|
20161013 |
900 |
1030 |
Opening Keynote |
Grand Ballroom A |
Keynote§§ |
Emil Eifrem |
NEO TECHNOLOGY |
CEO and Co-Creator of Neo4j |
Opening keynote and announcements |
|
|
20161013 |
1030 |
1100 |
Fika |
Main conference area |
Coffiee§§Break§§ |
All |
|
|
Fika is GraphConnect's Swedish coffee break. |
|
|
20161013 |
1100 |
1145 |
The Emergence of the Enterprise DataFabric |
Grand Ballroom A |
Case Study§§Recommendations§§Visualization§§Retail§§Business Intelligence§§ |
MARK KVAMME |
DRIVE CAPITAL |
CO-FOUNDER |
Companies have spent billions of dollars in their legacy enterprise software apps to manage their supply chain, sales forecasting, accounting management, customers relationships, and other aspects of their enterprise. In order to get business insight across their enterprise, companies have used old technology to build a complex business intelligence stack. |
http://graphconnect.com/speaker/mark-kvamme/ |
|
20161013 |
1100 |
1145 |
The Emergence of the Enterprise DataFabric |
Grand Ballroom A |
Case Study§§Recommendations§§Visualization§§Retail§§Business Intelligence§§ |
CLARK RICHEY |
FACTGEM |
CTO |
Companies have spent billions of dollars in their legacy enterprise software apps to manage their supply chain, sales forecasting, accounting management, customers relationships, and other aspects of their enterprise. In order to get business insight across their enterprise, companies have used old technology to build a complex business intelligence stack. |
http://graphconnect.com/speaker/clark-richey |
|
20161013 |
1100 |
1145 |
Accelerating Scientific Research Through Machine Learning and Graphs |
Bayview |
Case Study§§Health & Biotech§§Analytics§§Microservices§§Natural Language Processing§§ |
JORGE SOTO |
MICROCULUS |
CO-FOUNDER AND CTO |
Miroculus is a molecular diagnostics company that leverages the potential of microRNAs as biomarkers and has created the most easy to use and automated platform for their detection. MicroRNAs are small non-coding RNA molecules, whose primary role is to regulate the expression of our genes. |
http://graphconnect.com/speaker/antonio-molins/ |
|
20161013 |
1100 |
1145 |
Accelerating Scientific Research Through Machine Learning and Graphs |
Bayview |
Case Study§§Health & Biotech§§Analytics§§Microservices§§Natural Language Processing§§ |
ANTONIO MOLINS |
MICROCULUS |
VP OF DATA SCIENCE |
Miroculus is a molecular diagnostics company that leverages the potential of microRNAs as biomarkers and has created the most easy to use and automated platform for their detection. MicroRNAs are small non-coding RNA molecules, whose primary role is to regulate the expression of our genes. |
|
|
20161013 |
1100 |
1145 |
Core-Edge: Neo4j’s New Clustering Architecture |
Grand Ballroom B |
Tech & Integrations§§Neo4j 3.1§§Raft§§Clustering§§Scale§§ |
ALISTAIR JONES |
NEO TECHNOLOGY |
NEO4J ENGINEER |
We'll explore the exciting new Core-Edge clustering architecture for Neo4j. We'll see how Neo4j uses the Raft protocol for a robust underlay for intensive write operations, and how the new scale-out mechanism provides enormous power for very demanding graph workloads. |
http://graphconnect.com/speaker/alistair-jones/ |
|
20161013 |
1100 |
1145 |
Core-Edge: Neo4j’s New Clustering Architecture |
Grand Ballroom B |
Tech & Integrations§§Neo4j 3.1§§Raft§§Clustering§§Scale§§ |
MAX SUMRALL |
NEO TECHNOLOGY |
NEO4J ENGINEER |
We'll explore the exciting new Core-Edge clustering architecture for Neo4j. We'll see how Neo4j uses the Raft protocol for a robust underlay for intensive write operations, and how the new scale-out mechanism provides enormous power for very demanding graph workloads. |
