17469824
Extending OBDD graphs for composite event matching in content-based pub/sub systems
Content-based publish/subscribe offers a convenient abstraction for the information producers and consumers, supporting a large-scale system design and evolution by integrating several distributed independent application systems. Its core problem is how to match events by predicates on the content of events. In this paper, we propose three matching models, extend the simple matching to the multi-semantic matching and introduce the temporal constraint variable. The multi-semantic matching allows using different operations in accordance with different semantics; the temporal constraint variable supports processing the discrete events in the temporal sequence. Then, we extend OBDD graphs into hierarchy coloured OBDD graphs and prove the equivalence of the transformation. At last, based on OBDD graphs the composite matching algorithm is presented and analysed. By experiments, we show the proposed algorithm is efficient.
7297586
Diversifying and improving business process management systems by service cooperation middleware
Emerging service oriented business process management (BPM) systems require efficient middleware infrastructure. However, current techniques for middleware are not optimal for that because they all do not have service-oriented natures themselves. This paper introduces a novel service cooperation middleware (SCM), which is designed to meet such requirements. It improves the development and integration of loosely coupled service oriented BPM system by applying several concepts such as control flow semantic and projection operation, etc. Furthermore, based on the SCM architecture, the related process integration suit viz. ISProcessIntegration (PI) can be widely applied into all kinds of scenes, for example EAI, B2B, B2C, P2P, cluster, with different topologies.
7346557
Predictive Control for Dynamic Resource Allocation in Enterprise Data Centers
It is challenging to reduce resource over-provisioning for enterprise applications while maintaining service level objectives (SLOs) due to their time-varying and stochastic workloads. In this paper, we study the effect of prediction on dynamic resource allocation to virtualized servers running enterprise applications. We present predictive controllers using three different prediction algorithms based on a standard auto-regressive (AR) model, a combined ANOVA-AR model, as well as a multi-pulse (MP) model. We compare the properties of the predictive controllers with an adaptive integral (I) controller designed in our earlier work on controlling relative utilization of resource containers. The controllers are evaluated in a hypothetical virtual server environment where we use the CPU utilization traces collected on 36 servers in an enterprise data center. Since these traces were collected in an open-loop environment, we use a simple queuing algorithm to simulate the closed-loop CPU usage under dynamic control of CPU allocation. We also study the controllers by emulating the utilization traces on a test bed where a Web server was hosted inside a Xen virtual machine. We compare the results of these controllers from all the servers and find that the MP-based predictive controller performed slightly better statistically than the other two predictive controllers. The ANOVA-AR-based approach is highly sensitive to the existence of periodic patterns in the trace, while the other three methods are not. In addition, all the three predictive schemes performed significantly better when the prediction error was accounted for using a feedback mechanism. The MP-based method also demonstrated an interesting self-learning behavior
13542388
Efficient Adaptive Resource Allocation for Multiuser OFDM Systems with Minimum Rate Constraints
This paper investigates the adaptive resource allocation scheme for the downlink of multiuser OFDM systems. We focus on the problem of maximizing the overall spectral efficiency while maintaining the QoS requirements of users, including bit error rate and individual minimum rate requirements. Under the assumption of equal power allocation, an efficient algorithm is proposed to obtain the suboptimal solution of the resource allocation. In this algorithm, we first introduce some positive multipliers, one for each user, according to their minimum rate constraints (MRC), and then a parallel subcarrier-and-bit allocation scheme is designed using these multipliers with low complexity. The numerical results show that our algorithm not only substantially reduces the computational complexity of the existing algorithm, but also provides a noticeable performance improvement.
26522967
Maximum entropy model-based baseball highlight detection and classification
In this paper, we propose a novel system that is able to automatically detect and classify baseball highlights by seamlessly integrating image, audio, and speech clues using a unique framework based on maximum entropy model (MEM). What distinguishes our system is that we emphasize on the integration of multimedia features and the acquisition of domain knowledge through automatic machine learning processes. Our MEM-based framework provides a simple platform capable of integrating multimedia features as well as their contextual information in a uniform fashion. Unlike the Hidden Markov Model and the Bayes Network-based approaches, this framework does not need to explicitly segment and classify the input video into states during its data modeling process, and hence remarkably simplifies the training data creation and the highlight detection/classification tasks. Experimental evaluations demonstrate the superiority of our proposed baseball highlight detection system in its capability of detecting more major baseball highlights, and in its overall accuracy of detecting these major highlights.
1287667
Video object segmentation by motion-based sequential feature clustering
Segmentation of video foreground objects from background has many important applications, such as human computer interaction, video compression, multimedia content editing and manipulation. Most existing methods work on image pixels or color segments which are computationally expensive. Some methods require extensive manual inputs, static cameras, and/or rigid scenes. In this paper we propose a fully automatic foreground segmentation method based on sequential clustering of sparse image features. The sparseness makes the method computationally efficient. We use both edge and corner points extracted from each video frame. A joint spatio-temporal linear regression method is developed to compute sparse motion layers of M consecutive frames jointly under the temporal consistency constraint. Once the sparse motion layers have been identified for each frame, the corresponding dense motion layers are created using the Markov Random Field (MRF) model. The MRF model assigns the rest of the image pixels to the motion layers by considering both the color attributes and the spatial relations between each pixel and its surrounding edge/corner points. Experimental evaluations on videos taken by webcams show the effectiveness of the proposed method.
45173906
New Travel Demand Models with Back-Propagation Network
This paper explores the application of back-propagation network (BPN) to travel demand analysis. Models are developed to simulate travelers' inner-city behaviors and all of them adopt BPNs as main paradigm, for its virtue in non-linear analysis and prediction. Compared to the past researches, which were generally based on aggregate data, the models here are more comprehensive and developed based on disaggregate survey data. At first, three categories of models using BPNs are established to respectively realize trip generation, OD estimation and mode choice analysis-the first three steps in classical "four-step" models for travel demand forecasting. Furthermore, the integrated models are researched in two ways. One method is to use a simple combination of the former separate BPN models, and the other is to create a multilayer back-propagation network (MLBPN). Results show that BPN can be a feasible tool for travel demand analysis.