15503361
AB-Index: An Efficient Adaptive Index for Branching XML Queries
Query-adaptive XML indexing has been proposed and shown to be an efficient way to accelerate XML query processing, because it dynamically adapts to the workload. However, existing adaptive index lack of support for branching queries, and also with low efficiency for query processing and adaptation operations. In this paper, we propose a new Adaptive index for Branching queries, which is named as AB-Index. It is designed to support XML path queries with branching predicates. Efficient index construction, query processing, and index adaptation algorithms are proposed for AB-Index. In the experiments, the proposed index is demonstrated to outperform the state-of-the-art approach in the area of adaptive index in terms of query and adaptation efficiencies.
47178135
Efficient similarity joins for near duplicate detection
With the increasing amount of data and the need to integrate data from multiple data sources, a challenging issue is to find near duplicate records efficiently. In this paper, we focus on efficient algorithms to find pairs of records such that their similarities are above a given threshold. Several existing algorithms rely on the prefix filtering principle to avoid computing similarity values for all possible pairs of records. We propose new filtering techniques by exploiting the ordering information; they are integrated into the existing methods and drastically reduce the candidate sizes and hence improve the efficiency. Experimental results show that our proposed algorithms can achieve up to 2.6x - 5x speed-up over previous algorithms on several real datasets and provide alternative solutions to the near duplicate Web page detection problem.
3416923
Efficient Computation of the Skyline Cube
Skyline has been proposed as an important operator for multi-criteria decision making, data mining and visualization, and user-preference queries. In this paper. we consider the problem of efficiently computing a SKYCUBE, which consists of skylines of all possible non-empty subsets of a given set of dimensions. While existing skyline computation algorithms can be immediately extended to computing each skyline query independently, such "shared-nothing" algorithms are inefficient. We develop several computation sharing strategies based on effectively identifying the computation dependencies among multiple related skyline queries. Based on these sharing strategies, two novel algorithms, Bottom-Up and Top-Down algorithms, are proposed to compute SKYCUBE efficiently. Finally, our extensive performance evaluations confirm the effectiveness of the sharing strategies. It is shown that new algorithms significantly outperform the naive ones.
3251955
Containment join size estimation: models and methods
Recent years witnessed an increasing interest in researches in XML, partly due to the fact that XML has now become the de facto standard for data interchange over the internet. A large amount of work has been reported on XML storage models and query processing techniques. However, few works have addressed issues of XML query optimization. In this paper, we report our study on one of the challenges in XML query optimization: containment join size estimation. Containment join is well accepted as an important operation in XML query processing. Estimating the size of its results is no doubt essential to generate efficient XML query processing plans. We propose two models, the interval model and the position model, and a set of estimation methods based on these two models. Comprehensive performance studies were conducted. The results not only demonstrate the advantages of our new algorithms over existing algorithms, but also provide valuable insights into the tradeoff among various parameters.
2285434
Towards multidimensional subspace skyline analysis
The skyline operator is important for multicriteria decision-making applications. Although many recent studies developed efficient methods to compute skyline objects in a given space, none of them considers skylines in multiple subspaces simultaneously. More importantly, the fundamental problem on the semantics of skylines remains open: Why and in which subspaces is (or is not) an object in the skyline? Practically, users may also be interested in the skylines in any subspaces. Then, what is the relationship between the skylines in the subspaces and those in the super-spaces? How can we effectively analyze the subspace skylines? Can we efficiently compute skylines in various subspaces and answer various analytical queries?In this article, we tackle the problem of multidimensional subspace skyline computation and analysis. We explore skylines in subspaces. First, we propose the concept of Skycube, which consists of skylines of all possible nonempty subspaces of a given full space. Once a Skycube is materialized, any subspace skyline queries can be answered online. However, Skycube cannot fully address the semantic concerns and may contain redundant information. To tackle the problem, we introduce a novel notion of skyline group which essentially is a group of objects that coincide in the skylines of some subspaces. We identify the decisive subspaces that qualify skyline groups in the subspace skylines. The new notions concisely capture the semantics and the structures of skylines in various subspaces. Multidimensional roll-up and drill-down analysis is introduced. We also develop efficient algorithms to compute Skycube, skyline groups and their decisive subspaces. A systematic performance study using both real data sets and synthetic data sets is reported to evaluate our approach.
8670128
Top-k Set Similarity Joins
Similarity join is a useful primitive operation underlying many applications, such as near duplicate Web page detection, data integration, and pattern recognition. Traditional similarity joins require a user to specify a similarity threshold. In this paper, we study a variant of the similarity join, termed top-k set similarity join. It returns the top-k pairs of records ranked by their similarities, thus eliminating the guess work users have to perform when the similarity threshold is unknown before hand. An algorithm, topk-join, is proposed to answer top-k similarity join efficiently. It is based on the prefix filtering principle and employs tight upper bounding of similarity values of unseen pairs. Experimental results demonstrate the efficiency of the proposed algorithm on large-scale real datasets.
