Learn how JSON-LD and Schema.org enhance RAG and NLWeb with structured data. Discover howto use markdown for AI training data, boosting SEO, and creating a digital AI twin.
Discover how to expose enterprise data for AI indexing with Java and Spring using the jsonld-schemaorg-javatypes library for NLWeb. Learn to leverage Schema.org, JSON-LD, and OrientDB for semantic search, knowledge graphs, and interoperability, with sustainable Fair Code licensing.
The software industry confronts a defining moment. Open Source Software, as delineated by the Open Source Initiative (OSI), has long been a cornerstone of technological advancement, enabling collaborative triumphs like the Linux kernel. Yet, its open-access ethos harbors a persistent flaw: exploitation. Can we stop it?
Analysis and predictions of occupancy in public transport are essential in order to use vehicles intelligently […]
Business capabilities and opportunities result from these Big Data Landscape maturity levels of an enterprise. We describe what they are and how they matter.
This article will introduce the concept of Locality Sensitive Hashing (LSH) and the working principles of the algorithm.
The four horsemen represent the four levels of problems faced by Big Data-driven enterprises. We look at these problems and ways to solve them.
Big Time Series Data Applications crucial in enterprise digitization. Here, we discuss the 5 top known Time Series Applications as well as some use cases.
Concept drifts in Time Series Data are crucial for analysis and machine learning. We discuss concept drifts and their implications for machine learning.