Autodesk Netfabb Ultimate 2025 free crack

© 2023 Neo4j, Inc. All rights reserved.
Nouveautés
produits
Nicolas Rouyer, Field Engineer, Neo4j

Agenda
Nouvelles fonctionnalités Neo4j 5
2

Nouveautés Neo4j 5
Synthèse
3

Neo4j 5 -Téléchargement
4
https://neo4j.com/download-center/

NEO4J 5
Performances, Passage à l’échelle,
agilité
5
Performances
Élargies
Scalabilité
illimitée
sans effort
Flexibilité
Totale
des
Opérations
Répondre + vite à
des requêtes
+ complexes
Scalabilité H/V
+ automatique
-de coûts
d’infrastructure
Déployer,
exécuter et
exploiter Neo4j
simplement
et partout

Schéma du graphe
Nouvelles contraintes sur les nœuds,
les relations et les propriétés :
● Unique relationship property,
● Relationship key,
● Property data types
NOUVELLES FONCTIONNALITÉS NEO4J 5
Améliorations Base de
Données
6

Graph Pattern Matching
“La REGEX de la traversée”
Amélioration de l'expressivité de la navigation
dans les graphes avec les
Quantiﬁed Path Patterns (QPP).
Syntaxe + puissante et + performante pour
naviguer et parcourir votre graphe..
Données
7

Données
Exemple Graph Pattern Matching
8
MATCH path=(a:Account)-[rel:TRANSACTION*3..6]->(a)
WHERE size(apoc.coll.toSet(nodes(path))) = size(nodes(path)) - 1
AND ALL(idx in range(0, size(rel)-2)
WHERE (rel[idx]).date < (rel[idx+1]).date
AND (rel[idx+1].amount / rel[idx].amount) * 100 <= 20
)
RETURN path
Avant-QPP

Données
Exemple Graph Pattern Matching
9
QPP
MATCH path=(a:Account)-[:PERFORMS]->(first_tx)
((tx_i)-[:BENEFITS_TO]->(a_i)-[:PERFORMS]->(tx_j)
WHERE tx_i.date < tx_j.date
AND 0.80 <= tx_i.amount / tx_j.amount <= 1.00
){3,6}
(last_tx)-[:BENEFITS_TO]->(a)
WHERE size(apoc.coll.toSet([a]+a_i)) = size([a]+a_i)
RETURN path

Parallel
Runtime
10

Parallel Runtime : requêtes analytiques
accélérées x100
11

Parallel Runtime : requêtes analytiques
accélérées x100
12
+ de cores
Requêtes
+ rapides

Change Data
Capture (CDC)
13

CDC : Suivi automatisé en temps réel des
modiﬁcations
14

Perspectives :
Auto-Sharding
15

Auto-Sharding Fabric
16
A venir 2024+
Mapping
Règles de
Sharding
● Exploiter les indications du client pour créer une
stratégie de répartition
● Permettre la création de schéma/métadonnées pour le
sharding
● Augmenter automatiquement le nombre de shards en
fonction de la conﬁguration du client
● Créer un planner Fabric pour transférer les données
et les requêtes vers les bons shards
● Prendre en charge les transactions distribuées et les
proxy nodes
● Rendre disponible l’auto-sharding dans Aura

Neo4j 5
Détails
17

18
XP
Développeur
Data Scientist
Langage
et
Modèle
Performances
en
lecture/expor
t
Neo4j 5
Performances
en
écriture/import
Passage à l’
échelle et
Haute Dispo
Exploitation
Cloud Native
Gestion de
versions et
Support
Administratio
n
Exploitation
et
Observabilité
Sécurité

XP Développeur/Data Scientist
19
• Python driver export API (Aura)
• Export Neo4j result sets to Pandas dataframes
• Python driver support for asyncio
• ‘Retryable’ flag on Error objects (Aura)
• Query String and Parameters in Go driver results objects renamed
• Procedures can specify analyzer in Full-text queries (Aura)
• Notifications (Aura)
• Updates severity on some of the existing notifications