|
|
20161013 |
1100 |
1145 |
Neo4j as a Key Player in Human Capital Management |
Grand Ballroom C |
Case Study§§MDM§§RDBMS to Graphs§§HCM§§ |
LUANNE MISQUITTA |
GRAPHAWARE |
SENIOR CONSULTANT |
Graph databases are a perfect fit for HCM/People management solutions. In this talk, Luanne will present the challenges faced by these software vendors and how Neo4j can help them stay relevant and competitive. |
http://graphconnect.com/speaker/luanne-misquitta/ |
|
20161013 |
1100 |
1115 |
Utilizing user defined functions in APOC |
Market Foyer |
Tech & Integrations§§Neo4j 3.1§§Cypher§§APOC§§Lightning Talk§§ |
MICHAEL HUNGER |
NEO TECHNOLOGY |
HEAD OF EMEA DEVELOPER RELATIONS |
Neo4j 3.1 introduced the concept of user defined functions. These allow us to provide custom implementations for functions that can be used in any expression or predicate. |
http://graphconnect.com/speaker/michael-hunger/ |
|
20161013 |
1115 |
1130 |
Data Integration with APOC |
Market Foyer |
Tech & Integrations§§Neo4j 3.1§§Cypher§§APOC§§Lightning Talk§§ |
STEFAN ARMBRUSTER |
NEO TECHNOLOGY |
NEO4J FIELD ENGINEER |
Neo4j is frequently integrated with other databases. With APOC you have the means of accessing these data sources directly from Cypher. You can either import data into the graph, fetch additional information for your graph query from another source or just visualize the other database as a graph. |
http://graphconnect.com/speaker/stefan-armbruster/ |
|
20161013 |
1130 |
1145 |
Graph Algorithm Procedures Demo |
Market Foyer |
Tech & Integrations§§Neo4j 3.1§§Cypher§§Graph Algorithm§§Lightning Talk§§ |
WILL LYON |
NEO TECHNOLOGY |
NEO4J DEVELOPER RELATIONS |
While Neo4j is mostly used as OLTP database, there is an increasing interest to run graph algorithms directly on the graph data. With procedures iterative and parallel algorithms can be implemented efficiently and used directly from Cypher. |
http://graphconnect.com/speaker/will-lyon/ |
|
20161013 |
1150 |
1235 |
Building a High Performance Pricing Engine at Marriott |
Grand Ballroom A |
Case Study§§Impact Analysis§§ |
SCOTT GRIMES |
MARRIOTT |
ECOMMERCE AND CMS SENIOR DIRECTOR/ARCHITECT |
Marriott International, the world’s largest hospitality company, needed a new pricing engine to drive both revenue and competitive differentiation. The older pricing engine was being stretched by complex pricing rules that resulted in long pricing update cycles despite spending on massive amount of hardware and tuning the legacy application. |
http://graphconnect.com/speaker/scott-grimes/ |
|
20161013 |
1150 |
1225 |
Improving Service Quality for Cablevisión Customers with Network Topology Impact Analysis |
Bayview |
Case Study§§Network & IT§§Impact Analysis§§ |
HERNÁN PÉREZ MASCI |
CABLEVISION |
IT CLIENTS AND DEMAND MANAGER |
At Cablevision S.A we are using Neo4j to store the Graph of our Network Topology and then made some impact analysis and root cause when a network element fails. We have documented our topology in detail with different type of elements (Switches, fiber cables, amplifiers, Hubs and more) with location, metadata of the status of the element and the information about relationship between them. |
|
|
20161013 |
1150 |
1225 |
Improving Service Quality for Cablevisión Customers with Network Topology Impact Analysis |
Bayview |
Case Study§§Network & IT§§Impact Analysis§§ |
ANDRES NATANAEL SORIA |
CABLEVISION |
SENIOR SOFTWARE ARCHITECT |
At Cablevision S.A we are using Neo4j to store the Graph of our Network Topology and then made some impact analysis and root cause when a network element fails. We have documented our topology in detail with different type of elements (Switches, fiber cables, amplifiers, Hubs and more) with location, metadata of the status of the element and the information about relationship between them. |
