# DIKWP-TRIZ: A Revolution on Traditional TRIZ Towards Invention for Artificial Consciousness
## 4 Mapping and Coverage Analysis of TRIZ Rules in DIKWP
### Mapping the 40 TRIZ Principles to the DIKWP Model
#### 4.1.2 Data (D) \(\rightarrow\) Information (I)  
The transformation of Data into Information involves the cognitive subject distinguishing Data under different semantic contexts to obtain the corresponding Information semantics. This process requires not only technical support but also a deep understanding of the Knowledge embedded within the Data. Below are several TRIZ principles and their specific applications related to this transformation process:  
* **Principle 3: Local Quality** Focus on analyzing specific Data subsets to generate detailed Information. This approach can reveal insights that might be overlooked from a global perspective, providing in-depth understanding at a local level. For example, in large-scale market research Data, examining the behavior patterns of a particular consumer group can uncover unique preferences and needs, thereby informing product customization. For example, a study on large-scale software engineering Data demonstrated the benefits of splitting Data into smaller, more homogeneous subsets, resulting in improved model performance and more accurate predictions [47]. * **Principle 5: Merging** Integrate Data points from various sources to form a coherent and valuable Information system, facilitating a comprehensive understanding of the subject matter. For instance, combining user behavior Data, geographical Information, and social media feedback can help companies build a more comprehensive user profile, thereby enhancing the effectiveness of marketing strategies [48]. * **Principle 7: Nested Doll** Cognitive subjects are able to nest multiple data fields together, progressively forming contrasting content and constructing different semantic expressions. For example, a company's sales report can reflect the content that users want to understand and compare at once through multiple layers of nesting. * **Principle 9: Preliminary Anti-Action** The principle of preliminary anti-action in TRIZ, which involves preprocessing steps like filtering out noise or irrelevant Data, is crucial to enhancing the clarity and quality of Information. For instance, techniques such as noise reduction in various applications, like hyperspectral imaging, improve the analysis of key signals by eliminating non-white noise prior to formal analysis [49]. Additionally, noise filtering algorithms can significantly improve Data quality, especially in scenarios involving imbalanced Data or chaotic signals, thereby enhancing the subsequent Data analysis [50]. These preprocessing steps are essential for ensuring the authenticity and reliability of Information, reducing potential biases during analysis, and improving the overall quality of results [51]. * **Principle 17: Another Dimension** By utilizing multidimensional representations of data, such as visualization tools or multi-layer models, content with the same semantics can be presented and differentiated through another dimension, revealing deeper insights and providing rich and detailed understanding. For example, using 3D charts to display trend changes in time series data, or using heat maps to illustrate the strength of correlations between variables, can help analysts more intuitively grasp data characteristics [52]. * **Principle 28: Mechanics Substitution** Automate Data processing tasks using algorithms and tools to efficiently transform large amounts of Data into meaningful Information. With the advancement of machine learning and AI technologies, such automated methods have become an indispensable part of modern Data analysis, significantly enhancing the speed and accuracy of Information extraction [53]. * **Principle 35: Parameter Changes** By altering the measurement standards, formats, or parameters of Data, new Information semantics can be revealed. In financial market analysis, applying different smoothing parameters to historical trading Data can distinguish between short-term and long-term market fluctuations.