Langage et Modèle
20
• New Element ID for graph objects (Aura)
ID to uniquely identify the object and the database it is from.
• Graph Pattern Matching-relationship and label patterns (Aura)
(part of the Graph Pattern Matching long-term effort in Cypher)
• Additions to Cypher:
Underscores in numeric literals, count, exists(), isNaN
• APOC 5 Compatible Plugin (Aura)
Extended has been moved out to a community supported library
• Traversal API
Improved documentation and examples

Graph Pattern Matching
21
Neo4j 5
Simpler alternative syntax to navigate and traverse your
graph
- Disjunction (v:L1|L2)
- Negation (v:!L1)
- Grouping (v: (L1&L2)|(L3&!L4))
- Repetition ()* // 0..n ()+ //1..n
- Conjunction (v:L1&L2)

Performances en lecture/export
22
• Faster TEXT indexes with Trigram analyzer (Aura)
• Include Lists in FULLTEXT indexes (Aura)
• New RANGE and POINT indexes (Aura)
• Auto-parameterize partial parameterized queries (Aura)
• Faster K-Hop Queries in Cypher (Aura)
• Slotted Runtime for Community Edition

K-Hop Query optimization
23
In Neo4j 5: breadth first
Before: depth first
Benefit: orders of magnitude faster!
K-Hop query: variable length patterns.
Ex: MATCH (:LabelA)-[:RELTYPE*1..5]->(:LabelB)

Performances en écriture/import
24
• neo4j-admin import can now perform incremental
load
into an existing database
• CALL … {} … IN TRANSACTION subquery support
writes
in PIPELINED Runtime (Aura)
• No Eager for DETACH DELETE (n) in CALL IN
TRANSACTIONS (Aura)

Passage à l’échelle et Haute Disponibilité
25
• Autonomous Clustering (Enterprise)
The new clustering infrastructure decouples
servers from databases, improving scalability and
cloud-readiness.
• Create Database from URI
• COMPOSITE databases (Enterprise)
Fabric technology is used to improve the setting
and management of sharded and federated
databases with dynamic compositions of databases

Exploitation Cloud Native
26
• Immutable privileges (Aura)
Cloud operators can use sidecar design pattern to control
RBAC-based permissions.
• Server-side routing enabled by default (Aura)
Simplify operations in VPC, behind ﬁrewalls & network appliances
• Docker & Cloud Marketplace Publishing
Neo4j 4.4 and 5 published as a single image in AWS, GCP and Azure.
• ARM and Corretto JVM on AWS
Recommend AWS Corretto JVM for consistent write throughput

Gestion de versions et Support
27
• Neo4j 5 lifecycle and release cadence
• New supported platforms, require JDK 17 and Scala upgrade
• Any-to-any Neo4j 5.X version and patch release
• Neo4j 4.4 to Neo4j 5.X migration
• Improved eval/ commercial messaging
• Db.tx_state.memory_allocation
• Reorder transaction log ﬁle list in debug.log
• Cypher-shell Java Driver Logging
• Transaction log ﬁle rotation and retention policy change

Neo4j 5 Continuous Release
Support Model
28
5.LTS.0
Fixes ONLY
Fixes
Frequent
5.0.0
5.LTS+n.0
Features
LTS in June 2024
Final Features Released
End of Support Nov
2027
Upgrade to the latest version to receive latest ﬁxes from Support
Frequent (~monthly) releases for features and fixes

Administration, Exploitation et Observabilité
29
• neo4j-admin refresh
Improved features, more control and consistent set of arguments for
the admin
• Conﬁguration settings refresh
Settings have been reviewed, old and unnecessary settings have been
removed. A more consistent namespace structure has been introduce.
Settings in neo4j.conf have now strict validation.
• Improved procedures to check and set conﬁg values
• Fully integrated Log4j
The logging framework is fully integrated with Log4j, provides better
control of all the database logs.

