GraphDay Stockholm - iKnow Solutions - The Value Add of Graphs to Analytics and Information Management

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GraphDay Stockholm February 2017 iKnow Solutions

The Value Add of Graphs to Analytics and
Information Management

iKnow Solutions – short intro

iKnow Solutions - Our core skills

Why Neo4j?

A hard question to ask a relational DB…
Find friends of 3rd degree

SELECT P.Name, P3.Name FROM Person P
JOIN PersonFriend F1 ON P.PersonID =
F1.PersonID JOIN PersonFriend F2 ON
F1.FriendID = F2.PersonID JOIN PersonFriend F3
ON F2.FriendID = F3.PersonID
JOIN Person P3 ON F2.F3.PersonID =
P3.PersonID
MATCH (a:Person)-[:KNOWS*3..3]->(b)
RETURN a.name.b.name
SQL
Neo4j

Everything is Connected to Everything Else

Connecting people
Calls
Has
phone
Has
phone
Has
phone
Has
phone
Calls
Has
email
Has
email
Has
email
Has
email
Sends
email
Sends
email
A
B
C D
E
Meets
Has
email

Impact Analysis
Which elements have the most
dependencies?

Master Data Management
New relationships
are easily reflected

Connecting the Data Lake

Recommendations
Without a graph database, you can still create a
representation of a graph to be used for
recommendations.
But, that will be a static representation, and will
not be able to take into account events in the
current session.

Cognitive Analytucs
Neural Networks

Thank you

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Sara Mae O’Brien Scott and Tatiana Baquero Cakici, Senior Consultants at Enterprise Knowledge (EK), presented “AI Fast Track to Search-Focused AI Solutions” at the Information Architecture Conference (IAC24) that took place on April 11, 2024 in Seattle, WA. In their presentation, O’Brien-Scott and Cakici focused on what Enterprise AI is, why it is important, and what it takes to empower organizations to get started on a search-based AI journey and stay on track. The presentation explored the complexities of enterprise search challenges and how IA principles can be leveraged to provide AI solutions through the use of a semantic layer. O’Brien-Scott and Cakici showcased a case study where a taxonomy, an ontology, and a knowledge graph were used to structure content at a healthcare workforce solutions organization, providing personalized content recommendations and increasing content findability. In this session, participants gained insights about the following: Most common types of AI categories and use cases; Recommended steps to design and implement taxonomies and ontologies, ensuring they evolve effectively and support the organization’s search objectives; Taxonomy and ontology design considerations and best practices; Real-world AI applications that illustrated the value of taxonomies, ontologies, and knowledge graphs; and Tools, roles, and skills to design and implement AI-powered search solutions.

IAC 2024 - IA Fast Track to Search Focused AI Solutions

IAC 2024 - IA Fast Track to Search Focused AI Solutions

IAC 2024 - IA Fast Track to Search Focused AI Solutions

Enterprise Knowledge

The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

Artificial Intelligence: Facts and Myths

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Axa Assurance Maroc - Insurer Innovation Award 2024

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The 7 Things I Know About Cyber Security After 25 Years | April 2024

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GraphDay Stockholm - iKnow Solutions - The Value Add of Graphs to Analytics and Information Management

1. The Value Add of Graphs to Analytics and Information Management

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3. iKnow Solutions - Our core skills

4.

6. A hard question to ask a relational DB… Find friends of 3rd degree

7. SELECT P.Name, P3.Name FROM Person P JOIN PersonFriend F1 ON P.PersonID = F1.PersonID JOIN PersonFriend F2 ON F1.FriendID = F2.PersonID JOIN PersonFriend F3 ON F2.FriendID = F3.PersonID JOIN Person P3 ON F2.F3.PersonID = P3.PersonID MATCH (a:Person)-[:KNOWS*3..3]->(b) RETURN a.name.b.name SQL Neo4j

8. Everything is Connected to Everything Else

9. Connecting people Calls Has phone Has phone Has phone Has phone Calls Has email Has email Has email Has email Sends email Sends email A B C D E Meets Has email

10. Impact Analysis Which elements have the most dependencies?

11. Master Data Management New relationships are easily reflected

12. Connecting the Data Lake

13. Recommendations Without a graph database, you can still create a representation of a graph to be used for recommendations. But, that will be a static representation, and will not be able to take into account events in the current session.

14. Cognitive Analytucs Neural Networks