SlideShare a Scribd company logo
ingraph: Live Queries on Graphs
Gábor Szárnyas
GraphConnect 2017
Live Graph Queries
Railway operation model
Railway operation model
Railway operation model
Railway operation model
Railway operation model
Proximity detection
Railway operation model
Proximity detection
Railway operation model
Proximity detection
Railway operation model
Trailing the switch
Proximity detection
Railway operation model
Railway operation model
Railway operation model
c d e
g
fdiv
2
a b
1
Railway operation model
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
ON
a b
1
NEXT
ON
NEXT
Proximity detection
Proximity detection
≤ 𝟏 segment
Proximity detection
seg1
NEXT: 1..2
t1
ON
Proximity detection
seg2
t2
ON
≤ 𝟏 segment
Proximity detection
seg1
NEXT: 1..2
t1
ON
MATCH
(t1:Train)-[:ON]->(seg1:Segment)
-[:NEXT*1..2]->(seg2:Segment)
<-[:ON]-(t2:Train)
RETURN t1, t2, seg1, seg2
Proximity detection
seg2
t2
ON
≤ 𝟏 segment
Proximity detection
seg1
NEXT: 1..2
t1
ON
MATCH
(t1:Train)-[:ON]->(seg1:Segment)
-[:NEXT*1..2]->(seg2:Segment)
<-[:ON]-(t2:Train)
RETURN t1, t2, seg1, seg2
Proximity detection
seg2
t2
ON
≤ 𝟏 segment
Trailing the switch
Trailing the switch
seg div
t
STRAIGHT
ON
Trailing the switch
seg div
t
STRAIGHT
ON
MATCH (t:Train)-[:ON]->(seg:Segment)
<-[:STRAIGHT]-(sw:Switch)
WHERE sw.position = 'diverging'
RETURN t.number, sw
Trailing the switch
seg div
t
STRAIGHT
ON
MATCH (t:Train)-[:ON]->(seg:Segment)
<-[:STRAIGHT]-(sw:Switch)
WHERE sw.position = 'diverging'
RETURN t.number, sw
Trailing the switch
seg div
t
STRAIGHT
ON
MATCH (t:Train)-[:ON]->(seg:Segment)
<-[:STRAIGHT]-(sw:Switch)
WHERE sw.position = 'diverging'
RETURN t.number, sw
Evaluate
continuously
Batch vs. live queries
Batch vs. live queries
1. Client selects a query
2. Results are calculated
Batch queries
Results obtained on demand
Batch vs. live queries
1. Client selects a query
2. Results are calculated
1. Client registers queries
2. Graph is changed
3. Results are maintained
4. Goto 2
Batch queries Live queries
Results always available
Clients receive notifications
Results obtained on demand
Batch vs. live queries
1. Client selects a query
2. Results are calculated
1. Client registers queries
2. Graph is changed
3. Results are maintained
4. Goto 2
Batch queries Live queries
Results always available
Clients receive notifications
Results obtained on demand
Incremental query evaluation
Incremental query engines
CLIPS C NASA
Drools Java Red Hat
VIATRA Java/EMF BME & IncQuery Labs
Incremental query engines
CLIPS C NASA
Drools Java Red Hat
VIATRA Java/EMF BME & IncQuery Labs
INSTANS LISP/RDF Aalto University
i3QL Scala TU Darmstadt
IncQuery-D Scala/RDF BME
Incremental query engines
CLIPS C NASA
Drools Java Red Hat
VIATRA Java/EMF BME & IncQuery Labs
INSTANS LISP/RDF Aalto University
i3QL Scala TU Darmstadt
IncQuery-D Scala/RDF BME
No implementations for property graphs yet
ingraph
 PoC query engine
 Goals:
o Provide incremental query evaluation for graphs
o Parallel & distributed operation to allow scalability
ingraph
 PoC query engine
 Goals:
o Provide incremental query evaluation for graphs
o Parallel & distributed operation to allow scalability
ingraphClient
ingraph
 PoC query engine
 Goals:
o Provide incremental query evaluation for graphs
o Parallel & distributed operation to allow scalability
ingraphClient
register queries
ingraph
 PoC query engine
 Goals:
o Provide incremental query evaluation for graphs
o Parallel & distributed operation to allow scalability