Administration, Exploitation et
Observabilité
30
• Cypher-shell improvements
Access available when there is no leader, plus auto completion for
commands.
• SHOW/TERMINATE TRANSACTION improvements (Aura)
More information and control about running and terminating
transaction with the CALL … IN TRANSACTIONS
• Neo4j Operations Manager (Enterprise)
The new UI based tool to monitor, administer and operate Neo4j 4.4
and 5.x.
• Enhanced Backup (Enterprise)
Backup provides new compressed ﬁle format, an API, differential
backup, ‘restore until’ capabilities

Sécurité
31
• Execute and ExecuteBoosted privilege review (Enterprise)
Refresh of permissions for the execution of admin procedures
• Cypher-Shell supports impersonation (Enterprise)

Futur moteur de stockage au format block
32
MATCH (:Person {name:
'Bob'})-[r:LIKES]->(:Person {name:
'Alice'})

Neo4j Vector Index
● Les Neo4j vector indexes utilisent Apache Lucene (search, indexing).
● Lucene implémente l’algorithme Hierarchical Navigable Small World (HNSW) pour effectuer une
requête k~approximate nearest neighbors (k-ANN) sur les propriétés vectorielles.
CALL db.index.vector.createNodeIndex('abstract-embeddings', 'Abstract',
'embedding', 1536, 'cosine');
MATCH (title:Title)<--(:Paper)-->(abstract:Abstract)
WHERE toLower(title.text) = 'efficient and robust approximate nearest
neighbor search using hierarchical navigable small world graphs'
CALL db.index.vector.queryNodes('abstract-embeddings', 10, abstract.embedding)
YIELD node AS similarAbstract, score
MATCH (similarAbstract)<--(:Paper)-->(similarTitle:Title)
RETURN similarTitle.text AS title, score;
33

Intégration
Google Dataﬂow
34

Intégration GCP DataFlow
35
https://neo4j.com/blog/loading-data-neo4j-google-cloud-dataflow/
https://cloud.google.com/dataflow/docs/guides/templates/provided/google-cloud-to-neo4j?hl=fr

Nouveautés
Graph Data Science
36

Edge embeddings
Découvrir les relations
manquantes
● Users generate edge embeddings
using DGL, PyKeen, or PyTorch
Geometric
● Bring them into Neo4j Graph Data
Science or AuraDS stored as a
property.
● Use similarity functions to identify
possible relationship candidates
and check node pairs.
● Create relationships
Help predict missing
relationships between entities
Infer new relationships
Improve semantic
understanding and reasoning
Neo4j Inc. All rights reserved 2023
37
How it Helps You

Longest Path Algo
Find the path of maximum
length between two nodes
in a directed acyclic graph

Longest Path
Once a directed acyclic graph
is built, use either Graph Data
Science or the Python client to
call the Longest Path
Algorithm.
Find the path of maximum
length between two nodes in a
graph.
Help solve critical scheduling and
optimization problems by
understanding longest paths
How it Helps You

Topological Sort Algo
Sort nodes in a directed
acyclic graph by
traversing them in
order of dependency
Identify dependencies

Identify
Dependencies
Once a directed acyclic graph
is built, use either Graph Data
Science or the Python client to
call the topological sort
algorithm.
Find dependencies in the
graph.
Ensure that dependent tasks are
performed in the correct order
How it Helps You

Rappels LLM+Neo4j
42

Neo4j et l’IA Générative
1
2
3
L'IA générative adaptée à votre organisation
Utilisez Neo4j pour adapter l’IAGen à votre entreprise,
améliorer la précision et réduire les erreurs.
Démarrer un Graphe de Connaissances avec les LLMs
Utiliser LLMs + Neo4j pour l'extraction d'entités/relations
et la création de graphes de connaissances
Partenariats poussés
avec les fournisseurs cloud
pour l'IA générative

https://data.world/blog/generative-
ai-benchmark-increasing-the-accur
acy-of-llms-in-the-enterprise-with-
a-knowledge-graph/
Neo4j
Fiabilise
les LLMs
45

Merci!
nicolas.rouyer@neo4j.com
46

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