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Entity Resolution For Big Data

Entity resolution is the process of identifying the specific entities (e.g. rows, documents) that correspond to a given query. This can be done by inspecting the data or by using metadata.

When dealing with big data, entity resolution becomes an essential part of the data retrieval process. For example, if you want to know which rows from a table correspond to a particular document, you need to identify the document's id (or some other unique identifier). Take the time to visit a well known website such as

Entity resolution also plays a crucial role in analytics and data mining. For example, if you want to find all the documents that were modified within the last week, you need to identify which rows correspond to those documents.

Keep in mind that entity resolution isn't always simple or straightforward. In some cases, you might have to perform multiple rounds of queries in order to get all the results you need.

So don't hesitate to ask your database administrator for help with entity resolution – it's an important part of big data computing!  Entity resolution is a big data problem that has plagued companies for years. 

Traditionally, companies have used a variety of methods to try and find the right pieces of data to answer their questions. These methods can be time-consuming, error-prone, and expensive. In this blog post, we'll discuss how Entity Resolution can help solve these problems.

Traditional methods for finding the right pieces of data include using keyword searches or browsing through files manually. These methods can be time-consuming and error-prone. For example, if you're looking for information about employees, you may have to search through all the files in the database to find the information you're looking for. 

This process can be time-consuming and error-prone because there's a risk that you'll miss important information. Additionally, these methods can be expensive because they require manual labor to execute.