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Category: Computing & data (Page 51 of 80)

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.

AWS announces QuickSight Q

December 1, 2020 /

Amazon Web Services, Inc. (AWS) announced QuickSight Q to make it simpler for end-users to get more value from their business data using machine learning, among other new analytics analytics capabilities.

Amazon QuickSight Q is a machine learning-powered capability for Amazon QuickSight that lets users type natural language questions about their business data and receive accurate answers in seconds. As users begin typing their questions, Amazon QuickSight Q provides auto-complete suggestions with key phrases and business terms, and automatically performs spell-check and acronym and synonym matching, so users do not have to worry about typos or remembering the exact business terms for the data. Amazon QuickSight Q uses deep learning and machine learning (natural language processing, schema understanding, and semantic parsing for SQL code generation) to generate a data model that automatically understands the meaning of and relationships between business data, so users receive accurate answers to their business questions and do not have to wait for a data model to be built. Amazon QuickSight Q comes pre-trained on data from various domains and industries like sales, marketing, operations, retail, human resources, pharmaceuticals, insurance, and energy.

https://aws.amazon.com/quicksight/q, https://aws.amazon.com/big-data/datalakes-and-analytics/

The Internet Society and IETF announce new strategic agreement on Open Standards work

November 30, 2020 /

The Internet Society and the Internet Engineering Task Force (IETF) announced a new long term strategic agreement that will ensure the continuity of the IETF’s critical work in creating open standards that make the Internet work better. The IETF has been at the center of technical innovation for the global Internet for nearly 35 years. Open standards allow devices, services, and applications to work together across the tens of thousands interconnected networks that make up the Internet and everything it enables such as the Web and email.

The Internet Society provided the organisational home for the IETF until 2018 when the IETF Administration LLC was formed to support its ongoing operations. Under the new six-year agreement, the Internet Society will provide financial support in two areas: IETF’s work through the LLC, and a new donor match program in support of upcoming fundraising efforts for the IETF Endowment, which is designed to provide long term funding for the IETF’s mission. The new agreement will go into effect on January 1, 2021 and run through December 31, 2026.

https://www.internetsociety.org, https://www.ietf.org

The Open Group and ITU to develop digital government strategies

November 24, 2020 /

The Open Group, the vendor-neutral technology consortium, and the International Telecommunication Union (ITU), the United Nations agency for information and communication technologies (ICTs), announced a collaboration to accelerate public service innovation and transformation for better citizen outcomes and optimal utilization of ICT infrastructure. By working together, The Open Group and ITU will aim to promote, guide, and build capabilities for digital government strategies and citizen-centric Enterprise Architecture (EA) across the globe. With the necessary guidance and materials, resource-constrained countries will be better placed to convert digital strategies into implementable large-scale systems. As such, the strategic alliance between ITU and The Open Group will fill the gap between digital investments and best practice architectural approaches for the achievement of the SDGs.

The work undertaken as part of the collaboration will be executed by The Open Group Government EA Work Group. The Work Group will develop processes that enable seamless information flow across various government ecosystems, making existing EA resources – including guides, frameworks, use cases, and methodologies – easier to use and available to all. Through providing access to these resources, both The Open Group and ITU will help governments to build the capabilities needed to implement architectural approaches at scale, based on their country-specific needs.

https://www.opengroup.org, https://www.itu.int/en/Pages/default.aspx

Ontotext Platform 3.3 streamlines knowledge graph lenses & GraphQL interfaces

November 20, 2020 /

The new version of the Platform introduces a web-based administration tool that enables engineering teams to generate, enrich, validate and manage knowledge graph schemas, and comes with a major new component included, Ontotext Platform Workbench, a web-based administration interface to the platform. This simplifies the work of the subject matter experts by lowering the burden of knowing all platform configuration endpoints and commands and streamlines adoption with a graphical interface. The Ontotext Platform Workbench provides the ability to generate, validate and manage schemas using a wizard that guides the user through the process step by step.

Schemas, comprised of declarative definitions of semantic objects, are at the heart of the zero-code approach for access and management of knowledge graphs in Ontotext Platform 3. These schemas act like a lens to focus on specific parts of a large-scale knowledge graph, enabling querying and updates via GraphQL interfaces. This makes it easier for application developers to access knowledge graphs without tedious development of back-end APIs or complex SPARQL. The underlying Semantic Object service implements an efficient GraphQL to SPARQL translation as well as a generic configurable security model.

