diff --git a/_events/2024-0604-community-over-code-eu-documentation-design-templates.markdown b/_events/2024-0604-community-over-code-eu-documentation-design-templates.markdown new file mode 100644 index 0000000000..4c6ca32fa6 --- /dev/null +++ b/_events/2024-0604-community-over-code-eu-documentation-design-templates.markdown @@ -0,0 +1,13 @@ +--- +calendar_date: '2024-06-04' +eventdate: 2024-06-04 14:50:00 +0200 +title: Community Over Code Europe 2024 - Documentation to Use Open Source Design Templates +online: false +signup: + url: https://eu.communityovercode.org/sessions/2024/documentation-to-use-open-source-design-templates/ + title: Find out more +category: events +--- +Community Over Code Europe 2024 - Documentation to Use Open Source Design Templates + +In this session, Aparna Sundar shares best practices on the way to create bar raising documentation to guide users to use Figma and GitHub templates. To scale best practices in UX Research, designers of open source software create various design artifacts that can help software builders use and improve on the open source code and curated experience offerings. In this talk, she offers examples of OpenSearch research processes that can scale, documentation and creation of templates that designers and developers in the open source community can utilize in developing experiences for their users. Finally, she outlines best practices in developing documentation for the use of design templates such as the ones shared on Figma and GitHub. In this talk, Aparna shares examples of Figma templates offered to the open source community, and a way in which the open source community has utilized these in creating their software solution. diff --git a/_events/2024-0609-berlin-buzzwords-2024.markdown b/_events/2024-0609-berlin-buzzwords-2024.markdown new file mode 100644 index 0000000000..f0bc1c3e38 --- /dev/null +++ b/_events/2024-0609-berlin-buzzwords-2024.markdown @@ -0,0 +1,16 @@ +--- +calendar_date: '2024-06-09' +eventdate: 2024-06-09 01:50:00 +0200 +enddate: 2024-06-11 17:00:00 +0200 +title: Berlin Buzzwords 2024 +online: false +signup: + url: https://2024.berlinbuzzwords.de/ + title: See you there! +category: events +--- +Berlin Buzzwords 2024 + +Proud to be the Platinum Partner again this year for Germany’s most exciting conference on storing, processing, streaming and searching large amounts of digital data, with a focus on open source software projects. + +Come by the booth to learn more about the OpenSearch Project, and pick up a t-shirt or some stickers for your laptop. diff --git a/_events/2024-0610-berlin-buzzwords-2024-rediscover-your-keyword-search.markdown b/_events/2024-0610-berlin-buzzwords-2024-rediscover-your-keyword-search.markdown new file mode 100644 index 0000000000..4f2a3f89fa --- /dev/null +++ b/_events/2024-0610-berlin-buzzwords-2024-rediscover-your-keyword-search.markdown @@ -0,0 +1,15 @@ +--- +calendar_date: '2024-06-10' +eventdate: 2024-06-10 14:30:00 +0200 +title: Berlin Buzzwords 2024 - Rediscover your keyword search - Expand, Enrich and Rewrite +online: false +signup: + url: https://2024.berlinbuzzwords.de/sessions/?id=WLKNUS + title: Attend the session +category: events +--- +Berlin Buzzwords 2024 - Rediscover your keyword search: Expand, Enrich and Rewrite + +Praveen Mohan Prasad & Hajer Bouafif + +This presentation discusses about advanced methods to enhance keyword search by leveraging machine learning and large language models. The techniques involve utilising sparse models for document and query expansion, implementing metadata enrichment for text and images, and incorporating query rewriting using Large Language models. Through a live demonstration on a retail search scenario, we will illustrate how these approaches provide more relevant results by addressing the challenges of keyword search, such as vocabulary mismatches, unmatched queries against document attributes, and non-contextual search. The goal is to empower your existing keyword search system with semantic search capabilities without totally transforming to a costly and un-explainable, dense vector search system.