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.
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.
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.
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.
18316669
On Linear-Spline Based Histograms
Approximation is a very effective paradigm to speed up query processing in large databases. One popular approximation mechanism is data size reduction. There are three reduction techniques: sampling, histograms, and wavelets. Histogram techniques are supported by many commercial database systems, and have been shown very effective for approximately processing aggregation queries. In this paper, we will investigate the optimal models for building histograms based on linear spline techniques. We will firstly propose several novel models. Secondly, we will present efficient algorithms to achieve these proposed optimal models. Our experiment results showed that our new techniques can greatly improve the approximation accuracy comparing to the existing 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.
18410848
Network-adaptive scalable video streaming over 3G wireless network
Scalable video streaming over a wireless link with quality of service (QoS) is a very challenging task due to the time-varying characteristics of the wireless channel and limited battery resource in handheld devices. This paper proposes an end-to-end network-adaptive architecture for video streaming over 3G wireless networks. The proposed architecture not only dynamically estimates the varying network status on the fly, but also simultaneously performs application-level error control and transmission-level power control to protect against the random and fading error occurring across the 3G network. Distortion/power-minimized rate allocation is presented to achieve the minimum end-to-end distortion or minimum total power consumption based on the user's requirement. Simulation results demonstrate the effectiveness of our proposed scheme.
16216432
A Multiobjective Differential Evolution Based on Decomposition for Multiobjective Optimization with Variable Linkages
Although a number of multiobjective evolutionary algorithms have been proposed over the last two decades, not much effort has been made to deal with variable linkages in multiobjective optimization. Recently, we have suggested a general framework of multiobjective evolutionary algorithms based on decomposition (MOEA/D) [1]. MOEA/D decomposes a MOP into a number of scalar optimization subproblems by a conventional decomposition method. The optimal solution to each of these problems is a Pareto optimal solution to the MOP under consideration. An appropriate decomposition could make these individual Pareto solutions evenly distribute along the Pareto optimal front. MOEA/D aims at solving these scalar optimization subproblems simultaneously. In this paper, we propose, under the framework of MOEA/D, a multiobjective differential evolution based decomposition (MODE/D) for tackling variable linkages. Our experimental results show that MODE/D outperforms several other MOEAs on several test problems with variable linkages.
7585289
Proactive Scan: Fast Handoff with Smart Triggers for 802.11 Wireless LAN
It has been a challenging problem to support VoIP-type delay sensitive applications in an 802.11 wireless LAN, because the standard handoff procedure implemented in many current 802.11 products occurs a delay deem unacceptable to VoIP users. To reduce this delay, we have developed a fast handoff scheme called Proactive Scan. It employs two new techniques. The first is to decouple the time-consuming channel scan from the actual handoff, and to eliminate channel scan delay by doing scan early and interleaving it with ongoing traffic in a non-intrusive way. The second technique is a smart trigger that takes into account both uplink and downlink quality and explicitly addresses the link asymmetry which has yet not been touched in previous work. Through implementation and experimentation study, we have shown that Proactive Scan does provide fast handoff and satisfactory performance to VoIP applications. Further, it is a software-only client-only solution that any mobile device can use in any existing 802.11 networks.
1741788
A Compound TCP Approach for High-Speed and Long Distance Networks
Many applications require fast data transfer over high speed and long distance networks. However, standard TCP fails to fully utilize the network capacity due to the limitation in its conservative congestion control (CC) algorithm. Some works have been proposed to improve the connection 鈂s throughput by adopt- ing more aggressive loss-based CC algorithms. These algorithms, although can effectively improve the link utilization, have the weakness of poor RTT fairness. Further, they may severely de- crease the performance of regular TCP flows that traverse the same network path. On the other hand, pure delay-based ap- proaches that improve the throughput in high-speed networks may not work well when the traffic is mixed with both delay- based and greedy loss-based flows. In this paper, we propose a novel Compound TCP (CTCP) approach, which is a synergy of delay-based and loss-based approach. Specifically, we add a scal- able delay-based component into the standard TCP Reno conges- tion avoidance algorithm (a.k.a., the loss-based component). The sending rate of CTCP is controlled by both components. This new delay-based component can rapidly increase sending rate when network path is under utilized, but gracefully retreat in a busy network when bottleneck queue is built. Augmented with this delay-based component, CTCP provides very good bandwidth scalability with improved RTT fairness, and at the same time achieves good TCP-fairness, irrelevant to the windows size. We developed an analytical model of CTCP and implemented it on the Windows operating system. Our analysis and experiment results verify the properties of CTCP.
15428033
On Optimal Physical Carrier Sensing: Theoretical Analysis and Protocol Design
Traditional Physical Carrier Sensing (PCS), which aims at eliminating hidden terminals in wireless networks, causes too many exposed terminals and deteriorates the throughput per user seriously. Some existing work has proven that an aggressive PCS can improve the throughput by balancing the tradeoff between hidden terminals and exposed terminals. However, little work has been conducted to optimize the PCS according to the network conditions. In this paper, we develop an analytical model to study the behaviors of a user in the aggressive PCS scenario, with which the optimal PCS threshold can be derived. Then, to avoid the complicated computation, we present a heuristic algorithm, in which the parameters required for PCS tuning are estimated by the carrier sensing mechanism employed in IEEE 802.11. Simulation results show that the proposed heuristic algorithm can make the PCS threshold approach to the theoretical optimal one. Moreover, the proposed heuristic algorithm can obtain significant throughput gain compared to the traditional PCS threshold setting solutions.