Contexts as metadata for data management

数据的意义、可用性和质量以及数据清理都取决于上下文。信息系统应该为数据管理任务表示和使用上下文。然而，上下文的概念还没有被数据库社区完全正式化。上下文出现在数据管理中，具有一些明显的内涵，如时间和地理位置。一个完整的、一般的、抽象的形式化是缺失的，也是必要的。 元数据是关于数据的数据。它必须以计算术语表示和管理，存储在数据库中，并可供数据源访问。元数据的经典例子是关系模式和完整性约束。最近的例子是模式映射，视图定义，信任，出处，质量约束，本体。我们将上下文视为元数据的一种形式。因此，它必须在信息系统中得到适当的表示、管理和使用。 我们的长期目标是提供数据管理所需的上下文的一般的、逻辑的形式化，用于：（a）通过提供额外的含义、术语消歧等来理解数据。（B）解释数据。(c)数据的相关性。(d)捕捉关于数据的常识性假设。(e)为数据理解和使用提供适当的维度。我们的短期目标解决了上下文信息与它所描述的数据相结合的问题：1。使用上下文（包括质量约束）来指定数据源的预期质量内容。2.通过上下文查询数据源，使查询答案与上下文相关，特别是质量感知。3.用于数据分析和理解的数据维度的一般形式化，以及维度数据管理的应用。4.通过匹配依赖关系将数据交换与数据清理集成。

Meaning, usability and quality of data, and also data cleaning depend on the context. Information systems should represent and use contexts for data management tasks. However, the notion of context has not been fully formalized by the database community. Contexts appear in data management with a few obvious connotations, like time and geographic location. A full-fledged, general, and abstract formalization is missing and necessary. Metadata is data about data. It has to be represented and managed in computational terms, stored in databases, and made accessible for data sources. Classical examples of metadata are relational schemas and integrity constraints. More recent examples are schema mappings, view definitions, trust, provenance, quality constraints, ontologies. We see a context as a form of metadata. In consequence, it has to be properly represented, managed and used in information systems. Our long-term goal is to provide a general, logical formalization of context as required for data management, to be used for: (a) Making sense of data by providing additional meaning, term disambiguation, etc. (b) Explaining data. (c) Specifying relevance of data. (d) Capturing commonsense assumptions about data. (e) Providing appropriate dimensions for data understanding and usage. Etc. Our shorter-term goals address problems around the combination of contextual information with the data it describes: 1. Use of contexts, including quality constraints, to specify the intended, quality contents of a data source. 2. Querying data sources through a context, making query answers context-sensitive, in particular, quality-aware. 3. General formalization of data dimension for data analysis and understanding, with applications to management of dimensional data. 4. Integration of data exchange with data cleaning via matching dependencies.