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Category: Computing & data (Page 52 of 92)

Computing and data is a broad category. Our coverage of computing is largely limited to software, and we are mostly focused on unstructured data, semi-structured data, or mixed data that includes structured data.

Topics include computing platforms, analytics, data science, data modeling, database technologies, machine learning / AI, Internet of Things (IoT), blockchain, augmented reality, bots, programming languages, natural language processing applications such as machine translation, and knowledge graphs.

Related categories: Semantic technologies, Web technologies & information standards, and Internet and platforms.

Fluree and Lead Semantics announce TextDistil

May 27, 2021 /

Fluree, provider of an immutable semantic graph data platform, announced a technical partnership with Lead Semantics to provide an integrated solution, TextDistil, for enterprise data management teams building semantic-capable, secure data fabrics. A focus for the integrated solution includes regulated industries, with a greater magnitude and scope of requirements needed to prove compliance, including fintech, banking, insurance and the public sector.

Lead Semantics’ natural language processing (NLP) technology, powered by Fluree’s semantic graph database, will help convert unstructured data assets into semantic-capable enterprise knowledge. With TextDistil, Lead Semantics and Fluree are bringing unstructured data into the structured context of businesses’ respective operational transactional worlds with security, traceability, and audit-capabilities provided by Fluree’s immutable ledger. Key benefits:

  • With TextDistil, free text becomes yet another stable source of data with ‘structure’ much like data from a relational database in an enterprise.
  • Fluree’s trusted ledger, with the Fluree technology integration, companies using TextDistil will have secure and provable data, which is audit-friendly and can wrap legal contracts with blockchain-grade traceability.
  • Both follow standards-based data semantics, making them easily integratable. On the output side, TextDistil encodes text into knowledge according to a domain ontology. W3C semantic standards compliant knowledge-facts (RDF triples) are output to be loaded into Fluree Database enabling automatic semantic integration and standard SPARQL querying.

https://flur.ee ▪︎ https://leadsemantics.com

Squirro launches new Squirro App Studio

May 26, 2021 /

Squirro, an Augmented Intelligence solutions provider, has announced the launch of its new Squirro App Studio, a no code / low code platform to build and set up AI-powered apps such as Cognitive Search quickly and easily. The platform enables users with no background in data science to build a Cognitive Search app, leveraging Artificial Intelligence (AI), machine learning (ML), and natural language processing (NLP) to create a unique enterprise search experience. Cognitive Search offers a unique search experience that gathers data from internal and external sources, and understands the users’ intent and context whilst providing them with the correct information at the right time.

The platform is enhanced with an extensive set of connectors, allowing users to unify their data sources and extract actionable insights and recommendations. In one click, users can connect the Cognitive Search app with CRM systems such as Salesforce, premium market data such as Refinitiv, Pitchbook, and a range of different enterprise systems including OneDrive, SharePoint, Confluence, Jira, Google Drive, Gmail and Dropbox.

https://squirro.com/enterprise-search/

AtScale announces AtScale CloudStart

May 26, 2021 /

AtScale, a provider of semantic layer solutions for modern business intelligence and data science teams, announced the launch of AtScale CloudStart for building analytics infrastructure on cloud data platforms. This offering enables organizations to rapidly integrate AtScale’s semantic layer solution on cloud data management platforms. CloudStart provides customers a way to start with a smaller semantic layer investment aligned with entry points for cloud data platforms with the ability to scale seamlessly with your analytics infrastructure.

As enterprise data moves to the cloud, analytics teams are challenged to ensure performance and manage costs while capturing the value of democratizing data. AtScale’s semantic layer eliminates the friction of moving BI, artificial intelligence and machine learning workloads to the cloud. By leveraging a single source of enterprise business metrics, organizations can drive data literacy and self-service BI initiatives while aligning business intelligence and data science teams.

AtScale CloudStart is immediately available for Snowflake, Microsoft Azure Synapse SQL, Google BigQuery, Amazon Redshift, and DataBricks. Customers can leverage this offering to connect cloud data sources to BI tools including Tableau, Excel, Looker and Power BI (leveraging the recently announced Live Query support for Power BI). Accompanying services packages support training and rapid onboarding.

https://www.atscale.com

DataStax collaborates with NetApp

May 20, 2021 /

DataStax announced a collaboration with NetApp to deliver full lifecycle management for cloud native data in its DataStax Enterprise database as well as open source Apache Cassandra clusters. As part of this partnership, the two companies have worked together to integrate the NetApp Astra data management service for Kubernetes workloads with DataStax Enterprise and Cassandra to provide a single pane of glass management for Cassandra data in modern containerized environments. With the tested and certified DataStax and NetApp solution, enterprises can automate the implementation of Cassandra clusters as well as simplify operations and lifecycle management processes around applications, data and container images on Kubernetes. The DataStax and NetApp integration provides:

