Title: Using Graph Theory to understand User Intent
Subtitle: Graph-based Natural Language Processing applied to real-time Machine Learning
Abstract:
We are in a Graph Renaissance period. The advent of high-performance free/open-source software combined with inexpensive Cloud computing platforms enable graphs of information to be manipulated and utilised at scales never before seen. While use-cases like mining social and web data with graphs are common-place, their use in Natural Language Processing has largely been overlooked. In this presentation Michael Cutler will describe how TUMRA have used graph-based NLP algorithms as a core component of their upcoming digital marketing product TUMRA Optimize.
Presenter: Michael Cutler
Bio:
Michael is the CTO co-founder of TUMRA, a Data Science startup based in Chiswick, West London. First discovering Hadoop back in 2008, Michael has been following the bleeding edge of ‘Big Data’ technology since before it was called ‘Big Data’ and has applied it to solve real-world problems.
Before starting TUMRA, Michael was a senior researcher in the R&D labs for British Sky Broadcasting, inventing new technologies and solutions for everything from Satellite, Video and Network systems through to Web and Mobile-based applications.
Website: http://tumra.com http://cotdp.com
Twitter: @tumra @cotdp
The document discusses using graphs and Neo4j for natural language processing tasks. It describes representing text as a graph by connecting adjacent words, and using this representation to find word associations and do opinion mining. Graph-based summarization and content recommendation are also covered. The resources provided give examples of opinion summarization using shortest path algorithms on the graph representation of reviews.
This document summarizes a webinar about importing crime data from Chicago into Neo4j. It discusses loading a CSV file of crime data into Neo4j using LOAD CSV and creating nodes and relationships. It also describes using Spark to preprocess the CSV into multiple Neo4j-formatted files and bulk loading them using the Neo4j Import tool. The document then covers enriching the graph with additional crime data from JSON and updating the graph with new crimes.
This document provides a summary of using Apache Spark for continuous analytics and optimization. It discusses using Spark for collecting data from various sources, processing the data using Spark's capabilities for streaming, machine learning and SQL queries, and reporting insights. An example use case is presented for social media analysis using Spark Streaming to process a real-time data stream from Kafka and analyze the data using both the Spark SQL and core Spark APIs in Scala.
Using neo4j for enterprise metadata requirementsNeo4j
Metadata is everywhere yet traditionally approaches to managing it have been disparate, siloed and often ineffective.
In this talk James will discuss the opportunities for using graph technology to address the fundamental challenges and questions of metadata management such as impact analysis, data lineage and definitions.
Data to Value are a Data Consultancy based in London that specialise in applying lean and agile techniques to complex data requirements. Connected Data is a particular focus for the firm which they see as the new frontier for data leaders.
James Phare has over 15 years experience of creating and leading data teams in various roles in Financial Services. Prior to cofounding Data Consultancy Data to Value he was Head of Information Management and Data Architecture at Man Group – one of the world’s largest Hedge funds. James started his career at Thomson Reuters after graduating in Economics from the University of York.
The document provides an introduction and agenda for a Neo4j workshop about modeling Game of Thrones data. It discusses loading Game of Thrones character, episode and house data into Neo4j and demonstrates different Cypher queries to analyze relationships between these entities such as determining the most prominent characters or how houses are related. The workshop also covers more advanced topics like aggregation queries using the WITH clause and deleting or constraining data.
Right now in institutions around the world, some of the greatest minds in computer science and statistics are coming up with amazing new algorithms and mathematically beautiful solutions. However it's entirely possible that the solutions they conceive will be impracticable in industry. The reason is simple; "the best answer is useless if it arrives too late to do anything with it". The key principle here is the compromise between 'accuracy' and 'latency'. In this talk I will describe examples where this holds true, and how I am using real-time machine learning models to solve challenges in eCommerce, Financial Services and Media companies.
http://tumra.com/blog/real-time-machine-learning-at-industrial-scale
Use cases and examples using Apache Spark, presented at the Hadoop User Group (UK) November 2014 Hadoop Meetup
http://www.meetup.com/hadoop-users-group-uk/events/217791892/
This document summarizes a presentation about the graph database Neo4j. The presentation included an agenda that covered graphs and their power, how graphs change data views, and real-time recommendations with graphs. It introduced the presenters and discussed how data relationships unlock value. It described how Neo4j allows modeling data as a graph to unlock this value through relationship-based queries, evolution of applications, and high performance at scale. Examples showed how Neo4j outperforms relational and NoSQL databases when relationships are important. The presentation concluded with examples of how Neo4j customers have benefited.
