|Ph.D Student||Shraga Roee|
|Subject||(Artificial) Mind over Matter: Integrating Humans and|
Algorithms in Solving Matching Problems
|Department||Department of Industrial Engineering and Management||Supervisors||PROF. Avigdor Gal|
|ASSOCIATE PROF. Rakefet Ackerman|
|Full Thesis text|
The matching task is at the heart of data integration, in charge of aligning elements of data sources. Matching is a handy tool in multiple contemporary business and commerce applications and has been investigated in the fields of databases, AI, Semantic Web, and data mining for many years. The core challenge still remains the ability to create quality algorithmic matchers, automatic tools for identifying correspondences among data concepts (e.g., database attributes). Matching problems were traditionally performed in a semi-automatic manner, with correspondences being generated by matching algorithms and outcomes subsequently validated by human experts. Human-in-the-loop data integration has been recently challenged by the introduction of big data and recent studies have analyzed obstacles to effective human matching and validation. This research is devoted to the changing role of humans in matching, which is divided into two main approaches, namely Humans Out and Humans In. With the increase in amount and size of matching tasks, the role of humans as validators seems to diminish; thus Humans In questions the inherent need for humans in the matching loop. On the other hand, Humans Out focuses on overcoming human cognitive biases via algorithmic assistance. Above all, we observe that matching requires unconventional thinking demonstrated by advance machine learning methods to complement (and possibly take over) the role of humans in matching.