http://graphconnect.com/speaker/andres-natanael-soria/ |
|
20161013 |
1150 |
1235 |
Securing The Graph In Neo4j 3.1 |
Grand Ballroom B |
Tech & Integrations§§Neo4j 3.1§§Infosec§§Auth§§LDAP§§Active Directory§§ |
IGOR BOROJEVIC |
NEO TECHNOLOGY |
DIRECTOR OF PRODUCT MGMT, NEO4J |
Securing, monitoring and logging access to a system that stores customer data is the key part of enterprise security. In the upcoming Neo4j 3.1 release, we improve upon existing enterprise-class security features and raise the bar for safeguarding customer data and meeting compliance requirements. As part of the session, I will preview newly added support for AD/LDAP and time-permitting we will play with user defined roles and annotating user defined procedures with security roles. |
http://graphconnect.com/speaker/igor-borojevic/ |
|
20161013 |
1150 |
1225 |
Moving from Relational Schemas to Graphs |
Grand Ballroom C |
Tech & Integrations§§RDBMS to Graphs§§ |
PRAVEENA FERNANDES |
NEO TECHNOLOGY |
NEO4J ENGINEER |
With success stories of Neo4j, more people are eager to adopt Graph databases. More often than not, we get asked how do we begin migrating a relational database. |
http://graphconnect.com/speaker/praveena-fernandes/ |
|
20161013 |
1150 |
1205 |
Finding Insights through Keynumbers |
Market Foyer |
Tech & Integrations§§Graph Search§§Lightning Talk§§ |
JOHN POLJAK |
KEYNUMBERS |
FOUNDER |
Join us in exploring some of the ways Keynumbers is leveraging the power of Structr and Neo4j in developing unique solutions to meet these challenges |
http://graphconnect.com/speaker/john-poljak/ |
|
20161013 |
1205 |
1220 |
The Enterprise Information Graph: Managing Complex Metadata Using neo4j and Pitney Bowes Spectrum |
Market Foyer |
Case Study§§Finance§§Retail§§Lightning Talk§§ |
AARON WALLACE |
PITNEY BOWES |
PRINCIPAL PRODUCT MANAGER |
The world of business has changed, and far more value is now put on every single piece of data. In order to remain agile and respond quickly to new trends, organizations are striving to record and report on data as an asset, just like any other within the business. |
http://graphconnect.com/speaker/aaron-wallace/ |
|
20161013 |
1220 |
1235 |
Scale Without Limits Now |
Market Foyer |
Tech & Integrations§§Scale§§AWS§§Lightning Talk§§ |
BRAD NUSSBAUM |
GRAPHGRID |
COFOUNDER |
As Neo4j has advanced over the years so has the ability of the GraphGrid Data Platform to support an ever advancing system of capabilities ready to pave the way of the future scalability of Neo4j to a general audience. |
http://graphconnect.com/speaker/ben-nussbaum/ |
|
20161013 |
1230 |
1330 |
Lunch |
Main conference area |
Lunch§§Break§§ |
All |
|
|
|
|
|
20161013 |
1330 |
1415 |
Making the Right Connections: How the Financial Times Uses Graph Databases |
Grand Ballroom A |
Case Study§§Recommendations§§Golang§§Publishing§§RDF§§ |
DAN MURPHY |
FINANCIAL TIMES |
SENIOR DEVOPS |
In late 2015, the FT Semantic Metadata team investigated graph databases as an alternative to, or perhaps additional store for their existing semantic datastore. After a brief spike we picked Neo4j and then re-wrote a host of microservices in GoLang, from surprisingly memory hungry Java services. It was an interesting journey of changing culture, technologies, and learning. |
http://graphconnect.com/speaker/dan-murphy/ |
|
20161013 |
1330 |
1415 |
Neo4j Container Orchestration with Kubernetes, Docker Swarm, Mesos |
Bayview |
