Linked Data Spaces, Data Portability & Access

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Kingsley Uyi Idehen

Presentation covering issues of Data Access and Portability via Personally controlled points of presence, on Linked Data Networks called: Data Spaces.

Linked Data Spaces, Data Portability & Access © 2008 OpenLink Software, All rights reserved.

What’s The Problem? ,[object Object],[object Object],[object Object],© 2008 OpenLink Software, All rights reserved.

Problem Origins ,[object Object],[object Object],[object Object],[object Object],© 2008 OpenLink Software, All rights reserved.

Problem Vectors ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],© 2008 OpenLink Software, All rights reserved.

Aspects of Data Portability ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],© 2008 OpenLink Software, All rights reserved.

Aspects of Data Access ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],© 2008 OpenLink Software, All rights reserved.

Why is This Important? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],© 2008 OpenLink Software, All rights reserved.

How We Bust Data Silos ,[object Object],[object Object],[object Object],[object Object],[object Object],© 2008 OpenLink Software, All rights reserved.

How Web-Scale Data Access Should Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],© 2008 OpenLink Software, All rights reserved.

Personal Data Space Example using ODS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],© 2008 OpenLink Software, All rights reserved.

© 2008 OpenLink Software, All rights reserved.

Some ODS Specific Features ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],© 2008 OpenLink Software, All rights reserved.

Additional Information ,[object Object],[object Object],[object Object],[object Object],© 2008 OpenLink Software, All rights reserved.

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Tracing the root cause of a performance issue requires a lot of patience, experience, and focus. It’s so hard that we sometimes attempt to guess by trying out tentative fixes, but that usually results in frustration, messy code, and a considerable waste of time and money. This talk explains how to correctly zoom in on a performance bottleneck using three levels of profiling: distributed tracing, metrics, and method profiling. After we learn to read the JVM profiler output as a flame graph, we explore a series of bottlenecks typical for backend systems, like connection/thread pool starvation, invisible aspects, blocking code, hot CPU methods, lock contention, and Virtual Thread pinning, and we learn to trace them even if they occur in library code you are not familiar with. Attend this talk and prepare for the performance issues that will eventually hit any successful system. About authorWith two decades of experience, Victor is a Java Champion working as a trainer for top companies in Europe. Five thousands developers in 120 companies attended his workshops, so he gets to debate every week the challenges that various projects struggle with. In return, Victor summarizes key points from these workshops in conference talks and online meetups for the European Software Crafters, the world’s largest developer community around architecture, refactoring, and testing. Discover how Victor can help you on victorrentea.ro : company training catalog, consultancy and YouTube playlists.

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Retrieval augmented generation (RAG) is the most popular style of large language model application to emerge from 2023. The most basic style of RAG works by vectorizing your data and injecting it into a vector database like Milvus for retrieval to augment the text output generated by an LLM. This is just the beginning. One of the ways that we can extend RAG, and extend AI, is through multilingual use cases. Typical RAG is done in English using embedding models that are trained in English. In this talk, we’ll explore how RAG could work in languages other than English. We’ll explore French, Chinese, and Polish.

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The integration landscape is changing rapidly with the introduction of technologies like GraphQL, gRPC, stream processing, iPaaS, and platformless. However, not all existing applications and industries can keep up with these new technologies. Certain industries, like manufacturing, logistics, and finance, still rely on well-established EDI-based message formats. Some applications use XML or CSV with file-based communications, while others have strict on premises deployment requirements. This talk focuses on how Ballerina's built-in integration capabilities can bridge the gap between "old" and "new" technologies, modernizing enterprise applications without disrupting business operations.

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Linked Data Spaces, Data Portability & Access

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