The document discusses using graphs and Neo4j for natural language processing tasks. It describes representing text as a graph by connecting adjacent words, and using this representation to find word associations and do opinion mining. Graph-based summarization and content recommendation are also covered. The resources provided give examples of opinion summarization using shortest path algorithms on the graph representation of reviews.
This document summarizes a webinar about importing crime data from Chicago into Neo4j. It discusses loading a CSV file of crime data into Neo4j using LOAD CSV and creating nodes and relationships. It also describes using Spark to preprocess the CSV into multiple Neo4j-formatted files and bulk loading them using the Neo4j Import tool. The document then covers enriching the graph with additional crime data from JSON and updating the graph with new crimes.
This document provides a summary of using Apache Spark for continuous analytics and optimization. It discusses using Spark for collecting data from various sources, processing the data using Spark's capabilities for streaming, machine learning and SQL queries, and reporting insights. An example use case is presented for social media analysis using Spark Streaming to process a real-time data stream from Kafka and analyze the data using both the Spark SQL and core Spark APIs in Scala.
Using neo4j for enterprise metadata requirementsNeo4j
Metadata is everywhere yet traditionally approaches to managing it have been disparate, siloed and often ineffective.
In this talk James will discuss the opportunities for using graph technology to address the fundamental challenges and questions of metadata management such as impact analysis, data lineage and definitions.
Data to Value are a Data Consultancy based in London that specialise in applying lean and agile techniques to complex data requirements. Connected Data is a particular focus for the firm which they see as the new frontier for data leaders.
James Phare has over 15 years experience of creating and leading data teams in various roles in Financial Services. Prior to cofounding Data Consultancy Data to Value he was Head of Information Management and Data Architecture at Man Group – one of the world’s largest Hedge funds. James started his career at Thomson Reuters after graduating in Economics from the University of York.
The document provides an introduction and agenda for a Neo4j workshop about modeling Game of Thrones data. It discusses loading Game of Thrones character, episode and house data into Neo4j and demonstrates different Cypher queries to analyze relationships between these entities such as determining the most prominent characters or how houses are related. The workshop also covers more advanced topics like aggregation queries using the WITH clause and deleting or constraining data.
Right now in institutions around the world, some of the greatest minds in computer science and statistics are coming up with amazing new algorithms and mathematically beautiful solutions. However it's entirely possible that the solutions they conceive will be impracticable in industry. The reason is simple; "the best answer is useless if it arrives too late to do anything with it". The key principle here is the compromise between 'accuracy' and 'latency'. In this talk I will describe examples where this holds true, and how I am using real-time machine learning models to solve challenges in eCommerce, Financial Services and Media companies.
http://tumra.com/blog/real-time-machine-learning-at-industrial-scale
Use cases and examples using Apache Spark, presented at the Hadoop User Group (UK) November 2014 Hadoop Meetup
http://www.meetup.com/hadoop-users-group-uk/events/217791892/
This document summarizes a presentation about the graph database Neo4j. The presentation included an agenda that covered graphs and their power, how graphs change data views, and real-time recommendations with graphs. It introduced the presenters and discussed how data relationships unlock value. It described how Neo4j allows modeling data as a graph to unlock this value through relationship-based queries, evolution of applications, and high performance at scale. Examples showed how Neo4j outperforms relational and NoSQL databases when relationships are important. The presentation concluded with examples of how Neo4j customers have benefited.
Webinar: Large Scale Graph Processing with IBM Power Systems & Neo4jNeo4j
We live in a profoundly connected world. From supply chains to payment networks to digital business and complex portfolios, our ability to understand and navigate not just data, but relationships inside the data, play an increasingly important role in all aspects of business. Highly connected value chains that generate massive volumes of connected data create an opportunity for graph analysis, which Gartner describes as "the single most single most effective competitive differentiator for organizations pursuing data-driven operations and decisions." This talk will introduce the power of graph databases and share how the latest IBM Power Systems offerings featuring the POWER8 processor and CAPI-attached Flash enable unique scaling, performance and price-performance advantages for Neo4j workloads.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. Join this webinar to learn why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
Ryan Boyd, Developer Relations at Neo4j
Ryan is a SF-based software engineer focused on helping developers understand the power of graph databases. Previously he was a product manager for architectural software, built applications and web hosting environments for higher education, and worked in developer relations for twenty products during his 8 years at Google. He enjoys cycling, sailing, skydiving, and many other adventures when not in front of his computer.
