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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >TECHNIQUES FOR OPTIMIZATION OF QUERIES ON INTEGRATED BIOLOGICAL RESOURCES
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TECHNIQUES FOR OPTIMIZATION OF QUERIES ON INTEGRATED BIOLOGICAL RESOURCES

机译：综合生物资源查询优化技术

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摘要

Today, scientific data are inevitably digitized, stored in a wide variety of formats, and are accessible over the Internet. Scientific discovery increasingly involves accessing multiple heterogeneous data sources, integrating the results of complex queries, and applying further analysis and visualization applications in order to collect datasets of interest. Building a scientific integration platform to support these critical tasks requires accessing and manipulating data extracted from flat files or databases, documents retrieved from the Web, as well as data that are locally materialized in warehouses or generated by software. The lack of efficiency of existing approaches can significantly affect the process with lengthy delays while accessing critical resources or with the failure of the system to report any results. Some queries take so much time to be answered that their results are returned via email, making their integration with other results a tedious task. This paper presents several issues that need to be addressed to provide seamless and efficient integration of biomolecular data. Identified challenges include: capturing and representing various domain specific computational capabilities supported by a source including sequence or text search engines and traditional query processing; developing a methodology to acquire and represent semantic knowledge and metadata about source contents, overlap in source contents, and access costs; developing cost and semantics based decision support tools to select sources and capabilities, and to generate efficient query evaluation plans.
机译：如今，科学数据已不可避免地被数字化，以多种格式存储，并可通过Internet访问。科学发现越来越多地涉及访问多个异构数据源，集成复杂查询的结果以及应用进一步的分析和可视化应用程序以收集感兴趣的数据集。建立一个科学的集成平台来支持这些关键任务，需要访问和处理从平面文件或数据库提取的数据，从Web检索的文档以及在仓库中本地实现或由软件生成的数据。现有方法效率不足会在访问关键资源或系统无法报告任何结果的过程中长时间拖延，从而严重影响流程。某些查询要花很长时间才能回答，结果通过电子邮件返回，这使得它们与其他结果的集成变得繁琐。本文提出了一些问题，以提供无缝，有效的生物分子数据整合。已确定的挑战包括：捕获和表示由源支持的各种特定领域的计算能力，这些资源包括序列或文本搜索引擎以及传统的查询处理;开发一种方法来获取和表示有关源内容的语义知识和元数据，源内容的重叠以及访问成本;开发基于成本和语义的决策支持工具以选择源和功能，并生成有效的查询评估计划。

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