Causal knowledge integration is the process of combining information from different sources to understand not just what is happening, but why it is happening. This involves connecting data, theories, or observations to uncover cause-and-effect relationships. By integrating causal knowledge, people and systems can make better predictions and decisions by understanding underlying mechanisms.
Category: Data Science
Temporal Data Modeling
Temporal data modelling is the process of designing databases or data systems to capture, track and manage changes to information over time. It ensures that historical states of data are preserved, making it possible to see how values or relationships have changed. This approach is essential for systems where it is important to know not…
Graph-Based Predictive Analytics
Graph-based predictive analytics is a method that uses networks of connected data points, called graphs, to make predictions about future events or behaviours. Each data point, or node, can represent things like people, products, or places, and the connections between them, called edges, show relationships or interactions. By analysing the structure and patterns within these…
Multi-Domain Inference
Multi-domain inference refers to the ability of a machine learning model to make accurate predictions or decisions across several different domains or types of data. Instead of being trained and used on just one specific kind of data or task, the model can handle varied information, such as images from different cameras, texts in different…
Knowledge Representation Models
Knowledge representation models are ways for computers to organise, store, and use information so they can reason and solve problems. These models help machines understand relationships, rules, and facts in a structured format. Common types include semantic networks, frames, and logic-based systems, each designed to make information easier for computers to process and work with.
Dynamic Model Calibration
Dynamic model calibration is the process of adjusting a mathematical or computer-based model so that its predictions match real-world data collected over time. This involves changing the model’s parameters as new information becomes available, allowing it to stay accurate in changing conditions. It is especially important for models that simulate systems where things are always…
Graph-Based Feature Extraction
Graph-based feature extraction is a method used to identify and describe important characteristics or patterns from data that can be represented as a network or graph. In this approach, data points are seen as nodes and their relationships as edges, allowing complex connections to be analysed. Features such as node connectivity, centrality, or community structure…
Digital Performance Metrics
Digital performance metrics are measurements used to track how well digital systems, websites, apps, or campaigns are working. These metrics help businesses and organisations understand user behaviour, system efficiency, and the impact of their online activities. By collecting and analysing these numbers, teams can make informed decisions to improve their digital services and achieve specific…
AI-Driven Talent Analytics
AI-driven talent analytics uses artificial intelligence to collect, analyse, and interpret data about employees and job candidates. It helps organisations make better decisions about hiring, managing, and developing people by finding patterns in large sets of data. This approach can identify strengths, skills gaps, and predict which candidates or employees are most likely to succeed…
AI for Business Forecasting
AI for Business Forecasting uses computer systems that learn from past data to predict future trends for companies. These systems help businesses estimate sales, demand, costs, or other important numbers, making planning more accurate. By automating and improving predictions, AI can save time and reduce errors compared to manual forecasting methods.