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Description
Apache TinkerPop™ serves as a framework for graph computing, catering to both online transaction processing (OLTP) with graph databases and online analytical processing (OLAP) through graph analytic systems. The traversal language utilized within Apache TinkerPop is known as Gremlin, which is a functional, data-flow language designed to allow users to effectively articulate intricate traversals or queries related to their application's property graph. Each traversal in Gremlin consists of a series of steps that can be nested. In graph theory, a graph is defined as a collection of vertices and edges. Both these components can possess multiple key/value pairs referred to as properties. Vertices represent distinct entities, which may include individuals, locations, or events, while edges signify the connections among these vertices. For example, one individual might have connections to another, have participated in a certain event, or have been at a specific location recently. This framework is particularly useful when a user's domain encompasses a diverse array of objects that can be interconnected in various ways. Moreover, the versatility of Gremlin enhances the ability to navigate complex relationships within the graph structure seamlessly.
Description
GraphAware presents Hume, an innovative platform for data analytics and intelligence analysis that leverages graph technology to convert isolated structured and unstructured data into a cohesive web, enhancing insight and decision-making capabilities. Central to Hume's functionality are the principles of knowledge graphs and graph databases, which allow for the seamless ingestion, unification, and representation of data as interconnected networks of nodes and relationships, empowering analysts and data scientists to explore, query, and visualize complex connections and concealed patterns without the necessity of mastering intricate query languages. This platform provides a unified perspective of truth across various data sources, speeds up the identification of subtle relationships and behavioral patterns, and facilitates advanced graph data science techniques such as node influence analysis, link prediction, community detection, and automated alerting, all bolstered by integrated machine learning and features from large language models (LLMs). By streamlining the access and analysis of diverse data sets, Hume not only enhances the efficiency of data exploration but also opens up new avenues for strategic decision-making.
API Access
Has API
API Access
Has API
Integrations
Apache Groovy
Docker
G.V() Gremlin IDE
Java
Node.js
Python
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Apache Software Foundation
Country
United States
Website
tinkerpop.apache.org
Vendor Details
Company Name
GraphAware
Founded
2013
Country
United Kingdom
Website
graphaware.com
Product Features
Product Features
Data Analysis
Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics