15634346
Using high dimensional indexes to support relevance feedback based interactive images retrieval
Image retrieval has found more and more applications. Due to the well recognized semantic gap problem, the accuracy and the recall of image similarity search are often still low. As an effective method to improve the quality of image retrieval, the relevance feedback approach actively applies users' feedback to refine the search. As searching a large image database is often costly, to improve the efficiency, high dimensional indexes may help. However, many existing database indexes are not adaptive to updates of distance measures caused by users' feedback. In this paper, we propose a demo to illustrate the relevance feedback based interactive images retrieval procedure, and examine the effectiveness and the efficiency of various indexes. Particularly, audience can interactively investigate the effect of updated distance measures on the data space where the images are supposed to be indexed, and on the distributions of the similar images in the indexes. We also introduce our new B+-tree-like index method based on cluster splitting and iDistance.
10409272
Repairing Inconsistent XML Documents
XML document may contain inconsistencies that violate predefined integrity constraints, and there are two basic concepts for this problem: Repair is the data consistent with the integrity constraints, and also minimally differs from the original one. Consistent data is the data common for every possible repair. In this paper, first we give a general constraint model for XML, which can express functional dependencies, keys and multivalued dependencies. Next we provide a repair framework for inconsistent XML document with three basic update operations: node insertion, node deletion and value modification. Following this approach, we introduce the concept of repair for inconsistent XML document, discuss the chase process to generate repairs, and prove some important properties of the chase process. Finally we give a method to obtain the greatest lower bound of all possible repairs, which is sufficient for consistent data.
14792668
Personalization as a service: the architecture and a case study
Cloud computing has become a hot topic in the IT industry. Great efforts have been made to establish cloud computing platforms for enterprise users, mostly small businesses. However, there are few researches about the impact of cloud computing over individual users. In this paper we focus on how to provide personalized services for individual users in the cloud environment. We argue that a personalized cloud service shall compose of two parts. The client side program records user activities on personal de-vices such as PC. Besides that, the user model is also computed on the client side to avoid server overhead. The cloud side program fetches the user model periodically and adjusts its results accordingly. We build a personalized cloud data search engine prototype to prove our idea.
207162632
Utility-based anonymization for privacy preservation with less information loss
Privacy becomes a more and more serious concern in applications involving microdata. Recently, efficient anonymization has attracted much research work. Most of the previous methods use global recoding, which maps the domains of the quasi-identifier attributes to generalized or changed values. However, global recoding may not always achieve effective anonymization in terms of discernability and query answering accuracy using the anonymized data. Moreover, anonymized data is often used for analysis. As well accepted in many analytical applications, different attributes in a data set may have different utility in the analysis. The utility of attributes has not been considered in the previous methods.
 In this paper, we study the problem of utility-based anonymization. First, we propose a simple framework to specify utility of attributes. The framework covers both numeric and categorical data. Second, we develop two simple yet efficient heuristic local recoding methods for utility-based anonymization. Our extensive performance study using both real data sets and synthetic data sets shows that our methods outperform the state-of-the-art multidimensional global recoding methods in both discernability and query answering accuracy. Furthermore, our utility-based method can boost the quality of analysis using the anonymized data.
30881426
Constraint-Based Graph Mining in Large Database
Currently, constraints are increasingly considered as a kind of means of user- or expert-control for filtering those unsatisfied and redundant patterns rapidly during the web mining process. Recent work has highlighted the importance of constraint-based mining paradigm in the context of frequent itemsets, sequences, and many other interesting patterns in large database. However, it is still not clear how to push various constraints systematically into graph mining process. In this paper, we categorize various graph-based constraints into several major classes and develop a framework CabGin (i.e. C onstr a int- b ased G raph M in ing) to push them into mining process by their categories. Non-monotonic aggregates like average also can be pushed into CabGin with minor revision. Experimental results show that CabGin can prunes a large search space effectively by pushing graph-based constraints into mining process.
