Data profiling refers to the activity of collecting data about data,{ie}, metadata. Most IT
professionals and researchers who work with data have engaged in data profiling, at least …

Keys for graphs incorporate the topology and value constraints needed to uniquely identify
entities in a graph. They have been studied to support object identification, knowledge …

Given a knowledge graph and a fact (a triple statement), fact checking is to decide whether
the fact belongs to the missing part of the graph. Facts in real-world knowledge bases are …

Functional Dependencies define attribute relationships based on syntactic equality, and
when used in data cleaning, they erroneously label syntactically different but semantically …

Data currency is imperative towards achieving up-to-date and accurate data analysis. Data
is considered current if changes in real world entities are reflected in the database. When …

Data cleaning is a pervasive problem for organizations as they try to reap value from their
data. Recent advances in networking and cloud computing technology have fueled a new …

Nowadays, machine learning plays a key role in developing plenty of applications, eg, smart
homes, smart medical assistance, and autonomous driving. A major challenge of these …

Temporal Graph Functional Dependencies (TGFDs) are a class of data quality rules
imposing topological, attribute dependency constraints over a period of time. To make …

Regression testing is an important activity that aims to provide information about the quality
of the software product under test when changes occur. The two primary techniques for …

Keys for graphs are a class of data quality rules that use topological and value constraints to
uniquely identify entities in a data graph. They have been studied to support object …