Collaborative Development of Large-Scale Biomedical Ontologies

2011 ◽  
pp. 179-200 ◽  
Author(s):  
Tania Tudorache ◽  
Mark A. Musen
Keyword(s):  
Large Scale ◽  
Biomedical Ontologies ◽  
Collaborative Development

Resource Configuration for the Collaborative Development of Product

2012 ◽  
Vol 215-216 ◽  
pp. 540-543
Author(s):  
Fu Hong Zeng ◽  
Lan Hua Zhou
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Collaborative Planning ◽  
Matching Model ◽  
Collaborative Development ◽  
Resource Configuration ◽  
Manufacturing Enterprises

In order to meet the reasonable matching of resource for collaborative development of products in manufacturing enterprises including involvement of suppliers on a large scale, a Generalized Design Resource Pool (GDRP) and It’s Resource Particles (RP) are defined, a multi-project collaborative planning and resource particles constraint-matching model with realization algorithm is presented. Finally, a case of developing mobile phone to an enterprise is presented to verify the effectiveness and feasibility of the presented approach.

scholarly journals A Component Approach to Collaborative Scientific Software Development: Tools and Techniques Utilized by the Quantum Chemistry Science Application Partnership

2008 ◽  
Vol 16 (4) ◽  
pp. 287-296 ◽  
Author(s):  
Joseph P. Kenny ◽  
Curtis L. Janssen ◽  
Mark S. Gordon ◽  
Masha Sosonkina ◽  
Theresa L. Windus
Keyword(s):  
Software Engineering ◽  
Quantum Chemistry ◽  
Large Scale ◽  
Programming Model ◽  
Scientific Software ◽  
Collaborative Development ◽  
Common Component ◽  
Component Approach ◽  
Common Component Architecture ◽  
Science Application

Cutting-edge scientific computing software is complex, increasingly involving the coupling of multiple packages to combine advanced algorithms or simulations at multiple physical scales. Component-based software engineering (CBSE) has been advanced as a technique for managing this complexity, and complex component applications have been created in the quantum chemistry domain, as well as several other simulation areas, using the component model advocated by the Common Component Architecture (CCA) Forum. While programming models do indeed enable sound software engineering practices, the selection of programming model is just one building block in a comprehensive approach to large-scale collaborative development which must also address interface and data standardization, and language and package interoperability. We provide an overview of the development approach utilized within the Quantum Chemistry Science Application Partnership, identifying design challenges, describing the techniques which we have adopted to address these challenges and highlighting the advantages which the CCA approach offers for collaborative development.

scholarly journals Matching Biomedical Ontologies through Adaptive Multi-Modal Multi-Objective Evolutionary Algorithm

Biology ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1287
Author(s):  
Xingsi Xue ◽  
Pei-Wei Tsai ◽  
Yucheng Zhuang
Keyword(s):  
Evolutionary Algorithm ◽  
Large Scale ◽  
Biomedical Ontology ◽  
Biomedical Data ◽  
Biomedical Ontologies ◽  
Ontology Alignment ◽  
Multi Objective Optimization ◽  
Matching Problem ◽  
Multi Objective ◽  
Matching Techniques

To integrate massive amounts of heterogeneous biomedical data in biomedical ontologies and to provide more options for clinical diagnosis, this work proposes an adaptive Multi-modal Multi-Objective Evolutionary Algorithm (aMMOEA) to match two heterogeneous biomedical ontologies by finding the semantically identical concepts. In particular, we first propose two evaluation metrics on the alignment’s quality, which calculate the alignment’s statistical and its logical features, i.e., its f-measure and its conservativity. On this basis, we build a novel multi-objective optimization model for the biomedical ontology matching problem. By analyzing the essence of this problem, we point out that it is a large-scale Multi-modal Multi-objective Optimization Problem (MMOP) with sparse Pareto optimal solutions. Then, we propose a problem-specific aMMOEA to solve this problem, which uses the Guiding Matrix (GM) to adaptively guide the algorithm’s convergence and diversity in both objective and decision spaces. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s biomedical tracks to test aMMOEA’s performance, and comparisons with two state-of-the-art MOEA-based matching techniques and OAEI’s participants show that aMMOEA is able to effectively determine diverse solutions for decision makers.