36916040
A Decision-Tree-Based Online Speaker Clustering
When performing online speaker clustering, it is common to make clustering decision as soon as an audio segment is received. When the wrong decision is made, the error can propagate the posterior clustering. This paper describes a decision-tree-based online speaker clustering algorithm. Unlike typical online clustering approaches, the proposed method constructs a decision tree when an audio segment is received. A pruning strategy for candidate-elimination is also applied. Experiments indicate that the algorithm achieves good performance on both precision and speed. Finally, we discuss the relation between the performance and the width of the decision tree beam.
7408751
Quantitative Association Analysis Using Tree Hierarchies
Association analysis arises in many important applications such as bioinformatics and business intelligence. Given a large collection of measurements over a set of samples, association analysis aims to find dependencies of target variables to subsets of measurements. Most previous algorithms adopt a two-stage approach; they first group samples based on the similarity in the subset of measurements, and then they examine the association between these groups and the specified target variables without considering the inter-group similarities or alternative groupings. This can lead to cases where the strength of association depends significantly on arbitrary clustering choices. In this paper, we propose a tree-based method for quantitative association analysis. Tree hierarchies derived from sample similarities represent many possible sample groupings. They also provide a natural way to incorporate domain knowledge such as ontologies and to identify and remove outliers. Given a tree hierarchy, our association analysis evaluates all possible groupings and selects the one with strongest association to the target variable. We introduce an efficient algorithm, TreeQA, to systematically explore the search-space of all possible groupings in a set of input trees, with integrated permutation tests. Experimental results show that TreeQA is able to handlelarge-scale association analysis very efficiently and is more effective and robust in association analysis than previous methods.
37290278
cGridex: Efficient Processing of Continuous Range Queries over Moving Objects
In mobile computing environments, the continuous range query is one of the most important types of queries required to support various location-based services. With a large number of queries, the real-time response to query answers and the concurrent execution are two major challenges. In this paper, we propose a novel approach cGridex for efficient processing of continuous range queries over moving objects. cGridex abstracts the dynamic relations between moving objects and concurrent queries, and introduces the concept of coloring on a high level. With colored grids, it forms a direct mapping between moving objects and continuous queries, and can process both of them in a unified way, without redundant evaluation. By precomputing and materializing the color transferring relations, it avoids frequent operations between query sets and spatial join between objects and queries at each time interval, thus leading to fast query evaluation. The experimental results show that cGridex has good performance for the evaluation of continuous range queries.
29222863
An adaptive approach for image encryption and secure transmission over multirate wireless sensor networks
In this paper, we propose an innovative adaptive secure image delivery approach, to achieve effective and efficient image encryption based on an optimal transmission rate in terms of energy efficiency in wireless sensor networks (WSN). Our contribution is three folds—First, the proposed component based selective encryption scheme achieves high image security, while incurring little encryption overhead; furthermore, the proposed adaptive encryption scheme achieve the best security effort within delay bound; finally, the proposed transmission rate optimization improves energy efficiency, and feeds back effective path capacity parameters for adaptive encryption. Simulation results show a significant gain in both energy efficiency and security based on the proposed approach for image secure transmissions in WSN. Copyright # 2007 John Wiley & Sons, Ltd.
1194502
Energy-Aware Adaptive Watermarking for Real-Time Image Delivery in Wireless Sensor Networks
A secure image transmission mechanism is necessary when malicious intruders intend to access and modify content delivery over wireless networks. To assure data integrity, authentication is required for multimedia data delivered over wireless image sensor networks. Watermarking technique is an effective vehicle to assert and assure the image data authentications. There have been recent works reported on watermarking, but few with the consideration of energy cost in terms of the data communication and processing, which is a key constraint to many embedded systems and wireless sensor networks. Most watermarking systems only target at minimizing watermarked image distortion and increasing robustness at the source coding site for lossy image processing. The watermarked image distortion caused by error-prone wireless environments during transmission has not been fully considered. In this paper, an innovative energy-aware adaptive watermarking scheme for realtime image delivery is proposed in wireless multimedia sensor networks. This new scheme allocates network resource to protect the watermarked image transmission while embedding watermark coding redundancies into the images. Dynamic watermark thresholds are applied to be adaptive to the network condition (packet loss ratio) and the inter-frame correlation is exploited to reduce processing delay. The simulation results show that the proposed adaptive watermark system can achieve considerable energy efficiency and assure the data integrity.