Graph Gurus Episode 37: Modeling for Kaggle COVID-19 DatasetTigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-37
In this Graph Gurus Episode, we:
-Learn how to process text and extract entities (words and phrases) as well as classes linking the entities using SciSpacy, a Natural Language Processing (NLP) tool.
-Import the output of NLP and semantically link it in TigerGraph
-Run advanced analytics queries with TigerGraph to analyze the relationships and deliver insights
Graph Gurus Episode 35: No Code Graph Analytics to Get Insights from Petabyte...TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-35
By attending this webinar you will:
-Learn how to use TigerGraph’s no-code capabilities;
-Understand how TigerGraph is built for scale and performance;
-Get a deep dive into TigerGraph 3.0 feature enhancements.
Graph Gurus Episode 37: Modeling for Kaggle COVID-19 DatasetTigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-37
In this Graph Gurus Episode, we:
-Learn how to process text and extract entities (words and phrases) as well as classes linking the entities using SciSpacy, a Natural Language Processing (NLP) tool.
-Import the output of NLP and semantically link it in TigerGraph
-Run advanced analytics queries with TigerGraph to analyze the relationships and deliver insights
Graph Gurus Episode 35: No Code Graph Analytics to Get Insights from Petabyte...TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-35
By attending this webinar you will:
-Learn how to use TigerGraph’s no-code capabilities;
-Understand how TigerGraph is built for scale and performance;
-Get a deep dive into TigerGraph 3.0 feature enhancements.
Graph Gurus Episode 34: Graph Databases are Changing the Fraud Detection and ...TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-34
During this webinar we:
-Examine how graph analytics can lower the total cost of fraud;
-Describe how graph analytics can improve credit card fraud detection;
-Explore the application of graph analytics to an anti-money laundering use case.
Graph Gurus 24: How to Build Innovative Applications with TigerGraph CloudTigerGraph
This Graph Gurus episode walks you through the development of a simple application based on the TigerGraph Cloud Customer 360 Starter Kit. Specifically, we will:
-Share the use case for the Customer 360 Starter Kit.
-Walk you through a step-by-step tutorial based on the sample dataset, the prepackaged GSQL queries, the GraphStudio UI flow from the Starter Kit, and the integration process with a simple front-end application.
-Demonstrate the end-to-end full stack application development based on TigerGraph Cloud.
Graph Gurus Episode 27: Using Graph Algorithms for Advanced Analytics Part 2TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-27
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms. Join us for Part 2 of our five-part webinar series on using graph algorithms for advanced analytics.
By attending this webinar you will:
- Hear about use cases for centrality graph algorithms
- Learn how to select the right algorithm for your use case
- Be able to run and tailor GSQL graph algorithms
Graph Gurus Episode 17: Seven Key Data Science Capabilities Powered by a Nati...TigerGraph
This webinar will demonstrate seven key data science capabilities using TigerGraph’s intuitive GUI, GraphStudio and GSQL queries. In this episode, we:
-Share the capabilities and tie those to specific use cases across healthcare, pharmaceutical, financial services, Telecom, Internet and government industries.
-Walk you through a sample dataset, GraphStudio UI flow, and GSQL queries demonstrating the capabilities.
-Cover client case studies for Amgen, Intuit, China Mobile, Santa Clara County, and other enterprise customers
Petabytes to Personalization - Data Analytics with Qubit and LookerRittman Analytics
How do you turn petabytes of customer data into a personalized retail and e-commerce experience? With Qubit, the customer personalization platform that (with the help of Google Cloud Platform and Looker) gives customers the power of real-time ad-hoc analytics. Because of the scale of data enabled by GCP and the abstraction layer of Looker, Qubit customers are able to use their Live Tap product to to make every visitor experience relevant and engaging.
Commercial Analytics at Scale in Pharma: From Hackathon to MVP with Azure Dat...Databricks
GSK are a science-led global healthcare company with a special purpose: to help people do more, feel better, live longer.
