28378631
The Universal Medical Language System and the Gene Ontology: Some Critical Reflections
The Unified Medical Language System and the Gene Ontology are among the most widely used terminology resources in the biomedical domain. However, when we evaluate them in the light of simple principles for well-constructed ontologies we find a number of characteristic inadequacies. Employing the theory of granular partitions, a new approach to the understanding of ontologies and of the relationships ontologies bear to instances in reality, we provide an application of this theory in relation to an example drawn from the context of the pathophysiology of hypertension. This exercise is designed to demonstrate how, by taking ontological principles into account we can create more realistic biomedical ontologies which will also bring advantages in terms of efficiency and robustness of associated software applications.
14165673
Ontology for Task-Based Clinical Guidelines and the Theory of Granular Partitions
The theory of granular partitions (TGP) is a new approach to the understanding of ontologies and other classificatory systems. The paper explores the use of this new theory in the treatment of task-based clinical guidelines as a means for better understanding the relations between different clinical tasks, both within the framework of a single guideline and between related guidelines. We used as our starting point a DAML+OIL-based ontology for the WHO guideline for hypertension management, comparing this with related guidelines and attempting to show that TGP provides a flexible and highly expressive basis for the manipulation of ontologies of a sort which might be useful in providing more adequate Computer Interpretable Guideline Models (CIGMs) in the future.
16277175
Oncology Ontology in the NCI Thesaurus
The National Cancer Institute's Thesaurus (NCIT) has been created with the goal of providing a controlled vocabulary which can be used by specialists in the various sub-domains of oncology. It is intended to be used for purposes of annotation in ways designed to ensure the integration of data and information deriving from these various sub-domains, and thus to support more powerful cross-domain inferences. In order to evaluate its suitability for this purpose, we examined the NCIT's treatment of the kinds of entities which are fundamental to an ontology of colon carcinoma. We here describe the problems we uncovered concerning classification, synonymy, relations and definitions, and we draw conclusions for the work needed to establish the NCIT as a reference ontology for the cancer domain in the future.
8461087
Articulatory class based spectral envelope representation for voice fonts
Voice fonts are used to represent and transform the personality of speech. The spectral envelope is one of the main descriptors of voice fonts. We present a new technique to represent the spectral envelope in voice fonts which makes it possible to create a universal voice font for a given speaker which can be used for multiple languages. It enables voice fonts to be used for translingual personality transformation of speech. We evaluate the performance of the proposed approach through monolingual and translingual personality transformation experiments. We also compare the performance with the phoneme class based spectral envelope representation in voice fonts through various objective and subjective tests.
2583878
A data streaming algorithm for estimating subpopulation flow size distribution
Statistical information about the flow sizes in the traffic passing through a network link helps a network operator to characterize network resource usage, infer traffic demands, detect traffic anomalies, and improve network performance through traffic engineering. Previous work on estimating the flow size distribution for the complete population of flows has produced techniques that either make inferences from sampled network traffic, or use data streaming approaches. In this work, we identify and solve a more challenging problem of estimating the size distribution and other statistical information about arbitrary subpopulations of flows. Inferring subpopulation flow statistics is more challenging than the complete population counterpart, since subpopulations of interest are often specified a posteriori (i.e., after the data collection is done), making it impossible for the data collection module to "plan in advance".Our solution consists of a novel mechanism that combines data streaming with traditional packet sampling to provide highly accurate estimates of subpopulation flow statistics. The algorithm employs two data collection modules operating in parallel --- a NetFlow-like packet sampler and a streaming data structure made up of an array of counters. Combining the data collected by these two modules, our estimation algorithm uses a statistical estimation procedure that correlates and decodes the outputs (observations) from both data collection modules to obtain flow statistics for any arbitrary subpopulation. Evaluations of this algorithm on real-world Internet traffic traces demonstrate its high measurement accuracy.
33509544
Multiscale rough set data analysis with application to stock performance modeling
Multiscale Rough Set Data Analysis (MSRSDA) combines the feature selection and feature extraction ability of RSDA with the multiresolution property of the wavelet transform. RSDA removes the irrelevant (having no impact on prediction performance) and dependent (having no discriminatory power) market variables from the multivariate time series data matrix, thereby improving generalization capability of the forecasting engine (neural network, fuzzy inference system). A wavelet transform is used to decompose the time series into varying scales of resolution so that the underlying temporal structures of the original time series become more tractable; the decomposition is additive in details and approximation. Significance of market variables at various wavelet scales representing "detailed" short-term history and "general" long-term history of the time series, is computed using RSDA in wavelet space. Irrelevant and redundant variables at each scale are removed and a forecasting engine is then trained on each of the relevant resolution scales (i.e., those scales where significant events are detected); individual wavelets forecasts are recombined to form the overall forecast. The superior performance of MSRSDA is illustrated through stock performance modeling example. For prediction of S&P500 index, a data matrix of 21 variables has been used. MSRSDA gives 69% accuracy on stock-trend prediction.
5053302
Componentxchange: An E-Exchange for Software Components
A key challenge in Component-Based Software Engineering (CBSE) is finding appropriate software components for reuse. A component broker that allows component vendors to make available their modules, and also allows component integrators to search and select components matching their requirements will help solve this problem. In this paper, we describe an E-Exchange for buying and selling of software components over the World Wide Web. The ComponentXchange uses an XML based specification language for components, which is a semi-formal specification language that can describe a rich and an extensible set of component properties including functional properties, non-functional attributes and licensing aspects. ComponentXchange supports two models of component trading. A component can be used either by downloading it and integrating it into the client application or by accessing it remotely over the network. It has a licensing server that enforces the licensing terms and conditions. ComponentXchange can be easily extended to support different payment models like pay-per-use, pay-per-user, etc.
11169442
Linear Time Algorithms for Clustering Problems in Any Dimensions
We generalize the k-means algorithm presented by the authors [14] and show that the resulting algorithm can solve a larger class of clustering problems that satisfy certain properties (existence of a random sampling procedure and tightness). We prove these properties for the k-median and the discrete k-means clustering problems, resulting in O(2(k/e)O(1)dn) time (1+e)-approximation algorithms for these problems. These are the first algorithms for these problems linear in the size of the input (nd for n points in d dimensions), independent of dimensions in the exponent, assuming k and e to be fixed. A key ingredient of the k-median result is a (1+e)-approximation algorithm for the 1-median problem which has running time O(2(1/e)O(1)d). The previous best known algorithm for this problem had linear running time.