32109138
Rewriting Queries for XML Integration Systems
A data integration system typically creates a target XML schema to represent an application domain and source schemas are mapped to the target schema. A user poses a query over the target schema, and the system rewrites the query into a set of queries over the data sources. Existing algorithms generate a set of static rules based on the target schema and mappings, and rewrite the target query using these rules. We design a flexible and dynamic approach that rewrites XML queries directly based on the mappings between the target and source schemas. Theoretical analysis and experiments on both synthetic and real-world datasets indicate that the proposed approach is efficient and scalable.
28610395
A Path-Based Approach for Efficient Structural Join with Not-Predicates
There has been much research on XML query processing. However, there has been little work on the evaluation of XML queries involving not-predicates. Such queries are useful and common in many real-life applications. In this paper, we present a model called XQuery tree to model queries involving not-predicates and describe a path-based method to evaluate such queries efficiently. A comprehensive set of experiments is carried out to demonstrate the effectiveness and efficiency of the proposed solution.
15608177
Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method
MOTIVATION
We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm.


METHODS
Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples.


RESULTS
The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification.


AVAILABILITY
The method is available at http://dir.niehs.nih.gov/microarray/datamining


CONTACT
LI3@niehs.nih.gov
11582687
Web service enablement of communication services
In this paper, we introduce Web service application session services, WS-session, for Web service based application session management in communication. A two-way Web service framework is described. It supports the use of Web service in the situation of telecommunication where the communication endpoint can be both a client and a peer-to-peer server. We describe the interface design in two-way Web service interaction based on the concept of tightly coupled interface and loosely coupled interface solutions. The proposed approach provides full support of asynchronous outbound operations and event notifications in communication services. It is implemented in a research prototype system that provides both standard based (ECMA-348) services and Avaya service extensions. In addition, we investigated the integration of our proposed approach with BPEL4WS. It allows the user to create new services through a drag-n-drop based service creation environment (ECLIPSE/BPEL4WS). It leads to a paradigm of service creation through Web service composition. More sophisticated telecommunication services are created from composing the basic services.
5082292
Survival prediction of diffuse large-B-cell lymphoma based on both clinical and gene expression information
MOTIVATION
It is important to predict the outcome of patients with diffuse large-B-cell lymphoma after chemotherapy, since the survival rate after treatment of this common lymphoma disease is <50%. Both clinically based outcome predictors and the gene expression-based molecular factors have been proposed independently in disease prognosis. However combining the high-dimensional genomic data and the clinically relevant information to predict disease outcome is challenging.


RESULTS
We describe an integrated clinicogenomic modeling approach that combines gene expression profiles and the clinically based International Prognostic Index (IPI) for personalized prediction in disease outcome. Dimension reduction methods are proposed to produce linear combinations of gene expressions, while taking into account clinical IPI information. The extracted summary measures capture all the regression information of the censored survival phenotype given both genomic and clinical data, and are employed as covariates in the subsequent survival model formulation. A case study of diffuse large-B-cell lymphoma data, as well as Monte Carlo simulations, both demonstrate that the proposed integrative modeling improves the prediction accuracy, delivering predictions more accurate than those achieved by using either clinical data or molecular predictors alone.
37524397
Cluster-based estimation for sufficient dimension reduction
Sufficient dimension reduction (SDR) approaches such as ordinary least squares (OLS), sliced inverse regression, sliced average variance estimation and principal Hessian directions all require a linearity condition on the marginal distribution of the predictors X. This can be a significant limitation in practice. In this article we propose the use of clustering on the predictor space with the goal of breaking the nonlinearity among the predictors. We then estimate the dimension reduction subspace by combining estimates from individual clusters. OLS is used as a demonstration of the cluster-based estimation approach. Simulations have shown that the proposed method effectively mitigates the effects of nonlinearity; it thus has the potential to widen the applicative scope of existing SDR techniques.
2448001
The Instance Store: DL Reasoning with Large Numbers of Individuals
We present an application – the Instance Store – aimed at solving some of the scalability problems that arise when reasoning with the large numbers of individuals envisaged in the semantic web. The approach uses well-known techniques for reducing description logic reasoning with individuals to reasoning with concepts. Crucial to the implementation is the combination of a description logic terminological reasoner with a traditional relational database. The resulting form of inference, although specialised, is sound and complete and sufficient for several interesting applications. Most importantly, the application scales to sizes (over 100,000s individuals) where all other existing applications fail. This claim is substantiated by a detailed empirical evaluation of the Instance Store in contrast with existing alternative approaches.