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Querying on large and complex databases by content: Challenges on variety and veracity regarding real applications

机译：通过内容查询大型和复杂的数据库：关于实际应用的各种和准确性的挑战

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摘要

The amount and variety of digital data currently being generated, stored and analyzed, including images, videos, and time series, have brought challenges to data administrators, analysts and developers, who struggle to comply with the expectations of both data owners and end users. The majority of the applications demand searching complex data by taking advantage of queries that analyze different aspects of the data, and need the answers in a timely manner. Content-based similarity retrieval techniques are well-suited to handle large databases, because they enable performing queries and analyses using features automatically extracted from the data, without users' intervention. In this paper, we review and discuss the challenges posed to the database and related communities in order to provide techniques and tools that can meet the variety and veracity characteristics of big and complex data, while also considering the aspects of semantical preservation and completeness of the data. Examples and results obtained over a two-decade-long experience with real applications are presented and discussed. (C) 2019 Elsevier Ltd. All rights reserved.
机译：目前正在生成，存储和分析的数量和各种数据，包括图像，视频和时间序列，为数据管理员，分析师和开发人员带来了挑战，他们努力遵守数据所有者和最终用户的期望。大多数应用程序通过利用分析数据的不同方面的查询来搜索复杂数据，并及时需要答案。基于内容的相似性检索技术非常适合处理大型数据库，因为它们能够使用从数据中自动提取的功能进行查询和分析，而无需用户的干预。在本文中，我们审查并讨论了数据库和相关社区所带来的挑战，以便提供能够满足大型和复杂数据的品种和准确性特征的技术和工具，同时考虑了语义保存和完整性的方面数据。提出并讨论了通过两十年长的经验获得的实例和结果。 （c）2019 Elsevier Ltd.保留所有权利。

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