Tech & Integrations§§Docker§§Kubernetes§§Mesos§§Containers§§ |
DIPPY AGGARWAL |
UNIV OF CINCINNATI |
PH.D CANDIDATE |
The interest in Docker has increased significantly since its inception. According to a report compiled by a leading cloud-scale monitoring company, Datadog, two-thirds of the companies that try docker adopt it and the adopters have increased their container count by five times over a period of nine months. Neo4j has also embraced Docker by supporting official images and also offering specific images of its own. |
http://graphconnect.com/speaker/dippy-aggarwal/ |
|
20161013 |
1330 |
1415 |
Extend the Power of Neo4j with Stored Procedures and APOC |
Grand Ballroom B |
Tech & Integrations§§Java§§APOC§§ |
CLARK RICHEY |
FACTGEM |
CTO |
This talk will introduce crucial aspects of Neo4j stored procedures to developers. Stored procedures are a powerful and easy to implement feature that was made available in Neo4j 3.0. |
http://graphconnect.com/speaker/clark-richey/ |
|
20161013 |
1330 |
1415 |
Streamlining Processes with Neo4j at Glidewell Laboratories |
Grand Ballroom C |
Case Study§§MDM§§Workflow Engine§§Manufacturing§§ |
GALIT GONTAR |
GLIDEWELL LABORATORIES |
SOFTWARE ENGINEER |
By integrating a Neo4j powered generic workflow engine into its manufacturing process, Glidewell Laboratories has been able to take advantage of the traditional benefits of graph databases to streamline company processes, integrate departments and provide visibility throughout the organization. |
http://graphconnect.com/speaker/galit-gontar/ |
|
20161013 |
1330 |
1415 |
Streamlining Processes with Neo4j at Glidewell Laboratories |
Grand Ballroom C |
Case Study§§MDM§§Workflow Engine§§Manufacturing§§ |
ROBERT EDWARDS |
GLIDEWELL LABORATORIES |
MANAGER, SOFTWARE DEVELOPMENT |
|
http://graphconnect.com/speaker/robert-edwards/ |
|
20161013 |
1330 |
1345 |
Neo4j Connects Healthcare using Open Data |
Market Foyer |
Tech & Integrations§§Health & Biotech§§Cypher§§Lightning Talk§§ |
YAQI SHI |
UNIVERSITY OF SF |
MS CANDIDATE OF HEALTH INFORMATICS |
No one would argue that Healthcare is among one of the most complicated industries in the United States. While there are many public datasets can be found on FDA, CMS website, it is hard to see the general picture of how does each stakeholder, such as providers, drug manufacturers, lobbying systems, and governors link to each other. |
http://graphconnect.com/speaker/yaqi-shi/ |
|
20161013 |
1334 |
1400 |
How We Design Cypher |
Market Foyer |
Tech & Integrations§§Cypher§§Lightning Talk§§ |
STEFAN PLANTIKOW |
NEO TECHNOLOGY |
NEO4J ENGINEER |
The Cypher language is continuously evolved to be the perfect fit for the needs of the graph revolution. This talk looks at the process as well as the philosophy behind the design of Cypher. |
http://graphconnect.com/speaker/stefan-plantikow/ |
|
20161013 |
1400 |
1415 |
The openCypher Initiative |
Market Foyer |
Tech & Integrations§§openCypher§§Cypher§§Lightning Talk§§ |
STEFAN PLANTIKOW |
NEO TECHNOLOGY |
NEO4J ENGINEER |
The openCypher initiative is establishing Cypher as the industry standard for graph querying. Since the last GraphConnect San Francisco, we’ve been working on various artifacts that enable other vendors to adopt and implement Cypher in their products. |
http://graphconnect.com/speaker/stefan-plantikow/ |
|
20161013 |
1420 |
1505 |
How NASA Finds Critical Data Through a Knowledge Graph |
Grand Ballroom A |
Case Study§§MDM§§Linkurious§§R§§ |
DAVID MEZA |
NASA |
CHIEF KNOWLEDGE ARCHITECT |
Ask any project manager and they will tell you the importance of reviewing lessons learned prior to starting a new project. The lesson learned databases are filled with nuggets of valuable information to help project teams increase the likelihood of project success. |
