3992721
Error minimization in approximate range aggregates
Histogram techniques have been used in many commercial database management systems to estimate a query result size. Recently, it has been shown that they are very effective to support approximation of query processing especially aggregates. In this paper, we investigate the problem of minimizing average errors of approximate aggregates using histogram techniques. Firstly, we present a novel linear-spline histogram model that is more accurate than the existing models. Secondly, we propose a novel histogram construction technique for minimizing such average errors, which is shown to generate a near optimal histogram. Our experiment results demonstrate that the new histogram construction techniques lead to a great accuracy improvement on the existing techniques.
9724637
Selecting Stars: The k Most Representative Skyline Operator
Skyline computation has many applications including multi-criteria decision making. In this paper, we study the problem of selecting k skyline points so that the number of points, which are dominated by at least one of these k skyline points, is maximized. We first present an efficient dynamic programming based exact algorithm in a 2d-space. Then, we show that the problem is NP-hard when the dimensionality is 3 or more and it can be approximately solved by a polynomial time algorithm with the guaranteed approximation ratio 1-1/e. To speed-up the computation, an efficient, scalable, index-based randomized algorithm is developed by applying the FM probabilistic counting technique. A comprehensive performance evaluation demonstrates that our randomized technique is very efficient, highly accurate, and scalable.
2460253
Approximate processing of massive continuous quantile queries over high-speed data streams
Quantile computation has many applications including data mining and financial data analysis. It has been shown that an /spl epsi/-approximate summary can be maintained so that, given a quantile query (/spl phi/,/spl epsi/), the data item at rank /spl lceil//spl phi/N/spl rceil/ may be approximately obtained within the rank error precision /spl epsi/N over all N data items in a data stream or in a sliding window. However, scalable online processing of massive continuous quantile queries with different /spl phi/ and /spl epsi/ poses a new challenge because the summary is continuously updated with new arrivals of data items. In this paper, first we aim to dramatically reduce the number of distinct query results by grouping a set of different queries into a cluster so that they can be processed virtually as a single query while the precision requirements from users can be retained. Second, we aim to minimize the total query processing costs. Efficient algorithms are developed to minimize the total number of times for reprocessing clusters and to produce the minimum number of clusters, respectively. The techniques are extended to maintain near-optimal clustering when queries are registered and removed in an arbitrary fashion against whole data streams or sliding windows. In addition to theoretical analysis, our performance study indicates that the proposed techniques are indeed scalable with respect to the number of input queries as well as the number of items and the item arrival rate in a data stream.
11623385
Resource allocation for multimedia streaming over the Internet
This paper addresses the resource allocation problem for multiple media streaming over the Internet. First, we present an end-to-end transport architecture for multimedia streaming over the Internet. Second, we propose a new multimedia streaming TCP-friendly protocol (MSTFP), which combines forward estimation of network conditions with information feedback control to optimally track the network conditions. Third, we propose a novel resource allocation scheme to adapt media rate to the estimated network bandwidth using each media's rate-distortion function under various network conditions. By dynamically allocating resources according to network status and media characteristics, we improve the end-to-end quality of services (QoS). Simulation results demonstrate the effectiveness of our proposed schemes.
41461201
Edge Detection Models
In this paper, the Mumford-Shah (MS) model and its variations are studied for image segmentation. It is found that using the piecewise constant approximation, we cannot detect edges with low contrast. Therefore other terms, such as gradient and Laplacian, are included in the models. To simplify the problem, the gradient of the original image is used in the Rudin-Osher-Fatemi (ROF) like model. It is found that this approximation is better than the piecewise constant approximation for some images since it can detect the low contrast edges of objects. Linear approximation is also used for both MS and ROF like models. It is found that the linear approximation results are comparable with the results of the models using gradient and Laplacian terms.
27094711
An approach to multiple attribute decision making based on fuzzy preference information on alternatives
This paper investigates the multiple attribute decision making problem with preference information on alternatives, in which multiple decision makers give their preference information in three forms, i.e., preference orderings, utility values and fuzzy preference relation. A new approach is presented to make use of both the decision makers' social fuzzy preference relation on alternatives and decision matrix to form an optimization model. The optimization model can be used to determine the attribute weights and rank the alternatives. The approach provides a new way to reflect the decision makers' social preference information and the decision matrix. Finally, an example is used to illustrate the proposed approach.
36931032
A Roof Edge Detection Model
We have generalized the Mumford-Shah model to obtain a new model capable of detecting roof edges. In this new model, we have assumed a piecewise planar surface for each bounded region. We have also shown that this new model is less dependent on the scale parameter ν than the original Mumford-Shah model. We have proved that the gradient projection method can produce the minimal energy. The validity of the new model is demonstrated by experimental results.
29533358
On the discrete-time dynamics of a PCA learning algorithm
Abstract We propose a principal component analysis algorithm based on our previous work and analyze its deterministic discrete-time (DDT) system. We prove that its DDT system is always bounded for the small constant learning gain and can almost always deliver a principal component. The difference between Oja's algorithm and our proposed algorithm cannot be observed from their corresponding deterministic continuous-time systems.