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Category: Computing & data (Page 75 of 91)

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.

MariaDB releases MariaDB Community Server 10.5

MariaDB Corporation announced the general availability of MariaDB Community Server 10.5, a major release that brings high-performance analytics to the open source database. In a push to mainstream analytics and to make it as popular as MariaDB’s transactional engine, the company added a new, native columnar storage engine to the community database server and a new, native MariaDB Python Connector and Microsoft Power BI integration. All new analytical capabilities in MariaDB Community Server 10.5 are available for free with unrestricted use to broaden adoption of hybrid transactional and analytical processing, and modern analytical approaches.

MariaDB Server is compatible with the widely used MySQL database protocol and therefore supports native integrations with BI and data analysis tools and frameworks. This compatibility also enables access to data in any MariaDB storage engine, including ColumnStore. In addition, MariaDB released two new, native connectors to make data analysis with MariaDB easier and faster. MariaDB Community Server 10.5 including ColumnStore is available immediately for free direct download on the MariaDB website now and through Docker Hub by the end of June. MariaDB Connector/Python and MariaDB Power BI adapter can be downloaded from mariadb.com. For customers interested in MariaDB for demanding production environments with built-in high availability and massively parallel processing (MPP), please contact MariaDB for early access to MariaDB Enterprise Server 10.5.

https://mariadb.com

Adobe Sensei Product Recommendations integrate with Magento Page Builder

Adobe announced Product Recommendations for Magento Commerce has been integrated with Magento’s content creation tool, Page Builder, extending the embedded Magento Admin experience to streamline workflows. Magento merchants can now drag and drop Sensei-powered recommendation units on any position within content that’s being authored in Page Builder.

Adobe released Product Recommendations powered by Adobe Sensei, their AI and machine learning technology, to Magento Commerce customers globally in April. This feature allows merchants to deploy automated and intelligent recommendations across storefronts to help their customers discover new, relevant products throughout their shopping journey. Adobe plans additional Product Recommendations capabilities to bridge the gap between intelligent merchandizing and content management.

Key benefits of the Page Builder integration include:

  • Drag and drop functionality makes it simple to place product recommendations in any position within the content being edited via Page Builder.
  • Merchants can now easily add recommendation units to multiple content types including pages, blocks, dynamic blocks and individual fields.
    • Merchants can now target recommendations to specific customers by assigning dynamic blocks to various consumer segments.
    • With Page Builder, merchants can deploy specific recommendations to individual product pages instead of across all product pages.
    • Merchants can customize recommendation units within Page Builder to match their brand, including adding borders, colors, and custom headings.

Magento Commerce merchants that have implemented Product Recommendations can take advantage of the Page Builder integration today by updating their modules. Product Recommendations is exclusively available for Magento Commerce merchants.

https://magento.com/

Graph database

A graph database uses graph structures with nodes, edges, and properties to represent and store data. By definition, a graph database is any storage system that provides index-free adjacency. This means that every element contains a direct pointer to its adjacent element and no index lookups are necessary. General graph databases that can store any graph are distinct from specialized graph databases such as triplestores and network databases.

Semantic Web Company and Ontotext partner to advance enterprise knowledge graphs

Ontotext (OT) and Semantic Web Company (SWC) announced a strategic partnership to meet the requirements of enterprise architects such as deployment, monitoring, resilience, and interoperability with other enterprise IT systems and security. Users will be able to work with a feature-rich toolset to manage a graph composed of billions of edges that is hosted in data centers around the world. The companies have implemented an integration of the PoolParty Semantic SuiteTM v.8 with the GraphDB and Ontotext Platform, which offers benefits for numerous use cases:

  • GraphDB powering PoolParty: Most of the knowledge graph management tools out there bundle open-source solutions that are good at managing thousands of concepts, whereas PoolParty bundled with GraphDB manages millions of concepts and entities—without extra deployment overheads.
  • PoolParty linked to high-availability GraphDB cluster: GraphDB can now be used as an external store for PoolParty, which offers a combination of performance, scalability and resilience. This is particularly relevant for organizations intent on developing tailor-made knowledge graph platforms integrated into their existing data and content management infrastructure.
  • Dynamic text analysis using big knowledge graphs: PoolParty can be used to edit big knowledge graphs in order to tune the behavior of Ontotext’s text analysis pipelines, which employ vast amounts of domain knowledge to boost precision. This way the power and comprehensiveness of generic off-the-shelf natural language processing (NLP) pipelines can be custom-tailored to an enterprise.
  • GraphQL benefits for PoolParty: Application developers can now access the knowledge graph via GraphQL to build end-user applications or integrate knowledge graph services with the functionality of existing systems. Ontotext Platform uses semantic business objects, defined by subject matter experts and business analysts, to generate GraphQL interfaces and transform them into SPARQL.

