Data users are generally interested in two types of aggregated information: summarization of the selected attribute(s) for all considered entities, and retrieval and evaluation of entities by the requirements posed on the relevant attributes. Less statistically literate users (e.g. domain experts) and the business intelligence strategic dashboards can benefit from the linguistic summarization, i.e. a summary like the most of customers are middle–aged can be understood immediately. Evaluation of the mandatory and optional requirements of the structure ${P_{1}}$ and most of the other posed predicates should be satisfied is beneficial for analytical business intelligence dashboards and search engines in general. This work formalizes the integration of aforementioned quantified summaries and quantified evaluation into the concept of database queries to empower their flexibility by, e.g. the nested quantified query conditions on hierarchical data structures. Next, this approach contributes to the mitigation of the empty answer problem in data retrieval tasks. Thus, the strategic and analytical dashboards as well as query engines might benefit from the proposed approach. Finally, the obtained results are illustrated on examples, the internal and external trustworthiness is elaborated, and the future research topics and applicability are discussed. PDF  XML

中文翻译：

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评估基本条件，汇总数据和管理嵌套查询的语言摘要

数据用户通常对两种类型的汇总信息感兴趣：对所有考虑的实体的选定属性进行汇总，以及根据对相关属性提出的要求对实体进行检索和评估。缺乏统计知识的用户（例如领域专家）和商业智能战略仪表板可以从语言摘要中受益，即，可以像大多数客户一样是中年的摘要可以立即理解。应该满足对结构$ {P_ {1}} $ 和大多数其他谓词的强制性和可选性要求的评估总体上有利于分析型商业智能仪表板和搜索引擎。这项工作将上述量化摘要和量化评估的集成形式化为数据库查询的概念，以通过例如在分层数据结构上嵌套的量化查询条件来增强其灵活性。接下来，这种方法有助于减轻数据检索任务中的空答案问题。因此，战略和分析仪表板以及查询引擎可能会从建议的方法中受益。最后，通过实例说明了所获得的结果，阐述了内部和外部的可信度，并讨论了未来的研究主题和适用性。PDF XML