In data integration, data curation, and other data analysis tasks, users spend a considerable
amount of time converting data from one representation to another. For example US dates to …

While syntactic transformations require the application of a formula on the input values, such
as unit conversion or date format conversions, semantic transformations, such as" zip code …

Data transformation is a crucial step in data integration. While some transformations, such as
liters to gallons, can be easily performed by applying a formula or a program on the input …

Today, business analysts and data scientists increasingly need to clean, standardize and
transform diverse data sets, such as name, address, date time, and phone number, before …

Data transformation is a critical first step in modern data analysis: before any analysis can be
done, data from a variety of sources must be wrangled into a uniform format that is amenable …

The process of preparing potentially large and complex data sets for further analysis or
manual examination is often called data wrangling. In classical warehousing environments …

Data Transformation is a long-standing problem in data management. Recent work adopts
a" transform-by-example"(TBE) paradigm to infer transformation programs based on user …

Modern information systems often store data that has been transformed and integrated from
a variety of sources. This integration may obscure the original source semantics of data …

Data migration has become a priority in many industries, spawned by a variety of business
needs. Most of the existing tools for Extract, Transform and Load (ETL) process of data …

Though data analysis tools continue to improve, analysts still expend an inordinate amount
of time and effort manipulating data and assessing data quality issues. Such" data …