�ݺ�ߣshows by User: lyonwj

�ݺ�ߣshows by User: lyonwj / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: lyonwj / Fri, 11 Mar 2016 21:16:17 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: lyonwj Finding Insights In Connected Data: Using Graph Databases In Journalism /slideshow/finding-insights-in-connected-data-using-graph-databases-in-journalism/59440337 nicar2016-160311211617
When dealing with datasets, journalists have many options to choose from when moving beyond Excel. Usually the first step is using a relational (or SQL) database. While a relational database can be a good choice for some datasets, data analysts today turn to new tools to gain deeper insight. This talk will show how we can use a graph database to analyze highly connected data using examples from U.S. Congressional data and political email archives. Using the U.S. Congress data, we’ll show you how to explore the dataset using Cypher, the Neo4j query language, to discover legislator activity including bill sponsorship and voting activity. Building up our knowledge of Cypher as we progress, we’ll show how you can use principles from social network analysis to find influential legislators and discover what topics legislators have influence over. Finally, we will examine how to draw insights from the Hillary Clinton email dataset, released as part of a FOIA request earlier this year. We will explore this dataset as a graph of interactions among users, answering questions like: Who is communicating with Hillary the most? What are the topics of these emails? You’ll learn how to visualize these using the Neo4j browser to quickly make sense of the data as we are exploring. The goal of this talk is to provide a demonstration of database tools that any journalist can use to explore datasets and draw insights from connected datasets.]]>
When dealing with datasets, journalists have many options to choose from when moving beyond Excel. Usually the first step is using a relational (or SQL) database. While a relational database can be a good choice for some datasets, data analysts today turn to new tools to gain deeper insight. This talk will show how we can use a graph database to analyze highly connected data using examples from U.S. Congressional data and political email archives. Using the U.S. Congress data, we’ll show you how to explore the dataset using Cypher, the Neo4j query language, to discover legislator activity including bill sponsorship and voting activity. Building up our knowledge of Cypher as we progress, we’ll show how you can use principles from social network analysis to find influential legislators and discover what topics legislators have influence over. Finally, we will examine how to draw insights from the Hillary Clinton email dataset, released as part of a FOIA request earlier this year. We will explore this dataset as a graph of interactions among users, answering questions like: Who is communicating with Hillary the most? What are the topics of these emails? You’ll learn how to visualize these using the Neo4j browser to quickly make sense of the data as we are exploring. The goal of this talk is to provide a demonstration of database tools that any journalist can use to explore datasets and draw insights from connected datasets.]]> Fri, 11 Mar 2016 21:16:17 GMT /slideshow/finding-insights-in-connected-data-using-graph-databases-in-journalism/59440337 lyonwj@slideshare.net(lyonwj) Finding Insights In Connected Data: Using Graph Databases In Journalism lyonwj When dealing with datasets, journalists have many options to choose from when moving beyond Excel. Usually the first step is using a relational (or SQL) database. While a relational database can be a good choice for some datasets, data analysts today turn to new tools to gain deeper insight. This talk will show how we can use a graph database to analyze highly connected data using examples from U.S. Congressional data and political email archives. Using the U.S. Congress data, we’ll show you how to explore the dataset using Cypher, the Neo4j query language, to discover legislator activity including bill sponsorship and voting activity. Building up our knowledge of Cypher as we progress, we’ll show how you can use principles from social network analysis to find influential legislators and discover what topics legislators have influence over. Finally, we will examine how to draw insights from the Hillary Clinton email dataset, released as part of a FOIA request earlier this year. We will explore this dataset as a graph of interactions among users, answering questions like: Who is communicating with Hillary the most? What are the topics of these emails? You’ll learn how to visualize these using the Neo4j browser to quickly make sense of the data as we are exploring. The goal of this talk is to provide a demonstration of database tools that any journalist can use to explore datasets and draw insights from connected datasets. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nicar2016-160311211617-thumbnail.jpg?width=120&height=120&fit=bounds" /> When dealing with datasets, journalists have many options to choose from when moving beyond Excel. Usually the first step is using a relational (or SQL) database. While a relational database can be a good choice for some datasets, data analysts today turn to new tools to gain deeper insight. This talk will show how we can use a graph database to analyze highly connected data using examples from U.S. Congressional data and political email archives. Using the U.S. Congress data, we’ll show you how to explore the dataset using Cypher, the Neo4j query language, to discover legislator activity including bill sponsorship and voting activity. Building up our knowledge of Cypher as we progress, we’ll show how you can use principles from social network analysis to find influential legislators and discover what topics legislators have influence over. Finally, we will examine how to draw insights from the Hillary Clinton email dataset, released as part of a FOIA request earlier this year. We will explore this dataset as a graph of interactions among users, answering questions like: Who is communicating with Hillary the most? What are the topics of these emails? You’ll learn how to visualize these using the Neo4j browser to quickly make sense of the data as we are exploring. The goal of this talk is to provide a demonstration of database tools that any journalist can use to explore datasets and draw insights from connected datasets.

