Graph Data Structure

2011 ◽  
Keyword(s):  
Data Structure ◽  
Graph Data

scholarly journals Automatically Generating a MEP Logic Chain from Building Information Models with Identification Rules

2019 ◽  
Vol 9 (11) ◽  
pp. 2204 ◽  
Author(s):  
Ya-Qi Xiao ◽  
Sun-Wei Li ◽  
Zhen-Zhong Hu
Keyword(s):  
Data Structure ◽  
Topological Analysis ◽  
Failure Detection ◽  
Facility Management ◽  
Graph Data ◽  
Information Models ◽  
Average Accuracy ◽  
Connection Table ◽  
Building Information ◽  
Building Information Models

In mechanical, electrical, and plumbing (MEP) systems, logic chains refer to the upstream and downstream connections between MEP components. Generating the logic chains of MEP systems can improve the efficiency of facility management (FM) activities, such as locating components and retrieving relevant maintenance information for prompt failure detection or for emergency responses. However, due to the amount of equipment and components in commercial MEP systems, manually creating such logic chains is tedious and fallible work. This paper proposes an approach to generate the logic chains of MEP systems using building information models (BIMs) semi-automatically. The approach consists of three steps: (1) the parametric and nonparametric spatial topological analysis within MEP models to generate a connection table, (2) the transformation of MEP systems and custom information requirements to generate the pre-defined and user-defined identification rules, and (3) the logic chain completion of MEP model based on the graph data structure. The approach was applied to a real-world project, which substantiated that the approach was able to generate logic chains of 15 MEP systems with an average accuracy of over 80%.

scholarly journals Question-led approach in designing Dijkstra algorithm game-based learning: A pilot study

2020 ◽  
Vol 9 (4) ◽  
pp. 926
Author(s):  
Rosni Binti Ramle ◽  
D’oria Islamiah Rosli ◽  
Shelena Soosay Nathan ◽  
Mazniha Berahim
Keyword(s):  
Data Structure ◽  
Pilot Study ◽  
Teaching Tool ◽  
Game Model ◽  
Dijkstra Algorithm ◽  
Game Based Learning ◽  
Learning Context ◽  
Graph Data ◽  
Self Regulated Learning ◽  
Post Test

Dijkstra algorithm is important to be understood because of its many uses. However, understanding it is challenging. Various methods to teach and learn had been researched, with mixed results. The study proposes questionled approach of the algorithm in a game-based learning context. The game designed based on an existing game model, developed and tested by students. Pre- and post-game tests compared and game feedback survey analysed. Results showed that students’ performance in graph data structure Dijkstra algorithm improved after playing the game where post-test mark was higher than pre-test. Game feedback were mostly positive, with areas of improvement. Students may use the game as a learning tool for self-regulated learning. Educators may get some ideas on how to design teaching tool using question-led approach.

Simplification of Feature Based Models for Tolerance Analysis

1992 ◽  
Author(s):  
Peter Martino
Keyword(s):  
Data Structure ◽  
Graph Model ◽  
Tolerance Analysis ◽  
Solid Modeling ◽  
Computational Effort ◽  
Tree Graph ◽  
Graph Data ◽  
Computer Aided ◽  
Feature Based ◽  
Analysis Models

Abstract This paper presents a method for eliminating unnecessary parts, features, and dimensions from feature based models in computer aided tolerance analysis systems. A typical tolerance analysis involves a dozen or so parts, or subassemblies. Each part may have dozens of features, and hundreds of dimensions. Many of these subassemblies, parts, features, and dimensions do not effect the tolerance analysis, and therefore are not needed. Computational effort can be reduced by eliminating the unnecessary items from the model. Currently, tolerance analysis models are implicitly simplified by the user. The user examines the problem, and determines which parts, features, and dimensions can be ignored. The user then constructs his model, leaving out the unneeded items. This is true whether the analysis is accomplished with a computer aided tool, or with paper and pencil. Simplification of the model is essential. Practical tolerance analysis problems become overwhelmingly complex if every detail is included. The method discussed in this paper is intended for use in computer aided tolerance analysis systems that use feature based, and dimension driven, solid modeling. It uses a combined tree and graph data structure. The tree structure represents the hierarchy of assemblies, parts, and features in the model. The graphs represent the dependence between features in a part, or parts in an assembly. An algorithm has been developed that searches this tree/graph model, locating the parts and features needed to accomplish the tolerance analysis.

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