scholarly journals Matching Biomedical Ontologies: Construction of Matching Clues and Systematic Evaluation of Different Combinations of Matchers (Preprint)

2021 ◽  
Author(s):  
Peng Wang ◽  
Yunyan Hu ◽  
Shaochen Bai ◽  
Shiyi Zou
Keyword(s):  
Time Complexity ◽  
Large Scale ◽  
Empirical Studies ◽  
Representation Learning ◽  
Substantial Improvement ◽  
Systematic Evaluation ◽  
Ontology Matching ◽  
Biomedical Ontologies ◽  
Data Set ◽  
Combination Strategies

BACKGROUND Ontology matching seeks to find semantic correspondences between ontologies. With an increasing number of biomedical ontologies being developed independently, matching these ontologies to solve the interoperability problem has become a critical task in biomedical applications. However, some challenges remain. First, extracting and constructing matching clues from biomedical ontologies is a nontrivial problem. Second, it is unknown whether there are dominant matchers while matching biomedical ontologies. Finally, ontology matching also suffers from computational complexity owing to the large-scale sizes of biomedical ontologies. OBJECTIVE To investigate the effectiveness of matching clues and composite match approaches, this paper presents a spectrum of matchers with different combination strategies and empirically studies their influence on matching biomedical ontologies. Besides, extended reduction anchors are introduced to effectively decrease the time complexity while matching large biomedical ontologies. METHODS In this paper, atomic and composite matching clues are first constructed in 4 dimensions: terminology, structure, external knowledge, and representation learning. Then, a spectrum of matchers based on a flexible combination of atomic clues are designed and utilized to comprehensively study the effectiveness. Besides, we carry out a systematic comparative evaluation of different combinations of matchers. Finally, extended reduction anchor is proposed to significantly alleviate the time complexity for matching large-scale biomedical ontologies. RESULTS Experimental results show that considering distinguishable matching clues in biomedical ontologies leads to a substantial improvement in all available information. Besides, incorporating different types of matchers with reliability results in a marked improvement, which is comparative to the state-of-the-art methods. The dominant matchers achieve F1 measures of 0.9271, 0.8218, and 0.5 on Anatomy, FMA-NCI (Foundation Model of Anatomy-National Cancer Institute), and FMA-SNOMED data sets, respectively. Extended reduction anchor is able to solve the scalability problem of matching large biomedical ontologies. It achieves a significant reduction in time complexity with little loss of F1 measure at the same time, with a 0.21% decrease on the Anatomy data set and 0.84% decrease on the FMA-NCI data set, but with a 2.65% increase on the FMA-SNOMED data set. CONCLUSIONS This paper systematically analyzes and compares the effectiveness of different matching clues, matchers, and combination strategies. Multiple empirical studies demonstrate that distinguishing clues have significant implications for matching biomedical ontologies. In contrast to the matchers with single clue, those combining multiple clues exhibit more stable and accurate performance. In addition, our results provide evidence that the approach based on extended reduction anchors performs well for large ontology matching tasks, demonstrating an effective solution for the problem.

Managing Resource Allocation and Task Prioritization Decisions in Large Scale Virtual Collaborative Development Projects

2010 ◽  
Vol 23 (2) ◽  
pp. 53-76 ◽  
Author(s):  
Sharif H. Melouk ◽  
Uzma Raja ◽  
Burcu B. Keskin
Keyword(s):  
Resource Allocation ◽  
Open Source ◽  
Large Scale ◽  
Development Project ◽  
Operational Performance ◽  
Software Systems ◽  
Software System ◽  
Collaborative Development ◽  
Task Prioritization ◽  
And Task

The authors use a simulation approach to determine effective management of resource allocation and task prioritization decisions for the development of open source enterprise solutions software in the context of a large scale collaborative development project (CDP). Unlike traditional software systems where users have limited access to the development team, in open source environments, the resolution of issues is a collaborative effort among users and the team. However, as the project grows in size, complexity, and usage, effective allocation of resources and prioritization of tasks become a necessity to improve the operational performance of the software system. In this paper, by mining an open source software repository, the authors analyze the effects of collaborative issue resolution in a CDP and its effects on resource allocation of the team developers. This article examines several scenarios to evaluate the effects of forum discussions, resource allocation, and task prioritization on operational performance of the software system.

