Allegograph

AllegroGraph as a Graph Database
JansAasman, Ph.D.
CEO - Franz Inc
Ja@Franz.com

Contents
AllegroGraph as a
QuintupleStore (well OcttupleStore in 2011)
RDF store
Graph Database
Agraph architecture
Extreme use cases
AMDOCS … CRM on top a trillion triples
Pharmaceutic … explore connections in graph space
Demo

Agraph as a quintuple store
S, P, O, G + unique ID + transaction #
SPOG can be any data type
1 2.0 3 4
2001-12-12 after 010-12-12 +19258781444
Jans loves pizza file1 12
NoOne believes 12
And include very efficient geospatial and temporal representations and indices
6 default indices, 24 user controlled indices
Range indexing, Freetext Indexing
Neighborhood matrixes & UPI maps (for 1 ms access)
2011: time, security

Agraph as an RDF store
RDF store when you adhere to the RDF conventions.
Full Sparql 1.0, most of Sparql 1.1
RDFS++ reasoner
GeoSpatial and Temporal representations.
Prolog for Rules
Soon Common Logic (CLIF+)
As a usability layer on top of Prolog
Easier to combine Rules and Queries

Agraph as a Graph Database
If you want a Property Graph:
use the graph argument
Jans loves pizza gr1
gr1 weight 90
gr1 author Sophia

Schema
Node typing
Edge typing
Attributes (nodes)
Attributes (edges)
Directed edges
Undirected edges
Restricted edges
Loop edges
Attribute indexing
Starting node
Schema
Yes
Yes
Yes
Yes: A trusts B gr1, gr1 certainty 80.
Yes: A trusts B
Yes: if using RDFS symmetric property or generators
Yes, if it means there can be islands.
Yes, A loves A
Yes
No, although, is that a DB property?
Yes and No: On demand you can use Ontology and validation is straight forward

Querying
Language
Traversals
Lisp, Prolog, JavaScript and toy version of Gremlin
Yes, through adjacency lists and special indices.. This seems to be an implementation point and not a fundamental property

Database
Transactional
ACID
Fully Indexed
Distributed
Cache
Embeddable
Store-engine
Migration framework
Object mapping
Yes
Yes
Yes
Federation (in-machine, between machines), AG5
Yes, adjacency vectors (neighbourhoodmatrics)
Yes: 3.3, No: 4.2.x
Custom
From RDB to Graph DB? Various
Only in Lisp, not in clients.

Utilities
Shell
Algorithms
Benchmark
Protocols
RDF Store
OWL Store
IDE Integration
Admin tool
Importer
Exporter
Loader
Scripting Language
All from Lisp shell, some from cshell, wget/curl
Yes, JavaScript, Prolog and Lisp
Yes, but only for RDF stores and reasoning
REST/JSON
Yes
Yes
Yes
Yes, AGWebview
Yes, from various input formats
Yes, clients lets you dump triples
AGLoad, Gruff, AGWebview
Lisp and Javascript.

Languages
Java
Python
Ruby
C#
Scala
Clojure
Perl
PHP

Many graph algorithms using generator model
Because of Social Network Analysis requirements we implement many graph algorithms.
Using generators
A first class function that takes
One node as input
Returns all children
And neighbourhood matrices(or adjacency hash-tables) forspeed.

how far is Actor1 from Actor2?
Degrees of separation
How far is P1 from P2
Connection strength
How many shortest paths from P1 to P2 through a series of predicates and rules

In what groups is this actor?
Find the ego-network around a person or thing
Friend, friends of friends, etc.
Find all the fully connect graphs around a personor thing

Questions in SNA: How Important is an actor?
In-degree, out-degree
Actor degree centrality
I have the most connections in a group so I am more important
Actor closeness centrality
I have more shortest paths to anyone else in the group so I am more important
Actor betweenness centrality
I am more often on the shortest path between other people in the group so I am more important. I can control flow of information better than other people

Has the group a leader, is the group cohesive?
Group centralization
How centralized is this group?
Does this group have a leader
Is there someone controllingthe information flow
Group cohesiveness
How strong and well connected is this group
Are most people connected
What is the density

All search and SNA functions use Generators
Generator
Input: one node
Output: list of nodes
Fully functional, can be complex sparql or prolog queries
Or just predicates and indication of direction