ingraphClient
register queries
query results
ingraph
 PoC query engine
 Goals:
o Provide incremental query evaluation for graphs
o Parallel & distributed operation to allow scalability
ingraphClient
register queries
query results
update graph
ingraph
 PoC query engine
 Goals:
o Provide incremental query evaluation for graphs
o Parallel & distributed operation to allow scalability
ingraphClient
register queries
query results
change notifications
update graph
Under the Hood
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON
div
STRAIGHT
Trailing the switch
ON
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
a
1
ON
e
2
ON
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e2
ON
a1
ON
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
a
1
ON
e
2
ON
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e2
ON
a1
ON
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e2
ON
a1
ON
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
e div
STRAIGHT
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e2
ON
a1
ON
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
e div
STRAIGHT
e div
STRAIGHT
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
e2
ON
a1
ON
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
e2
ON
a1
ON
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
e div
STRAIGHT
e2
ON
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
e2
ON
a1
ON
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
e div
STRAIGHT
e2
ON
e div
2
STRAIGHT
ON
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
e2
ON
a1
ON
e div
STRAIGHT
2
ON
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
e div
STRAIGHT
e2
ON
e div
2
STRAIGHT
ON
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
e2
ON
a1
ON
div
STRAIGHTON
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
e2 div
STRAIGHTON
e2
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
e2
ON
a1
ON
div
STRAIGHTON
e div
STRAIGHT
2
ON
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
e2 div
STRAIGHTON
e2
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
e2
ON
a1
ON
e div
STRAIGHT
2
ON
e div
STRAIGHT
2
ON
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
div
2
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
e2
ON
a1
ON
e div
STRAIGHT
2
ON
e div
STRAIGHT
2
ON
div2
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
div
2
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
e2
ON
a1
ON
e div
STRAIGHT
2
ON
e div
STRAIGHT
2
ON
div2
c d e
g
fdiv
2
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
ON
div
STRAIGHT
Trailing the switch
ON
div
2
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
e2
ON
a1
ON
e div
STRAIGHT
2
ON
e div
STRAIGHT
2
ON
div2
c e
g
fdiv
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
div
STRAIGHT
Trailing the switch
ON
div
ON
2
d
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
e2
ON
a1
ON
e div
STRAIGHT
2
ON
e div
STRAIGHT
2
ON
div2
c e
g
fdiv
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
div
STRAIGHT
Trailing the switch
ON
div
ON
2
d
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
a1
ON
e div
STRAIGHT
2
ON
e div
STRAIGHT
2
ON
div2
c e
g
fdiv
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
div
STRAIGHT
Trailing the switch
ON
div
ON
2
d
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
d2
ON
a1
ON
e div
STRAIGHT
2
ON
e div
STRAIGHT
2
ON
div2
c e
g
fdiv
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
div
STRAIGHT
Trailing the switch
ON
div
ON
2
d
