31062388
Content Selection Model for Adaptive Content Delivery
In order to adapt content delivery to different client capabilities and preferences, we propose a content selection model to automatically classify HTML content based on its functionality, then map client descriptions on preferences and device capabilities into our classification scheme, and finally selectively deliver the content which users want and which devices can handle. The experiment shows that our content selection model could reduce HTML object size, object latency and page latency. Therefore, it is effective in saving network resources and improving clients’ access experiences.
209398610
Beyond the traditional query operators.
10076076
Quantitative Modeling for Web Objects' Cacheability
In this paper, we propose a mathematical model, called the E-Cacheability Index, to quantify and compare the effectiveness of caching web objects on Internet. Unlike previous related work on web object cacheability, our study is based on a comprehensive study of all object attributes related to the HTTP protocol transfer, content reuse, freshness re-validation as well as the inter-relationship/dependency among these attributes in the final caching decision. This model is important because it not only estimates the cacheability effectiveness of web content, but also provides hints on web caching optimization.
9546816
A Framework of HTML Content Selection and Adaptive Delivery
In this paper, we propose a content selection and adaptive delivery framework to adapt content delivery to different client capabilities and preferences. The framework provides language support to content publishers with XML-based markup language to describe the content and control the transformation and delivery process. Automatic content classification and selective delivery based on match between content class and user capability or preference are also implemented. Thus, when content descriptions from server are not available, adaptive content delivery is still possible. The framework is generic and scalable. Performance studies on content classification and selection model show that it is effective in saving network resources and improving clients’ access experiences.
13552447
Reverse mapping of referral links from storage hierarchy for Web documents
Due to the lack of back-pointers, information about the referral parent(s) of a given Web page is not usually available to Web surfers. This significantly reduces the effectiveness of Web surfing and information discovery. We propose a mechanism to locate the referral parent(s) for a given fragment of a Web document (in terms of an URL address) from the client side. The basic idea is to explore the hierarchical hyperlink structure of a Web document fragment from its storage path directory information that is embedded in its URL. Two mechanisms were proposed: exhaustibility first search (EFS) and generalization first search (GFS). Results showed that through direct mapping of the storage hierarchy path to the URL address and then performing reverse tracing along the hyperlink structure, the chance of discovering a referral parent ranged from 41% to 51%. Compared to EFS, GFS was found to reduce the average search space from 26 pages to 4 pages.
19735283
A generalized site ranking model for Web IR
Normally, the unit for a ranking model in a Web IR system is a Web page, which is, sometimes, just an information fragment. A larger unit considering the linkage information may be desired to reduce the cognitive overload for users to identify the complete information from the interconnected Web. We propose a ranking model to measure the relevance of the whole Web site. We take some illustrations to show the idea and provide evidences to indicate its effectiveness.
2510063
Software behavior oriented parallelization
Many sequential applications are difficult to parallelize because of unpredictable control flow, indirect data access, and input-dependent parallelism. These difficulties led us to build a software system for behavior oriented parallelization (BOP), which allows a program to be parallelized based on partial information about program behavior, for example, a user reading just part of the source code, or a profiling tool examining merely one or few executions.
 The basis of BOP is programmable software speculation, where a user or an analysis tool marks possibly parallel regions in the code, and the run-time system executes these regions speculatively. It is imperative to protect the entire address space during speculation. The main goal of the paper is to demonstrate that the general protection can be made cost effective by three novel techniques: programmable speculation, critical-path minimization, and value-based correctness checking. On a recently acquired multi-core, multi-processor PC, the BOP system reduced the end-to-end execution time by integer factors for a Lisp interpreter, a data compressor, a language parser, and a scientific library, with no change to the underlying hardware or operating system.
807817
A spectral method to separate disconnected and nearly-disconnected web graph components
Separation of connected components from a graph with disconnected graph components mostly use breadth-first search (BFS) or depth-first search (DFS) graph algorithms. Here we propose a new algebraic method to separate disconnected and nearly-disconnected components. This method is based on spectral graph partitioning, following a key observation that disconnected components will show up, after properly sorted, as step-function like curve in the lowest eigenvectors of the Laplacian matrix of the graph. Following an perturbative analysis framework, we systematically analyzed the graph structures, first on the disconnected subgraph case, and second on the effects of adding edges sparsely connecting different subgraphs as a perturbation. Several new results are derived, providing insights to spectral methods and related clustering objective function. Examples are given illustrating the concepts and results our methods. Comparing to the standard graph algorithms, this method has the same O(‖E ‖ + ‖V‖log(‖V‖)) complexity, but is easier to implement (using readily available eigensolvers). Further more the method can easily identify articulation points and bridges on nearly-disconnected graphs. Segmentation of a real example of Web graph for query amazon is given. We found that each disconnected or nearly-disconnected components forms a cluster on a clear topic.
11783493
Unsupervised Feature Selection Via Two-way Ordering in Gene Expression Analysis
MOTIVATION
Selection of genes most relevant and informative for certain phenotypes is an important aspect in gene expression analysis. Most current methods select genes based on known phenotype information. However, certain set of genes may correspond to new phenotypes which are yet unknown, and it is important to develop novel effective selection methods for their discovery without using any prior phenotype information.


RESULTS
We propose and study a new method to select relevant genes based on their similarity information only. The method relies on a mechanism for discarding irrelevant genes. A two-way ordering of gene expression data can force irrelevant genes towards the middle in the ordering and thus can be discarded. Mechanisms based on variance and principal component analysis are also studied. When applied to expression profiles of colon cancer and leukemia, the unsupervised method outperforms the baseline algorithm that simply uses all genes, and it also selects relevant genes close to those selected using supervised methods.


SUPPLEMENT
More results and software are online: http://www.nersc.gov/~cding/2way.
18159100
Using accurate arithmetics to improve numerical reproducibility and stability in parallel applications
Numerical reproducibility and stability of large scale scientific simulations, especially climate modeling, on distributed memory parallel computers are becoming critical issues. In particular, global summation of distributed arrays is most susceptible to rounding errors, and their propagation and accumulation cause uncertainty in final simulation results. We analyzed several accurate summation methods and found that two methods are particularly effective to improve (ensure) reproducibility and stability: Kahan's self-compensated summation and Bailey's double-double precision summation. We provide an MPI operator MPLSUMDD to work with MPI collective operations to ensure a scalable implementation on large number of processors. The final methods are particularly simple to adopt in practical codes.
16446006
A similarity-based probability model for latent semantic indexing
A dual probability model is constructed for the Latent Semantic Indexing (LSI) using the cosine similarity measure. Both the document-document similarity matrix and the term-term similarity matrix naturally arise from the maximum likelihood estimation of the model parameters, and the optimal solutions are the latent semantic vectors of of LSI. Dimensionality reduction is justi ed by the statistical signi cance of latent semantic vectors as measured by the likelihood of the model. This leads to a statistical criterion for the optimal semantic dimensions, answering a critical open question in LSI with practical importance. Thus the model establishes a statistical framework for LSI. Ambiguities related to statistical modeling of LSI are clari ed.