12276338
User-oriented reliability modeling for a Web system
We develop a user-oriented reliability model, a simple yet effective approach, to address huge amounts of Web contents, and more importantly tackle users' dynamic navigation behaviors for a Web system. The users' intrinsic capabilities and behaviors, unlike programs' common recurring behaviors, call for a new approach for modeling the reliability of an organization's Web system. Our approach differs in the modeling scope of the Web system and the means of modeling user behaviors. We exploit the idea of cyclomatic complexity to cover the maximum number of independent paths, which overcome the less likely repetitive user navigations, and still maintain the dynamic nature of Web surfing. The modeling process takes Web senders' access, error, and referrer information as inputs, and translates them into a flow graph. A transition tree is then derived from the flow graph, based on depth-first traversal and dynamic programming. Accordingly, the reliability of the Web system can be computed by excluding those unreliable paths. This model is further applied to prioritize the error removal sequence to assist in allocating resources and effort.
42677463
Heterogeneous software reliability modeling
A number of Markov-based software reliability models have been developed for measuring software reliability. However, the application of these models is strictly limited to software that satisfies the Markov properties. The objective of our work is to expand the application domain of the Markov-based models, so that most software can be modeled and software reliability can be measured at the architecture level. To overcome the limitations of Markov properties, our model takes execution history into account and addresses both deterministic and probabilistic software behaviors. Each state represents the executions of one or more components depending on the architectural styles. In addition, the executions of one component are depicted by using distinctive states, when such executions are influenced by past states. Furthermore, we construct loops to eliminate the likelihood of unlimited state expansion and utilize a binomial tree structure to account for all the different execution paths. We show that Markov models are applicable even to software that does not fully satisfy the Markov properties. Therefore, we significantly improve the state of the art in architecture-based software reliability modeling.
14919460
Interpolation on quadric surfaces with rational quadratic spline curves
Abstract Given a sequence of points { X i } i =1 n on a regular quadric S : X T AX = 0 ⊂ E d , d ⩾ 3, we study the problem of constructing a G 1 rational quadratic spline curve lying on S that interpolates { X i } i =1 n . It is shown that a necessary condition for the existence of a nontrivial interpolant is ( X 1 T AX 2 )( X i T AX i +1 ) > 0, i = 1,2…, n − 1. Also considered is a Hermite interpolation problem on the quadric S : a biarc consisting of two conic arcs on S joined with G 1 continuity is used to interpolate two points on S and two associated tangent directions, a method similar to the biarc scheme in the plane (Bolton, 1975) or space (Sharrock, 1987). A necessary and sufficient condition is obtained on the existence of a biarc whose two arcs are not major elliptic arcs. In addition, it is shown that this condition is always fulfilled on a sphere for generic interpolation data.
385307
STING: A Statistical Information Grid Approach to Spatial Data Mining
Spatial data mining, i.e., discovery of interesting characteristics and patterns that may implicitly exist in spatial databases, is a challenging task due to the huge amounts of spatial data and to the new conceptual nature of the problems which must account for spatial distance. Clustering and region oriented queries are common problems in this domain. Several approaches have been presented in recent years, all of which require at least one scan of all individual objects (points). Consequently, the computational complexity is at least linearly proportional to the number of objects to answer each query. In this paper, we propose a hierarchical statistical information grid based approach for spatial data mining to reduce the cost further. The idea is to capture statistical information associated with spatial cells in such a manner that whole classes of queries and clustering problems can be answered without recourse to the individual objects. In theory, and confirmed by empirical studies, this approach outperforms the best previous method by at least an order of magnitude, especially when the data set is very large.
16657065
Collaborative Web caching based on proxy affinities
With the exponential growth of hosts and traffic workloads on the Internet, collaborative web caching has been recognized as an efficient solution to alleviate web page server bottlenecks and reduce traffic. However, cache discovery, i.e., locating where a page is cached, is a challenging problem, especially in the fast growing World Wide Web environment, where the number of participating proxies can be very large. In this paper, we propose a new scheme which employs proxy affinities to maintain a dynamic distributed collaborative caching infrastructure. Web pages are partitioned into clusters according to proxy reference patterns. All proxies which frequently access some page(s) in the same web page cluster form an “information group”. When web pages belonging to a web page cluster are deleted from or added into a proxy's cache, only proxies in the associated information group are notified. This scheme can be shown to greatly reduce the number of messages and other overhead on individual proxies while maintaining a high cache hit rate. Finally, we employ trace driven simulation to evaluate our web caching scheme using three web access trace logs to verify that our caching structure can provide significant benefits on real workloads.
2768791
Finding representative set from massive data
In the information age, data is pervasive. In some applications, data explosion is a significant phenomenon. The massive data volume poses challenges to both human users and computers. In this project, we propose a new model for identifying representative set from a large database. A representative set is a special subset of the original dataset, which has three main characteristics: It is significantly smaller in size compared to the original dataset. It captures the most information from the original dataset compared to other subsets of the same size. It has low redundancy among the representatives it contains. We use information-theoretic measures such as mutual information and relative entropy to measure the representativeness of the representative set. We first design a greedy algorithm and then present a heuristic algorithm that delivers much better performance. We run experiments on two real datasets and evaluate the effectiveness of our representative set in terms of coverage and accuracy. The experiments show that our representative set attains expected characteristics and captures information more efficiently.
3126850
Recent innovations in speech-to-text transcription at SRI-ICSI-UW
We summarize recent progress in automatic speech-to-text transcription at SRI, ICSI, and the University of Washington. The work encompasses all components of speech modeling found in a state-of-the-art recognition system, from acoustic features, to acoustic modeling and adaptation, to language modeling. In the front end, we experimented with nonstandard features, including various measures of voicing, discriminative phone posterior features estimated by multilayer perceptrons, and a novel phone-level macro-averaging for cepstral normalization. Acoustic modeling was improved with combinations of front ends operating at multiple frame rates, as well as by modifications to the standard methods for discriminative Gaussian estimation. We show that acoustic adaptation can be improved by predicting the optimal regression class complexity for a given speaker. Language modeling innovations include the use of a syntax-motivated almost-parsing language model, as well as principled vocabulary-selection techniques. Finally, we address portability issues, such as the use of imperfect training transcripts, and language-specific adjustments required for recognition of Arabic and Mandarin