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Category: Computing & data (Page 4 of 56)

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.

MongoDB unveils vision for a developer data platform

MongoDB, Inc. unveiled its developer data platform vision with new capabilities, helping development teams with a wider set of use cases, servicing more of the data lifecycle, optimizing for modern architectures, and implementing sophisticated levels of data encryption. MongoDB 6.0 has extended its approach of working with data beyond operational and transactional use cases to serve search and analytics use cases within a unified platform and consistent developer experience to reduce the complexity of data infrastructure required for modern applications. Upcoming capabilities include:

  • Making it easier for developers to leverage in-app analytics. Column Store Indexes will enable users to create and maintain a purpose-built index that speeds up many analytical queries without requiring changes to the document structure. 
  • Time series collections will support secondary indexes on measurements, and feature read performance improvements and optimizations for sorting time-based data more quickly.
  • With Search Facets, developers are able to rapidly build search experiences that allow end users to more seamlessly browse, narrow down or refine their results.

MongoDB also announced new products and capabilities that enable development teams to better analyze, transform, and move their data in Atlas while reducing reliance on batch processes and ETL jobs.

https://www.mongodb.com/new

Algolia launches additional AI models in Algolia Recommend

Algolia, an API-First Search & Discovery platform, unveiled additional AI (artificial intelligence) models and capabilities in its Recommend Spring Release 2022. Algolia Recommend introduces AI models powered by behavioral insights. When coupled with Algolia’s fast indexing capabilities, customers are able to immediately put their most relevant and up to date content into motion for end-users. From a single dashboard, merchandisers, digital content managers, or digital business leaders can choose the model that is right for them, deploy it, and track the results. The release includes:

  • Popular Trends – AI models that detects emerging trends based on users’ behavioral data as they interact with various brands, categories of products and content, and topics of interest.
  • Business Rules – Low-code/no-code functionality for controlling AI and activating unique business strategies. This provides greater flexibility for category merchandisers, online retail strategists, and content specialists to generate new recommendations.
  • Hybrid Recommend Engine – This is a combination of collaborative filtering algorithms and content-based filtering algorithms to increase the relevancy and accuracy of recommendations. Recommendations can be presented to users as the content-based data is indexed. Availability of behavioral information either at this initial stage or later can further help fine-tune and enrich the quality of recommendations.

https://www.algolia.com/about/news/new-ai-based-recommendation-models-overcome-cold-start-challenge/

Stardog updates enterprise knowledge graph platform

Stardog, an Enterprise Knowledge Graph platform provider, unveiled Stardog 8.0, with new innovations to streamline data exploration and discovery for all citizen data users.

The new Advanced Query tool in Stardog Explorer empowers citizen data users to ask complex business questions via the semantic layer more easily. By removing the need to learn a graph query language, users can self-serve from across their enterprise data landscape. Also new in Explorer is the ability to see what’s in your Stardog database by browsing data source and virtual graph metadata in the new Stardog Data Catalog graph. Additional updates include:

  • Project resources (imported CSV files and virtual graphs) can be previewed and refreshed to see updates in the data.
  • Enhanced support for project collaboration through exporting, importing, and duplicating projects.
  • Our new query profiler is available and shows you the query plan, allows you to interrupt slow queries, and can show you partial results.
  • Error notifications now stay displayed and the error text can be copied for troubleshooting.
  • A new Stardog Data Catalog graph is built from the metadata about data sources and virtual graphs within a Stardog database.
  • Improved performance for querying multiple virtual graphs and SPARQL update queries.

https://www.stardog.com/blog/introducing-stardog-8.0/

MariaDB and MindsDB collaborate on machine learning

MariaDB Corporation and MindsDB, a provider of in-database machine learning tools, together announced a technology collaboration that makes machine learning predictions easy and accessible to cloud database users.  By using MindsDB in SkySQL, MariaDB’s fully managed cloud database service, data science and data engineering teams can increase their organization’s predictive capabilities to plan for and address business issues. MariaDB database users will now be able to add machine learning based predictions directly into their datasets stored in SkySQL. This simplifies the task of analyzing and predicting future trends, putting machine learning capabilities into the hands of MariaDB users, no matter their role. The use cases for business predictions cut across every business function such as finance, sales, risk analysis, logistics, operations, and marketing.

