AWS Database Blog

Category: Artificial Intelligence

Supercharge your knowledge graph using Amazon Neptune, Amazon Comprehend, and Amazon Lex

Knowledge graph applications are one of the most popular graph use cases being built on Amazon Neptune today. Knowledge graphs consolidate and integrate an organization’s information into a single location by relating data stored from structured systems (e.g., e-commerce, sales records, CRM systems) and unstructured systems (e.g., text documents, email, news articles) together in a […]

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Diagram shows a Lambda function consuming the DynamoDB streams and interacting with Amazon Comprehend and with Kinesis Firehose.

Integrate your Amazon DynamoDB table with machine learning for sentiment analysis

Amazon DynamoDB is a non-relational database that delivers reliable performance at any scale. It’s a fully managed, multi-Region, multi-active database that provides consistent single-digit millisecond latency and offers built-in security, backup and restore, and in-memory caching. DynamoDB offers a serverless and event-driven architecture, which enables you to use other AWS services to extend DynamoDB capability. […]

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As we discussed earlier, the class column differentiates between bots and humans: class=1 is bot acceleration, class=0 is human acceleration.

Accelerating your application modernization with Amazon Aurora Machine Learning

Organizations that store and process data in relational databases are making the shift to the cloud. As part of this shift, they often wish to modernize their application architectures and add new cloud-based capabilities. Chief among these are machine learning (ML)-based predictions such as product recommendations and fraud detection. The rich customer data available in […]

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The following diagram is a Neptune Workbench visualization of the relationship between a document, a corporate acquisition event, and the organizations (with their roles) involved in that event.

Building a knowledge graph in Amazon Neptune using Amazon Comprehend Events

Organizations that need to keep track of financial events, such as mergers and acquisitions or bankruptcy or leadership change announcements, do so by analyzing multiple documents, news articles, SEC filings, or press releases. This data is often unstructured or semi-structured text, which is hard to analyze without a predefined data model. You can use Amazon […]

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Getting started with Amazon DocumentDB (with MongoDB compatibility); Part 4 – using Amazon SageMaker notebooks

In this post, we demonstrate how to use Amazon SageMaker notebooks to connect to Amazon DocumentDB for a simple, powerful, and flexible development experience. We walk through the steps using the AWS Management Console, but also include an AWS CloudFormation template to add an Amazon SageMaker notebook to your existing Amazon DocumentDB environment.

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Building and querying the AWS COVID-19 knowledge graph

This blog post details how to recreate the AWS COVID-19 knowledge graph (CKG) using AWS CloudFormation and Amazon Neptune, and query the graph using Jupyter notebooks hosted on Amazon SageMaker in your AWS account. The CKG aids in the exploration and analysis of the COVID-19 Open Research Dataset (CORD-19), hosted in the AWS COVID-19 data […]

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Exploring scientific research on COVID-19 with Amazon Neptune, Amazon Comprehend Medical, and the Tom Sawyer Graph Database Browser

COVID-19 is a global crisis that has affected us all. A massive research effort is underway to gain knowledge on every facet of the virus, including symptoms, treatments, and risk factors. To aid in the relief effort, AWS has created the public COVID-19 data lake, which contains various datasets you can use to help in the […]

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Let Me Graph That For You – Part 1 – Air Routes

We’re pleased to announce the start of a multi-part series of posts for Amazon Neptune in which we explore graph application datasets and queries drawn from many different domains and problem spaces. Amazon Neptune is a fast and reliable, fully-managed graph database, optimized for storing and querying highly connected data. It is ideal for online […]

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Analyze Amazon Neptune Graphs using Amazon SageMaker Jupyter Notebooks

Whether you’re creating a new graph data model and queries, or exploring an existing graph dataset, it can be useful to have an interactive query environment that allows you to visualize the results. In this blog post we show you how to achieve this by connecting an Amazon SageMaker notebook to an Amazon Neptune database. […]

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