Next-Generation Data Fabric Architecture
With the ever increasing velocity and diversity of data, organizations need to catalog, manage and apply advanced analytics across diverse, distributed sources in order to gain critical insights and enable high-value business decisions.
Introducing Vyasa Layar, a next-generation data fabric architecture that can be deployed across cloud and on-prem environments to enable secure, highly-scalable data management, cataloging, metadata tagging, analytics and content indexing across the full landscape of an organization’s most critical asset, its data.
Seeing, managing and utilizing the full tapestry of data within an organization is challenging. Vyasa Layar brings together cutting edge A.I. analytics with powerful cloud-native data management and cataloging to give organizations a bird’s eye view of their critical data assets, where they reside, their metadata properties and their A.I. derived content patterns, thereby providing valuable insights for critical business use cases and management functions.
Biomedical Reference Data Fabric
Access to Pre-Built Sources
Connect to millions of life-science and legal texts for research and analytics, continuously analyzed by Layar using advanced deep learning text analytics algorithms. Retrieve answers from documents within PubMed, Clinical Trials, the US Patent and Trademark Office, and more.
Dynamic Data Connector Framework
Connect Your Data Fabric to Disparate Data Storage
The flexible and extensible nature of Layar’s data fabric architecture allows for the creation of secure connections from Layar to a wide range of sources including Google BigQuery, FHIR healthcare data repositories, AWS S3 buckets, internal document repositories, and live data streams, thereby enabling dynamic continuous availability and visibility into your aggregated Layar data fabric.
Flexible Deployment for Cloud Infrastructure
Layar can be deployed as a fully containerized Helm chart for a diverse collection of cloud environments including Google Cloud, AWS, Azure, and on-premise.
Access to the Layar APIs
Extract greater value from integrated external and internal data with Layar’s extensive set of APIs for text and small compound analytics.
Try each of our APIs in our Demo App
There is a wealth of biomedical information available in public life sciences and healthcare data sources. At Vyasa we’re building the world’s first deep learning modeled catalog of these sources for natural language querying.
Try our open source demo app live or download it on Github to build novel ways of connecting to the Vyasa Biomedical Reference Data Fabric.Try our Demo App
System Admin Panel
Manage Roles, Permissions, & Data Sources.
System administrators can manage tenants on their instance, assign privileges to users and groups, give or deny access to confidential nodes in the fabric, and monitor health checks for online status across the entire collection of nodes in their data fabric.
Named Entity Recognition (NER)
Identify & Categorize Data Fabric Concepts
Layar leverages NER to identify and categorize terms and phrases from content integrated in your data fabric. Layar identifies a wide array of life science NER concepts (proteins, cells lines, diseases, etc.) as well as several business development concepts (organizations, people, locations). NER concepts are continuously updated and refined by our deep learning models to reflect the current language of the domain, and novel terms previously not mentioned in literature (such as COVID-19).
Table & Image Extraction
Draw Deeper Insights from PDFs.
Layar automatically pulls all figures and images from a PDF article and creates a new image file for that figure, which is directly connected to the original article. For tables, users can create a CSV file from a PDF using the table extraction feature. These images and tables are then linked to the original PDF, and users can annotate the new data with additional information.
Curate Datasets for Downstream Analytics
Run a comprehensive search for data and perform the downstream analysis another day. Collect a series of documents into a single set for review until the user is ready.
Dynamic Compute Technology
Greater Efficiency with CPU/GPU Parallelization
Deep learning models typically require substantial computing power for pre-training and ingestion of novel texts, which can be expensive if a company attempts to build and train algorithms from scratch. Layar’s hybrid GPU/CPU architecture and GPU Smart Switching capabilities allow deep learning training and model utilization to run seamlessly and efficiently. If you are looking to build your own complex and powerful models, we will work with you to create an efficient parallelization strategy for your deep learning tasks.
Detection of breast cancer on screening mammography is challenging as an image classification task because cancerous tissue only represents a small portion of the tissue in the image. Retina rises to the challenge with localized tiling to deliver state of the art results.
Automated identification of emergent technologies, patents, and companies from unstructured text can be challenging. Synapse can uncover even the most obscured similarities between technologies hidden deep in documents, making discovery efficient and effective.
Deep Learning A.I. Data Fabric
A next-generation deep learning A.I. data fabric that connects diverse data stores and third-party applications via a range of built-in connectors to perform sophisticated text and image analytics.
Find more support in our help center about:
- The Basics of Layar
- System Admin Panel Support
- Setting Up Connectors in Layar