Category: Enterprise search & search technology (Page 7 of 60)

Elastic announced updates across the Elastic Search Platform, a data analytics platform for search-powered solutions, including:

• Simplifying the Elastic Cloud on AWS Experience Enabling customers to ingest data from any AWS service into Elastic Cloud on AWS directly from the AWS Marketplace with just three clicks.
• Improving search relevance with machine learning-based hybrid scoring
• Combining traditional keyword scoring with vector search scoring capabilities.

They also announced plans to develop stateless Elasticsearch, a new, fully cloud-native architecture. The stateless architecture will fully decouple compute and storage services, enabling customers to store and search all of their data in stateless object storage services such as Amazon S3, Azure Blob Store, and Google Cloud Storage.

A private beta version of a new Universal Profiling and additional synthetic monitoring capabilities to provide visibility into how application code and infrastructure are performing at all times, in production and across a wide range of languages, in both containerized and non-containerized environments was introduced, as well as a new managed testing infrastructure within Elastic Uptime to enable customers to schedule tests from a global network of testing agents for greater visibility into regional variances, also in beta.

https://www.elastic.co

Elastic, the company behind Elasticsearch, announced enhancements to its cross-cluster search and cross-cluster replication capabilities with interoperability between self-managed deployments and Elastic Cloud, now generally available. Customers can seamlessly search data across multiple Elasticsearch clusters deployed on-premises, on Kubernetes, and in the cloud.

Cross-cluster search enables users to search across multiple clusters and visualize data in one coherent view for deeper insights. Cross-cluster replication allows customers to replicate data between clusters regardless of their physical location (cloud or on-premises) and deployment model. Features include:

• Streamlining workflows with a single user interface to search and replicate data between Elasticsearch clusters regardless of environment—on-premises, public cloud, hybrid, and multi-cloud.
• Enabling customers to minimize risk and increase operational efficiency while retaining complete visibility of their data throughout the gradual migration of on-premises workloads to the cloud.
• Optimizing customers’ ability to troubleshoot production applications, analyze security events, and manage where their sensitive data resides.
• Improving disaster recovery scenarios where data redundancy and business continuity are critical, while increasing service resilience and lowering latency.

https://www.elastic.co/blog/search-and-replicate-data-between-your-elastic-cloud-and-on-prem-deployments

Enterprise search provider Sinequa announced the addition of advanced neural search capabilities to its Search Cloud Platform, for better relevance and accuracy to enterprises. As an optional capability of Sinequa’s Search Cloud platform, Neural Search uses four deep learning language models. These models are pre-trained and ready to use in combination with Sinequa’s Natural Language Processing (NLP) and semantic search.

Sinequa optimized the models and collaborated with the Microsoft Azure and NVIDIA AI/ML teams to deliver a high performance, cost-efficient infrastructure to support intensive Neural Search workloads without a huge carbon footprint. Neural Search is optimized for Microsoft Azure and the latest NVIDIA A10 or A100 Tensor Core GPUs to efficiently process large amounts of unstructured data as well as user queries.

Sinequa’s Neural Search improves relevance and is often able to directly answer natural language questions. It does this with deep neural nets that go beyond word-based search to better leverage meaning and context. Sinequa’s Search Cloud platform combines neural search with its extensive NLP and statistical search. This unified approach provides more accurate and comprehensive search results across a broader range of content and use cases.

https://www.sinequa.com/product-enterprise-search/neural-search/