17384175
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. Unlike in the traditional address-based unicast or multicast, its core problem is how to match events by predicates on the content of events. In existing matching approaches, matching predicates are composed by the conjunction and disjunction of non-semantic constraints. But, in context of enterprise application integration, although they can match events by their contents, this traditional matching predicates are not enough expressive in manipulating the complex event matching, such as the "one-to-many" and "many-to-one" matching. Therefore, traditional matching approaches should be extended to solve the complex matching problems. After analyzing information matching patterns in enterprise application integration, 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. Based on the extended OBDD graphs, the composite matching algorithm is presented and analysed. By experiments, we show the proposed algorithm is efficient
2507502
An Analytical Performance Model of IP-SAN
In this paper we propose a component-based analytical performance model for IP-SAN. We model four key components within IP-SAN individually and they are IP network, cache, RAID and disks. We validate these component models and compose them to give an entire IP-SAN performance model. The entire IP-SAN model analyzes the performance of an IP-SAN configuration under multiple synchronous workloads by using closed queueing network model and under asynchronous workload by using open queueing network model. We also validate the entire IP-SAN performance model against a real IP-SAN system. The theoretical results are compared with the results from a real IP-SAN using a variety of synthetic workloads. These experiments prove that the component-based method is an appropriate way to build performance model for a complex storage system. To our knowledge, this is the first exploration into building analytical performance model for entire IP-SAN system and validating against a real system. We also identify areas for future research in the performance analysis of real IP-SAN.
17485840
A framework for application server based Web services management
Web services are considered as solution for solving the interoperability problem and the challenge of integration. How to manage Web services more efficiently is a key problem to the applications based on Web services at present. This paper presents a framework for application server based Web services management named FASWSM. The main contribution of FASWSM is Web service adaptation, so that a Web service can be plugged into the application server (AS) as a Web service adapter which enables a Web service to be managed by the application server. Therefore, the Web services management efforts are greatly reduced by leveraging various services provided by the AS. The FASWSM also provides mechanisms for dynamical reconfiguration which improve the extensibility and flexibility of Web services management. The FASWSM has been applied to OnceAS 2.0 application server, and the experiments data show that FASWSM outperforms JAX-RPC in terms of Web service invocation.
46077798
A Component-Based Analytical Performance Model of IP-Based SAN
In this paper we propose a component-based analytical performance model for IP-based SAN. This model evaluates and analyzes the behavior of IP network, cache, RAID and disks under multiple synchronous I/O workloads by using closed queueing networks. The theoretical results are compared with the results from a real IP-based SAN using a variety of synthetic workloads. These experiments prove that the component-based method is an appropriate way to build performance model for a complex storage system. Using the model, we can find out that the bottleneck of SAN performance is network or storage node under different workloads or applications. We also identify areas for future research in the performance analysis of real IP-based SAN.
38303932
Fuzzy inference based on fuzzy concept lattice
In this paper, a fuzzy inference method based on the notion of fuzzy concept lattice is studied. We first propose a new form of fuzzy concept lattice, and then based on three kinds of known fuzzy concept lattices and our new fuzzy concept lattice, two coherent fuzzy inference methods, the lower approximate fuzzy inference and the upper approximate fuzzy inference, are proposed, and the combined use of the two methods will make the fuzzy inference more precise.
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.
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.
12613317
Research on the Status Quo and System Architecture of the Web Information Resource Evaluation
Web information resource evaluation (website evaluation) becomes more and more important, as the web information resources become larger and more complex. Thus, a lot of theory and methods have been developed to evaluate websites. In this paper, the author analyzes the primary methods applied currently in the evaluation of the web information resource and points out the main problems in this area from the perspectives of qualitative evaluation, quantitative evaluation, synthetic evaluation and automatic evaluation, after making an in-depth and comprehensive research of the literature and evaluation tools at home and abroad. Furthermore, the paper concludes that the evaluation of information resource on the Internet should take the internal and external characteristics into consideration, in the meantime, the paper puts forward a system architecture scheme of the automatic website evaluation based on the usability engineering.
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.
517875
Detecting Unsafe Driving Patterns using Discriminative Learning
We propose a discriminative learning approach for fusing multichannel sequential data with application to detect unsafe driving patterns from multi-channel driving recording data. The fusion is performed using a discriminatively trained graphical model -conditional random field (CRF). The proposed approach offers several advantage over existing information fusing approaches. First, it derives its classification power by directly modelling and maximizing the conditional probability. Second, it represents the variable dependency in an undirected graph, which is very efficient in inference. Third, it does not require to label all the training data and utilizes both labelled and unlabelled data efficiently by semi-supervised learning algorithms. The proposed approach is evaluated on driving recording data collected from driving simulator -STISIM. Experiments show it outperforms the simple discriminative classifier (SVM) and generative model (HMM).
850350
Online System Problem Detection by Mining Patterns of Console Logs
We describe a novel application of using data mining and statistical learning methods to automatically monitor and detect abnormal execution traces from console logs in an online setting. Different from existing solutions, we use a two stage detection system. The first stage uses frequent pattern mining and distribution estimation techniques to capture the dominant patterns (both frequent sequences and time duration). The second stage use principal component analysis based anomaly detection technique to identify actual problems. Using real system data from a 203-node Hadoop [1] cluster, we show that we can not only achieve highly accurate and fast problem detection, but also help operators better understand execution patterns in their system.