面向多学科协作的数据世系建模及溯源关键问题研究

Deep organization, iterative reuse and source-tracing of data and knowledge with different maturity are crucial for multi-disciplinary scientific collaboration. Emerging features such as high complexity of collaboration process, strong relevancy of cooperative data and persistent effect of abnormal data fundamentally challenge the existing collaborative information processing technology in terms of data modeling & source-tracing, consistency maintenance & query and quality management. This project focuses on the key scientific problem of "how to build a formal data provenance model with capabilities of covering the whole multi-disciplinary collaboration process, comprehensively describing the data relevancies, rigorously reasoning the source-tracing paths and accurately locating the data problems”. Based on the theory, techniques and methods of computer supported cooperative work (CSCW), this project propose research on key issues of multi-disciplinary collaboration oriented data provenance modeling and tracing, which consists of building multi-disciplinary collaboration oriented formal data provenance model, investigating data provenance based relevant data consistency maintenance algorithms, query techniques and data quality analysis methods, and developing a prototype system for proof of technique and validation. The expected outcome of the project will lay a fundamental underpinning of core theories, models, techniques and methods for the research and development of large-scale scientific collaboration platforms and data management & digital mock-up systems, which is of great significance, importance and value for both research and practice of multi-disciplinary collaboration.

多学科协作中协同数据与知识的深度组织、迭代重用和溯源管理是支撑多学科协作的关键。针对多学科协作过程的高复杂性、协同数据的强关联性和异常数据影响的持续辐射性给现有协同信息处理技术在多学科协作数据建模与溯源、一致性维护与查询和质量管理等方面带来的全新技术挑战，本项目聚焦“如何建立能覆盖多学科协作全过程、全面刻画数据关联关系、严格推理数据溯源路径、精确定位数据质量问题的形式化数据世系模型”这一关键科学问题，基于计算机支持的协同工作（CSCW）的理论、技术和方法，开展面向多学科协作的数据世系建模和溯源关键问题研究，构建面向多学科协作的数据世系形式化模型，研究基于数据世系模型的关联数据一致性维护算法、查询技术以及质量分析技术等，并开发技术验证原型系统。项目研究成果将为大型科研协同平台及知识管理和数字样机系统的研发提供数据世系建模与溯源的核心理论、模型、技术和方法支撑，具有重要的理论价值与实际意义。

本项目组针对多学科协作过程中的高复杂性、协同数据的强关联性和异常数据影响的持续辐射性给现有协同信息处理技术在多学科协作数据建模与溯源、一致性维护与查询和质量管理等方面带来的全新挑战，开展面向多学科协作的数据世系建模和溯源关键问题的研究。主要创新成果包括：面向多学科协作场景的数据世系图模型及其世系图摘要方法；跨学科场景下多粒度关联世系数据的一致性维护算法；基于数据世系的数据质量评估、分析与推荐技术；面向多学科社区的用户行为数据世系管理、溯源与分析验证。这些成果发表在高水平国际会议和期刊上，其中CCF A类会议论文3篇，CCF A类期刊论文1篇，SCI期刊论文2篇。在本基金项目资助下，项目组共发表学术论文16篇，其中SCI收录1篇，EI收录10篇；申请国家发明专利4项；培养博士2名、硕士6名；组织国外学术研讨会2次，参加国际学术会议22人次。

内容获取失败，请点击重试