The Research archive provides access to all Research articles published in past issues of Communications of the ACM.
Magellan's key insight is that a successful entity matching system must offer a versatile system building paradigm for entity matching that can be easily adapted for different application needs.
Do we need a completely new database system to support machine learning?
We show that by making just a few changes to a parallel/distributed relational database system, such a system can become a competitive platform for scalable linear algebra.
Entity matching can be viewed as a special class of data science problems and thus can benefit from system building ideas in data science.