This application of bigRing uses utility and functions of bigRing primarily for biomedical domain. Data integration tool helps unify data from various structured, semi-structured, and unstructured sources into a single bigRing Graph enriched with causal relationships. It significantly expands the scope for creative insight into biological contexts and biological and medical reality for users and widely opens doors for real personalization of medicine . Users can select preferred data sources and areas of interest, without needing extensive IT expertise or support.
• A tool for integrating and consolidating a huge amount of related data from a wide variety of sources in any biomedical field
• Connects consolidated data into causal relationships and more broadly connected complexes
• A tool for knowledge base development and development of known connection patterns by less known or unknown contexts
• It is a no-code platform
• Displays knowledge in the form of graphs, in varying degrees of detail and in various contexts (according to the user’s choice)
• A tool for increasing the effectiveness of research activities by reducing cognitive load and speeding up discovery and evaluation processes
The system’s uniqueness lies in its integration of advanced technologies such as AI, ML, RL, DL, graph theory, OLTP, and OLAP within one platform, offering user-friendly interfaces for business users. The synergy between the expert and users allows real-time knowledge application and model refinement, fostering continuous learning and innovation. Furthermore, its ability to generate synthetic knowledge enables creation of new insights from existing data, providing a competitive advantage in decision-making processes.
Configuration search for causal connection
Found possible paths of causality
Overview of the entire resulting causal diagram
Tools for customizing the display
Investigated pathway of metabolic processes
The model based on meta-heuristic principles communicates knowledge in a format intelligible to machines, computers, and also among humans. The distinctive characteristic of this model lies in its explicit interpretation of reality, which is consistent across all disciplines. This allows the sharing of knowledge or information even at the more complex conceptual levels – the realms of wisdom.
The expert working with the meta-heuristic model is a specialist in data integration, capable of unifying disparate data sources into comprehensive models. They employ personal insights and advanced algorithms to generate synthetic knowledge, creating new insights from existing data. This professional is unique in their ability to verify the applicability of these insights through both research and practical application, enhancing the effectiveness and relevance of the model.
The result of the expert’s work is a causal mechanisms model, acting as a meta-source code for the BigRing platform and BigBrain computer. This model deciphers cause-effect relationships in data, laying the foundation for efficient processing and intelligent analysis within these advanced systems, thereby enhancing their overall performance.
The expert continuously refines the causal mechanisms model within the BigRing platform, engaging in constant analysis and exploration. Collaborating with business users on the platform, they collectively apply knowledge to generate tailored solutions, enhancing the platform’s efficacy and impact.
The causal mechanisms model is processed by the BigBrain computer, a hub of various integrated technologies and algorithms. It incorporates elements of Artificial Intelligence (AI), Machine Learning (ML), Reinforcement Learning (RL), Deep Learning (DL), graph theory, Online Transaction Processing (OLTP), and Online Analytical Processing (OLAP). These diverse technologies and methodologies converge within the BigBrain, working in harmony to dissect and understand the model. The BigBrain communicates seamlessly with the BigRing platform, ensuring synchronized operation between these two advanced systems.
The business user applies all these components in practice when utilizing knowledge to address real-world problems. This application is facilitated by generic user interfaces, which serve as conduits between the user and the sophisticated technologies of AI, ML, RL, DL, graph theory, OLTP, and OLAP. These interfaces allow the user to seamlessly interact with the integrated technologies, enabling them to harness the power of the BigBrain computer and the BigRing platform in a practical, user-friendly manner.
Data Unification
Unifying data from structured, semi-structured, and unstructured sources may pose significant challenges in terms of data compatibility, integrity, and normalization.
Data Interpretation
Converting various types of data into interpretable information reflective of real-world biophysical relationships can be complex.
User-Interface Optimization
Maintaining a user-friendly interface while offering complex functionalities may pose UX/UI challenges.
Data Source Selection
Helping users select preferred data sources without needing extensive IT support could be demanding, given the likely diversity and complexity of these sources.
Data Import Simplification
Streamlining the data import process with intuitive visual tools for data extraction could present technical challenges.
Error Identification
Ensuring the software is capable of accurately identifying potential errors in the data import model or the source might be challenging, particularly with a diversity of data formats.
Manual Correction of Errors
Allowing for manual corrections in a manner that doesn’t compromise data integrity may be a challenge.
Integration of Proprietary Information
Merging external data with proprietary information could raise concerns about data security and privacy, especially in sensitive fields like biotechnology and pharmaceuticals.
Real-Time Data Analysis
Offering real-time analysis features, especially for large and diverse datasets, could be computationally challenging and resource-intensive.
Customizable Visualization
Ensuring effective customization options for data visualization that don’t sacrifice clarity or accuracy could pose a design challenge.
Understanding Complex Relationships
Facilitating a deeper understanding of complex biophysical relationships and improving data processing efficiency may be technically challenging.
Reducing Cognitive Burden
Creating a software that significantly reduces the cognitive burden on researchers, enabling them to focus on specific causal pathways, might be challenging given the inherent complexity of the field.