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
d2
ON
a1
ON
e div
STRAIGHT
2
ON
e div
STRAIGHT
2
ON
div2
c e
g
fdiv
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
div
STRAIGHT
Trailing the switch
ON
div
ON
2
d
e div2
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
d2
ON
a1
ON
e div
STRAIGHT
2
ON
div2
c e
g
fdiv
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
div
STRAIGHT
Trailing the switch
ON
div
ON
2
d
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
d2
ON
a1
ON
e div
STRAIGHT
2
ON
div2
c e
g
fdiv
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
div
STRAIGHT
Trailing the switch
ON
div
ON
2
d
e div2
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
d2
ON
a1
ON
div2
c e
g
fdiv
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
div
STRAIGHT
Trailing the switch
ON
div
ON
2
d
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
d2
ON
a1
ON
div2
c e
g
fdiv
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
div
STRAIGHT
Trailing the switch
ON
div
ON
2
d
div2
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
d2
ON
a1
ON
c e
g
fdiv
NEXT NEXT
STRAIGHT TOP
a b
1
NEXT NEXT
ON
div
STRAIGHT
Trailing the switch
ON
div
ON
2
d
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
d2
ON
a1
ON
div
STRAIGHT
Trailing the switch
ON
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
d2
ON
a1
ON
div
STRAIGHT
Trailing the switch
ON
Actors
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
d2
ON
a1
ON
div
STRAIGHT
Trailing the switch
ON
Actors
Async messages
πt.number, sw
σsw.position = ′diverging′
⋈
STRAIGHTON e div
STRAIGHT
d2
ON
a1
ON
div
STRAIGHT
Trailing the switch
ON
Actors
Async messages
Szárnyas, G. et al.
IncQuery-D: A distributed incremental model query framework in the cloud.
MODELS 2014
Architecture
ingraph’s query language
openCypher: an open specification of the Cypher language
 Cypher Reference Documentation
 Grammar specification
 TCK (Technology Compatibility Kit)
 Cypher language specification
ingraph’s query language
openCypher: an open specification of the Cypher language
 Cypher Reference Documentation
 Grammar specification
 TCK (Technology Compatibility Kit)
 Cypher language specification
Work-in-progress,
no formal semantics
Formalisation of openCypher
 First work to formalise of openCypher
 Covers most standard constructs
Marton, J., Szárnyas, G. and Varró, D.:
Formalising openCypher Graph Queries in Relational Algebra,
Preprint on arXiv
MATCH (t:Train)-[:ON]->(seg:Segment)
<-[:STRAIGHT]-(sw:Switch)
WHERE sw.position = 'diverging'
RETURN t.number, sw
openCypher
query
MATCH (t:Train)-[:ON]->(seg:Segment)
<-[:STRAIGHT]-(sw:Switch)
WHERE sw.position = 'diverging'
RETURN t.number, sw
openCypher
query
Query
syntax tree
MATCH (t:Train)-[:ON]->(seg:Segment)
<-[:STRAIGHT]-(sw:Switch)
WHERE sw.position = 'diverging'
RETURN t.number, sw
Query
parser
openCypher
query
Query
syntax tree
MATCH (t:Train)-[:ON]->(seg:Segment)
<-[:STRAIGHT]-(sw:Switch)
WHERE sw.position = 'diverging'
RETURN t.number, sw
Query
parser
openCypher
query
Relational
Graph
Algebra
Query
syntax tree
MATCH (t:Train)-[:ON]->(seg:Segment)
<-[:STRAIGHT]-(sw:Switch)
WHERE sw.position = 'diverging'
RETURN t.number, sw
Relational
algebra
builder
Query
parser
openCypher
query
Relational
Graph
Algebra
Query
syntax tree
MATCH (t:Train)-[:ON]->(seg:Segment)
<-[:STRAIGHT]-(sw:Switch)
WHERE sw.position = 'diverging'
RETURN t.number, sw
Relational
algebra
builder
Query
parser
openCypher
query
Relational
Graph
Algebra
Query
syntax tree