Deploying Massive Scale Graphs for Realtime InsightsNeo4j
Graph databases have been at the forefront of helping organizations manage and generate insights from data relationships, and applying those insights in real-time to drive competitive advantage. As organizations gain value in deploying graph databases, the data volumes managed are growing exponentially pushing the limits of large-scale in-memory graph processing. Neo4j and IBM Power Systems combined forces to deliver a market leading scalable graph database platform capable of affordably storing and processing graphs of extremely large size and offering real-time insights, using flash and FPGA accelerators. In this session we will cover the use cases driving the need for this extremely scalable platform and how this platform offers an easy to deploy model for extreme scale graph databases.
Neo4j graphs in the real world - graph days d.c. - april 14, 2015Neo4j
This document discusses several real-world use cases for graph databases across different industries:
1) It describes how graph databases have been used for master data management by companies like die Bayerische insurance and Classmates social network to create a unified view of customer and organizational data.
2) Graphs have also been applied to network and IT operations management by the Royal Netherlands Meteorological Institute to optimize infrastructure and by Telenor for identity and access management.
3) Fraud detection in industries like banking, insurance, and ecommerce is another common use case, with graphs helping to connect discrete user accounts and transactions to detect rings of fraudulent activity.
This introduction to graph databases is specifically designed for Enterprise Architects who need to map business requirements to architectural components like graph databases. It explains how and why graphs matter for Enterprise Architecture and reviews the architectural differences between relational and graph models.
Searching on Intent: Knowledge Graphs, Personalization, and Contextual Disamb...Trey Grainger
Search engines frequently miss the mark when it comes to understanding user intent. This talk will walk through some of the key building blocks necessary to turn a search engine into a dynamically-learning "intent engine", able to interpret and search on meaning, not just keywords. We will walk through CareerBuilder's semantic search architecture, including semantic autocomplete, query and document interpretation, probabilistic query parsing, automatic taxonomy discovery, keyword disambiguation, and personalization based upon user context/behavior. We will also see how to leverage an inverted index (Lucene/Solr) as a knowledge graph that can be used as a dynamic ontology to extract phrases, understand and weight the semantic relationships between those phrases and known entities, and expand the query to include those additional conceptual relationships.
As an example, most search engines completely miss the mark at parsing a query like (Senior Java Developer Portland, OR Hadoop). We will show how to dynamically understand that "senior" designates an experience level, that "java developer" is a job title related to "software engineering", that "portland, or" is a city with a specific geographical boundary (as opposed to a keyword followed by a boolean operator), and that "hadoop" is the skill "Apache Hadoop", which is also related to other terms like "hbase", "hive", and "map/reduce". We will discuss how to train the search engine to parse the query into this intended understanding and how to reflect this understanding to the end user to provide an insightful, augmented search experience.
Topics: Semantic Search, Apache Solr, Finite State Transducers, Probabilistic Query Parsing, Bayes Theorem, Augmented Search, Recommendations, Query Disambiguation, NLP, Knowledge Graphs
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. Join this webinar to learn why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships
This tutorial will provide you with a basic understanding of graph database technology and the ability to quickly begin development of a graph database application. You will have the capability to recognize graph-based problems and present the benefits of using graph technology for problem resolution.
The tutorial will give you an understanding of:
• Graph theory - origins and concepts
• Benefits of graph databases
• Different types of graph databases
• Typical graph database API
• Programming basics
• Use cases
Bring your laptops for a hands-on opportunity to practice some sample codes. A basic understanding of Java programming is a recommended prerequisite to understand this course. This session is led by the InfiniteGraph technical team and the demonstration code will be drawn from InfiniteGraph examples, however the broader educational presentation is product-neutral and not a commercial presentation of their products.
To participate in the hands-on portion of the graph tutorial users must have:
• Java programming experience
• Java Developer Kit (JDK)
• Current InfiniteGraph installed on laptop. (To download visit www.objectivity.com/infinitegraph)
• HelloGraph test – Upon installing IG, run HelloGraph to test the install. (HelloGraph can be found online at http://wiki.infinitegraph.com/2.1/w/index.php?title=Download_Sample_Code)
Leon Guzenda was one of the founding members of Objectivity in 1988 and one of the original architects of Objectivity/DB. He currently works with Objectivity's major customers to help them effectively develop and deploy complex applications and systems that use the industry's highest-performing, most reliable DBMS technology, Objectivity/DB. He also liaises with technology partners and industry groups to help ensure that Objectivity/DB remains at the forefront of database and distributed computing technology. Leon has more than 35 years experience in the software industry. At Automation Technology Products, he managed the development of the ODBMS for the Cimplex solid modeling and numerical control system. Before that, he was Principal Project Director for International Computers Ltd. in the United Kingdom, delivering major projects for NATO and leading multinationals. He was also design and development manager for ICL's 2900 IDMS product. He spent the first 7 years of his career working in defense and government systems. Leon has a B.S. degree in Electronic Engineering from the University of Wales.