In earlier versions the setup of the Platform license required some technical skills, and often users without a strong IT operations background had difficulties configuring the license in the docker compose file. With the new version all users can use the Workbench and set up the license much easier and avoid issues related to license path, operation system specifics and others.

https://www.ontotext.com/company/news/ontotext-platform-3-3-streamlines-the-building-of-knowledge-graph-lenses-and-graphql-ui/

VMware announces enterprise blockchain platform

November 18, 2020 /

VMware, Inc. announced the commercial availability of VMware Blockchain to provide a digital foundation so enterprises can build business networks and deploy business-critical decentralized applications. Recognizing the importance of day-0 and day-2 operations to enable enterprises to bring their blockchain solutions to production, VMware provides a comprehensive set of operational capabilities, including ease of deployment, monitoring, management, upgradability, and world class support. VMware Blockchain’s Scalable Byzantine Fault Tolerance (SBFT), an enterprise-grade consensus engine developed internally by VMware Research, is designed to solve the problems of scale and performance in blockchain solutions while preserving fault-tolerance and defense against malicious attacks. SBFT maintains decentralized trust and supports ongoing governance in multi-party networks.

VMware Blockchain brings a layered architecture that decouples the ledger from the smart contract language. VMware Blockchain supports DAML, an open source smart contract language created by VMware Blockchain technology partner, Digital Asset. VMware Blockchain’s platform architecture includes a virtual smart contract execution engine that is designed to easily extend the platform to support additional smart contract languages.

https://www.vmware.com/blockchain

Clarabridge CX Analytics now on Oracle Cloud Marketplace

November 18, 2020 /

Clarabridge, provider of AI text and speech analytics and a member of Oracle PartnerNetwork (OPN), announced that Clarabridge Customer Experience Analytics is available on Oracle Cloud Marketplace and is fully integrated into Oracle Cloud CX Service. Clarabridge CX Analytics enables companies to understand customer feedback from multiple data sources including calls, surveys, messages, chats, emails and social media platforms. By leveraging Clarabridge CX Analytics in combination with Oracle Cloud CX Service, users can connect to and analyze hundreds of customer feedback sources in one place and route insights into Oracle Cloud CX Service. An integrated view of customer feedback allows business users to identify how customer sentiment, effort, intent, and emotion impacts purchasing decisions, customer lifetime value, churn and overall satisfaction.

http://www.oracle.com/partnernetwork

Snowflake announces support for unstructured data and more

November 17, 2020 /

Snowflake announced new features to enable Snowflake customers to work with more types of data, have a more powerful developer experience, deliver more control over data, and access data services within the Data Cloud. Features include:

Unstructured Data – In addition to structured and semi-structured data, Snowflake announced support for unstructured data such as audio, video, pdfs, imaging data and more – which will provide the ability to orchestrate pipeline executions of that data. Unstructured data management in Snowflake means customers will be able to avoid accessing and managing multiple systems, deploy fine-grained governance over unstructured files and metadata, and gain more complete insights. This feature is currently in private preview.

Snowpark – A new developer experience that will allow data engineers, data scientists, and developers to write code in their languages of choice, using familiar programming concepts, and then execute workloads such as ETL/ELT, data preparation, and feature engineering on Snowflake. Snowpark is currently available in testing environments only.

Data Services on Snowflake Data Marketplace – Snowflake Data Marketplace enables any Snowflake customer to discover and access live, ready-to-query, third-party data sets from more than 100 data providers, without needing to copy files or move the data. Now the marketplace also features data service providers.

Row Access Policies – Customers will be able to advance their data governance across all data objects and workloads in Snowflake. Row access policies will give Snowflake customers the ability to create policies for restricting returned result sets when queries are executed. Row access policies are designed to mitigate risk, improve governance, and help organizations better adhere to regional and industry-specific data privacy regulations. Snowflake’s row access policies feature is expected to be in private preview later this year.

https://www.snowflake.com

Information Model
Gilbane Advisor 6-9-21 — stacks & aggregation, JavaScript for data, news

Stardog announces cloud-native Enterprise Knowledge Graph Platform

November 17, 2020 /

Stardog announced Stardog Cloud, cloud-native Enterprise Knowledge Graph Platform. Stardog Cloud connects data in every cloud as well as on-premise environments. Deployed as a managed service, Stardog Cloud transforms existing enterprise data infrastructure into a comprehensive data fabric and answers complex queries across data silos, unifies data across the enterprise ecosystem based on its meaning, and context to create a connected network of knowledge. Highlights of Stardog include:

  • Data Virtualization: Allows organizations to leave data within existing data sources and silos and query it where it lives – whether on-premise or in the cloud – and perform complex queries across silos.
  • Semantic Models: Rationalizes the meaning between legacy applications on-premise, and new remote, cloud or on-premise applications in a flexible scalable way. Seamlessly supports multiple apps and data models in order to bring context to data and support better decision-making.
  • Inference Engine: Connects data without having to rely only on explicit key matching. Leverages machine learning and inferencing regardless of the data domain or subject area and then uses this rich web of information to discover new relationships.

https://www.stardog.com

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