MATCH (t:Train)-[:ON]->(seg:Segment)
<-[:STRAIGHT]-(sw:Switch)
WHERE sw.position = 'diverging'
RETURN t.number, sw
Relational
algebra
builder
Query
parser
openCypher
query
Relational
Graph
Algebra
Query
syntax tree
Relational
Graph
Algebra
Rete
network
Rete
network
model
Relational
Graph
Algebra
Rete
network
Rete
network
model
Transformer
and optimizer
Relational
Graph
Algebra
Rete
network
Rete
network
model
Transformer
and optimizer
VIATRA
Relational
Graph
Algebra
Rete
network
Rete
network
model
Transformer
and optimizer
Query
deployer
VIATRA
Use Cases
Constraint validation
 Design-time constraints
in a railway network
 Scalable graph generator
 EMF (Eclipse modelling)
 Property graph
 RDF (semantic web)
 SQL
 Validation queries and
model transformations
 Implemented for 12+ tools
Szárnyas, G. et al.: The Train Benchmark:
cross-technology performance evaluation
of continuous model queries, SOSYM 2017
ftsrg/trainbenchmark
Static analysis of JavaScript
var foo = 1 / 0;
ftsrg/codemodel-rifle
Static analysis of JavaScript
VariableDeclarator
BindingIdentifier
name = `foo`
BinaryExpression
operator = `Div`
LNExpression
value = 1.0
LNExpression
value = 0.0
var foo = 1 / 0;
ftsrg/codemodel-rifle
Static analysis of JavaScript
VariableDeclarator
BindingIdentifier
name = `foo`
BinaryExpression
operator = `Div`
LNExpression
value = 1.0
LNExpression
value = 0.0
var foo = 1 / 0;
MATCH
(binding:BindingIdentifier)<-[:binding]-()-->
(be:BinaryExpression)-[:right]->
(right:LNExpression)
WHERE be.operator = 'Div'
AND right.value = 0.0
RETURN binding
ftsrg/codemodel-rifle
Static analysis of JavaScript
VariableDeclarator
BindingIdentifier
name = `foo`
BinaryExpression
operator = `Div`
LNExpression
value = 1.0
LNExpression
value = 0.0
binding be
right
var foo = 1 / 0;
MATCH
(binding:BindingIdentifier)<-[:binding]-()-->
(be:BinaryExpression)-[:right]->
(right:LNExpression)
WHERE be.operator = 'Div'
AND right.value = 0.0
RETURN binding
ftsrg/codemodel-rifle
Static analysis of JavaScript
VariableDeclarator
BindingIdentifier
name = `foo`
BinaryExpression
operator = `Div`
LNExpression
value = 1.0
LNExpression
value = 0.0
binding be
right
var foo = 1 / 0;
MATCH
(binding:BindingIdentifier)<-[:binding]-()-->
(be:BinaryExpression)-[:right]->
(right:LNExpression)
WHERE be.operator = 'Div'
AND right.value = 0.0
RETURN binding
ECMAScript 6
• Dead code detection
• Type inferencing
ftsrg/codemodel-rifle
Summary
ftsrg/ingraph
Summary
 Consider live graph queries for
o Large graph
o Complex queries
o Continuous changes
ftsrg/ingraph
Summary
 Consider live graph queries for
o Large graph
o Complex queries
o Continuous changes
 Current goals
o Test with the openCypher Technology Compatibility Kit
o Performance evaluation using the LDBC Social Network Benchmark
ftsrg/ingraph
Summary
 Consider live graph queries for
o Large graph
o Complex queries
o Continuous changes
 Current goals
o Test with the openCypher Technology Compatibility Kit
o Performance evaluation using the LDBC Social Network Benchmark
 Use cases wanted
o Example datasets
o Fraud detection queries
ftsrg/ingraph
Open-source projects
Incremental Graph Engine: github.com/ftsrg/ingraph
Train Benchmark: github.com/ftsrg/trainbenchmark
Codemodel-Rifle: github.com/ftsrg/codemodel-rifle
openCypher TCK: github.com/bme-db-lab/opencypher-tck-tests
This project was supported by the
MTA-BME Lendület Research Group on Cyber-Physical Systems.
Thanks to the openCypher team and the contributors of ingraph.