https://mindsdb.comhttps://mariadb.com/products/skysql/

SimInsights launches no-code XR authoring platform

SimInsights announced general availability (GA) of HyperSkill, a no-code 3D simulation software for Virtual and Augmented Reality and Artificial Intelligence powered training. HyperSkill was created to enable instructional designers and subject matter experts to author immersive, interactive and intelligent training content without having to learn programming or technical skills in machine learning and artificial intelligence. HyperSkill enables non-programmers to author VR/AR/AI-powered content, automatically optimize it and publish it across many devices and audiences and collect and visualize experience data for assessment and evaluation. HyperSkill has been used by customers in healthcare, manufacturing and education and has been developed with their close collaboration and feedback. HyperSkill includes:

  • No-code authoring: Faster and cheaper to author compared to programming with 3D game engines
  • Reusable repository: Growing public repository of XR-ready 3D assets, including virtual environments, virtual persons and virtual objects
  • AI-Powered: Natural Language Processing (NLP) and Computer Vision to simplify authoring, enhance experiences and unlock new use cases
  • Cross Platform: Author once, deliver everywhere, including emerging AR/VR headsets as well as web, desktop and mobile platforms.
  • Multiplayer: Enables synchronous learning scenarios, including collaborative and instructor-led training.

HyperSkill is a SaaS (Software as a Service) product available for a free trial.

https://siminsights.com/hyperskill/

Deepnote data notebook comes out of beta

Deepnote, an early-stage startup backed by Accel and Index Ventures, launched version 1.0, opening up to the general availability of collaborative data science notebooks to data teams. Data team efficacy relies on the process of access to, exploration of, and collaboration around data—for example, when an organization needs to make a data-informed decision, it will rely on data teams to explore datasets and share insights that lead to action. This process is siloed within a single department, findings are inconsistent, and insights quickly become out of date. Deepnote makes data collaboration a reality improving three pain points of traditional data science notebooks:

  1. Collaboration: Sharing analysis and collaboration is as easy as sending a link because everything is hosted in a fully-managed cloud environment. Analysis is done in real-time with multiplayer mode if needed. And everything is organized and hosted in a single place.
  2. Connectivity: With dozens of native integrations to tools in the modern data stack—Snowflake, BigQuery, Postgres, S3, GitHub—data teams can seamlessly connect to the tools they’re already using.
  3. Productivity: Underserved data analysts and scientists are now equipped with productivity features—reproducibility, autocomplete, scheduling, version control—to do better work in less time.

https://deepnote.com/

Ontotext gets growth funding to meet demand for graph technology

Ontotext, an enterprise knowledge graph technology and semantic database engine provider, announced that Integral Venture Partners (Integral), a capital investment firm, announced this week that an Integral–led investment consortium has entered into a definitive agreement with our mother company Sirma Group Holding, to acquire Ontotext as a global supplier of a deep-tech enterprise software, operating in the graph databases space and the Artificial Intelligence market. The Integral-led international investment consortium also includes PortfoLion Capital Partners, the venture capital and private equity arm of OTP Bank, and Carpathian Partners, a specialized technology-focused investment platform based in London. The Consortium’s investment in excess of €30 million will be structured as a combination of a capital increase and a secondary share purchase. The transaction is not subject to any regulatory approvals and is expected to close by August 2022.

Supported by new capital, Ontotext will accelerate its international expansion and go-to-market operations, focusing on the US market. We will invest in further development of our vertical product stack — end-to-end solutions for specific industries starting with Life Sciences and Financial Services. Last but not least, we will further strengthen our position as global provider of knowledge graph technology.

https://www.ontotext.com

Komprise automates unstructured data discovery with Smart Data Workflows

Komprise announced Komprise Smart Data Workflows, a systematic process to discover relevant file and object data across cloud, edge and on-premises datacenters and feed data in native format to AI and machine learning (ML) tools and data lakes.

Komprise has expanded Deep Analytics Actions to include copy and confine operations based on Deep Analytics queries, added the ability to execute external functions such as running natural language processing functions via API and expanded global tagging and search to support these workflows. Komprise Smart Data Workflows allow you to define and execute a process with as many of these steps needed in any sequence, including external functions at the edge, datacenter or cloud. Komprise Global File Index and Smart Data Workflows together reduce the time it takes to find, enrich and move the right unstructured data. Komprise Smart Data Workflows are relevant across many sectors. Here’s an example from the pharmaceutical industry.

https://www.komprise.com/komprise-automates-unstructured-data-discovery-with-smart-data-workflows/

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