You gather your data, choose the best layout, and find an analysis method that gets your users the insight they need. But before you can declare victory, you need to assign meaning to the objects in your graph. Adding labels to nodes and edges might seem easy enough, but sometimes—especially with graph edge labels—their placement can cause readability issues. Since a “messy” presentation can compromise the message and meaning of your graph visualization, you’ll want to do all you can to prevent these.
We can’t quit you, baseball! The season might be over, but we want more. So, we’re dipping into the baseball data to see what else we can learn. Read on for one more run around the bases!
Put Me In, Coach
This season, all anyone talked about was home runs. There were 6,770 homers hit during the regular season this year. That’s 665 MORE than the previous record! And exactly half of the teams in the league set franchise home run records. Holy homer!
But, do all these home runs lead teams to the playoffs? We looked at the past five years (2014-2019), pulling the top five teams for home runs in each year. Edge colors indicate:
Blue Edge: Appeared in playoffs
Green Edge: Won the World Series
Orange Edge: Neither won nor appeared in the playoffs
Here’s the result:
How do other stats affect a team’s success? To find out, we layered in Earned Run Average (ERA), Strikeouts (SO), and Runs Batted In (RBI) one by one.
2019 marks the 115th year for World Series baseball in the United States. In a year when home run records were smashed with authority, we thought we’d take a look at America’s pastime through the lens of graph visualization.
Simple trade history of Washington Nationals star pitcher “Mad Max” Scherzer
Over the next several weeks, we’ll ask two fundamental questions about Major League Baseball (MLB):
Who’s on the playoff team rosters? Do most players start on other teams? Which players have been traded and from which teams?
What’s with all the homers? 6,770 home runs were hit during the regular season this year–a new record! Sure, they’re fun to watch, but do they lead teams to the playoffs or the World Series?
Let’s pull some data, make some graphs, and see what we can figure out. Today we’ll:
walk through the over-arching process, using MLB datasets as our subject
drop some knowledge about how the graphs were created
show how we used the customization tools resident in Tom Sawyer Perspectives to easily tailor the graphs to show important statistics we can use in our analysis
At AWS re:Invent, we revealed our partnership with Amazon Web Services (AWS) that supports the integration of Tom Sawyer Graph Database Browser and Tom Sawyer Perspectives with Amazon Neptune. Customers can take advantage of the integration and build applications with Perspectives for Amazon’s newest cloud-based graph database service, Neptune.
Amazon Neptune is a fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. Our flagship product, Tom Sawyer Perspectives, is designed specifically for building and deploying web applications that visualize and analyze highly related datasets. The two products are tightly integrated.
Tom Sawyer Graph Database Browser makes it easy for anyone to load data into Amazon Neptune and start analyzing the graphs. The application is the first and only to visualize and analyze data in Amazon Neptune as well as automatically execute native Neptune load commands to import data directly from Amazon Simple Storage Service (S3) into the database.
Together with AWS Amazon Neptune, our deep integration creates a super convenient and easy-to-use cloud-based, fast, and scalable graph database and visualization solution.
Want to try visualizing AWS Amazon Neptune Data?
It’s easy to test drive the Tom Sawyer Graph Database Browser using a free trial on AWS Marketplace. It works with other graph databases too.
The Graph Database Browser includes a number of unique capabilities that go beyond a typical database browser to enhance the AWS Amazon Neptune user experience. Below are just a few.
I recently learned about Dr. Ioannis Tollis’ impressive career in graph analytics and computer science. He is a pioneer in graph analytics technology, and a Professor at the University of Crete. Dr. Tollis, Ph.D., spoke with us about exciting developments and trends in the field. Read on to find out more.
You’ve been a leader in the global graph analytics technology community since before it hit the mainstream. What are some of the trends you’ve noticed over time?
There are many trends in graph analytics technology that appeared in the past. Some have disappeared, while others are still with us today.
We are excited to announce that you can now download the Beta release of Tom Sawyer Perspectives, Version 8.0, Java Edition.
This version represents a strategic upgrade to our core graph and data visualization architecture and adds new enterprise solutions for systems engineers and architects, and business process analysts and users. With an underlying Spring architecture, this version lays the foundation for further innovation and enterprise-wide deployment.
The Beta release is your chance for early access to powerful new capabilities and solutions, and to influence the future direction of Tom Sawyer Perspectives.
Version 8.0 Delivers Web-based, Automated Business Process Modeling (BPM) and Model-based Engineering (MBE) Solutions Built on Tom Sawyer Perspectives
Our team members across three continents have invested more than 50,000 hours designing, developing, and testing to bring this new version to you.
