Keming Li

2069-29, Donald Bren Hall

University of California, Irvine

CA, 92617, US

kemingl1@uci.edu

likm2020@mail.sustech.edu.cn

Keming Li is a computer science PhD student at the University of California, Irvine (UCI), supervised by Professor Sharad Mehrotra. Before that, he earned his bachelor and master degree at Southern University of Science and Technology (SUSTech), supervised by Professor Bo Tang. He interned at Huawei during his undergraduate studies and received a coding award. His research interests include spatial database and graph database, and he is excited to learn and apply his knowledge to solve real-world problems.

He had worked with Professor Kyriakos Mouratidis, Professor Bo Tang, and Doctor Jiahao Zhang on some research work. They were published in SIGMOD’21, SIGMOD’22, and VLDBJ. The work in SIGMOD’21 is to develop new query operators that combine the benefits of top-k and skyline queries and are flexible, personalized, and controllable in output size. Performance is also a challenge of this work. The work in SIGMOD’22 is to construct a space efficient index for top-k retrieval related queries, experiments of plenty queries were conducted. The work in VLDBJ aims to assess its competitiveness with regard to different possible preferences. They define measures of competitiveness, utilize heatmap to show the competitiveness, and show side-applications of the work on multi-object querying.

Currently he is a PhD student researching topic on database collecting data from IoT devices. The target of this project is to develop an intelligent IoT database answering OLAP and OLTP queries with billion scale of data.

Teaching. I serviced as a teaching assistant in 5 department core courses including Database, Computer Programming Fundamentals, C/C++ programming, Data Structures and Algorithms, and Operating Systems. I won the Southern University of Science and Technology Excellent Teaching Assistan Award at SUSTech for 2018 and 2019.

My resume can be found here. My github account is ghlkm.

news

Nov 11, 2024	Paper [Marrying Top-k with Skyline Queries: Relaxing the Preference Input while Producing Output of Controllable Size] was accepted by TODS!
Sep 25, 2023	I join ISG@UCI as a PhD student under supervisor of Professor Sharad Mehrotra.
Jun 19, 2023	I graduated from SUSTech and earned a master degree of computer science.
Jun 19, 2023	Paper Quantifying the competitiveness of a dataset in relation to general preferences was accepted by VLDBJ!
Jun 11, 2022	Paper \(\tau\)-LevelIndex: Towards Efficient Query Processing in Continuous Preference Space was accepted by SIGMOD 2022!
Jun 18, 2021	Paper Marrying Top-k with Skyline Queries: Relaxing the Preference Input while Producing Output of Controllable Size was accepted by SIGMOD 2021!
Nov 5, 2020	I helped organizing the Greater Bay Area Database Day (GBDBD), where there were outstanding researchers from famous Chinese universities such as Peking University and Huazhong University of Science and Technology.
Jul 1, 2020	I received my BS degree in Computer Science and Technology at SUSTech (BS 2016). I won SUSTech CSE Outstanding Academic Achievement Award for Undergraduates (2020) and SUSTech Zhiren College Outstanding Undergraduate Award (2020).
Nov 5, 2019	I won the third place in SUSTech Artificial Intelligence IMP contest (2019) and the fifth place in SUSTech Artificial Intelligence CARP contest (2019).
Sep 20, 2019	I got the qualification of graduate student and dicided to join Professor Bo Tang’s Database Group at SUSTech!
Jul 1, 2019	I got an internship in Huawei and won the freshman Coding Award of Huawei (2019).
Jan 20, 2019	My team won the third prize of SUSTech Da Vinci Robotic 2019 Winter Camp.
Dec 12, 2018	I won the SUSTech Excellent Teaching Assistant Award (2018) and University Merit Student Scholarship (2018).
Dec 31, 2017	I won the National Endeavor Scholarship (2017) and University Merit Student Scholarship (2017).
Mar 26, 2017	Our team sucessfully held the SUSTech Regional Cultural Festival
Dec 10, 2016	I won the third place in the 100 meter race, second place in the 4*100 meter race, and fifth place in long jump race of the SUSTech.
Sep 1, 2016	I was admitted by Southern University of Science and Technology as an undergraduate. 😆
Jul 1, 2015	I won the first prize of Guangdong Division of National High School Mathematics League.

selected publications

T-LevelIndex: Towards Efficient Query Processing in Continuous Preference Space

Jiahao Zhang, Bo Tang, Man Lung Yiu, Xiao Yan, and Keming Li

In Proceedings of the 2022 International Conference on Management of Data, 2022

Abs

Top-k related queries in continuous preference space (e.g., k-shortlist preference query kSPR, uncertain top-k query UTK, output-size specified utility-based query ORU) have numerous applications but are expensive to process. Existing algorithms process each query via specialized optimizations, which are difficult to generalize. In this work, we propose a novel and general index structure T-LevelIndex, which can be used to process various queries in continuous preference space efficiently. We devise efficient approaches to build the T-LevelIndex by fully exploiting the properties of continuous preference space. We conduct extensive experimental studies on both real- and synthetic- benchmarks. The results show that (i) our proposed index building approaches have low costs in terms of both space and time, and (ii) T-LevelIndex significantly outperforms specialized solutions for processing a spectrum of queries in continuous preference space, and the speedup can be two to three orders of magnitude.
Marrying Top-k with Skyline Queries: Relaxing the Preference Input While Producing Output of Controllable Size

Kyriakos Mouratidis, Keming Li, and Bo Tang

In Proceedings of the 2021 International Conference on Management of Data, 2021

Abs

The two most common paradigms to identify records of preference in a multi-objective setting rely either on dominance (e.g., the skyline operator) or on a utility function defined over the records’ attributes (typically, using a top-k query). Despite their proliferation, each of them has its own palpable drawbacks. Motivated by these drawbacks, we identify three hard requirements for practical decision support, namely, personalization, controllable output size, and flexibility in preference specification. With these requirements as a guide, we combine elements from both paradigms and propose two new operators, ORD and ORU. We perform a qualitative study to demonstrate how they work, and evaluate their performance against adaptations of previous work that mimic their output.