More Related Content

Similar to ingraph: Live Queries on Graphs

GraphBolt: Dependency-Driven Synchronous Processing of Streaming Graphs
GraphBolt: Dependency-Driven Synchronous Processing of Streaming GraphsGraphBolt: Dependency-Driven Synchronous Processing of Streaming Graphs
GraphBolt: Dependency-Driven Synchronous Processing of Streaming Graphs
Mugilan Mariappan
 
Learning Timed Automata with Cypher
Learning Timed Automata with CypherLearning Timed Automata with Cypher
Learning Timed Automata with Cypher
Gábor Szárnyas
 
Digital design slides for engineering
Digital  design   slides for engineeringDigital  design   slides for engineering
Digital design slides for engineering
kasheen2803
 
quick and merge.pptx
quick and merge.pptxquick and merge.pptx
quick and merge.pptx
LakshayYadav46
 
motor_2.ppt
motor_2.pptmotor_2.ppt
motor_2.ppt
VikramJit13
 
Pivot Sampling in Dual-Pivot Quicksort
Pivot Sampling in Dual-Pivot QuicksortPivot Sampling in Dual-Pivot Quicksort
Pivot Sampling in Dual-Pivot Quicksort
Sebastian Wild
 
2D viewing
2D viewing2D viewing
2D viewing
HiteshJain007
 
7-conversion-of-ff.pdf
7-conversion-of-ff.pdf7-conversion-of-ff.pdf
7-conversion-of-ff.pdf
satyaSatyant234
 
The_ERICSSON_commands_listed_below_are_f (1) (1).pdf
The_ERICSSON_commands_listed_below_are_f (1) (1).pdfThe_ERICSSON_commands_listed_below_are_f (1) (1).pdf
The_ERICSSON_commands_listed_below_are_f (1) (1).pdf
ssuser340a0c
 
The making of the Perfect MOSFET Final
The making of the Perfect MOSFET FinalThe making of the Perfect MOSFET Final
The making of the Perfect MOSFET FinalAlan Elbanhawy
 
Chapter4flipflop forstudents-131112193906-phpapp02
Chapter4flipflop forstudents-131112193906-phpapp02Chapter4flipflop forstudents-131112193906-phpapp02
Chapter4flipflop forstudents-131112193906-phpapp02
Seshu Chakravarthy
 
Single elctron transisto PHASE 2.pptx
Single elctron transisto PHASE 2.pptxSingle elctron transisto PHASE 2.pptx
Single elctron transisto PHASE 2.pptx
ssuser1580e5
 
2005 PT Design Of Interior Permanant Magnet Machines For Hybrid Electric Vehi...
2005 PT Design Of Interior Permanant Magnet Machines For Hybrid Electric Vehi...2005 PT Design Of Interior Permanant Magnet Machines For Hybrid Electric Vehi...
2005 PT Design Of Interior Permanant Magnet Machines For Hybrid Electric Vehi...
csezenoglu
 
Chapter 7: Matrix Multiplication
Chapter 7: Matrix MultiplicationChapter 7: Matrix Multiplication
Chapter 7: Matrix Multiplication
Heman Pathak
 
fdocuments.in_the-ericsson-commands.pdf
fdocuments.in_the-ericsson-commands.pdffdocuments.in_the-ericsson-commands.pdf
fdocuments.in_the-ericsson-commands.pdf
SaidHaman
 
Set Algebra for Service Behavior
Set Algebra for Service BehaviorSet Algebra for Service Behavior
Set Algebra for Service Behavior
Universität Rostock
 
Lesson_11_Low-level_Flight_Control hexa copt best ppt.pdf
Lesson_11_Low-level_Flight_Control hexa copt best ppt.pdfLesson_11_Low-level_Flight_Control hexa copt best ppt.pdf
Lesson_11_Low-level_Flight_Control hexa copt best ppt.pdf
BorchalaDareje
 
Roaster calculation for shifting 24x7.pptx
Roaster calculation for shifting 24x7.pptxRoaster calculation for shifting 24x7.pptx
Roaster calculation for shifting 24x7.pptx
ssuser1c9455
 
Cassandra : to be or not to be @ TechTalk
Cassandra : to be or not to be @ TechTalkCassandra : to be or not to be @ TechTalk
Cassandra : to be or not to be @ TechTalk
Andriy Rymar
 

