# 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
#### 4.1.1 Data (D) \(\rightarrow\) Data (D)  
The transformative process explores the conversion of Data, as a set under the same semantics, into various interpretations within a cognitive subject, as well as how existing Knowledge guides the collection and interpretation of these Data. This bidirectional transformation process emphasizes the dynamic interaction among Data and aims to establish a continuous cognitive improvement cycle. The following are several TRIZ principles related to this transformation process, along with their application examples:  
* **Principle 1: Segmentation** This principle involves dividing large datasets into smaller, more manageable segments to facilitate detailed analysis and obtain targeted insights. This strategy helps optimize resource utilization and enhances the efficiency of the analytical process [37]. * **Principle 2: Extraction** Extracting necessary subsets or key Data elements from large datasets to focus attention on core Information, avoiding distractions caused by irrelevant Data [38; 39]. This method significantly improves both the speed and effectiveness of Data processing. * **Principle 5: Merging** Integrating datasets from different sources to create a more complete and robust dataset. This not only enhances the overall accuracy of the Data but also increases its utility, providing a solid foundation for decision making [40]. * **Principle 10: Preliminary Actions** Preprocessing the Data, such as cleansing, standardization, or structural adjustment, prior to formal analysis to ensure the smoothness and effectiveness of subsequent Data operations. This step is essential in Data analysis, as it directly impacts the quality of the final results. For instance, in Data mining, preprocessing steps like Data cleaning, normalization, and integration are essential for reducing noise and inconsistencies, which are common in large datasets. Proper preprocessing improves the performance of Data analysis and mining algorithms, leading to more robust findings. Additionally, preprocessing also includes standardization, which ensures that all Data points are on the same scale, enhancing the accuracy of analytical models and decision-making processes [41]. * **Principle 12: Equipotentiality** We have extended the semantic content of this principle here, which is to reduce excessive content flow, complexity, and inconsistency through certain optimization strategies, such as layering or preset boundaries, in order to improve the coordination of the entire system. For example, in software development in Domain-Driven Design [42] mode, all sub-business domain models with the same level are divided into the same equipotential surface. In addition, a unified domain modeling language can be established to ensure that domain experts and developers have the same understanding of the same term. * **Principle 18: Mechanical vibration** We have expanded the semantic content of this principle by adding a specific mechanism to randomize or denoise the data content, ensuring that the entire system operates at its optimal state. For example, in the protection algorithm for Differential Privacy [43], researchers use Differential Privacy technology to protect individual data without significantly affecting the statistical results and try to avoid individual data leakage as much as possible [44]. * **Principle 26: Copying** The main principle is to use a simple, inexpensive, and easy-to-manufacture replica instead of an expensive or complex original. This principle is reflected in the concept of metaverse [45], which models display targets through computer graphics technology to form data-driven content, allowing users to achieve immersive experiences through virtual reality- or augmented reality-related technologies.