The document discusses using a graph database foundation for customer information management. It outlines how traditional approaches using rigid SQL databases lack agility and require long implementation cycles. A graph database like Neo4j allows for a more flexible data model that can be visually modeled to the business and provide multi-dimensional, contextual views of customer data. It also discusses how the graph database can integrate diverse data sources, apply data quality processes, and provide insights through querying and visualization of the knowledge graph.
An Introduction to NOSQL, Graph Databases and Neo4jDebanjan Mahata
Neo4j is a graph database that stores data in nodes and relationships. It allows for efficient querying of connected data through graph traversals. Key aspects include nodes that can contain properties, relationships that connect nodes and also contain properties, and the ability to navigate the graph through traversals. Neo4j provides APIs for common graph operations like creating and removing nodes/relationships, running traversals, and managing transactions. It is well suited for domains that involve connected, semi-structured data like social networks.
Relational databases power most applications, but new use-cases have requirements that they are not well suited for.
That's why new approaches like graph databases are used to handle join-heavy, highly-connected and realtime aspects of your applications.
This talk compares relational and graph databases, show similarities and important differences.
We do a hands-on, deep-dive into ease of data modeling and structural evolution, massive data import and high performance querying with Neo4j, the most popular graph database.
I demonstrate a useful tool which makes data import from existing relational databases with a non-denormalized ER-model a "one click"-experience.
Which leaves biggest challenge for people coming from a relational background is to adapt some of their existing database experience to new ways of thinking.
Importing Data into Neo4j quickly and easily - StackOverflowNeo4j
In this GraphConnect presentation Mark and Michael show several ways to import large amounts of highly connected data from different formats into Neo4j. Both Cypher's LOAD CSV as well as the bulk importer is demonstrated along with many tips.
We use the well know StackOverflow Q&A site data which is interestingly very graphy.
This document introduces Neo4j, the world's leading graph database. It discusses Neo4j's product and company details, how graph databases are different than other databases by focusing on relationships between connected data. Common use cases for Neo4j are also summarized, such as recommendations, master data management, network operations, identity and access management, and fraud detection. The document provides examples of how customers use Neo4j and discusses patterns of fraud that Neo4j can help detect.
The document discusses using Neo4j and graph databases for fraud detection solutions. It describes how Neo4j allows for agile development, high productivity, and real-time response times when working with connected fraud data. The document outlines a fraud detection demo using Neo4j to load operational data, inject fraud cases, generate alerts, and export detected fraud data for investigation. It proposes using Neo4j as the foundation for a 360-degree fraud prevention solution integrated with other systems and data sources.
The document provides an overview of the internal workings of Neo4j. It describes how the graph data is stored on disk as linked lists of fixed size records and how two levels of caching work - a low-level filesystem cache and a high-level object cache that stores node and relationship data in a structure optimized for traversals. It also explains how traversals are implemented using relationship expanders and evaluators to iteratively expand paths through the graph, and how Cypher builds on this but uses graph pattern matching rather than the full traversal system.
This document contains the agenda for the Neo4j Partner Day event in Amsterdam on March 16th, 2017. The agenda includes sessions on the business potential for graph database partners, real-world Neo4j applications, an overview of the Neo4j partner program, and networking sessions.
Max De Marzi gave an introduction to graph databases using Neo4j as an example. He discussed trends in big, connected data and how NoSQL databases like key-value stores, column families, and document databases address these trends. However, graph databases are optimized for interconnected data by modeling it as nodes and relationships. Neo4j is a graph database that uses a property graph data model and allows querying and traversal through its Cypher query language and Gremlin scripting language. It is well-suited for domains involving highly connected data like social networks.