We have three global businesses that discover, develop and manufacture innovative pharmaceutical medicines, vaccines and consumer healthcare products.
In this talk i will share our experience in the Pharmaceutical business delivering commercial analytics going from hackathon to MVP.
From the initial ideas and business discussions through delivery of a hackathon as an accelerator, on to building an MVP. Using the Azure cloud platform and Databricks to rapidly ingest data and prototype.
I will touch on the challenges, opportunities and learning points of the process we went through to deliver commercial analytics at scale in Pharma.
Using Graph Algorithms for Advanced Analytics - Part 2 CentralityTigerGraph
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.
At the CodeTalks conference 2017 in Hamburg, LeanIX presented their lessons learned for GraphQL, a new alternative for building REST APIs which was introduced by Facebook.
Graph Gurus Episode 34: Graph Databases are Changing the Fraud Detection and ...TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-34
During this webinar we:
-Examine how graph analytics can lower the total cost of fraud;
-Describe how graph analytics can improve credit card fraud detection;
-Explore the application of graph analytics to an anti-money laundering use case.
Graph Gurus 24: How to Build Innovative Applications with TigerGraph CloudTigerGraph
This Graph Gurus episode walks you through the development of a simple application based on the TigerGraph Cloud Customer 360 Starter Kit. Specifically, we will:
-Share the use case for the Customer 360 Starter Kit.
-Walk you through a step-by-step tutorial based on the sample dataset, the prepackaged GSQL queries, the GraphStudio UI flow from the Starter Kit, and the integration process with a simple front-end application.
-Demonstrate the end-to-end full stack application development based on TigerGraph Cloud.
Graph Gurus Episode 27: Using Graph Algorithms for Advanced Analytics Part 2TigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-27
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms. Join us for Part 2 of our five-part webinar series on using graph algorithms for advanced analytics.
By attending this webinar you will:
- Hear about use cases for centrality graph algorithms
- Learn how to select the right algorithm for your use case
- Be able to run and tailor GSQL graph algorithms
Graph Gurus Episode 17: Seven Key Data Science Capabilities Powered by a Nati...TigerGraph
This webinar will demonstrate seven key data science capabilities using TigerGraph’s intuitive GUI, GraphStudio and GSQL queries. In this episode, we:
-Share the capabilities and tie those to specific use cases across healthcare, pharmaceutical, financial services, Telecom, Internet and government industries.
-Walk you through a sample dataset, GraphStudio UI flow, and GSQL queries demonstrating the capabilities.
-Cover client case studies for Amgen, Intuit, China Mobile, Santa Clara County, and other enterprise customers
Petabytes to Personalization - Data Analytics with Qubit and LookerRittman Analytics
How do you turn petabytes of customer data into a personalized retail and e-commerce experience? With Qubit, the customer personalization platform that (with the help of Google Cloud Platform and Looker) gives customers the power of real-time ad-hoc analytics. Because of the scale of data enabled by GCP and the abstraction layer of Looker, Qubit customers are able to use their Live Tap product to to make every visitor experience relevant and engaging.
Commercial Analytics at Scale in Pharma: From Hackathon to MVP with Azure Dat...Databricks
GSK are a science-led global healthcare company with a special purpose: to help people do more, feel better, live longer.
We have three global businesses that discover, develop and manufacture innovative pharmaceutical medicines, vaccines and consumer healthcare products.
In this talk i will share our experience in the Pharmaceutical business delivering commercial analytics going from hackathon to MVP.
From the initial ideas and business discussions through delivery of a hackathon as an accelerator, on to building an MVP. Using the Azure cloud platform and Databricks to rapidly ingest data and prototype.
I will touch on the challenges, opportunities and learning points of the process we went through to deliver commercial analytics at scale in Pharma.
