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Related Experiment Video

Updated: Jun 2, 2026

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

Algorithms and Time Complexity of the Request-Service Problem.

Chunmei Liu1, Legand Burge, Ajoni Blake

  • 1Department of Systems and Computer Science, Howard University, Washington, DC 20059, USA.

Journal of Combinatorial Optimization
|May 6, 2011
PubMed
Summary
This summary is machine-generated.

This study presents a polynomial-time algorithm for the minimum-cost request-service problem with reciprocal service constraints. However, maximizing satisfied users becomes NP-hard when no feasible solution exists.

Related Experiment Videos

Last Updated: Jun 2, 2026

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

Area of Science:

  • Computer Science
  • Operations Research
  • Algorithm Analysis

Background:

  • The request-service problem involves users providing and requesting services with associated costs.
  • A key constraint is the requirement for reciprocal service provision (direct or indirect).

Purpose of the Study:

  • To analyze the computational complexity of the request-service problem with reciprocal constraints.
  • To determine the efficiency of finding minimum-cost feasible solutions.

Main Methods:

  • Developed a polynomial-time algorithm to find minimum-cost feasible solutions.
  • Investigated the complexity of maximizing satisfied users when feasibility is not guaranteed.

Main Results:

  • A polynomial-time algorithm exists for finding minimum-cost feasible solutions.
  • The problem of maximizing satisfied users is NP-hard when no feasible solution exists.

Conclusions:

  • Efficient solutions are possible for feasible request-service scenarios.
  • Resource allocation and user satisfaction maximization can be computationally challenging.