David Tudor's Rainforest: An Evolving Exploration of Resonance

2004 ◽  
Vol 14 ◽  
pp. 25-30 ◽  
Author(s):  
John Driscoll ◽  
Matt Rogalsky
Keyword(s):  
Large Scale ◽  
Public Performance ◽  
Collaborative Development

Of the works of David Tudor, none would seem to be better known than Rainforest IV, his large-scale performed installation of the 1970s. Although it has received widespread and well documented public performance, Rainforest's germination in the mid-1960s in elements of Bandoneon! (1966) and its evolution over a period of 10 years, from versions I (1968), II (1968–1969), III (1972) and IV (1973) through Forest Speech (1976), have not yet been adequately assessed. This paper follows Rainforest's trajectory chronologically: Matt Rogalsky focuses on the early versions of the work, and John Driscoll describes the collaborative development of Rainforest IV.

scholarly journals Matching Biomedical Ontologies: Construction of Matching Clues and Systematic Evaluation of Different Combinations of Matchers

10.2196/28212 ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. e28212
Author(s):  
Peng Wang ◽  
Yunyan Hu ◽  
Shaochen Bai ◽  
Shiyi Zou
Keyword(s):  
Time Complexity ◽  
Large Scale ◽  
Empirical Studies ◽  
Representation Learning ◽  
Substantial Improvement ◽  
Systematic Evaluation ◽  
Ontology Matching ◽  
Biomedical Ontologies ◽  
Data Set ◽  
Combination Strategies

Background Ontology matching seeks to find semantic correspondences between ontologies. With an increasing number of biomedical ontologies being developed independently, matching these ontologies to solve the interoperability problem has become a critical task in biomedical applications. However, some challenges remain. First, extracting and constructing matching clues from biomedical ontologies is a nontrivial problem. Second, it is unknown whether there are dominant matchers while matching biomedical ontologies. Finally, ontology matching also suffers from computational complexity owing to the large-scale sizes of biomedical ontologies. Objective To investigate the effectiveness of matching clues and composite match approaches, this paper presents a spectrum of matchers with different combination strategies and empirically studies their influence on matching biomedical ontologies. Besides, extended reduction anchors are introduced to effectively decrease the time complexity while matching large biomedical ontologies. Methods In this paper, atomic and composite matching clues are first constructed in 4 dimensions: terminology, structure, external knowledge, and representation learning. Then, a spectrum of matchers based on a flexible combination of atomic clues are designed and utilized to comprehensively study the effectiveness. Besides, we carry out a systematic comparative evaluation of different combinations of matchers. Finally, extended reduction anchor is proposed to significantly alleviate the time complexity for matching large-scale biomedical ontologies. Results Experimental results show that considering distinguishable matching clues in biomedical ontologies leads to a substantial improvement in all available information. Besides, incorporating different types of matchers with reliability results in a marked improvement, which is comparative to the state-of-the-art methods. The dominant matchers achieve F1 measures of 0.9271, 0.8218, and 0.5 on Anatomy, FMA-NCI (Foundation Model of Anatomy-National Cancer Institute), and FMA-SNOMED data sets, respectively. Extended reduction anchor is able to solve the scalability problem of matching large biomedical ontologies. It achieves a significant reduction in time complexity with little loss of F1 measure at the same time, with a 0.21% decrease on the Anatomy data set and 0.84% decrease on the FMA-NCI data set, but with a 2.65% increase on the FMA-SNOMED data set. Conclusions This paper systematically analyzes and compares the effectiveness of different matching clues, matchers, and combination strategies. Multiple empirical studies demonstrate that distinguishing clues have significant implications for matching biomedical ontologies. In contrast to the matchers with single clue, those combining multiple clues exhibit more stable and accurate performance. In addition, our results provide evidence that the approach based on extended reduction anchors performs well for large ontology matching tasks, demonstrating an effective solution for the problem.

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