Neo4j Partner Tag Berlin - Potential für System-Integratoren und Berater Neo4j
This document summarizes a Neo4j partner event. It includes an agenda with sessions on the business potential of Neo4j for system integrators and consultants, the Neo4j partner program, and a case study on using Neo4j to analyze the Panama Papers. There are also sessions on quickly gaining value from Neo4j and on modeling logistics processes with Neo4j.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Webinar: Large Scale Graph Processing with IBM Power Systems & Neo4jNeo4j
We live in a profoundly connected world. From supply chains to payment networks to digital business and complex portfolios, our ability to understand and navigate not just data, but relationships inside the data, play an increasingly important role in all aspects of business. Highly connected value chains that generate massive volumes of connected data create an opportunity for graph analysis, which Gartner describes as "the single most single most effective competitive differentiator for organizations pursuing data-driven operations and decisions." This talk will introduce the power of graph databases and share how the latest IBM Power Systems offerings featuring the POWER8 processor and CAPI-attached Flash enable unique scaling, performance and price-performance advantages for Neo4j workloads.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. Join this webinar to learn why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
Ryan Boyd, Developer Relations at Neo4j
Ryan is a SF-based software engineer focused on helping developers understand the power of graph databases. Previously he was a product manager for architectural software, built applications and web hosting environments for higher education, and worked in developer relations for twenty products during his 8 years at Google. He enjoys cycling, sailing, skydiving, and many other adventures when not in front of his computer.
Deploying Massive Scale Graphs for Realtime InsightsNeo4j
Graph databases have been at the forefront of helping organizations manage and generate insights from data relationships, and applying those insights in real-time to drive competitive advantage. As organizations gain value in deploying graph databases, the data volumes managed are growing exponentially pushing the limits of large-scale in-memory graph processing. Neo4j and IBM Power Systems combined forces to deliver a market leading scalable graph database platform capable of affordably storing and processing graphs of extremely large size and offering real-time insights, using flash and FPGA accelerators. In this session we will cover the use cases driving the need for this extremely scalable platform and how this platform offers an easy to deploy model for extreme scale graph databases.
Neo4j graphs in the real world - graph days d.c. - april 14, 2015Neo4j
This document discusses several real-world use cases for graph databases across different industries:
1) It describes how graph databases have been used for master data management by companies like die Bayerische insurance and Classmates social network to create a unified view of customer and organizational data.
2) Graphs have also been applied to network and IT operations management by the Royal Netherlands Meteorological Institute to optimize infrastructure and by Telenor for identity and access management.
3) Fraud detection in industries like banking, insurance, and ecommerce is another common use case, with graphs helping to connect discrete user accounts and transactions to detect rings of fraudulent activity.
This introduction to graph databases is specifically designed for Enterprise Architects who need to map business requirements to architectural components like graph databases. It explains how and why graphs matter for Enterprise Architecture and reviews the architectural differences between relational and graph models.
Searching on Intent: Knowledge Graphs, Personalization, and Contextual Disamb...Trey Grainger
Search engines frequently miss the mark when it comes to understanding user intent. This talk will walk through some of the key building blocks necessary to turn a search engine into a dynamically-learning "intent engine", able to interpret and search on meaning, not just keywords. We will walk through CareerBuilder's semantic search architecture, including semantic autocomplete, query and document interpretation, probabilistic query parsing, automatic taxonomy discovery, keyword disambiguation, and personalization based upon user context/behavior. We will also see how to leverage an inverted index (Lucene/Solr) as a knowledge graph that can be used as a dynamic ontology to extract phrases, understand and weight the semantic relationships between those phrases and known entities, and expand the query to include those additional conceptual relationships.
As an example, most search engines completely miss the mark at parsing a query like (Senior Java Developer Portland, OR Hadoop). We will show how to dynamically understand that "senior" designates an experience level, that "java developer" is a job title related to "software engineering", that "portland, or" is a city with a specific geographical boundary (as opposed to a keyword followed by a boolean operator), and that "hadoop" is the skill "Apache Hadoop", which is also related to other terms like "hbase", "hive", and "map/reduce". We will discuss how to train the search engine to parse the query into this intended understanding and how to reflect this understanding to the end user to provide an insightful, augmented search experience.
Topics: Semantic Search, Apache Solr, Finite State Transducers, Probabilistic Query Parsing, Bayes Theorem, Augmented Search, Recommendations, Query Disambiguation, NLP, Knowledge Graphs
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. Join this webinar to learn why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships
This tutorial will provide you with a basic understanding of graph database technology and the ability to quickly begin development of a graph database application. You will have the capability to recognize graph-based problems and present the benefits of using graph technology for problem resolution.