http://graphconnect.com/speaker/david-meza/ |
|
20161013 |
1420 |
1505 |
Using Neo4j for Cloud Management at Scale |
Bayview |
Tech & Integrations§§Continuous Deployment§§Cloud§§ |
DAVID BRIAN WARD |
TELEGRAPH HILL SOFTWARE |
CEO & FOUNDER |
This talks explores how Telegraph Hill Software uses Neo4j to create a live, active, self-updating repository service, containing nearly all virtual hardware, network and software components and their dependencies, enabling continuous deployment in any cloud environment at scale |
http://graphconnect.com/speaker/david-brian-ward/ |
|
20161013 |
1420 |
1505 |
Hardening your Information Security: Analyzing Active Directory |
Grand Ballroom B |
Case Study§§Network & IT§§Infosec§§Active Directory§§ |
CHRIS UPKES |
NEO TECHNOLOGY |
NEO4J FIELD ENGINEER |
This session covers the typical security questions that are difficult to answer using the existing AD / LDAP management tools used by the enterprise. I will provide an overview on the process of adapting the graph as part of a practical approach to answering these questions. As part of the session I will detail an AD reference model developed in conjunction with one of our important AD customers and I will show how to leverage cypher and visualization to develop better insight into how AD domains can be better understood, refactored and managed. |
http://graphconnect.com/speaker/chris-upkes/ |
|
20161013 |
1420 |
1505 |
Neo4j JDBC Driver: Integrate with ETL, BI, Visualization & more |
Grand Ballroom C |
Tech & Integrations§§Visualization§§QlikView§§Talend§§Business Intelligence§§ETL§§ |
STEFAN ARMBRUSTER |
NEO TECHNOLOGY |
NEO4J FIELD ENGINEER |
Using Neo4j’s new binary BOLT protocol in Neo4j 3.0, our friends from Larus IT in Italy developed a JDBC driver, which was just released in early July. This talk briefly explains the architecture on the JDBC driver. After that we'll demo various JDBC enables tools like SquirrelSQL, Pentaho, QlikView, Talend, JasperReports, and take a look at how they access your graph. |
http://graphconnect.com/speaker/stefan-armbruster/ |
|
20161013 |
1420 |
1435 |
Cypher: Write Fast and Furious |
Market Foyer |
Tech & Integrations§§Cypher§§Lightning Talk§§ |
CHRISTOPHE WILLEMSEN |
GRAPHAWARE |
SENIOR CONSULTANT |
Ever struggle with writes performance in Cypher? This Lightning talk is for you! In only 15 minutes, Christophe will show you some tips and tricks for making your Cypher write transactions as fast as possible. |
http://graphconnect.com/speaker/christophe-willemsen/ |
|
20161013 |
1435 |
1450 |
Hetionet awakens: Integrating all of Biology into a Public Neo4j Database |
Market Foyer |
Tech & Integrations§§Health & Biotech§§Lightning Talk§§ |
DANIEL HIMMELSTEIN |
UNIV OF PENNSYLVANIA |
POSTDOCTORAL FELLOW |
We created Hetionet — a network that encodes decades of knowledge from biomedical research. Hetionet is a hetnet — a network with multiple node and relationship types. Version 1.0 contains 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. |
http://graphconnect.com/speaker/daniel-himmelstein/ |
|
20161013 |
1450 |
1505 |
Uncovering Hidden Relationships with Tom Sawyer Perspectives |
Market Foyer |
Tech & Integrations§§Lightning Talk§§Visualization§§ |
KEVIN MADDEN |
TOM SAWYER SOFTWARE |
CHIEF SOFTWARE ENGINEER |
See how Tom Sawyer Perspectives can help investigators find out the people and hidden relationships involved in the Panamanian offshore banking scandal. |
http://graphconnect.com/speaker/kevin-madden/ |
|
20161013 |
1505 |
1525 |
