Tales of Graph Analytics with Neo4j

• What we'll do
18:00 - 18:30 - Mingling
18:30 - 19:15 - An Enterprise Graph around Topics to enable Deep Work - Yehonathan Sharvit @ harmon.ie
19:15 - 20:00 - Demo of Analytical research data on Neo4j - Tal Shainfeld and Svetlana Yaroshevsky @ Deloitte Analytics Israel

Title: An Enterprise Graph around Topics to enable Deep Work

Abstract:
harmon.ie is a Israeli startup that builds an Enterprise Graph around topics.

The Topics Enterprise Graph connects artifacts from different Office 365 apps (Exchange, Sharepoint, Teams ...) around common topics and build a smart feed and notifications based on topics. In this talk, we will explain our graph model and present how we were able to leverage graph algorithm to provide business-related insights.

Bio:
Yehonathan has been in the Hi-Tech since 2000.
He has worked in various management positions from team leader to VP R&D, mostly in startups, combining strong technical knowledge with management skills. His management style is mostly influenced by Agile methodology and Lean Startup philosophy.
As a functional programming expert, Yehonathan is a regular speaker at Tech events around the world and he is the maintainer of Klipse - a popular github open source project. He blogs about programming languages at http://blog.klipse.tech.
He has strong academic background: M.Sc. in Mathematics and B.Sc. in Electrical Engineering from Technion institute in Israel.

Title: Demo of Analytical research data on Neo4j - Tal Shainfeld and Svetlana Yaroshevsky @ Deloitte Analytics Israel Abstract:
As data scientist we use Neo4g Graph database to represent and visualize the analytical model.
Via graph database we can represent the clusters the correlations and the probabilities between the entities.
We can query the statistical model outcomes and find new patterns.
The main use cases we use graph model are: Next best offer, fraud and prediction. Bio:

Svetlana Yaroshevsky is Senior Data Scientist with more than 10 years' experience conducting research, mining data, enhancing data collection procedures, cleaning and verifying the integrity of data, creating data visualization graphs, developing automated data anomaly detectors, building algorithms and using machine learning tools.
Svetlana responsible for data science team members utilizing advanced statistical and machine learning methods to answer business questions and deliver insightful solutions to complex problems, for clients in retail, energy, manufacturing, telecom, healthcare, and financial services
Svetlana has extensive experience in handling complex analytics research involving CRM analytics, churn modeling, customer retention strategies, customer segmentation and Survey analysis.
Holds a Master degree in Applied Sociology from the University of Haifa. Tal Shainfeld - Solution Architect
Tal brings over 15 years of experience as an application manager and CIO at medium and large multi-national companies.
Tal leads the solution architecture, at Deloitte an

• What to bring
Nothing

• Important to know