Mission
Our mission is to connect data, graphs, and AI, transforming information into knowledge and enabling more intelligent systems, decisions, and discoveries.
Together we are exploring how data can be organised, connected, and transformed into knowledge. With a collective experience of more than 4 decades, this is the site where we share experiences, ideas, methods, and lessons learned across data architecture, knowledge graphs, and AI.
Latest Articles
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Enterprise GenAI Assessment: Practical Experiences with AI‑Powered IDEs
Executive Summary This document presents an enterprise-level assessment of AI-powered Integrated Development Environments (IDEs) such as Cursor and AWS Kiro, based on direct implementation experiences while building an end-to-end web application. The intent is to document observed behaviours, highlight architectural implications, and outline risks and considerations relevant to solution architects, engineering leaders, and governance bodies…
4–6 minutes -
Querying an Ontology from R
This article explores how we can query an ontology in GraphDB from R opening it up for analytics. Step-by-step: Query competency questions using R GraphDB exposes a SPARQL endpoint to which you can send SPARQL queries using HTTP POST and parse the JSON results. The endpoint is located at: http://192.168.1.102:7200/repositories/banking The entire R code is…
1–2 minutes -
The FtM Data Model (Part 1)
The Follow the Money Model consists of four main concepts:entities, references, interstitial entities, and streams 1. Node entities These are the core objects: like Person, Passport, Company as in Figure 1. Each one of them is a standalone entity with its own id, schema and properties (usually, multi-valued lists of strings). 2. Edges (formed via…
3–4 minutes
Meet ANALYTI UK®
We invite researchers, practitioners and individuals to collaborate. The content in the site are owned by the respective authors.
Acknowledgements
ANALYTI® is an independent initiative sustained through collaborative projects and expert engagement. At its core, it is an attempt to foster collaborative analytical thinking, encouraging diverse perspectives to work together in understanding complex challenges and generating meaningful insights.
We’re open to collaboration. If you have an experience to share, please contribute.
Meet the people behind
ANALYTI®
Dr. Sreekumar Pillai
Principal Architect
Data Engineering, Machine Learning, Deep Learning, Analytics, Knowledge Graphs
Aji S.
Enterprise Archiect
Enterprise Architecture, Digital Technology, Applied AI
Shrawan S.
Data & AI Consultant
Anaplan, Data Engineering, Machine Learning and AI
Visiting Collaborators
Network
Partnerships, Coauthorship, Shared datasets
Funding & Partners
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F.A.Q.
What’s the difference between Field Notes and Publications?
Field Notes are working-facing posts—method notes, reflections, and updates written for readability and practical reuse. Publications are formal research outputs (journal articles, reports, and working papers) with citation details and official links.
Can I reuse your datasets, protocols, or templates?
Often, yes. Each resource lists its license and any access conditions. If a dataset is restricted (e.g., sensitive locations or participant privacy), we’ll note how to request access.
How should I cite something from this site?
Use the citation information on the relevant page when available (publication citation, dataset version, or post date).
When in doubt, cite the page title, Analyti®, the year, and the URL.
How do I collaborate or request access to restricted materials?
Email admin@analyti.uk with a short description of your project and what you need (e.g., dataset version, intended use, timeline). We typically respond within 3–5 working days.