Using Graph Algorithms for Advanced Analytics - Part 2 CentralityTigerGraph
What does finding the best location for a warehouse/office/retail store have in common with finding the most influential person in a referral network? Answer: they are both Centrality problems and can be solved with graph algorithms.
At the CodeTalks conference 2017 in Hamburg, LeanIX presented their lessons learned for GraphQL, a new alternative for building REST APIs which was introduced by Facebook.
Reuse Strategy for MBSE Data - GPDIS 2022SodiusWillert
The largest asset of any organization making today’s complex products is the expertise found in the engineering repositories and documents that govern the development of those products. The only way for these organizations to continue to compete and innovate in the future is to reuse much of this information.
Depending on the structure of these assets, the changes in the engineering processes going forward, and the intended use of that information, the way in which that data will be reused will be different.
This presentation will explore the different use cases for reusing data and present best practices for implementing reuse strategies in the organization.
Learn more: https://bit.ly/3MqJXYS
Data Natives Frankfurt v 11.0 | "Competitive advantages with knowledge graphs...Dataconomy Media
The challenges of increasing complexity of organizations, companies and projects are obvious and omnipresent. Everywhere there are connections and dependencies that are often not adequately managed or not considered at all because of a lack of technology or expertise to uncover and leverage the relationships in data and information. In his presentation, Axel Morgner talks about graph technology and knowledge graphs as indispensable building blocks for successful companies.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Predictive Analytics Project in Automotive IndustryMatouš Havlena
Original article: http://www.havlena.net/en/business-analytics-intelligence/predictive-analytics-project-in-automotive-industry/
I had a chance to work on a predictive analytics project for a US car manufacturer. The goal of the project was to evaluate the feasibility to use Big Data analysis solutions for manufacturing to solve different operational needs. The objective was to determine a business case and identify a technical solution (vendor). Our task was to analyze production history data and predict car inspection failures from the production line. We obtained historical data on defects on the car, how the car moved along the assembly line and car specific information like engine type, model, color, transmission type, and so on. The data covered the whole manufacturing history for one year. We used IBM BigInsights and SPSS Modeler to make the predictions.
Streamlining Feature Engineering Pipelines with Open SourceSoledad Galli
We introduce the main feature engineering and creation techniques used to prepare variables to train machine learning models. We then present the challenges of preparing feature engineering pipelines. And finally, we discuss how open sources libraries can help us streamline our machine learning pipelines.
Design Dynamics: Elevating UiPath Apps with UX WireframesDianaGray10
Become a better developer through proper preparation. Our session focuses on three key areas: discovering the best wireframing tools, mastering their usage for optimal design, and effectively translating these designs into functional UiPath Apps. Gain practical insights and skills to elevate your app development process.
This session will cover the following topics:
• Preferred Wireframing Tools
• Translate Requirements to Designs
• Using that wireframe to speed up app development
Speaker:
David Kroll, Director, Product Marketing @Ashling Partners and UiPath MVP
Cloud Task Execution at Scale with example from quant financeJohn Holden
Calculating CVA (Credit valuation adjustment) in a timely manner poses a big performance challenge for financial institutions. The number of trades encompasses the entire institution, not just an individual desk’s book(s). It is needed intraday as positions and markets update.
