publications

2023

1. 

   Quantifying the competitiveness of a dataset in relation to general preferences

   Kyriakos Mouratidis, Keming Li, and Bo Tang

   The VLDB Journal, 2023

2022

1. 

   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.

2021

1. 

   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.