Tracking technology innovation with multi-dimensional data aggregation

• Main Author: Huang, Shurui
• Other Authors: Chen Songlin
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Mechanical engineering
• Online Access: https://hdl.handle.net/10356/149615
• Institution: Nanyang Technological University

Current markets are becoming more diverse as a result of rapid technological and economic development. When customers want to buy something, they are frequently perplexed by dazzling products. People find it difficult to choose from thousands of products due to the market’s diversity. Choosing from a plethora of options is becoming increasingly time-consuming and complicated. It is possible to spend an hour deciding where to eat lunch. This issue is becoming more serious as the market's diversity grows. In the expanding market, there are more options. Furthermore, because each industry has a large number of companies that sell similar products, it is difficult to track the rate of development for a specific category of products. However, there are numerous benefits to tracking development trends. Companies can use the trend to predict the strengths and weaknesses of their competitors. They can also monitor the entire market to predict whether a technology is approaching a bottleneck or still has a lot of room to grow, allowing companies to better plan their future research. In the modern society, data can be found around every corner and it is easy to approach. If used correctly, people can discover many secrets and gain insights into massive databases. They can not only provide a better picture of the situation and tell how things are going, but they can also provide advice on how to make decisions and create plans to achieve specific goals in the business world. Nonetheless, in the majority of everyday situations, more than one criterion and attribute can be considered. For example, when a family wishes to travel and select the best location for themselves. There are numerous factors to consider, such as local food, attractions, price, holiday traffic, and so on. Even if they do not have a data analysis method, they will still have a similar system in their heads and weigh these various factors to see which are more critical for them and which are not. This paper seeks to introduce Analytic Hierarchy Process (referred as AHP) approach on data aggregation for multi-level and multi-criteria data. This project uses smartphone as a case study to present how the aggregation process works. The AHP method is based on this line of thought. Users can get the best decision using this method simply by answering a few questions that will be used to determine the weightages of all influencing factors. The system will do the rest. It will collect the scores for each factor and associate them with their respective weightages, resulting in an overall score for each travel destination in the previous travel place selection situation. The one with the highest score is the best option for this family at this time. This is a high-level overview of AHP methodology. This paper uses a smartphone as a case study to demonstrate this data aggregation method in depth.