  Relational database management system (RDBMS) is a major undergraduate course
taught in many universities worldwide as part of their computer science
program. A core component of such course is the design and implementation of
the query optimizer in a RDBMS. The goal of the query optimizer is to
automatically identify the most efficient execution strategies for executing
the declarative SQL queries submitted by users. The query optimization process
produces a query execution plan (QEP) which represents an execution strategy
for the query. Due to the complexity of the underlying query optimizer,
comprehension of a QEP demands that a student is knowledgeable of
implementation-specific issues related to the RDBMS. In practice, this is an
unrealistic assumption to make as most students are learning database
technology for the first time. Hence, it is often difficult for them to
comprehend the query execution strategy undertaken by a DBMS by perusing the
QEP, hindering their learning process. In this demonstration, we present a
novel system called NEURON that facilitates natural language interaction with
QEPs to enhance its understanding. NEURON accepts a SQL query (which may
include joins, aggregation, nesting, among other things) as input, executes it,
and generates a simplified natural language-based description (both in text and
voice form) of the execution strategy deployed by the underlying RDBMS.
Furthermore, it facilitates understanding of various features related to the
QEP through a natural language-based question answering framework. We advocate
that such tool, world's first of its kind, can greatly enhance students'
learning of the query optimization topic.