Similar to ingraph: Live Queries on Graphs (20)

GraphBolt: Dependency-Driven Synchronous Processing of Streaming Graphs
GraphBolt: Dependency-Driven Synchronous Processing of Streaming GraphsGraphBolt: Dependency-Driven Synchronous Processing of Streaming Graphs
GraphBolt: Dependency-Driven Synchronous Processing of Streaming Graphs
 
Learning Timed Automata with Cypher
Learning Timed Automata with CypherLearning Timed Automata with Cypher
Learning Timed Automata with Cypher
 
Digital design slides for engineering
Digital  design   slides for engineeringDigital  design   slides for engineering
Digital design slides for engineering
 
quick and merge.pptx
quick and merge.pptxquick and merge.pptx
quick and merge.pptx
 
motor_2.ppt
motor_2.pptmotor_2.ppt
motor_2.ppt
 
Pivot Sampling in Dual-Pivot Quicksort
Pivot Sampling in Dual-Pivot QuicksortPivot Sampling in Dual-Pivot Quicksort
Pivot Sampling in Dual-Pivot Quicksort
 
05viewing2d
05viewing2d05viewing2d
05viewing2d
 
2D viewing
2D viewing2D viewing
2D viewing
 
7-conversion-of-ff.pdf
7-conversion-of-ff.pdf7-conversion-of-ff.pdf
7-conversion-of-ff.pdf
 
The_ERICSSON_commands_listed_below_are_f (1) (1).pdf
The_ERICSSON_commands_listed_below_are_f (1) (1).pdfThe_ERICSSON_commands_listed_below_are_f (1) (1).pdf
The_ERICSSON_commands_listed_below_are_f (1) (1).pdf
 
The making of the Perfect MOSFET Final
The making of the Perfect MOSFET FinalThe making of the Perfect MOSFET Final
The making of the Perfect MOSFET Final
 
Chapter4flipflop forstudents-131112193906-phpapp02
Chapter4flipflop forstudents-131112193906-phpapp02Chapter4flipflop forstudents-131112193906-phpapp02
Chapter4flipflop forstudents-131112193906-phpapp02
 
Single elctron transisto PHASE 2.pptx
Single elctron transisto PHASE 2.pptxSingle elctron transisto PHASE 2.pptx
Single elctron transisto PHASE 2.pptx
 
2005 PT Design Of Interior Permanant Magnet Machines For Hybrid Electric Vehi...
2005 PT Design Of Interior Permanant Magnet Machines For Hybrid Electric Vehi...2005 PT Design Of Interior Permanant Magnet Machines For Hybrid Electric Vehi...
2005 PT Design Of Interior Permanant Magnet Machines For Hybrid Electric Vehi...
 
Chapter 7: Matrix Multiplication
Chapter 7: Matrix MultiplicationChapter 7: Matrix Multiplication
Chapter 7: Matrix Multiplication
 
fdocuments.in_the-ericsson-commands.pdf
fdocuments.in_the-ericsson-commands.pdffdocuments.in_the-ericsson-commands.pdf
fdocuments.in_the-ericsson-commands.pdf
 
Set Algebra for Service Behavior
Set Algebra for Service BehaviorSet Algebra for Service Behavior
Set Algebra for Service Behavior
 
Lesson_11_Low-level_Flight_Control hexa copt best ppt.pdf
Lesson_11_Low-level_Flight_Control hexa copt best ppt.pdfLesson_11_Low-level_Flight_Control hexa copt best ppt.pdf
Lesson_11_Low-level_Flight_Control hexa copt best ppt.pdf
 
Roaster calculation for shifting 24x7.pptx
Roaster calculation for shifting 24x7.pptxRoaster calculation for shifting 24x7.pptx
Roaster calculation for shifting 24x7.pptx
 
Cassandra : to be or not to be @ TechTalk
Cassandra : to be or not to be @ TechTalkCassandra : to be or not to be @ TechTalk
Cassandra : to be or not to be @ TechTalk
 