The tutorial will give you an understanding of:
• Graph theory - origins and concepts
• Benefits of graph databases
• Different types of graph databases
• Typical graph database API
• Programming basics
• Use cases
Bring your laptops for a hands-on opportunity to practice some sample codes. A basic understanding of Java programming is a recommended prerequisite to understand this course. This session is led by the InfiniteGraph technical team and the demonstration code will be drawn from InfiniteGraph examples, however the broader educational presentation is product-neutral and not a commercial presentation of their products.
To participate in the hands-on portion of the graph tutorial users must have:
• Java programming experience
• Java Developer Kit (JDK)
• Current InfiniteGraph installed on laptop. (To download visit www.objectivity.com/infinitegraph)
• HelloGraph test – Upon installing IG, run HelloGraph to test the install. (HelloGraph can be found online at http://wiki.infinitegraph.com/2.1/w/index.php?title=Download_Sample_Code)
Leon Guzenda was one of the founding members of Objectivity in 1988 and one of the original architects of Objectivity/DB. He currently works with Objectivity's major customers to help them effectively develop and deploy complex applications and systems that use the industry's highest-performing, most reliable DBMS technology, Objectivity/DB. He also liaises with technology partners and industry groups to help ensure that Objectivity/DB remains at the forefront of database and distributed computing technology. Leon has more than 35 years experience in the software industry. At Automation Technology Products, he managed the development of the ODBMS for the Cimplex solid modeling and numerical control system. Before that, he was Principal Project Director for International Computers Ltd. in the United Kingdom, delivering major projects for NATO and leading multinationals. He was also design and development manager for ICL's 2900 IDMS product. He spent the first 7 years of his career working in defense and government systems. Leon has a B.S. degree in Electronic Engineering from the University of Wales.
The document discusses using a graph database foundation for customer information management. It outlines how traditional approaches using rigid SQL databases lack agility and require long implementation cycles. A graph database like Neo4j allows for a more flexible data model that can be visually modeled to the business and provide multi-dimensional, contextual views of customer data. It also discusses how the graph database can integrate diverse data sources, apply data quality processes, and provide insights through querying and visualization of the knowledge graph.
An Introduction to NOSQL, Graph Databases and Neo4jDebanjan Mahata
Neo4j is a graph database that stores data in nodes and relationships. It allows for efficient querying of connected data through graph traversals. Key aspects include nodes that can contain properties, relationships that connect nodes and also contain properties, and the ability to navigate the graph through traversals. Neo4j provides APIs for common graph operations like creating and removing nodes/relationships, running traversals, and managing transactions. It is well suited for domains that involve connected, semi-structured data like social networks.
Relational databases power most applications, but new use-cases have requirements that they are not well suited for.
That's why new approaches like graph databases are used to handle join-heavy, highly-connected and realtime aspects of your applications.
This talk compares relational and graph databases, show similarities and important differences.
We do a hands-on, deep-dive into ease of data modeling and structural evolution, massive data import and high performance querying with Neo4j, the most popular graph database.
I demonstrate a useful tool which makes data import from existing relational databases with a non-denormalized ER-model a "one click"-experience.
Which leaves biggest challenge for people coming from a relational background is to adapt some of their existing database experience to new ways of thinking.
Importing Data into Neo4j quickly and easily - StackOverflowNeo4j
In this GraphConnect presentation Mark and Michael show several ways to import large amounts of highly connected data from different formats into Neo4j. Both Cypher's LOAD CSV as well as the bulk importer is demonstrated along with many tips.
We use the well know StackOverflow Q&A site data which is interestingly very graphy.
This document introduces Neo4j, the world's leading graph database. It discusses Neo4j's product and company details, how graph databases are different than other databases by focusing on relationships between connected data. Common use cases for Neo4j are also summarized, such as recommendations, master data management, network operations, identity and access management, and fraud detection. The document provides examples of how customers use Neo4j and discusses patterns of fraud that Neo4j can help detect.
The document discusses using Neo4j and graph databases for fraud detection solutions. It describes how Neo4j allows for agile development, high productivity, and real-time response times when working with connected fraud data. The document outlines a fraud detection demo using Neo4j to load operational data, inject fraud cases, generate alerts, and export detected fraud data for investigation. It proposes using Neo4j as the foundation for a 360-degree fraud prevention solution integrated with other systems and data sources.