In this talk we highlight the tools needed to achieve the performance required to deliver CVA on time. We will focus on Origami – a DAG Execution Engine. Origami is a light-weight task execution framework which is easy to use and maintain. Users combine tasks into a DAG. Origami can execute the DAG on an ad hoc cluster of workstations on the local network, on a dedicated in-house grid, on production cloud, or on a hybrid of all these.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
Today's Speakers
Benyue (Emma) Liu
Product Manager
● BS in Engineering from Harvey Mudd College, MS
in Engineering Systems from MIT
● Prior work experience at Oracle and MarkLogic
● Focus - Cloud, Containers, Enterprise Infra,
Monitoring, Management, Connectors
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3. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
1 No Code Graph Analytics Introduction
Demo
Use Case and Graph Features
Today’s Outline
3
2
3
7. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
No Code Migration from Relational Database to TigerGraph
● User Friendly and Speedy
Transition from Relational
Data Stores to TigerGraph via
Simple Clicks
● Intuitive Steps to New Graph
Model through Auto
Generated Schema and Data
Mapping
● Accelerated ETL Process and
Built-in Data Loading
● Flexibility and Agility via
Customizable Options
Overview Video - tigergraph.com/nocode
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8. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
No Code Graph Analytics with Visual Query Builder
● Intuitive Approach to Graph
Analytics
○ Translate a question into an
executable query and get to the
Graph Insights. E.g. “I want to find all
the people who are friends of me and
my manager in Company X and Y”
● Speed to Business Value for Graph
Use Cases
○ Output Graph Insights through Graph
Patterns
● Decrease Learning Curve to Graph
Analytics
○ Drag and Drop - No need to write
GSQL Queries
Overview Video - tigergraph.com/nocode
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9. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
TigerGraph No Code Data Workflow
GraphStudio - TigerGraph Visual SDK
No Code
RDBMS
to Graph
Auto Populated
Graph Schema
Graph Data Mapping
Business
Intelligence
Analytics
Visualization
Dashboards
Reports
Data
Warehouses
Master Data
Stores
Machine
Learning
Relational
Data
Stores
No Code
Visual Query
Builder
Drag-and-Drop
Graph Patterns
Auto Generated
GSQL Queries
RESTful
APIs
Relational
Schema
& Data
DBs
Spark
Streams
Files ETL Data
Loader
User Customized
Graph Schema
Graph Data Mapping
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10. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
Who Are We Designing For?
Business Analysts who want to
explore common Graph
Analytics
Developers who are learning
and using TigerGraph and
GSQL, and want to reuse
Graph Patterns
Data Architects who are
transitioning from RDBMS to
TigerGraph, and want
auto-generated Graph
Schemas
Data Scientists who want to
examine Graph Features for
Machine Learning Models
Ease of
Use
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12. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
Anti Fraud and Money
Laundering Starter Kit
at tgcloud.io
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13. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
Count the number
of trust_score=0 of
referrer for a given
user, order the
users from high to
low according to
the low score
referrer count and
output the top 10
Graph Pattern: Low Score Referrer Count
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14. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
For a given payment
instrument, find user with
trust_score larger than
0.5, and their
transactions which are
larger than 10
Graph Pattern: High Transactions Trusted Users For A Given Device
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15. | GRAPHAIWORLD.COM | #GRAPHAIWORLD | 15
Find users who have
trust_score less than
0.15 and connected
to payment
instrument with
trust_score less than
0.1; and calculate
users’ total received
transaction amount,
and find the top 10
users with the
highest total amount
of transaction
Graph Pattern: High Transaction Amount For Low Trust Score
16. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
Find users who are in the
6 hop transaction ring
(potentially fraud ring)
Graph Pattern - Fraud Ring
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17. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
Build Graph Patterns In Simple Steps
Step 1: Analyze Your Inquiry
Step 2: Identify Entities and Relationships
Step 3: Pick and Merge Vertices and Edges
Step 4: Add Filters/Aggregations/Orders/Limits/Parameters/Widgets
Step 5: Output and Verify Your Results
Overview Video - tigergraph.com/nocode
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19. | GRAPHAIWORLD.COM | #GRAPHAIWORLD |
No Code Visual Query Builder and RDBMS to Graph Tool
Faster and Intuitive Graph Feature Design
Call to Action: Try V3.0 No Code GUI at tgcloud.io
19
20. Get Started for Free
● Try TigerGraph Cloud
● Take a Test Drive - Online Demo
● Get TigerGraph Certified
● Join the Community
@TigerGraphDB /tigergraph /TigerGraphDB /company/TigerGraph
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