More from Neo4j

Atelier - Architecture d’applications de Graphes - GraphSummit Paris
Atelier - Architecture d’applications de Graphes - GraphSummit ParisAtelier - Architecture d’applications de Graphes - GraphSummit Paris
Atelier - Architecture d’applications de Graphes - GraphSummit Paris
Neo4j
 
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissancesAtelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Neo4j
 
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j
 
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j
 
FLOA - Détection de Fraude - GraphSummit Paris
FLOA -  Détection de Fraude - GraphSummit ParisFLOA -  Détection de Fraude - GraphSummit Paris
FLOA - Détection de Fraude - GraphSummit Paris
Neo4j
 
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...
Neo4j
 
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...
ADEO -  Knowledge Graph pour le e-commerce, entre challenges et opportunités ...ADEO -  Knowledge Graph pour le e-commerce, entre challenges et opportunités ...
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...
Neo4j
 
GraphSummit Paris - The art of the possible with Graph Technology
GraphSummit Paris - The art of the possible with Graph TechnologyGraphSummit Paris - The art of the possible with Graph Technology
GraphSummit Paris - The art of the possible with Graph Technology
Neo4j
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Neo4j
 
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Neo4j
 
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Neo4j
 
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neo4j
 
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Neo4j
 
GraphAware - Transforming policing with graph-based intelligence analysis
GraphAware - Transforming policing with graph-based intelligence analysisGraphAware - Transforming policing with graph-based intelligence analysis
GraphAware - Transforming policing with graph-based intelligence analysis
Neo4j
 
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product UpdatesGraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates
Neo4j
 
KLARNA - Language Models and Knowledge Graphs: A Systems Approach
KLARNA -  Language Models and Knowledge Graphs: A Systems ApproachKLARNA -  Language Models and Knowledge Graphs: A Systems Approach
KLARNA - Language Models and Knowledge Graphs: A Systems Approach
Neo4j
 
INGKA DIGITAL: Linked Metadata by Design
INGKA DIGITAL: Linked Metadata by DesignINGKA DIGITAL: Linked Metadata by Design
INGKA DIGITAL: Linked Metadata by Design
Neo4j
 
Your enemies use GenAI too - staying ahead of fraud with Neo4j
Your enemies use GenAI too - staying ahead of fraud with Neo4jYour enemies use GenAI too - staying ahead of fraud with Neo4j
Your enemies use GenAI too - staying ahead of fraud with Neo4j
Neo4j
 
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptxBT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx
Neo4j
 
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit MilanWorkshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
Neo4j
 

More from Neo4j (20)

Atelier - Architecture d’applications de Graphes - GraphSummit Paris
Atelier - Architecture d’applications de Graphes - GraphSummit ParisAtelier - Architecture d’applications de Graphes - GraphSummit Paris
Atelier - Architecture d’applications de Graphes - GraphSummit Paris
 
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissancesAtelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissances
 
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
 
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
 
FLOA - Détection de Fraude - GraphSummit Paris
FLOA -  Détection de Fraude - GraphSummit ParisFLOA -  Détection de Fraude - GraphSummit Paris
FLOA - Détection de Fraude - GraphSummit Paris
 
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...
 
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...
ADEO -  Knowledge Graph pour le e-commerce, entre challenges et opportunités ...ADEO -  Knowledge Graph pour le e-commerce, entre challenges et opportunités ...
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...
 
