Lecture

Data Accuracy: Assessing Faithfulness and Error Detection

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In course

CS-422: Database systems

This course is intended for students who want to understand modern large-scale data analysis systems and database systems. It covers a wide range of topics and technologies, and will prepare students to be able to build such systems as well as read and und ...

Description

This lecture covers the assessment of data accuracy, focusing on the faithfulness of records within a dataset, error detection taxonomy including outliers and duplicates, handling outliers by deletion or default setting, correlations within records, functional dependencies, FD violation detection, FD discovery, Tane algorithm for FD discovery, conditional functional dependencies, matching dependencies, denial constraints, detecting denial constraint violations, data repairing techniques, and the minimality of repairs principle in data repairing automation.

Instructor

Anastasia Ailamaki

Expertise Database management systems, scientific applications, computer architecture Teaching & PhD PhD Students Hamish Nicholson, Anna Herlihy, Yi Jiang, Eleni Zapridou, Georgiy Konstantinovich Lebedev, Ioanna Tsakalidou Past EPFL PhD Students Farhan Tauheed, Radu Ioan Stoica, Manos Athanassoulis, Pinar Tözün, Adrian Daniel Popescu, Ioannis Alagiannis, Renata Borovica-Gajic, Danica Porobic, Iraklis Psaroudakis, Manos Karpathiotakis, Matthaios Alexandros Olma, Mirjana Pavlovic, Eleni Tzirita Zacharatou,

Official source

Related concepts (32)

Data cleansing

Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting or a data quality firewall. After cleansing, a data set should be consistent with other similar data sets in the system.

Data wrangling

Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. The goal of data wrangling is to assure quality and useful data. Data analysts typically spend the majority of their time in the process of data wrangling compared to the actual analysis of the data.

Data transformation (computing)

In computing, data transformation is the process of converting data from one format or structure into another format or structure. It is a fundamental aspect of most data integration and data management tasks such as data wrangling, data warehousing, data integration and application integration. Data transformation can be simple or complex based on the required changes to the data between the source (initial) data and the target (final) data. Data transformation is typically performed via a mixture of manual and automated steps.

Data mapping

In computing and data management, data mapping is the process of creating data element mappings between two distinct data models.

Data integration

Data integration involves combining data residing in different sources and providing users with a unified view of them. This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data integration appears with increasing frequency as the volume (that is, big data) and the need to share existing data explodes.

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