207627601
A logical design methodology for relational databases using the extended entity-relationship model
A database design methodology is defined for the design of large relational databases. First, the data requirements are conceptualized using an extended entity-relationship model, with the extensions being additional semantics such as ternary relationships, optional relationships, and the generalization abstraction. The extended entity-relationship model is then decomposed according to a set of basic entity-relationship constructs, and these are transformed into candidate relations. A set of basic transformations has been developed for the three types of relations: entity relations, extended entity relations, and relationship relations. Candidate relations are further analyzed and modified to attain the highest degree of normalization desired.
The methodology produces database designs that are not only accurate representations of reality, but flexible enough to accommodate future processing requirements. It also reduces the number of data dependencies that must be analyzed, using the extended ER model conceptualization, and maintains data integrity through normalization. This approach can be implemented manually or in a simple software package as long as a "good" solution is acceptable and absolute optimality is not required.
7609568
Mining Maximal Correlated Member Clusters in High Dimensional Database
Mining high dimensional data is an urgent problem of great practical importance. Although some data mining models such as frequent patterns and clusters have been proven to be very successful for analyzing very large data sets, they have some limitations. Frequent patterns are inadequate to describe the quantitative correlations among nominal members. Traditional cluster models ignore distances of some pairs of members, so a pair of members in one big cluster may be far away. As a combination and complementary of both techniques, we propose the Maximal-Correlated-Member-Cluster (MCMC) model in this paper. The MCMC model is based on a statistical measure reflecting the relationship of nominal variables, and every pair of members in one cluster satisfy unified constraints. Moreover, in order to improve algorithm's efficiency, we introduce pruning techniques to reduce the search space. In the first phase, a Tri-correlation inequation is used to eliminate unrelated member pairs, and in the second phase, an Inverse-Order-Enumeration-Tree (IOET) method is designed to share common computations. Experiments over both synthetic datasets and real life datasets are performed to examine our algorithm's performance. The results show that our algorithm has much higher efficiency than the naive algorithm, and this model can discover meaningful correlated patterns in high dimensional database.
8277660
Web Service Composition Using Markov Decision Processes
This paper discusses a method for dynamic web service composition, which is based on Markov Decision Processes (MDP). It is defined on the base of QoS description and addresses the issue of selecting web services for the purpose of their composition. Web service composition patterns including sequential, conditional, parallel and iterative are modeled in MDP. Two computation algorithms for MDP are introduced and implemented. One is backward recursive iteration and the other is forward iteration, both called Value Iteration. Experiments are conducted to evaluate the performance of MDP methods for service composition. Experimental results show that the methods are efficient. The effect of average failure probability of service invocation on performance is also examined in this paper.
133221
Discovery of Frequent XML Query Patterns with DTD Cardinality Constraints
Common query patterns of multiple XML queries can be stored and shared to accelerate the query execution efficiently. We present a new technique for efficiently mining frequent XML query patterns with DTD cardinality constraints. We first introduce a new definition of extended subtree for XML query patterns (EST), which provides a model for supporting special characters of ”common” XML query pattern. Then we ”push” the DTD cardinality constraints deep into the mining process to prune the search space and still ensure the completeness of the answers. Finally, we propose an algorithm FEST for effectively mining frequent ESTs to improve the query performance for XML data management system.
24223451
Towards a Semantic e-Community
With the extensive application of the World Wide Web, an important application is community management, many governments have built district-oriented websites to serve the citizens who live, work in the community, named as e-community. The e-community should be up-to-date, personalized and flexible to provide information. Furthermore, they should be embedded with knowledge that can be utilized for content analysis and marketing intelligence and above all with minimal administrative overhead.
2822268
Online Mining in Sensor Networks
Online mining in large sensor networks just starts to attract interest. Finding patterns in such an environment is both compelling and challenging. The goal of this position paper is to understand the challenges and to identify the research problems in online mining for sensor networks. As an initial step, we identify the following three problems to work on: (1) sensor data irregularities detection; (2) sensor data clustering; and (3) sensory attribute correlations discovery. We also outline our preliminary proposal of solutions to these problems.
6642477
Optimizing state allocation for multicast communications
We propose a new forwarding mechanism for IP multicast based on explicit multicast (Xcast). Compared with traditional IP multicast, our mechanism achieves a more flexible allocation of multicast forwarding states among routers. Previous works only focus on reducing the number of routers which store the forwarding states, ignoring the distribution of forwarding states among routers. The number of forwarding states stored in each router may be unbalanced. We formulate the assignment of the routers with forwarding states as two optimization problems. The first one is to minimize the number of routers with the forwarding states in each multicast tree. The second one is to minimize the maximum number of forwarding states stored in a router. We design several algorithms for both problems. We prove that our algorithms can find the optimal solution to the first problem. By simulation, we also show that our algorithms perform well in the second problem. Moreover, we prove that assigning all branching routers as the only routers with the forwarding states is a special case in our mechanism. Due to higher flexibility, we show that our mechanism use fewer forwarding states, and the distribution of forwarding states is more balanced.
7172030
Information-theoretic inequalities for contoured probability distributions
We show that for a special class of probability distributions that we call contoured distributions, information-theoretic invariants and inequalities are equivalent to geometric invariants and inequalities of bodies in Euclidean space associated with the distributions. Using this, we obtain characterizations of contoured distributions with extremal Shannon and Renyi entropy. We also obtain a new reverse information-theoretic inequality for contoured distributions.
2295358
Optimal Placement and Selection of Camera Network Nodes for Target Localization
The paper studies the optimal placement of multiple cameras and the selection of the best subset of cameras for single target localization in the framework of sensor networks. The cameras are assumed to be aimed horizontally around a room. To conserve both computation and communication energy, each camera reduces its image to a binary “scan-line” by performing simple background subtraction followed by vertical summing and thresholding, and communicates only the center of the detected foreground object. Assuming noisy camera measurements and an object prior, the minimum mean squared error of the best linear estimate of the object location in 2-D is used as a metric for placement and selection. The placement problem is shown to be equivalent to a classical inverse kinematics robotics problem, which can be solved efficiently using gradient descent techniques. The selection problem on the other hand is a combinatorial optimization problem and finding the optimal solution can be too costly to implement in an energy-constrained wireless camera network. A semi-definite programming approximation for the problem is shown to achieve close to optimal solutions with much lower computational burden. Simulation and experimental results are presented.
28045056
Orderings on Graphs and Game Coloring Number
Many graph theoretic algorithms rely on an initial ordering of the vertices of the graph which has some special properties. We discuss new ways to measure the quality of such orders, give methods for constructing high quality orders, and provide applications for these orders. While our main motivation is the study of game chromatic number, there have been other applications of these ideas and we expect there will be more.
1017528
Parametric interpolator versus linear interpolator for precision CNC machining
Abstract The paper presents a comparative study of linear and parametric interpolators used for command generation during CNC machining. For many decades, linear interpolators have been used in NC machines for command generation. Recently, a parametric interpolator has been developed that is designed for command generation for parametric curves (or surfaces). Two algorithms for the online implementation of this parametric interpolator are introduced. The comparison is mainly based on memory size, feedrate fluctuation and CPU time. Other considerations include tracking errors and jerk magnitude. Results show that the parametric interpolator is favourable in the machining of free-form geometry in terms of high speed and tight tolerancing.