Fika |
Main conference area |
Coffiee§§Break§§ |
All |
|
|
Fika is GraphConnect's Swedish coffee break. |
|
|
20161013 |
1525 |
1610 |
Connecting the Dots in Early Drug Discovery at Novartis |
Grand Ballroom A |
Case Study§§Health & Biotech§§ |
STEPHAN REILING |
NOVARTIS |
SENIOR SCIENTIST |
We have created a large Neo4J database that integrates the results from textmining, experimental data, and biological background knowledge. The utility of this graph is twofold: 1.Identify promising compounds to be tested as a starting point for drug development. 2. Better understand the results of large scale compound testing in cellular assays using imaging technology. |
http://graphconnect.com/speaker/stephan-reiling/ |
|
20161013 |
1525 |
1610 |
Lessons Learned: Building a Scalable Platform for Sharing 500 Million Photos |
Bayview |
Case Study§§Recommendations§§Scale§§Retail§§ |
RUBEN HEUSINKVELD |
ALBUMPRINTER (VISTAPRINT) |
TECHNICAL LEAD |
The following might sound all too familiar: we’re taking and sharing more photos than ever before, and as a result we’re drowning in our photos. Somewhere in that pile or stream of photos are the ones that really matter – if only we could find them. To make this easier Albumprinter created a solution which recently launched with more than 500 million photos for 1 million existing customers. |
|
|
20161013 |
1525 |
1610 |
Lessons Learned: Building a Scalable Platform for Sharing 500 Million Photos |
Bayview |
Case Study§§Recommendations§§Scale§§Retail§§ |
WOUTER CROOY |
ALBUMPRINTER (VISTAPRINT) |
TECHNICAL LEAD |
The following might sound all too familiar: we’re taking and sharing more photos than ever before, and as a result we’re drowning in our photos. Somewhere in that pile or stream of photos are the ones that really matter – if only we could find them. To make this easier Albumprinter created a solution which recently launched with more than 500 million photos for 1 million existing customers. |
http://graphconnect.com/speaker/wouter-crooy/ |
|
20161013 |
1525 |
1610 |
Graph Algorithms: Make Election Data Great Again |
Grand Ballroom B |
Tech & Integrations§§Analytics§§Visualization§§Data Journalism§§R§§APOC§§Java§§ |
JOHN SWAIN |
RIGHT RELEVANCE |
PRODUCT MANAGER, DATA SCIENCE & ANALYTICS |
This session will briefly discuss the value of those algorithms, and then dive into how we used them to analyze the US Presidential Election based on Twitter data. You'll see a mixture of code, visualizations, and thoughtful analysis to better understand the conversations happening around the election. |
|
|
20161013 |
1525 |
1610 |
Graph Algorithms: Make Election Data Great Again |
Grand Ballroom B |
Tech & Integrations§§Analytics§§Visualization§§Data Journalism§§R§§APOC§§Java§§ |
RYAN BOYD |
NEO TECHNOLOGY |
HEAD OF N. AMERICA DEVELOPER RELATIONS |
This session will briefly discuss the value of those algorithms, and then dive into how we used them to analyze the US Presidential Election based on Twitter data. You'll see a mixture of code, visualizations, and thoughtful analysis to better understand the conversations happening around the election. |
|
|
20161013 |
1525 |
1610 |
Deploying Massive Scale Graphs for Real Time Insights |
Grand Ballroom C |
Tech & Integrations§§Scale§§IBM Power Systems§§ |
BRAD BRECH |
IBM |
DISTINGUISHED ENGINEER |
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. |
http://graphconnect.com/speaker/brad-brech/ |
|
20161013 |
1525 |
1540 |
InterActing with Neo4j using a Graph-Driven User Interface |
Market Foyer |
Tech & Integrations§§Lightning Talk§§Visualization§§ |
TOM ZEPPENFELDT |
GRAPHILEON |
DIRECTOR & FOUNDER |