8249361
SUDEPHIC: Self-Tuning Density-Based Partitioning and Hierarchical Clustering
Clustering is one of the primary techniques in data mining, for which to find the user expecting result is a major issue. However, to dynamically specify the parameters for clustering algorithms presents an obstacle for users. This paper firstly introduces a novel density-based partitioning and hierarchical algorithm, which makes it easy to employ synthetic feedback mechanism in clustering. Additionally, by investigating into the relation between parameters and the clustering result, we propose a self-tuning technique for the setting of parameters. Meanwhile, the density distribution within a cluster can be expressed in the result for the user to specify the cluster’s feature. The algorithm is both evaluated in theory and practice. It outperforms many existing algorithms both in efficiency and quality.
2440328
Image transmissions with security enhancement based on region and path diversity in wireless sensor networks
Transmissions of large sized images can be a bottleneck for a wireless sensor network (WSN) due to its limited resources. Security can be another concern. This paper proposes a collaborative transmission scheme for image sensors to utilize inter-sensor correlations to decide the transmission and security sharing patterns based on the path diversities. Our proposed approach for secret image sharing on multiple node-disjoint paths for image delivery is to achieve high security without any key distribution and management, and thus the key management related problems do not exist. The energy efficiency is another major contribution made in this paper. This scheme does not only allow each image sensor to transmit optimal fractions of overlapped images through appropriate transmission paths in an energy-efficient way, but also provides unequal protection to overlapped image regions by path selections and adaptive bit error rate (BER) requirement. The simulation results show that the proposed scheme can achieve considerable gains in terms of network lifetime extension, image transmission security enhancement, image quality improvement, and energy efficiency for wireless sensor networks.
876538
Dependent link padding algorithms for low latency anonymity systems
Low latency anonymity systems are susceptive to traffic analysis attacks. In this paper, we propose a dependent link padding scheme to protect anonymity systems from traffic analysis attacks while providing a strict delay bound. The covering traffic generated by our scheme uses the minimum sending rate to provide full anonymity for a given set of flows. The relationship between user anonymity and the minimum covering traffic rate is then studied via analysis and simulation. When user flows are Poisson processes with the same sending rate, the minimum covering traffic rate to provide full anonymity to m users is O(log m). For Pareto traffic, we show that the rate of the covering traffic converges to a constant when the number of flows goes to infinity. Finally, we use real Internet trace files to study the behavior of our algorithm when user flows have different rates.
16734183
An Optimal Approach for Image Transmission in Multi-Rate Wireless Sensor Network
Many sensor applications such as monitoring and surveillance may require image sensor array to conduct collaborative image transmissions in Wireless sensor network (WSN). The large size image transmission in WSN is a bottleneck due to the limited energy resources. In this paper, we propose an optimal scheme for image sensors to utilize inter-sensor correlations to decide transmission patterns based on a multi-rate least cost path (MRLCP) routing scheme, which achieves high energy efficiencies and longer network lifetime. This optimization scheme allows each image sensor to transmit optimal fractions of the overlapped images through appropriate rate- based routing paths. A specific genetic algorithm is designed to solve such discrete optimization problem. The simulation results show that the proposed image transmission scheme can achieve considerable gains in terms of the WSN energy efficiency and network lifetime extension.
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.
2629783
Optimal Rate-Based Image Transmissions via Multiple Paths in Wireless Sensor Network
Many sensor applications such as monitoring and surveillance may require image sensor array to conduct collaborative image transmissions in wireless sensor network (WSN). The large size image transmission in WSN is a bottleneck due to the limited energy resources. In this paper, we propose an optimal scheme for image sensors to utilize inter-sensor correlations to decide transmission patterns based on a multi-level rate-oriented routing (MLRR) routing scheme, which achieves high energy efficiencies and longer network lifetime. This optimization scheme allows each image sensor to transmit optimal fractions of the overlapped images through appropriate multiple rate-oriented routing paths. The simulation results by using GA algorithm show that the proposed image transmission scheme can achieve considerable gains in terms of the WSN energy efficiency and network lifetime extension.