November 23-24


Daniel Wrigley

Lead Consultant Search & Analytics

SHI, Germany


Daniel Wrigley works as a Lead Search & Analytics Consultant. He mostly deals with search and advanced analytics applications with a strong focus on modern open source projects such as NiFi, Solr, Spark, Kafka or Zeppelin. His experience as a Solr trainer enabled him to co-author the first German book on Solr. His weakness for search and natural language processing originates from his computational linguistics studies at Ludwig-Maximilians-University Munich where he graduated in 2012.


Keyword Search is Dead! And so are Solr and Elasticsearch?

With Solr and Elasticsearch, two technologies form the current standard in the open-source search area, which rely on an algorithm that has its origins in the 70s for their relevance calculation: BM25.

However, this keyword-based approach cannot represent the complexity of natural language. In recent years, technologies have evolved that enable semantic indexing of language by vectorizing text.

Do such approaches find their way into the current developments of Solr & Elasticsearch? How can AI combined with Vector Similarity Search efficiently deliver more relevant search results than conventional methods? For which cases is there an economic gain from their application? To answer these and other questions, he will provide an overview of the current state and an outlook into the future possibilities of new technologies and reveal how search applications can get a boost with the help of AI.

Session Keywords

🔑 Natural Language Processing (NLP)
🔑 Vector Similarity Search
🔑 Elasticsearch
🔑 Solr

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