CAREER: Exploration of Dynamic Sequences in Scientific Databases

职业：探索科学数据库中的动态序列

• 批准号: 0546713
• 负责人: Hakan Ferhatosmanoglu
• 金额: $ 45.5万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0546713&HistoricalAwards=false
• 关键词: CAREER; Exploration; Dynamic; Sequences; Scientific

Scientific data repositories increasingly involve large amounts of images and streams of empirical measurements generated by a diverse set of data sources. The goal of this project is to develop online structures and algorithms to dynamically maintain and analyze data sequences for scientific discovery and monitoring purposes. The implementation focuses on specific applications from physical and biological sciences that generate vast amounts of multi-dimensional data sequences. For scientific discoveries, an iterative querying framework is developed for modeling of the sequences of observations. The framework optimally utilizes access structures to execute queries ranging from a simple max aggregate to complex scientific queries. Interactive tools are implemented where researchers are able to incorporate domain specific knowledge into the search process. For real-time monitoring, one-pass summaries that can be updated in constant-time are developed. The structures are designed to be self-adaptive with respect to the workload changes and to handle heterogeneous and incomplete information. The project involves collaborations with domain experts in focus areas and is expected to advance the state-of-the-art knowledge in the application domains. For example, the gene expression analysis tools implemented in this project have already enhanced the ability of the collaborative researchers in their studies of Haemophilus Influenzae (first described in 1892 by Dr. Richard Pfeiffer during an influenza pandemic) in order to understand it role in a wide range of clinical diseases, so that effective vaccines can be developed. This research project is integrated with education through significant educational and outreach activities. The developed toolkits, findings, and methods of the project will be communicated in a broader context and to an expanded audience through the project website (http://www.cse.ohio-state.edu/~hakan/Career.html).

科学数据储存库越来越多地涉及由一组不同的数据源产生的大量图像和经验测量流。该项目的目标是开发在线结构和算法，以动态维护和分析数据序列，用于科学发现和监测目的。该实施侧重于产生大量多维数据序列的物理和生物科学的具体应用。对于科学发现，开发了一个迭代查询框架来对观测序列进行建模。该框架优化地利用访问结构来执行从简单的最大聚集到复杂的科学查询的查询。实施了交互式工具，其中研究人员能够将特定领域的知识纳入搜索过程。对于实时监控，开发了可持续更新的一遍摘要。这些结构被设计为根据工作量的变化自适应，并处理不同种类和不完整的信息。该项目涉及与重点领域的领域专家合作，预计将促进应用领域的最新知识。例如，该项目中实施的基因表达分析工具已经增强了合作研究人员对流感嗜血杆菌(1892年由Richard Pfeiffer博士在流感大流行期间首次描述)的研究能力，以便了解它在广泛的临床疾病中的作用，从而开发有效的疫苗。这一研究项目通过重大的教育和外联活动与教育相结合。项目开发的工具包、结果和方法将在更广泛的背景下传播，并通过项目网站(http://www.cse.ohio-state.edu/~hakan/Career.html).向更多的受众传播