Finding Insights In Connected Data: Using Graph Databases In Journalism from William Lyon

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0 Congressional PageRank: Graph Analytics of US Congress With Neo4j /slideshow/congressional-pagerank-graph-analytics-of-us-congress-with-neo4j/57473383 congressional-pagerank-graphdaytexas-160125182540
Interactions among members of any large organization are naturally a graph, yet the tools we use to analyze data about these organizations often ignore the graphiness of the domain and instead map the data into structures (such as relational databases) that make taking advantage of the relationships in the data much more difficult when it comes time for analysis. Collaboration networks are a perfect example. This talk will focus on analyzing one of the most powerful collaboration networks in the world, the US Congress. We will show how to model US Congressional data (legislators, bills, committees and the interactions among them) as a graph, how to import the data into the Neo4j graph database and how to write ad-hoc queries to answer simple questions such as “What are the topics of bills referred to committees on which California House Representatives serve?”. We will then see how we can combine a graph processing engine (Apache Spark) with Neo4j to run graph algorithms like PageRank on our data stored in Neo4j. This will allow us to identify influential legislators in the network and the topics over which they exert influence. This talk will touch on topics related to graph data modeling, graph databases, graph processing, and social network analysis that can be applied to many different domains.]]>
Interactions among members of any large organization are naturally a graph, yet the tools we use to analyze data about these organizations often ignore the graphiness of the domain and instead map the data into structures (such as relational databases) that make taking advantage of the relationships in the data much more difficult when it comes time for analysis. Collaboration networks are a perfect example. This talk will focus on analyzing one of the most powerful collaboration networks in the world, the US Congress. We will show how to model US Congressional data (legislators, bills, committees and the interactions among them) as a graph, how to import the data into the Neo4j graph database and how to write ad-hoc queries to answer simple questions such as “What are the topics of bills referred to committees on which California House Representatives serve?”. We will then see how we can combine a graph processing engine (Apache Spark) with Neo4j to run graph algorithms like PageRank on our data stored in Neo4j. This will allow us to identify influential legislators in the network and the topics over which they exert influence. This talk will touch on topics related to graph data modeling, graph databases, graph processing, and social network analysis that can be applied to many different domains.]]> Mon, 25 Jan 2016 18:25:40 GMT /slideshow/congressional-pagerank-graph-analytics-of-us-congress-with-neo4j/57473383 lyonwj@slideshare.net(lyonwj) Congressional PageRank: Graph Analytics of US Congress With Neo4j lyonwj Interactions among members of any large organization are naturally a graph, yet the tools we use to analyze data about these organizations often ignore the graphiness of the domain and instead map the data into structures (such as relational databases) that make taking advantage of the relationships in the data much more difficult when it comes time for analysis. Collaboration networks are a perfect example. This talk will focus on analyzing one of the most powerful collaboration networks in the world, the US Congress. We will show how to model US Congressional data (legislators, bills, committees and the interactions among them) as a graph, how to import the data into the Neo4j graph database and how to write ad-hoc queries to answer simple questions such as “What are the topics of bills referred to committees on which California House Representatives serve?”. We will then see how we can combine a graph processing engine (Apache Spark) with Neo4j to run graph algorithms like PageRank on our data stored in Neo4j. This will allow us to identify influential legislators in the network and the topics over which they exert influence. This talk will touch on topics related to graph data modeling, graph databases, graph processing, and social network analysis that can be applied to many different domains. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/congressional-pagerank-graphdaytexas-160125182540-thumbnail.jpg?width=120&height=120&fit=bounds" /> Interactions among members of any large organization are naturally a graph, yet the tools we use to analyze data about these organizations often ignore the graphiness of the domain and instead map the data into structures (such as relational databases) that make taking advantage of the relationships in the data much more difficult when it comes time for analysis. Collaboration networks are a perfect example. This talk will focus on analyzing one of the most powerful collaboration networks in the world, the US Congress. We will show how to model US Congressional data (legislators, bills, committees and the interactions among them) as a graph, how to import the data into the Neo4j graph database and how to write ad-hoc queries to answer simple questions such as “What are the topics of bills referred to committees on which California House Representatives serve?”. We will then see how we can combine a graph processing engine (Apache Spark) with Neo4j to run graph algorithms like PageRank on our data stored in Neo4j. This will allow us to identify influential legislators in the network and the topics over which they exert influence. This talk will touch on topics related to graph data modeling, graph databases, graph processing, and social network analysis that can be applied to many different domains.

