Parameterized Complexity and Approximation in Data Management

Dear Colleagues,

Parameterized complexity is a subfield of computational complexity theory that focuses on analyzing the inherent difficulty of computational problems by considering both input size and additional parameters. Unlike traditional complexity theory, which measures problem hardness solely based on input size, parameterized complexity introduces a more refined approach by examining how problem difficulty scales with respect to a specific parameter. Approximation algorithms are designed to solve NP-hard problems by providing feasible solutions in polynomial time while guaranteeing a quantifiable performance bound relative to the optimal solution. Both parameterized complexity and approximation algorithms are feasible methods for tackling computationally intractable problems. They have many natural applications in data management, which range from traditional conjunctive query processing to novel data mining problems.

This Special Issue of Mathematics aims to provide an overview of recent advances of parameterized complexity and approximation algorithms in data management. We welcome research articles and review papers that address theoretical developments, algorithmic approaches, and the application of theoretical techniques in practical data management applications.

We look forward to receiving your contributions.

Dr. Xianmin Liu
Guest Editor

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