Abstract

Schema discovery is finding the structure of data. It helps users understand the meaning of data and write queries to manipulate it. This is typically easy for relational databases, but complex for non-relational (NoSQL) databases with documents. For relational databases, the schema is predefined because the data they contain is structured, but for NoSQL databases, data is usually unstructured or semi-structured. Here, we focus on a type of semi-structured data called JSON, which is a collection of documents that consists of nested key-value pairs. A JSON key and value are similar to a column name and its associated data instance in a relational database. In a JSON dataset, the structure of one document can be completely different from another. Several algorithms ere have been developed to discover schemas from JSON documents, but they provide a physical structure and semantic information that is insufficient for data understanding and analysis. In this thesis, we enumerate the major techniques used to extract a schema from documents and present some of the next challenges that need to be addressed within the field of schema discovery to enhance the quality of the discovered schemas. These challenges are (1) distinguishing when keys are data or metadata, (2) detecting frequent patterns (groups of keys that commonly appear together), and (3) matching semantically related data that is structurally different. We propose solutions to address all three challenges.

Publication Date

2026

Document Type

Dissertation

Student Type

Graduate

Degree Name

Computing and Information Sciences (Ph.D.)

Department, Program, or Center

Computing and Information Sciences Ph.D, Department of

College

Golisano College of Computing and Information Sciences

Advisor

Travis J Desell

Advisor/Committee Member

Carlos R. Rivero

Advisor/Committee Member

Michael J. Mior

Campus

RIT – Main Campus

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