3994784
Error minimization for approximate computation of range aggregates
Histogram techniques are widely used in commercial database management systems for an estimation of query results. Recently, they have been also used in approximately, processing database queries, especially aggregation queries. Existing research results in this area have been mainly focused on constructing a histogram to approximately represent, as accurate as possible on an intuitive base, the original data frequencies. We propose a novel histogram construction method aiming to minimize the average approximate aggregation errors; and we have developed an efficient algorithm to construct near optimal histograms to achieve this goal. Our experiment results showed that the new histogram construction techniques lead to more accurate results than those by existing histogram techniques, and also out-perform the existing wavelet techniques.
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.
4916717
Building Data Synopses Within a Known Maximum Error Bound
The constructions of Haar wavelet synopses for large data sets have proven to be useful tools for data approximation. Recently, research on constructing wavelet synopses with a guaranteed maximum error has gained attention. Two relevant problems have been proposed: One is the size bounded problem that requires the construction of a synopsis of a given size to minimize the maximum error. Another is the error bounded problem that requires a minimum sized synopsis be built to satisfy a given error bound. The optimum algorithms for these two problems take O(N2) time complexity. In this paper, we provide new algorithms for building error-bounded synopses. We first provide several property-based pruning techniques, which can greatly improve the performance of optimal error bounded synopses construction. We then demonstrate the efficiencies and effectiveness of our techniques through extensive experiments.
4018890
Clustering Moving Objects for Spatio-temporal Selectivity Estimation
Many spatio-temporal applications involve managing and querying moving objects. In such an environment, predictive spatio-temporal queries become an important query class to be processed to capture the nature of moving objects. In this paper, we investigated the problem of selectivity estimation for predictive spatio-temporal queries. We propose a novel histogram technique based on a clustering paradigm. To avoid expensive computation costs, we developed linear time heuristics to construct such a histogram. Our performance study indicated that the new techniques improve the accuracy of the existing techniques by one order of magnitude.
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.
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.
21818900
Trunk Reservation in Multi-service Networks with BPP Traffic
In this paper we develop approximate models for trunk reservation in multi-service systems with BPP (Binomial-Poisson-Pascal) multi-rate traffic streams. The approximation is a generalization of previous work by Tran-Gia & Hubner who assumed Poisson arrival processes. It is based on a generalized algorithm which allows for calculation of individual performance measures for each service, in particular the traffic congestion. The algorithm is numerically robust and requires a minimum of computer memory and computing time. The approximation is good when the services have equal mean service times.
13918699
Constructing irregular surfaces to enclose macromolecular complexes for mesoscale modeling using the discrete surface charge optimization (DISCO) algorithm
Salt-mediated electrostatics interactions play an essential role in biomolecular structures and dynamics. Because macromolecular systems modeled at atomic resolution contain thousands of solute atoms, the electrostatic computations constitute an expensive part of the force and energy calculations. Implicit solvent models are one way to simplify the model and associated calculations, but they are generally used in combination with standard atomic models for the solute. To approximate electrostatics interactions in models on the polymer level (e.g., supercoiled DNA) that are simulated over long times (e.g., milliseconds) using Brownian dynamics, Beard and Schlick have developed the DiSCO (Discrete Surface Charge Optimization) algorithm. DiSCO represents a macromolecular complex by a few hundred discrete charges on a surface enclosing the system modeled by the Debye-Hückel (screened Coulombic) approximation to the Poisson-Boltzmann equation, and treats the salt solution as continuum solvation. DiSCO can represent the nucleosome core particle (>12,000 atoms), for example, by 353 discrete surface charges distributed on the surfaces of a large disk for the nucleosome core particle and a slender cylinder for the histone tail; the charges are optimized with respect to the Poisson-Boltzmann solution for the electric field, yielding a approximately 5.5% residual. Because regular surfaces enclosing macromolecules are not sufficiently general and may be suboptimal for certain systems, we develop a general method to construct irregular models tailored to the geometry of macromolecules. We also compare charge optimization based on both the electric field and electrostatic potential refinement. Results indicate that irregular surfaces can lead to a more accurate approximation (lower residuals), and the refinement in terms of the electric field is more robust. We also show that surface smoothing for irregular models is important, that the charge optimization (by the TNPACK minimizer) is efficient and does not depend on the initial assigned values, and that the residual is acceptable when the distance to the model surface is close to, or larger than, the Debye length. We illustrate applications of DiSCO's model-building procedure to chromatin folding and supercoiled DNA bound to Hin and Fis proteins. DiSCO is generally applicable to other interesting macromolecular systems for which mesoscale models are appropriate, to yield a resolution between the all-atom representative and the polymer level.
2128570
Peer-to-peer based multimedia distribution service
Recently, there are many research interests in providing efficient and scalable multimedia distribution service. However, stringent quality-of-service (QoS) requirements for media distribution, as well as dynamically changing and heterogeneous network capacity in today's best effort Internet, bring many challenges. In this paper, we introduce a novel framework for multimedia distribution service based on peer-to-peer (P2P) networks. A topology-aware overlay is proposed in which hosts self-organize into groups. End hosts within the same group have similar network conditions and can easily collaborate with each other to achieve QoS awareness. In order to improve media delivery quality and provide high service availability, we further propose two distributed heuristic replication strategies, intergroup replication and intragroup replication, based on this topology-aware overlay. Specifically, intergroup replication is aimed to improve the efficiency of media content delivery between the group where a request is issued and the group where the content is stored. Also, intragroup replication is targeted at improving the availability of the content. Extensive simulation results show that the latency in our proposed architecture is 20% less than that of the FreeNet and 50% less than that of the randomly replication system. Simulation results also show that the video quality in our system is much better than that in the other two systems. Our P2P-based approach is also distributed, scalable, cost effective, and aware of the performance.
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.
9684949
Network-adaptive rate control with TCP-friendly protocol for multiple video objects
Delivering visual content over the Internet with acceptable quality is an important and challenging task. In order to maintain good quality of service (QoS) in the current best-effort Internet, bandwidth adaptation to the varying network conditions is desired for streaming video over the Internet. In this paper, we propose a new rate control scheme for streaming MPEG4 video containing multiple video objects in conjunction with a TCP-friendly transport protocol that dynamically estimates available network bandwidth on the fly. Furthermore, network packet loss rate is taken into account in rate adaptation so that the end-to-end distortion is minimized. Simulation results demonstrate the effectiveness of our proposed scheme.