Entity Resolution Techniques

This lecture covers entity resolution techniques, including dealing with duplicate entities, name/attribute ambiguity, data deduplication, and similarity metrics like edit distance and Jaccard similarity. It also explains duplicate elimination with clustering, possible repairs, and the computational cost of duplicate detection. The use of blocking techniques, q-gram set join, and string similarity to q-gram similarity are discussed, along with examples of standard blocking and q-gram set join in action. The lecture concludes with insights on scaling out similarity joins, data transformations, data accuracy, and recommended reading materials.

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Related concepts (30)

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

String metric

In mathematics and computer science, a string metric (also known as a string similarity metric or string distance function) is a metric that measures distance ("inverse similarity") between two text strings for approximate string matching or comparison and in fuzzy string searching. A requirement for a string metric (e.g. in contrast to string matching) is fulfillment of the triangle inequality. For example, the strings "Sam" and "Samuel" can be considered to be close.

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 quality

Data quality refers to the state of qualitative or quantitative pieces of information. There are many definitions of data quality, but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning". Moreover, data is deemed of high quality if it correctly represents the real-world construct to which it refers. Furthermore, apart from these definitions, as the number of data sources increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose.

Data mapping

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

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.