15163337
A Database Encryption Scheme for Enhanced Security and Easy Sharing
Database encryption is a crucial technique in the security mechanisms of database. It is widely recognized as one of the key issues of data security. Current techniques of sharing the keys and the encrypted data for databases are neither convenient nor flexible in the real applications. Inspired by the PGP technique, we propose a novel database encryption scheme for enhanced data sharing inside a database, while preserving data privacy. It is characterized by both the fast speed of the conventional encryption and the convenience of key distribution of public key encryption. It also provides secured storage for security related data and effective key management, which enables the encrypted data to be shared conveniently. The scheme has been implemented and successfully applied on Oscar, a commercial DBMS developed by us
5747087
Efficient Processing of Warping Time Series Join of Motion Capture Data
Discovering non-trivial matching subsequences from two time series is very useful in synthesizing novel time series. This can be applied to applications such as motion synthesis where smooth and natural motion sequences are often required to be generated from existing motion sequences. We first address this problem by defining it as a problem of l-e-join over two time series. Given two time series, the goal of l-e-join is to find those non-trivial matching subsequences by detecting maximal l-connections from the e-matching matrix of the two time series. Given a querying motion sequence, the l-e-join can be applied to retrieve all connectable motion sequences from a database of motion sequences. To support efficient l-e-join of time series, we propose a two-step filter-and-refine algorithm, called Warping Time Series Join (WTSJ) algorithm. The filtering step serves to prune those sparse regions of the e-matching matrix where there are no maximal l-connections without incurring costly computation. The refinement step serves to detect closed l-connections within regions that cannot be pruned by the filtering step. To speed up the computation of e-matching matrix, we propose a block-based time series summarization method, based on which the block-wise e-matching matrix is first computed. Lots of pairwise distance computation of elements can then be avoided by applying the filtering algorithm on the block-wise e-matching matrix.Extensive experiments on l-e-join of motion capture sequences are conducted. The results confirm the efficiency and effectiveness of our proposed algorithm in processing l-e-join of motion capture time series.
15447660
A logical replication-based intrusion detection approach for CSCW system
With the rapid development of CSCW techniques, CSCW system security has been of much attention. Its major challenge comes from malicious attacks, which cannot be handled by traditional security mechanisms, such as authorization, access control, encryption, etc. Although there existed some intrusion detection systems, current researches on intrusion detection are still insufficient in accuracy and efficiency for two reasons: firstly, most of them implement malicious transaction detection only by matching samples with predefined patterns; secondly, most current researches mainly resort to semantic analyzing or even only manual interventions to judge the malicious attacks. Inspired by the replication technique, we propose and implement a novel logical replication-based intrusion detection solution for CSCW system in this paper. It provides an additional layer of defense against application attacks, especially program attacks. The approach entitles the system to detect malicious intrusions efficiently at low false positive rate.
18102767
Resilient Rights Protection for Product Data
With the development of advance manufacture applications, enterprise must interact with their suppliers and customers to share more product data information. Consequently, copyright and integrity protection over product data is becoming an urgent requirement. Product data information consists of two parts: product structure and part content, besides protecting the three-dimensional (3D) model of part by watermarking, we also need to protect product structure information, which describes the relations among product parts and can be represented as a tree. In this paper, based on the 3D polygonal meshes watermark technology, the structure/semi-structure data watermark technologies and the idea of Fractal, we propose a novel watermark scheme for tree-structure product data with the value lying both in product structure and in part node content, which gives a more comprehensive and resilient right protection for both part node content and product structure. Moreover it is particularly effective when resisting invertibility attacks
15894335
An adaptive load balancing framework for parallel database systems based on collaborative agents
Load balancing is a key technique in parallel computer supported collaborative work (CSCW) systems, parallel database system and P2P system for instance, to boost performance and improve scalability. In order to reduce total cost of ownership (TCO), adaptive/self-tuning administration techniques are gradually and extensively expected in the cyberspace. In parallel database systems, adaptive load balancing techniques are proposed to face the change in data storage patterns and access patterns in a dynamic real environment. The techniques utilized in both shared-nothing and shared-disk parallel database systems are discussed, and a general flexible framework based on collaborative agents is studied to support these techniques in both architectures. The framework supports two kinds of load balancing - one is passively executing query statements balancedly, and the other one is proactively adjusting data placement and task execution scheme, by means of data and task migration, whenever load unbalance is detected. Three categories of agents, scheduling agents, monitoring agents and task agents, are identified in the framework. The collaboration protocols and scheduling algorithms to support adaptive load balancing are described. The framework also applies to other parallel systems such as P2P systems and shared file processing systems due to their underlying commonness.