GraphSummit Paris - The art of the possible with Graph Technology
GraphSummit Paris - The art of the possible with Graph TechnologyGraphSummit Paris - The art of the possible with Graph Technology
GraphSummit Paris - The art of the possible with Graph Technology
 
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
 
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
 
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
 
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024GraphSummit Singapore | The Art of the  Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
 
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
 
GraphAware - Transforming policing with graph-based intelligence analysis
GraphAware - Transforming policing with graph-based intelligence analysisGraphAware - Transforming policing with graph-based intelligence analysis
GraphAware - Transforming policing with graph-based intelligence analysis
 
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product UpdatesGraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product Updates
 
KLARNA - Language Models and Knowledge Graphs: A Systems Approach
KLARNA -  Language Models and Knowledge Graphs: A Systems ApproachKLARNA -  Language Models and Knowledge Graphs: A Systems Approach
KLARNA - Language Models and Knowledge Graphs: A Systems Approach
 
INGKA DIGITAL: Linked Metadata by Design
INGKA DIGITAL: Linked Metadata by DesignINGKA DIGITAL: Linked Metadata by Design
INGKA DIGITAL: Linked Metadata by Design
 
Your enemies use GenAI too - staying ahead of fraud with Neo4j
Your enemies use GenAI too - staying ahead of fraud with Neo4jYour enemies use GenAI too - staying ahead of fraud with Neo4j
Your enemies use GenAI too - staying ahead of fraud with Neo4j
 
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptxBT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptx
 
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit MilanWorkshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit Milan
 

Recently uploaded

LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
DanBrown980551
 
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
James Anderson
 
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Jeffrey Haguewood
 
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
Product School
 
Assuring Contact Center Experiences for Your Customers With ThousandEyes
Assuring Contact Center Experiences for Your Customers With ThousandEyesAssuring Contact Center Experiences for Your Customers With ThousandEyes
Assuring Contact Center Experiences for Your Customers With ThousandEyes
ThousandEyes
 
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
BookNet Canada
 
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
Product School
 
Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*
Frank van Harmelen
 
The Future of Platform Engineering
The Future of Platform EngineeringThe Future of Platform Engineering
The Future of Platform Engineering
Jemma Hussein Allen
 
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Thierry Lestable
 
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...
Ramesh Iyer
 
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
Product School
 
Key Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdfKey Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdf
Cheryl Hung
 
DevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA ConnectDevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA Connect
Kari Kakkonen
 
Leading Change strategies and insights for effective change management pdf 1.pdf
Leading Change strategies and insights for effective change management pdf 1.pdfLeading Change strategies and insights for effective change management pdf 1.pdf
Leading Change strategies and insights for effective change management pdf 1.pdf
OnBoard
 
Monitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR EventsMonitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR Events
Ana-Maria Mihalceanu
 
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdfFIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance
 
How world-class product teams are winning in the AI era by CEO and Founder, P...
How world-class product teams are winning in the AI era by CEO and Founder, P...How world-class product teams are winning in the AI era by CEO and Founder, P...
How world-class product teams are winning in the AI era by CEO and Founder, P...
Product School
 
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMsTo Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
Paul Groth
 
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdfFIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance
 

Recently uploaded (20)

LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
 
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
 
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
 
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
 
Assuring Contact Center Experiences for Your Customers With ThousandEyes
Assuring Contact Center Experiences for Your Customers With ThousandEyesAssuring Contact Center Experiences for Your Customers With ThousandEyes
Assuring Contact Center Experiences for Your Customers With ThousandEyes
 
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
 
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
 
Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*
 
The Future of Platform Engineering
The Future of Platform EngineeringThe Future of Platform Engineering
The Future of Platform Engineering
 
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
 
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...
 
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
 
Key Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdfKey Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdf
 
DevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA ConnectDevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA Connect
 
Leading Change strategies and insights for effective change management pdf 1.pdf
Leading Change strategies and insights for effective change management pdf 1.pdfLeading Change strategies and insights for effective change management pdf 1.pdf
Leading Change strategies and insights for effective change management pdf 1.pdf
 
Monitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR EventsMonitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR Events
 
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdfFIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
 
How world-class product teams are winning in the AI era by CEO and Founder, P...
How world-class product teams are winning in the AI era by CEO and Founder, P...How world-class product teams are winning in the AI era by CEO and Founder, P...
How world-class product teams are winning in the AI era by CEO and Founder, P...
 
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMsTo Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
 
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdfFIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
 

ingraph: Live Queries on Graphs