Process digitisation analytics refers to the use of data analysis tools and techniques to monitor, measure, and improve business processes that have been converted from manual to digital formats. It focuses on collecting and analysing data generated during digital workflows to identify inefficiencies, bottlenecks, and opportunities for improvement. By using analytics, organisations can make informed…
Category: Data Science
Graph Knowledge Propagation
Graph knowledge propagation is a way of spreading information through a network of connected items, called nodes, based on their relationships. Each node can share what it knows with its neighbours, helping the whole network learn more about itself. This method is used in computer science and artificial intelligence to help systems understand complex structures,…
Quantum Feature Mapping
Quantum feature mapping is a technique used in quantum computing to transform classical data into a format that can be processed by a quantum computer. It involves encoding data into quantum states so that quantum algorithms can work with the information more efficiently. This process can help uncover patterns or relationships in data that may…
AI for Forecasting
AI for forecasting uses artificial intelligence techniques to predict future events or trends based on data. It can analyse patterns from large amounts of past information and automatically learn which factors are important. This helps make more accurate predictions for things like sales, weather, or demand without needing manual calculations. Businesses and organisations use AI…
Quantum Data Mapping
Quantum data mapping is the process of transforming classical data into a format that can be used by a quantum computer. This involves encoding everyday information, such as numbers or images, into quantum bits (qubits) so it can be processed in quantum algorithms. The choice of mapping method affects how efficiently the quantum computer can…
Graph Signal Processing
Graph Signal Processing is a field that extends traditional signal processing techniques to data structured as graphs, where nodes represent entities and edges show relationships. Instead of working with signals on regular grids, like images or audio, it focuses on signals defined on irregular structures, such as social networks or sensor networks. This approach helps…
Privacy-Preserving Analytics
Privacy-preserving analytics refers to methods and technologies that allow organisations to analyse data and extract useful insights without exposing or compromising the personal information of individuals. This is achieved by using techniques such as data anonymisation, encryption, or by performing computations on encrypted data so that sensitive details remain protected. The goal is to balance…
Graph-Based Inference
Graph-based inference is a method of drawing conclusions by analysing relationships between items represented as nodes and connections, or edges, on a graph. Each node might stand for an object, person, or concept, and the links between them show how they are related. By examining how nodes connect, algorithms can uncover hidden patterns, predict outcomes,…
Differential Privacy Metrics
Differential privacy metrics are methods used to measure how much private information might be exposed when sharing or analysing data. They help determine if the data protection methods are strong enough to keep individuals’ details safe while still allowing useful insights. These metrics guide organisations in balancing privacy with the usefulness of their data analysis.
Knowledge Fusion Models
Knowledge fusion models are systems or algorithms that combine information from multiple sources to create a single, more accurate or comprehensive dataset. These models help resolve conflicts, fill in gaps, and reduce errors by evaluating the reliability of different inputs. They are commonly used when data comes from varied origins and may be inconsistent or…