16833900
Securely Sharing Data in Encrypted Databases
Collaborative design and application integration emphasize the demand for database security. Database encryption is widely adopted to ensure data privacy, which can prevent attacks from both outside intruders and inside malicious users. Current researches on this area are mainly focusing on encryption algorithms, key management and encryption efficiency. However, data sharing nature of database system is usually neglected. In this paper, we propose an access control model, 3S-RBAC, which enables data sharing while guaranteeing privacy. The model has many features: the novel concept of strong permission and weak permission; the hierarchy of database objects and keys; the permission and key inheritance; the binding of keys and permissions. Implementation in OSCAR Secure DBMS shows that the model is flexible, secure, practical, and can be integrated easily into existing enterprise applications
15986478
MATLAB Simulink Modeling and Simulation of Zhang Neural Network for Online Time-Varying Matrix Inversion
Recently, a special kind of recurrent neural networks (RNN) with implicit dynamics has been proposed by Zhang et al for online time-varying problems solving (such as time-varying matrix inversion). Such a neural-dynamic system is elegantly designed by defining a matrix-valued error function rather than the usual scalar-valued norm-based error function. Its computational error can be made decrease to zero globally and exponentially. For the final purpose of field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) realization, we investigate in this paper the MATLAB Simulink modeling and simulative verification of such a Zhang neural network (ZNN). By using click-and-drag mouse operations, it is easier to model and simulate in comparison with MATLAB coding. Both convergence and robustness properties of such a ZNN model are analyzed, which substantiate the effectiveness of Zhang neural network on inverting the time-varying matrices.
16708333
MATLAB Simulation and Comparison of Zhang Neural Network and Gradient Neural Network for Time-Varying Lyapunov Equation Solving
This paper presents a new kind of recurrent neural network proposed by Zhang et al.for solving online Lyapunov equation with time-varying coefficient matrices. Global exponential convergence could be achieved by such a recurrent neural network when solving the time-varying problems in comparison with gradient neural networks (GNN). MATLAB simulation of both neural networks for the real-time solution of time-varying Lyapunov equation is then investigated through several important techniques. Computer-simulation results substantiate the theoretical analysis and demonstrate the efficacy of such a Zhang neural network (ZNN) on time-varying Lyapunov equation solving, especially when using power-sigmoid activation functions.
1589839
Optimization of the Sliding Window Size for Protein Structure Prediction
Sliding window based methods are relatively often applied in prediction of various aspects related to protein structure. Despite their wide spread use, researchers did not establish a standard related to the size of the window, i.e., window sizes ranging between 7 and 17 residues were used in the past. To this end, this paper performs a computational study based on a probabilistic approach that aims at finding an optimal sliding window size. The results shows that formation of helical structure can be affected by amino acids (AAs) that are up to 9 positions away in the sequence, while the formation of coils and strands can be affected by AAs that are up to 3 and 6 positions away, respectively. Overall, our results suggest that a sliding window with 19 residues is optimal for secondary structure prediction, while for a specific prediction tasks, such as prediction of p-strands, a smaller window size is sufficient. Finally, the 20 AAs are categorized into five groups based on their influence of formation of the secondary structure. The finding related to the optimal window size was confirmed based on an independent experimental study related to the prediction of secondary protein structure
15363041
An Ensemble of Competitive Learning Networks with Different Representations for Temporal Data Clustering
Temporal data clustering provides useful techniques for condensing and summarizing information conveyed in temporal data, which is demanded in various fields ranging from time series analysis to sequential data understanding. In this paper, we propose a novel approach to temporal data clustering by an ensemble of competitive learning networks incorporated by different representations of temporal data. In our approach, competitive learning networks of the rival-penalized learning mechanism are employed for clustering analyses based on different temporal data representations while an optimal selection function is applied to find out a final consensus partition from multiple partition candidates yielded by applying alternative consensus functions to results of competitive learning on different representations. Thanks to its capability of the rival penalized learning rules in automatic model selection and the synergy of fusing diverse partitions on different representations, our ensemble approach yields favorite results, which has been demonstrated in time series and motion trajectory clustering tasks.
5841267
The Data Mining Technology Based on CIMS and its Application on Automotive Remanufacturing
Nowadays, with the development of computer technology, data mining has been widely used in various fields. This paper describes a CIMSMINER that combines the data mining with CIMS (computer integrated manufacturing system) and instructs its objectives, model, physical architecture and methods. Considering the characteristics of remanufacturing of automotive products in China, the CIMSMINER is used to get the information concourse together and obtain the data mining results to help the improvement of products. The application in automotive remanufacturing is a reform to make the automotive product information chain be not only an information carrier, but also an information miner. Currently, the government strongly emphasizes energy saving and emission reducing, which is closely related to the sustainable development of China. Obviously, CIMSMINER is an effective tool to support the implementation of this policy.