particular phenomenon. The query index utilizes the phenomenon index to maintain a query deployment map in which each input stream is aware of the set of continuous queries that the stream contributes to their answers. Both indices are updated dynamically in response to the evolving nature of phenomena and to the mobility of the stream sources. Experimental results show the efficiency of this approach with respect to the accuracy of the query result and the resource utilization of the DSMS

We propose a new automatic image segmentation method. Color edges in an image are first obtained automatically by combining an improved isotropic edge detector and a fast entropic thresholding technique. After the obtained color edges have provided the major geometric structures in an image, the centroids between these adjacent edge regions are taken as the initial seeds for seeded region growing (SRG). These seeds are then replaced by the centroids of the generated homogeneous image regions by incorporating the required additional pixels step by step. Moreover, the results of color-edge extraction and SRG are integrated to provide homogeneous image regions with accurate and closed boundaries. We also discuss the application of our image segmentation method to automatic face detection. Furthermore, semantic human objects are generated by a seeded region aggregation procedure which takes the detected faces as object seeds

In a growing number of relational domains, the data record temporal sequences of interactions among entities. For example, in citation domains authors publish scientific papers together each year and in telephone fraud detection domains people make calls to each other each day. The temporal dynamics of these interactions contain information that can improve predictive models (e.g., people publishing together frequently are likely to be publishing on the same topic) but to date there has been little effort to incorporate timevarying dependencies into relational models. Past work in relational learning has focused primarily on static “snapshots” of relational data. In this paper, we present an initial approach to modeling dynamic relational data graphs in predictive models of attributes. More specifically, we use a two-step process that first summarizes the dynamic graph with a weighted static graph and then incorporates the link weights in a relational Bayes classifier. We evaluate our approach on the Cora dataset (where co-author and citation links vary over time) showing that our approach results in significant performance gains over a baseline snapshot approach that ignores the temporal component of the data.

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.

Many evolving database applications warrant the use of non-traditional indexing mechanisms beyond B+-trees and hash tables. SP-GiST is an extensible indexing framework that broadens the class of supported indexes to include disk-based versions of a wide variety of space-partitioning trees, e.g., disk-based trie variants, quadtree variants, and kd-trees. This paper presents a serious attempt at implementing and realizing SP-GiST-based indexes inside PostgreSQL. Several index types are realized inside PostgreSQL facilitated by rapid SP-GiST instantiations. Challenges, experiences, and performance issues are addressed in the paper. Performance comparisons are conducted from within PostgreSQL to compare update and search performances of SP-GiST-based indexes against the B+-tree and the R-tree for string, point, and line segment data sets. Interesting results that highlight the potential performance gains of SPGiST- based indexes are presented in the paper.

Synchronized multimedia applications play an important role in a digital library environment, since they allow one to efficiently disseminate knowledge among differently skilled users through an approach, which is more direct than the classic ‘static’ documents. In this paper, we propose a new authoring approach based on an innovative presentation structure and a new class of content-based constraints. Thanks to a flexible heuristic process, such features allow the author to easily combine several multimedia objects into a multi-topic presentation, whose different contents can be freely chosen by end users according to their preferences or skills

This paper develops a language and a reference architecture supporting the management and enforcement of authentication policies. Such language directly supports multi-factor authentication and the high level specification of authentication factors, in terms of conditions against the features of the various authentication mechanisms and modules. In addition the language supports a rich set of constraints; by using these constraints, one can specify for example that a subject must be authenticated by two credentials issued by different authorities. The paper presents a logical definition of the language and its corresponding XML encoding. It also reports an implementation of the proposed authentication system in the context of the FreeBSD Unix operating system (OS). Critical issues in the implementation are discussed and performance results are reported. These results show that the implementation is very efficient.

Content-based video database access control is becoming very important, but it depends on the progresses of the following related research issues: (a) efficient video analysis for supporting semantic visual concept representation; (b) effective video database indexing structure; (c) the development of suitable video database models; and (d) the development of access control models tailored to the characteristics of video data. In this paper, we propose a novel approach to support multilevel access control in video databases. Our access control technique combines a video database indexing mechanism with a hierarchical organization of visual concepts (i.e., video database indexing units), so that different classes of users can access different video elements or even the same video element with different quality levels according to their permissions. These video elements, which, in our access control mechanism, are used for specifying the authorization objects, can be a semantic cluster, a subcluster, a video scene, a video shot, a video frame, or even a salient object (i.e., region of interest). In the paper, we first introduce our techniques for obtaining these multilevel video access units. We also propose a hierarchical video database indexing technique to support our multilevel video access control mechanism. Then, we present an innovative access control model which is able to support flexible multilevel access control to video elements. Moreover, the application of our multilevel video database modeling, representation, and indexing for MPEG-7 is discussed.

Periodicity mining is used for predicting trends in time series data. Periodicity detection is an essential process in periodicity mining to discover potential periodicity rates. Existing periodicity detection algorithms do not take into account the presence of noise, which is inevitable in almost every real-world time series data. In this paper, we tackle the problem of periodicity detection in the presence of noise. We propose a new periodicity detection algorithm that deals efficiently with all types of noise. Based on time warping, the proposed algorithm warps (extends or shrinks) the time axis at various locations to optimally remove the noise. Experimental results show that the proposed algorithm outperforms the existing periodicity detection algorithms in terms of noise resiliency.

A locality-preserving mapping (LPM) from the multi-dimensional space into the one-dimensional space is beneficial for many applications (e.g., range queries, nearest-neighbor queries, clustering, and declustering) when multi-dimensional data is placed into one-dimensional storage (e.g., the disk). The idea behind a locality-preserving mapping is to map points that are nearby in the multi-dimensional space into points that are nearby in the one-dimensional space. For the past two decades, fractals (e.g., the Hilbert and Peano space-filling curves) have been considered the natural method for providing a locality-preserving mapping to support efficient answer for range queries and similarity search queries. In this paper, we go beyond the idea of fractals. Instead, we investigate a locality-preserving mapping algorithm (The Spectral LPM) that uses the spectrum of the multi-dimensional space. This paper provably demonstrates how Spectral LPM provides a globally optimal mapping from the multi-dimensional space to the one-dimensional space, and hence outperforms fractals. As an application, in the context of range queries and nearest-neighbor queries, empirical results of the performance of Spectral LPM validate our analysis in comparison with Peano, Hilbert, and Gray fractal mappings.