We demonstrate XClean, a data cleaning system specifically geared towards cleaning XML data. XClean’s approach is based on a set of cleaning operators. Users may specify cleaning programs by combining operators using the declarative XClean/PL language, which is then compiled into XQuery. We plan to show XClean in action on several scenarios based on real-world data. A graphical user interface s...

The goal of data cleaning is to make fit for purpose, i.e., improve quality, through updates and transformations, such that downstream analyses can be conducted lead trustworthy results. A transparent reusable workflow save time effort automation, subsequent on new less errorprone. However, reusability workflows has received little no attention in the research community. We identify some challe...

HIGHLIGHTS Proposing a drinking-water data cleaning model with the combination of nonlinear partial differential equations and CART decision tree. Preprocessing by removing outlier elements not following normal distribution. missing modified classifier AdaBoost. Implementing proposed Big Data technology based on Hadoop architecture. Overall performance method competes other similar cutting-edge...

Enterprises have been acquiring large amounts of data from a variety of sources to build their own “Data Lakes”, with the goal of enriching their data asset and enabling richer and more informed analytics. The pace of the acquisition and the variety of the data sources make it impossible to clean this data as it arrives. This new reality has made data cleaning a continuous process and a part of...

Analysts often clean dirty data iteratively–cleaning some data, executing the analysis, and then cleaning more data based on the results. We explore the iterative cleaning process in the context of statistical model training, which is an increasingly popular form of data analytics. We propose ActiveClean, which allows for progressive and iterative cleaning in statistical modeling problems while...