Congressional PageRank: Graph Analytics of US Congress With Neo4j from William Lyon

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0 Natural Language Processing with Graph Databases and Neo4j /slideshow/natural-language-processing-with-graph-databases-and-neo4j/57472034 nlpneo4jdataday-160125175402
Originally presented at DataDay Texas in Austin, this presentation shows how a graph database such as Neo4j can be used for common natural language processing tasks, such as building a word adjacency graph, mining word associations, summarization and keyword extraction and content recommendation.]]>
Originally presented at DataDay Texas in Austin, this presentation shows how a graph database such as Neo4j can be used for common natural language processing tasks, such as building a word adjacency graph, mining word associations, summarization and keyword extraction and content recommendation.]]> Mon, 25 Jan 2016 17:54:02 GMT /slideshow/natural-language-processing-with-graph-databases-and-neo4j/57472034 lyonwj@slideshare.net(lyonwj) Natural Language Processing with Graph Databases and Neo4j lyonwj Originally presented at DataDay Texas in Austin, this presentation shows how a graph database such as Neo4j can be used for common natural language processing tasks, such as building a word adjacency graph, mining word associations, summarization and keyword extraction and content recommendation. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nlpneo4jdataday-160125175402-thumbnail.jpg?width=120&height=120&fit=bounds" /> Originally presented at DataDay Texas in Austin, this presentation shows how a graph database such as Neo4j can be used for common natural language processing tasks, such as building a word adjacency graph, mining word associations, summarization and keyword extraction and content recommendation.

Natural Language Processing with Graph Databases and Neo4j from William Lyon

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0 Neo4j + MongoDB. Neo4j Doc Manager for Mongo Connector - GraphConnect SF 2015 /slideshow/neo4j-mongodb-neo4j-doc-manager-for-mongo-connector-graphconnect-sf-2015/54365201 mongoconnectorgc-151026005301-lva1-app6891
Polyglot persistence is all about taking advantage of the strengths of multiple database technologies together to enhance your application. The Neo4j Doc Manager for Mongo Connector allows application developers to use the Neo4j graph database alongside the MongoDB document database to add functionality to applications.]]>
Polyglot persistence is all about taking advantage of the strengths of multiple database technologies together to enhance your application. The Neo4j Doc Manager for Mongo Connector allows application developers to use the Neo4j graph database alongside the MongoDB document database to add functionality to applications.]]> Mon, 26 Oct 2015 00:53:01 GMT /slideshow/neo4j-mongodb-neo4j-doc-manager-for-mongo-connector-graphconnect-sf-2015/54365201 lyonwj@slideshare.net(lyonwj) Neo4j + MongoDB. Neo4j Doc Manager for Mongo Connector - GraphConnect SF 2015 lyonwj Polyglot persistence is all about taking advantage of the strengths of multiple database technologies together to enhance your application. The Neo4j Doc Manager for Mongo Connector allows application developers to use the Neo4j graph database alongside the MongoDB document database to add functionality to applications. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mongoconnectorgc-151026005301-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Polyglot persistence is all about taking advantage of the strengths of multiple database technologies together to enhance your application. The Neo4j Doc Manager for Mongo Connector allows application developers to use the Neo4j graph database alongside the MongoDB document database to add functionality to applications.

Neo4j + MongoDB. Neo4j Doc Manager for Mongo Connector - GraphConnect SF 2015 from William Lyon

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0 Neo4j + MongoDB - SF Graph Database Meetup Group Presentation /slideshow/neo4j-mongodb-sf-graph-database-meetup-group-presentation/53778443 mongoslides-151010202436-lva1-app6891
Gain better insight from connected data using a document database (MongoDB) alongside a graph database (Neo4j) with the new Neo4j Doc Manager project.]]>
Gain better insight from connected data using a document database (MongoDB) alongside a graph database (Neo4j) with the new Neo4j Doc Manager project.]]> Sat, 10 Oct 2015 20:24:36 GMT /slideshow/neo4j-mongodb-sf-graph-database-meetup-group-presentation/53778443 lyonwj@slideshare.net(lyonwj) Neo4j + MongoDB - SF Graph Database Meetup Group Presentation lyonwj Gain better insight from connected data using a document database (MongoDB) alongside a graph database (Neo4j) with the new Neo4j Doc Manager project. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mongoslides-151010202436-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Gain better insight from connected data using a document database (MongoDB) alongside a graph database (Neo4j) with the new Neo4j Doc Manager project.

Neo4j + MongoDB - SF Graph Database Meetup Group Presentation from William Lyon

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0 https://cdn.slidesharecdn.com/profile-photo-lyonwj-48x48.jpg?cb=1529874353 Software developer with experience building data intensive applications for mobile and the web. Interests in iOS programming, machine learning, recommender systems, graph data processing and natural language processing applications. lyonwj.com https://cdn.slidesharecdn.com/ss_thumbnails/nicar2016-160311211617-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/finding-insights-in-connected-data-using-graph-databases-in-journalism/59440337 Finding Insights In Co... https://cdn.slidesharecdn.com/ss_thumbnails/congressional-pagerank-graphdaytexas-160125182540-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/congressional-pagerank-graph-analytics-of-us-congress-with-neo4j/57473383 Congressional PageRank... https://cdn.slidesharecdn.com/ss_thumbnails/nlpneo4jdataday-160125175402-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/natural-language-processing-with-graph-databases-and-neo4j/57472034 Natural Language Proce...