Related Experiment Videos
An artificial intelligence-driven holistic multi-criteria framework for data asset valuation
Yan Gong1, Zhinan Li1, Wei Zhang2
1Institute of Science and Technology Information, Beijing Academy of Science and Technology, Beijing, 100044, China.
Scientific Reports
|July 10, 2026
Summary
This study introduces an AI framework for data asset valuation, improving prediction accuracy. The novel approach combines knowledge graphs and machine learning, offering practical tools for finance and data management.
Area of Science:
- Artificial Intelligence
- Data Science
- Financial Technology
Background:
- Data asset valuation is complex due to intangible and context-dependent factors.
- Traditional valuation methods struggle with the unique characteristics of data assets.
Purpose of the Study:
- To develop a novel AI-driven hybrid framework for accurate data asset valuation.
- To integrate knowledge graphs, preference learning, and Support Vector Regression (SVR).
Main Methods:
- Constructed a three-tier indicator system with 18 metrics using knowledge graph technology.
- Employed an AI-based preference learning mechanism to reduce subjectivity in weight determination.
- Validated the framework on Bitcoin and stock index datasets, comparing against AHP and SVR benchmarks.
Main Results:
- The AI framework demonstrated superior predictive performance.
- Achieved significant reductions in Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) compared to benchmarks.
- Reduced RMSE by 1.446 (Bitcoin) and 1.0673 (stock index).
- Reduced MAE by 2.4004 (Bitcoin) and 0.803 (stock index).
Conclusions:
- The proposed AI framework offers a robust solution for data asset valuation.
- Findings provide practical tools for enterprise data asset management and portfolio optimization.
- The framework has potential applications in cross-sector governance and financial technology.
Related Concept Videos
01:09Decision Making: P-value Method
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
01:20Actuarial Approach
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
01:20Lazarus's Cognitive Appraisal Theory
Cognitive psychologist Richard Lazarus proposed the cognitive-mediational theory of emotions, which emphasizes how individuals' assessments of stressors significantly affect their experience of stress. According to Lazarus, the stress response is determined by a two-step appraisal process: primary appraisal and secondary appraisal. These cognitive appraisals help individuals evaluate the potential impact of a stressor and determine the adequacy of their coping resources.
Primary Appraisal:...
Primary Appraisal:...
01:21Heuristics
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
01:03Data Validation
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
Nursing assessment guides are generally based on holistic models rather than medical...
01:20Decision Making
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
Automatic decision-making is fast, intuitive, and relies on gut feelings...