Interactive applications for exploration and visualisation of the content of a graph-store require multiple components like diagrams, charts and tables that are connected to each other. This is exactly what Graphileon’s InterActor offers, using an extensible set of UI and backend functions that are connected by triggers to create meaningful interactions. |
http://graphconnect.com/speaker/tom-zeppenfeldt/ |
|
20161013 |
1540 |
1555 |
Taming text with Neo4j: The Graphaware NLP Framework |
Market Foyer |
Tech & Integrations§§NLP§§Lightning Talk§§ |
ALESSANDRO NEGRO |
GRAPHAWARE |
CHIEF SCIENTIST |
A great part of the world’s knowledge is stored using text in natural language, but using it in an effective way is still a major challenge. Natural Language Processing (NLP) techniques provide the basis for harnessing this huge amount of data and converting it into a useful source of knowledge for further processing. |
http://graphconnect.com/speaker/alessandro-negro/ |
|
20161013 |
1555 |
1610 |
Graphs in time and Space: A Visual Guide |
Market Foyer |
Tech & Integrations§§Visualization§§Geospatial§§Lightning Talk§§ |
COREY LANUM |
CAMBRIDGE INTELLIGENCE |
COMMERCIAL DIRECTOR |
Corey will use visual examples to explain the quirks (and importance) of dynamic and geospatial graphs, and how they can be stored, explored and queried in Neo4j. He will then show how graph visualization tools empower users to explore connections between people, events, locations and times. |
http://graphconnect.com/speaker/corey-lanum/ |
|
20161013 |
1615 |
1700 |
A Panama Papers & Beyond: Unveiling Secrecy with Graphs |
Grand Ballroom |
Tech & Integrations§§Linkurious§§Visualization§§Data Journalism§§ |
MAR CABRA |
ICIJ |
HEAD OF DATA & RESEARCH UNIT |
The media start-up International Consortium of Investigative Journalists has been breaking the secrecy surrounding tax havens for the past four years, but graph databases helped take their investigative power to the next level. |
http://graphconnect.com/speaker/mar-cabra/ |
|
20161013 |
1615 |
1700 |
Enabling the Cisco Decoder Ring: Using Neo4j to Automate Master Data Management Across the Enterprise |
Bayview |
Case Study§§MDM§§ |
ANDREW CHAPPELL |
CISCO |
TECHNICAL PROGRAM MGR |
Launched in 2009, Cisco’s Hierarchy Management Platform aimed at consolidating and improving master data management by creating a one-stop shop for Enterprise hierarchies. Fast forward seven years and the mission has expanded to something even more intriguing: utilizing cross-hierarchy relationships to simplify and automate Cisco’s functional processes. |
http://graphconnect.com/speaker/andrew-chappell |
|
20161013 |
1615 |
1700 |
How Semantic is Your Graph? |
Grand Ballroom B |
Tech & Integrations§§RDF§§Semantic Graph§§Triple Store§§ |
JESÚS BARRASA |
NEO TECHNOLOGY |
NEO4J FIELD ENGINEER |
There is a wall that separates the Graph Databases and the RDF & Semantics worlds. A significant portion of the bricks in this wall are made of misconceptions and assumptions that need to be clarified, and the reality is that analysts and vendors have historically failed to help in this task. |
http://graphconnect.com/speaker/jesus-barrasa/ |
|
20161013 |
1615 |
1700 |
Driving Predictive Roadway Analytics with the Power of Neo4j |
Grand Ballroom C |
Case Study§§GPS§§Mobile§§Open Street Map (OSM)§§ |
BLAKE NELSON |
FOUNDER |
WAVEONICS |
This talk will present how a small start-up (Waveonics) with no prior graphical database experience employed the simplicity, versatility and flexibility of Neo4j to handle the complex and dynamic roadway conditions found in crowdsourced street map data. |
http://graphconnect.com/speaker/blake-nelson/ |