The document provides an overview of the internal workings of Neo4j. It describes how the graph data is stored on disk as linked lists of fixed size records and how two levels of caching work - a low-level filesystem cache and a high-level object cache that stores node and relationship data in a structure optimized for traversals. It also explains how traversals are implemented using relationship expanders and evaluators to iteratively expand paths through the graph, and how Cypher builds on this but uses graph pattern matching rather than the full traversal system.
This document contains the agenda for the Neo4j Partner Day event in Amsterdam on March 16th, 2017. The agenda includes sessions on the business potential for graph database partners, real-world Neo4j applications, an overview of the Neo4j partner program, and networking sessions.
Max De Marzi gave an introduction to graph databases using Neo4j as an example. He discussed trends in big, connected data and how NoSQL databases like key-value stores, column families, and document databases address these trends. However, graph databases are optimized for interconnected data by modeling it as nodes and relationships. Neo4j is a graph database that uses a property graph data model and allows querying and traversal through its Cypher query language and Gremlin scripting language. It is well-suited for domains involving highly connected data like social networks.
Neo4j Partner Tag Berlin - Potential für System-Integratoren und Berater Neo4j
This document summarizes a Neo4j partner event. It includes an agenda with sessions on the business potential of Neo4j for system integrators and consultants, the Neo4j partner program, and a case study on using Neo4j to analyze the Panama Papers. There are also sessions on quickly gaining value from Neo4j and on modeling logistics processes with Neo4j.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3Data Hops
Free A4 downloadable and printable Cyber Security, Social Engineering Safety and security Training Posters . Promote security awareness in the home or workplace. Lock them Out From training providers datahops.com
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Choosing The Best AWS Service For Your Website + API.pptx
Using Graph theory to understand Intent & Concepts - Neo4j User Group (January 2013)
1. Using Graph Theory to understand Intent & Concepts – January 2013
tumra.com
2. UNDERSTANDING INTENT & CONCEPTS
• Use case:
- Enhancing Social TV user experience
- Matching users to content that interests them
• Topics we’ll cover:
- Natural Language Processing
- Graph Theory
- Machine Learning
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3. USE CASE ENHANCED SOCIAL TV
• Objectives:
- Increase engagement with content
- Enhance multi-channel user experience
• We built a prototype solution:
- Mines unstructured data in real-time
- Understands:
- What interests individual users
- Entities & Concepts (People, Places, Events)
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4. THE CHALLENGE
THANKS FORtoLISTENING
Help users to “follow the story” regardless of the
news outlet, integrated web / second-screen
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Photo Credit: byrion on Flickr (cc)
6. THE PROBLEM
• Little useful data to work with…
- Streams of continuous live TV
- Have to create metadata
• Where did we start?
- Ingest several live news channels
- Extract whatever data was available:
- In-video text using OCR
- Subtitles / Closed Captions
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7. STEP 1 NAMED ENTITY RECOGNITION
We used a simple N-Gram model for exact matches;
then Apache Lucene for everything else…
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8. EXAMPLE N.E.R.
“David Cameron and the German
Chancellor Angela Merkel meets to
discuss the debt crisis and signal
their approval for greater eurozone
integration.”
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9. EXAMPLE N.E.R.
“David Cameron and the German
Chancellor Angela Merkel meets to
discuss the debt crisis and signal
their approval for greater eurozone
integration.”
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10. INITIAL SOLUTION
NoSQL
Unstructured
Awesomeness!
Data
NER
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12. DISAMBIGUATION
• Which “David Cameron”?
- We have many in our Knowledgebase
- Sportsmen, actors, painters & characters…
• Our initial simplistic approach was naïve
- Works great with unambiguous matches
- Best-case returns top-scoring entity
• We needed a smarter approach
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13. RECAP
• We have an effectively ‘flat’ KB of Entities
- “David Cameron” -> Politician (Person)
- “Angela Merkel” -> Politician (Person)
- “German Chancellor” -> Political office (Concept)
- “Debt” -> Economic concept (Concept)
- “Eurozone” -> Economic area (Place)
• We needed a way to find relationships
between Entities
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14. THE BIG IDEA
Graphs allow us to store relationships between entities, and
graph algorithms allow us to interrogate those connections…
15. GRAPH DATABASES
Graph
Neo4J
Lab
Apache Golden
Giraph Orb
… of course there are many more open-source & proprietary ones
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16. SO, WHICH ONE?
???
… it had to be fast, scalable, active development
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17. STEP 2 BUILDING RELATIONSHIPS
We had 250 million Nodes, and 4 billion Edges…
great initial results but horrendously inefficient!
