ﻻ يوجد ملخص باللغة العربية
Real-world datasets are dirty and contain many errors. Examples of these issues are violations of integrity constraints, duplicates, and inconsistencies in representing data values and entities. Learning over dirty databases may result in inaccurate models. Users have to spend a great deal of time and effort to repair data errors and create a clean database for learning. Moreover, as the information required to repair these errors is not often available, there may be numerous possible cle
Relational databases are valuable resources for learning novel and interesting relations and concepts. In order to constraint the search through the large space of candidate definitions, users must tune the algorithm by specifying a language bias. Un
Data cleaning is the initial stage of any machine learning project and is one of the most critical processes in data analysis. It is a critical step in ensuring that the dataset is devoid of incorrect or erroneous data. It can be done manually with d
Big data analysis has become an active area of study with the growth of machine learning techniques. To properly analyze data, it is important to maintain high-quality data. Thus, research on data cleaning is also important. It is difficult to automa
This tutorial overviews the state of the art in learning models over relational databases and makes the case for a first-principles approach that exploits recent developments in database research. The input to learning classification and regression
F-IVM is a system for real-time analytics such as machine learning applications over training datasets defined by queries over fast-evolving relational databases. We will demonstrate F-IVM for three such applications: model selection, Chow-Liu trees, and ridge linear regression.