|
20161013 |
1615 |
1630 |
Structr is the Neo4j Workbench: New Features to Make Developers Happy |
Market Foyer |
Tech & Integrations§§CMS§§Lightning Talk§§ |
AXEL MORGNER |
STRUCTR |
COFOUNDER AND CEO |
Find about some of Structr's new features in the upcoming 2.1 release which are targeted mainly at software developers. |
http://graphconnect.com/speaker/axel-morgner/ |
|
20161013 |
1630 |
1645 |
Best of Both Worlds: Neo4j and Spark |
Market Foyer |
Tech & Integrations§§Spark§§Flink§§Lightning Talk§§ |
MICHAEL HUNGER |
NEO TECHNOLOGY |
HEAD OF EMEA DEVELOPER RELATIONS |
Streaming data processing that takes data from many sources, analyses it and trains models of understanding is used in more and more places. The popularity of frameworks like Apache Spark and Flink throughout the industry demonstrates that. |
http://graphconnect.com/speaker/michael-hunger/ |
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20161013 |
1505 |
1525 |
Fika |
Main conference area |
Coffiee§§Break§§ |
All |
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Fika is GraphConnect's Swedish coffee break. |
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20161013 |
1730 |
1830 |
A Closing Keynote |
Grand Ballroom |
Keynote§§ |
JIM WEBBER |
NEO TECHNOLOGY |
NEO4J CHIEF SCIENTIST |
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20161014 |
900 |
1700 |
Intro to Neo4j Training |
Training Room 1 |
Training§§ |
DAVID FAUTH |
NEO TECHNOLOGY |
NEO4J FIELD ENGINEER |
Build a good knowledge of graph databases, beginning with the core functionality of the Neo4j graph database. |
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20161014 |
900 |
1700 |
Graph Data Modeling with Neo4j Training |
Training Room 2 |
Training§§ |
MAX DE MARZI |
NEO TECHNOLOGY |
NEO4J FIELD ENGINEER |
How to design and implement a graph data model and associated queries. |
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20161014 |
900 |
1700 |
Graph Data Modeling with Neo4j Training |
Training Room 3 |
Training§§ |
JESÚS BARRASA |
NEO TECHNOLOGY |
NEO4J FIELD ENGINEER |
How to design and implement a graph data model and associated queries. |
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20161014 |
900 |
1700 |
Advanced Neo4j Deployment Training |
Training Room 4 |
Training§§ |
ALISTAIR JONES |
NEO TECHNOLOGY |
NEO4J ENGINEER |
Deploy and maintain Neo4j Enterprise edition in production. |
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20161014 |
900 |
1700 |
Building a Recommendation System with Neo4j |
Training Room 5 |
Training§§ |
WILLIAM LYON |
NEO TECHNOLOGY |
NEO4J DEVELOPER RELATIONS |
Build a real-time recommendation engine from the ground up. |
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20161014 |
900 |
1700 |
Building a Recommendation System with Neo4j |
Training Room 6 |
Training§§ |
PRAVEENA FERNANDES |
NEO TECHNOLOGY |
NEO4J ENGINEER |
Build a real-time recommendation engine from the ground up. |
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20161014 |
900 |
1700 |
Advanced Cypher Queries Training |
Training Room 7 |
Training§§ |
MICHAEL HUNGER |
NEO TECHNOLOGY |
HEAD OF EMEA DEVELOPER RELATIONS |
Build upon your existing knowledge of the Cypher query language and set you on the path to mastery. |
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20161014 |
900 |
1700 |
Advanced Cypher Queries Training |
Training Room 7 |
Training§§ |
EVE FREEMAN |
TEKSYSTEMS |
NEO4J AND DATA VISUALIZATION CONSULTANT |
Build upon your existing knowledge of the Cypher query language and set you on the path to mastery. |
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