CoCo: Interactive Exploration of Conformance Constraints for Data Understanding and Data Cleaning
CoCo: Interactive Exploration of Conformance Constraints for Data Understanding and Data Cleaning
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CoCo：数据理解和数据清理的一致性约束的交互式探索
DOI：
10.1145/3448016.3452750
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Gulwani, Sumit
中科院分区：
文献类型：
--
作者：
Fariha, Anna;Tiwari, Ashish;Meliou, Alexandra;Radhakrishna, Arjun;Gulwani, Sumit
数据分析是指提取技术元数据或配置文件的任务，并具有许多应用程序，如数据理解，验证，集成和清理。虽然在文献中存在一些数据分析原语，但它们中的大多数仅限于分类属性。一些技术考虑数值属性;但是，它们要么关注涉及一对属性的简单关系（例如，相关性）或将数值属性的连续语义转换为离散语义，这导致信息丢失。为了捕获涉及数值属性的更复杂的关系，我们开发了一种新的数据分析原语，称为一致性约束，它可以对涉及多个数值属性的线性算术关系进行建模。我们提出了CoCo，一个系统，允许交互式发现和探索的约束，了解涉及数据集的数值属性的趋势，特别注重数据清洗的应用。通过一个简单的界面，CoCo使用户能够根据自己的喜好指导一致性约束发现。用户可以检查新的（可能是脏的）数据集满足或违反所发现的一致性约束的程度。此外，CoCo提供了用于清理脏数据元组的有用建议，其中用户可以交互地更改单元格值，并通过检查由于更改而导致的一致性约束违反的变化来进行验证。我们演示了CoCo如何帮助理解数据趋势，并使用一致性约束帮助用户进行交互式数据清理。
Data profiling refers to the task of extracting technical metadata or profiles and has numerous applications such as data understanding, validation, integration, and cleaning. While a number of data profiling primitives exist in the literature, most of them are limited to categorical attributes. A few techniques consider numerical attributes; but, they either focus on simple relationships involving a pair of attributes (e.g., correlations) or convert the continuous semantics of numerical attributes to a discrete semantics, which results in information loss. To capture more complex relationships involving the numerical attributes, we developed a new data-profiling primitive called conformance constraints, which can model linear arithmetic relationships involving multiple numerical attributes. We present CoCo, a system that allows interactive discovery and exploration of Conformance Constraints for understanding trends involving the numerical attributes of a dataset, with a particular focus on the application of data cleaning. Through a simple interface, CoCo enables the user to guide conformance constraint discovery according to their preferences. The user can examine to what extent a new, possibly dirty, dataset satisfies or violates the discovered conformance constraints. Further, CoCo provides useful suggestions for cleaning dirty data tuples, where the user can interactively alter cell values, and verify by checking change in conformance constraint violation due to the alteration. We demonstrate how CoCo can help in understanding trends in the data and assist the users in interactive data cleaning, using conformance constraints.
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