Example: “David Cameron” & “Angela Merkel”
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18.
19.
20. INITIAL IMPROVEMENTS
• We didn’t need everything… just:
- People: “David Cameron”, “Angela Merkel”
- Places: “London”, “Downing Street”, “Eurozone”
- Concepts: “Debt”, “President”, “Eurozone”
- Things: Companies, Products etc.
• Pruned the graph using Map/Reduce
• This reduced the number of Entities…
- … but we still had billions of connections
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21. EXAMPLE PEOPLE, PLACES, CONCEPTS
“David Cameron and the German
Chancellor Angela Merkel meets to
discuss the debt crisis and signal
their approval for greater eurozone
integration.”
tumra.com
22. EXAMPLE PEOPLE, PLACES, CONCEPTS
“David Cameron and the German
Chancellor Angela Merkel meets to
discuss the debt crisis and signal
their approval for greater eurozone
integration.”
Concepts Places
People
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23. DISAMBIGUATION
Angela
Merkel
David
Cameron
(painter) Living
Person Politician
Head of
State
David
Cameron David
(footballer) David
Cameron Cameron
(actor) (politician)
Possibilities: shortest path, number of common connections etc.
24. STEP 3 SIMPLIFYING THE GRAPH
Sure all that extra metadata was tasty but we didn’t
need it all to solve the use-case…
So we used Map/Reduce to count the common
connections
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25. SIMPLIFIED
Angela
Merkel
David
Cameron
(painter)
1
3
1
David
Cameron David
(footballer) David
Cameron Cameron
(actor) (politician)
Woah … that looks a lot like Least Cost Routing problem
26. LEAST COST PATH
Angela
Merkel
David
Cameron
(painter)
1/1
1/3
1/1
David
Cameron David
(footballer) David
Cameron Cameron
(actor) (politician)
1 / number of common connections = cost
27. UPDATED SOLUTION
Neo4J NoSQL
Unstructured
Disambiguation Awesomeness!
Data
NER
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28. RECAP
• Graphs allow us to interrogate relationships
- Disambiguate when faced with multiple possibilities
- Infer more about the context of what’s happening
• Went through iterations of improvements
- Kept our Entity data in NoSQL = TB’s
- Used the Graph as an index of sorts = GB’s
• Neo4j was a great fit for our needs
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29. STEP 4 MAKING IT WORK REAL-TIME
Some queries were taking ‘seconds’ and we needed
to go a lot faster because TV wont wait for us …
Do we really need to check the Graph everytime?
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30. ENTER MACHINE LEARNING
• We can use simple predictors to estimate
the likelihood of Entities occurring
- i.e. every time we’ve looked for “David Cameron” in
the past the best match was the Politician
• Keeping a ‘probabilistic context’ of recent
Entities allows us to detect shifts in topics
- Works especially well on News channels
- Reduces the demand on Graph lookups
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31. BAYES THEOREM
Looks complicated, but its basically just counting & division
Photo Credit: mattbuck007 on Flickr (cc)
32. STEP 5 MAKING IT WORK WORLDWIDE
We solved the problem for English, but what about
other languages?
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33. LANGUAGE
• Our core Entities of ‘People’, ‘Places’, &
‘Concepts’ are language agnostic…
• We needed a way to ditch ‘language’ and
jump straight to entities…
- The colour ‘Red’ means the same thing regardless of
you calling it ‘Rot’, ‘Rouge’ or ‘赤’
• Again, Graphs could solve the problem
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35. PROBLEM SOLVED
Typical response time ~30ms … relevancy improves
over time and learns new entities ‘online’
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36. FINAL SOLUTION
Neo4J NoSQL
Unstructured Language Model Disambiguation
Awesomeness!
Data
Machine Learning
NER
tumra.com
37. ABOUT US
• We’ve built a product…
- Our ‘Digital Marketing Optimization’ platform
improves conversion rates & customer satisfaction
for eCommerce & Marketing campaigns
- Launches Q1 2013
• What else do we do?
- ‘Big Data’ & ‘Data Science’ professional services
- Bespoke prototype & solution development
“TUMRA” is a transliteration of the Sanskrit word for “BIG”;
we thought it’s a great name … ( and the .COM was available )
tumra.com
38. TUMRA
You?
THANKS FOR LISTENING
We’re hiring!
Data Scientists & Developers
work@tumra.com
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39. THANKS FOR LISTENING
Questions?
tumra.com
hello@tumra.com
twitter.com/tumra
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