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Jess Tan

dblp:328/5974 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Query processing and optimization · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Query processing and optimization › query planning
physical operator selection
0.612022
MOCHA: A Tool for Visualizing Impact of Operator Choices in Query Execution Plans for Database Education · Proc. VLDB Endow. 2022
Query processing and optimization
query execution
0.612022
MOCHA: A Tool for Visualizing Impact of Operator Choices in Query Execution Plans for Database Education · Proc. VLDB Endow. 2022
Computing education › computer science curriculum
database education
0.212022
MOCHA: A Tool for Visualizing Impact of Operator Choices in Query Execution Plans for Database Education · Proc. VLDB Endow. 2022

Methods — techniques the papers use, named apart from their topics

query plan visualization · 1.1alternative plan comparison · 1.1
YearPublicationVenuePosition
2022 MOCHA: A Tool for Visualizing Impact of Operator Choices in Query Execution Plans for Database Education
abstract
The database systems course is offered in many major universities. A key learning goal of learners taking such a course is to understand how sql queries are processed in an RDBMS in practice. To this end, comprehension of the impact of various physical operators on the selected query execution plan (QEP) of a query is paramount. Unfortunately, off-the-shelf RDBMS typically only expose the QEP to users without revealing information about the impact of alternative choices of various physical operators on it in a user-friendly manner to aid learning. In this demonstration, we present a novel system called MOCHA that facilitates exploration and visualization of the impact of alternative physical operator choices on the QEP of a given SQL query. MOCHA accepts an SQL query as input, and compares and visualizes the QEP and alternative plans which are selected based on learner-specified operator preferences. Furthermore, it intuitively explains why the key operators in a QEP are chosen by connecting them to established knowledge in the literature.
Jess Tan, Desmond Yeo, Rachael Neoh, Huey-Eng Chua, Sourav S. Bhowmick
Proc. VLDB Endow.1