https://www.ontotext.com/, https://www.poolparty.biz

DataStax unveils Vector: AIOps for Apache Cassandra

DataStax announced the private beta of Vector, an AIOps service for Apache Cassandra. Vector continually assesses the behavior of a Cassandra cluster to provide developers and operators with automated diagnostics and advice, helping them be consistently successful with Cassandra and DataStax Enterprise (DSE) clusters. Vector provides recommendations with detailed background knowledge and offers multiple ways to fix a problem. With this embedded knowledge base, Vector is able to analyze individual nodes, compare behavior to other nodes in the cluster, and serve up recommendations, such as: Cassandra and operating system configuration, schema design, and Cassandra performance and query techniques. Vector features:

  • Automated expert advice – Proactively identifies current and potential issues to help developers and operators solve problems quickly. Automated advice provides contextual learning with background knowledge to build skills.
  • Continuous updates – Rules and advice are continuously updated, deployed to SaaS and on-premises applications, and automatically applied to clusters.
  • Hands-off management – Advanced visualizations of system usage with insightful charting to understand tables, keyspaces, and nodes. Vector helps developers and operators see and understand how the cluster is performing and its configuration without having to log into Cassandra nodes.
  • Cassandra skills development – Helps strengthen Cassandra skills and knowledge by providing detailed advice and recommendations. Vector helps to reduce unexpected and unplanned items.

https://www.datastax.com/

ABBYY open-sources machine learning library NeoML

ABBYY announced the launch of NeoML, an open-source library for building, training, and deploying machine learning models. Available now on GitHub, NeoML supports both deep learning and traditional machine learning algorithms. The cross-platform framework is optimized for applications that run in cloud environments, on desktop and mobile devices. Compared to a popular open-source library (according to internal tests) NeoML offers 15-20% faster performance for pre-trained image processing models. Developers can use NeoML to build, train, and deploy models for object identification, classification, semantic segmentation, verification, and predictive modeling.

NeoML is designed as a universal tool to process and analyze data in a variety of formats including text, image, video, and others. It supports ​​C++, Java, and Objective-C programming languages; Python will be added shortly. NeoML’s neural network models support over 100 layer types. It also offers 20+ traditional ML algorithms such as classification, regression, and clustering frameworks. The library is cross-platform – a single code base that can be run on popular operating systems including Windows, Linux, macOS, iOS, and Android – and optimized for both CPU and GPU processors.

NeoML supports the Open Neural Network Exchange (ONNX), a global open ecosystem for interoperable ML models. The ONNX standard is supported jointly by Microsoft, Facebook, and other partners as an open source project. ABBYY invites developers, data scientists, and business analysts to use and contribute to NeoML on GitHub, where its code is licensed under the Apache License 2.0. The company offers developer support, ongoing review of reports, regular updates, and performance enhancements.

https://github.com/neoml-lib, https://www.abbyy.com/

SAS and Microsoft partner on analytics and AI

Microsoft Corp. and SAS announced an extensive technology and go-to-market strategic partnership. The two companies will enable customers to run their SAS workloads in the cloud, and will migrate SAS’ analytical products and industry solutions onto Microsoft Azure as the preferred cloud provider for the SAS Cloud. SAS’ industry solutions and expertise will also bring value to Microsoft’s customers across health care, financial services and many other industries. This will include optimizing SAS Viya, the latest release of the company’s cloud-native offering, for Azure as well as integrating SAS’ deep portfolio of industry solutions into the Azure Marketplace. Additionally, Microsoft and SAS will explore opportunities to integrate SAS analytics capabilities, including industry-specific models, within Azure and Dynamics 365 and build new market-ready joint solutions for customers that are natively integrated with SAS services across multiple vertical industries. Microsoft and SAS are already supporting customers with solutions that help them capitalize on the vast amount of data being generated by the Internet of Things by combining Microsoft’s Azure IoT platform with SAS’ edge-to-cloud IoT analytics and AI capabilities. Additional SAS products and solutions will begin rolling out later this year.

https://www.sas.com/, https://news.microsoft.com

GIGXR announces new immersive learning system

GIGXR, Inc., a provider of extended reality (XR) learning systems for instructor-led teaching and training, announced the availability of its GIG Immersive Learning System for the Fall 2020 Northern Hemisphere academic year. The cloud-based System was created to enhance learning outcomes while simplifying complex, real-life teaching and training scenarios in medical and nursing schools, higher education, healthcare and hospitals. The GIG Immersive Learning System is available for demos and pre-order now, and includes three core components:

  • Remote and Socially Distanced Learning: Enables teaching and training with students in a distributed classroom through extended reality. Students can be co-located, remote or safely socially distanced, and participate in sessions anywhere using 3D mixed reality immersive devices and mobile phones, tablets or laptops for a 2.5D experience.
  • Mixed Reality Applications: GIGXR’s products HoloPatient and HoloHuman run on Microsoft’s HoloLens 2, placing the 3D digital world in a collaborative physical space for safe development of clinical skills and exploration into human pathologies and anatomies.
  • Immersive Learning Platform: Cloud-based infrastructure that supports GIGXR’s mixed reality applications and remote learning capabilities with additional features such as visual login, instructor content creation, holographic content management, session planning, roles and rights, license management, security, privacy, and long-term data management.

https://www.gigxr.com/

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