InfoQ Homepage Search Content on InfoQ
-
Elastic 8.6 Released with Improvements to Observability, Security, and Search
Elastic has released Elastic 8.6 with improvements across the entire Elastic Search Platform including Elastic Enterprise Search, Elastic Observability, Elastic Security, and Kibana. The release includes additional connector clients, better observability of dependencies, improvements to alerts generated from prebuilt security rules, and temporary data views.
-
Amazon Announces Preview of OpenSearch Serverless
AWS recently announced the preview of OpenSearch Serverless, a new option of OpenSearch service that automatically provisions and scales the resources for data ingestion and query responses. The minimum capacity required for the serverless option raised some concerns in the community.
-
Amazon OpenSearch Adds Anomaly Detection for Historical Data
Amazon OpenSearch recently introduced the support of anomaly detection for historical data. The machine learning based feature helps identifying trends, patterns, and seasonality in OpenSearch data.
-
Pinecone 2.0 Aims to Bring Vector Similarity Search to Production
Pinecone recently introduced version 2.0 of its vector similarity search solution aiming to make it easier for companies to build recommendation systems, image search, and similar applications. InfoQ has taken the chance to speak with Edo Liberty, founder and CEO of Pinecone.
-
AWS Renames Amazon Elasticsearch Service to Amazon OpenSearch Service
Recently AWS announced that it would rename Amazon Elasticsearch Service to Amazon OpenSearch Service. With the renaming, the company releases the service with OpenSearch 1.0 support and makes it the successor to Amazon Elasticsearch Service.
-
ElasticSearch Fork OpenSearch is Generally Available
Amazon has recently announced the general availability of OpenSearch 1.0, the Apache 2.0-licensed fork of Elasticsearch that was created after Elastic changed their license.
-
Better SEO with Structured Data and Rich Snippets
Martin Splitt, search developer advocate for Google, recently explained at the Chrome Developer Summit 2020 how to use structured data to make a website eligible for rich results in Google Search. Rich results support semantic searches, stand out from ordinary search results, and may increase the click-through rate.
-
Pinterest Describes an Architecture for Efficient Retrieval of Hierarchical Documents
In a recent blog post, Pinterest engineers describe how they implemented an efficient two-stage retrieval architecture to retrieve hierarchical documents in a home-grown search engine. They accomplished it by combining index flattening, index normalization, and index denormalization.
-
Space-Efficient Full-Text Search with Rust and WebAssembly
Matthias Endler, backend engineer for Trivago, published a client-side full-text search engine designed for space efficiency by leveraging Bloom filters. Tinysearch is written in Rust, transpiled to WebAssembly, and used in the browser. Tinysearch claims sizes between 50 and 100KB and can only index full words.
-
Elasticsearch 7.7 Brings Asynchronous Search, Secure Keystore and More
Elastic, the search company, has released Elasticsearch 7.7.0. This release introduces asynchronous search, password protected keystore, performance improvement on time sorted queries, two new aggregates and first release of packaging for ARM(non x86) platform.
-
AWS Releases its Machine Learning Powered Enterprise Search Service Kendra into General Availability
Recently Amazon announced the general availability of its enterprise search service Kendra on AWS. With the GA release of Amazon Kendra, the public cloud provider added a few new specialized features and improved service accuracy.
-
Amazon Announces General Availability of UltraWarm for Its Elastic Search Service on AWS
Recently, Amazon announced the general availability of UltraWarm for its Elasticsearch Service on AWS. Ultrawarm is a low cost warm storage tier, and extension to the Elasticsearch Service - offering up to three petabytes of storage, at almost a 90% cost reduction over existing options.
-
Dropbox Predicts What File You Need Next with Content-Specific ML Pipelines
The Dropbox machine learning team shared how the company improved the model behind their content suggestions feature. The enhancements allow Dropbox to deal with different types of content, incorporate folder suggestions into the existing file suggestions model and handle cloud-based documents resulting from relatively recent partnerships.
-
Google Applies NLP Algorithm BERT to Search
BERT, Google's latest NLP algorithm, will power Google search and make it better at understanding user queries in a way more similar to how humans would understand them, writes Pandu Nayak, Google fellow and vice president for Search, with one in 10 queries providing a different set of results.
-
Google Research Use of Concept Vectors for Image Search
Google recently released research about creating a tool for searching Similar Medical Images Like Yours (SMILY). The research uses embeddings for image-based search and allows users to influence the search through the interactive refinement of concepts.