  • Faster delivery of business applications through automatic storage provisioning and storage class setup processes
  • Improved application unit and system testing efficiency with cloning and migration of application clusters
  • Rich data management services, including data protection, business continuity and disaster recovery, active cloning, activity log. This supports rapid recovery from a disaster, or point-in-time copy recovery of DataStax Enterprise or Apache Cassandra clusters
  • Seamless portability and migration for Cassandra clusters, supporting enterprises with moving Kubernetes workloads and data between cloud locations
  • Consistent data management user interface with clear visualization of data protection status.

https://www.datastax.com ▪︎ https://www.netapp.com

Voicify Releases third major update

May 11, 2021 /

Voicify has released the third major update to the platform since its inception in 2017. Voicify 3.0 is notable in its delivery of features that categorize it as an enterprise solution, sitting within the ecosystem of digital platforms responsible for managing the customer experience. In 3.0, Voicify enables brands to engage customers as individuals through personalization and connectivity to systems like CRM, PIM, CMS etc. Conditional logic available within Voicify ranges from simple session information (first time versus returning) to specific user data (like transaction history).

Additionally, Voicify makes it easier for brands to get started in voice with the advent of VUX (Voice User Experience) Spark Templates. Spark is the componentry of Voicify that allows for the templatization of conversation flows that may be re-usable into an even simpler UI. To further support quick and simple voice app spin up, Voicify created a marketplace where VUX Spark Templates and Integrations are available to be added to customer voice apps. This release establishes over 100 major features to the platform and is immediately accessible.

https://voicify.com

DataRobot acquires Zepl to enhance enterprise capabilities for data scientists

May 11, 2021 /

DataRobot announced the acquisition of Zepl, a cloud data science and analytics platform. The acquisition will unlock new capabilities within DataRobot’s enterprise AI platform for the world’s most advanced data scientists. Zepl was founded by the creators of Apache Zeppelin, an open source notebook for data and analytics. Zepl provides a self-service data science notebook solution for advanced data scientists to do exploratory, code-centric work in Python, R, and Scala with enterprise-ready features such as collaboration, versioning, and security.

DataRobot will incorporate Zepl as a cloud-native, self-service notebook in its enterprise AI platform to drive productivity, efficiency, and collaboration for multiple personas. This allows for data scientists who prefer to code by allowing them to write their own tasks and custom models extending the out-of-the-box capabilities provided by DataRobot. With the integration of Zepl, business analysts will be able to build models using DataRobot’s automation and then collaborate with their advanced data science colleagues for additional customization if desired, on the same platform. It will also provide a more transparent view of the code behind DataRobot blueprints, further enhancing trust and explainability. In addition to the acquisition, DataRobot also unveiled new platform enhancements, including Composable AI.

https://www.datarobot.com ▪︎ https://www.zepl.com

Appian updates Low-code Automation Platform

May 11, 2021 /

Appian unveiled the latest version of the Appian Low-code Automation Platform. The release includes the introduction of low-code data, a new code-free approach to unifying enterprise data, enhanced AI-driven Intelligent Document Processing (IDP), new design guidance and developer collaboration features, and enhanced DevSecOps capabilities.

  • Low-code Data: Appian makes integrating data as easy as building apps. Source data from anywhere, without needing to migrate it. Visually combine, extend, and model relationships between varied data sources, and automatically optimize data sets for performance, without coding or database programming.
  • IDP: Appian Intelligent Document Processing (IDP) delivers efficiency gains via straight-through processing of large volumes of unstructured data. IDP now features native Optical Character Recognition (OCR).
  • Low-code RPA: Appian customers can now automate tasks faster with new Low-code RPA Windows actions and additional new libraries of actions that can be downloaded directly from the Appian AppMarket.
  • Low-code Apps: New developer collaboration capabilities simplify co-creation of apps, while enhanced design guidance optimizes app performance, security, and testing.
  • Low-code DevSecOps: Enhanced simplified movement of software packages between development, test, and production environments with one-click compare and deploy to accelerate secure, governed deployments.

https://www.appian.com/platform/free-trial/

Expert.ai adds emotion analysis to natural language API

May 7, 2021 /

Expert.ai announced advanced features enhancing analysis capabilities through its cloud-based natural language (NL) API. The new extension addresses one of the biggest challenges artificial intelligence developers face in the NL ecosystem – extracting emotions in large-scale texts and identifying stylometric data driving a complete fingerprint of content.

The expert.ai NL API captures a range of 117 different traits, providing a rich emotional and behavioral taxonomy. Emotional Traits are categorized into 8 different groups (anger, fear, disgust, sadness, happiness, joy, nostalgia, shame…). Behavioral Traits are divided into 7 groups (sociality, action, openness, consciousness, ethics, indulgence and capability) and the API assigns 3 levels of polarity (low, fair, high) to further indicate the level of each trait extracted.

The emotions and traits extension can be useful to make media content categorization more effective by capturing new needs or advancing analytics by providing more detailed forecasting and enabling more effective recommendation tailoring for e-commerce. The expert.ai NL API writeprint extension performs a deep linguistic style analysis (or stylometric analysis) ranging from document readability and vocabulary richness to verb types and tenses, registers, sentence structure and grammar. Compare multiple documents to identify unique writing style and author invariants to streamline authorship analysis, establish the author of a specific text or isolate characteristics such as education level.

https://www.expert.ai

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