How to get from A to E??
subjpredobj
a dinner-with b
a kissed-with c
c movie-with e
b kissed-with d
d movie-with e
e dinner-with a
(defgenerator knows (node)
(objects-of :p dinner-with))
(objects-of :p dinner-with)
(subjects-of :p dinner-with))

How to get from A to E??
(defgenerator knows ()
(object-of :p dinner-with)
(subject-of :p dinner-with)
(object-of :p movie-with)
(subject-of :p movie-with)
(object-of :p kissed-with)
(subject-of :p kissed-with))
(defgenerator knows ()
(undirected (dinner-with movie-with kissed-with)))

Declaratively specify
(generator knows (node)
(select (?x)
(q ??node movie-with ?x)
(q ??node dinner-with ?x)
(not (q ??node kissed-with ?x)))
(select (?x)
(q ?x movie-with ??node)
(q- ?x dinner-with ??node)
(not (q- ?x kissed-with ??node)))

Sample SNA functions
(Ego-group actor generator depth ?group)
- binds ?group to group of nodes
(Ego-group-members actor generator depth ?a)
- bind ?a to every member in the group
(Cliques actor generator min-depth ?cl)
- binds ?cl to all cliques
(Clique-members actor generator min-depth ?cl ?a)
- binds ?cl to cliques and then iterates of ever member ?a in ?cl
(Actor-centrality actor group generator ?num)
- binds ?num to actorcentrality
(Actor-centrality-members group ?actor ?num)
- binds ?actor to every actor in group, ?centrality is centrality of
that actor, we start with the actor with highest centrality.
(Group-centrality group generator ?num)
Actor = single node
Group = list of nodes
Depth = number
Generator = generator

Integrated in Prolog and Common Logic (CLIF)
(undirected :p (!fr:dinner-with !fr:kissed-with)))
(select (?x)
(ego-group-members !person:jans knows ?x 2)
(q ?x !geo:place ?y)
(geo-box-around !geoname:Berkeley ?y 5 miles))
(select (?x)
(ego-group !person:jans knows ?group 2)
(actor-centrality-members ?group knows ?x ?num)
(q ?x !geo:place ?y)
(geo-box-around !geoname:Berkeley ?y 5 miles))

Where we use this?
Amdocs: Know everything about every customer
Partitioned on customer
Most graph search centered in client
Pfizer: help me find connections between drugs, diseases, genes, side effects in a sea of clinical trials
Just a mess of data
All graph search in server

Traditional Business Intelligence
Can tell you ALL about
the average customer
but NOTHING about
the individual.

Can you in < 1 second with one push of a button
Predict the three most likely reasons why Joe Smith from Kansas is calling the call center? Bill unexpectedly high, loosing connection too often, doesn’t know how to use new subscription service?
The ten last events that happened for JS? Phone calls, sms, downloads of movie, device stopped working, payment of bill, looking at map, search for local store.
What is the likelyhoodthat he will change from T-Mobile to Sprint or AT&T?
What are his ten most important friends and what devices do they have. And who is the first to change and who follows?

Can you in < 1 second with one push of a button
What are the usual daily locations for this person? What kind of shops?
What kind of services does he download, what kind of movies/music/games does he like, what products does he buy?
Is his plan the right plan for him?
Is he in a good mood?
Is he a valuable customer, is he a good payer, what is your margin on him, how many times per month does he call a call center, does he look up help for mail on the internet? Can you predict if he is going to pay the bill?

Architecture
Decision Engine
Actions
Events
SBA Application Server
Container
Amdocs
Event Collector
Container
Inference
Engine(Business
Rules)
Amdocs
Integration
Framework
Event
Ingestion
Events
Scheduled
Events
Bayesian
Belief
Network
CRM
RM
OMS
CRM
“Sesame”
Operational Systems
NW
Web 2.0
AllegroGraph
Triple Store DB
Event Data Sources

Work for Pharma

Gruff Demo

What about Scalability

Architecture overview
Java:
Sesame Jena
Python
Ruby
C#
ClojureScala
Perl
REST
Backup/Restore
Replication
Warm Failover
Security
Management
Sparql
Prolog
Rules Clif++
Geo
SNA
Time
RDFS+
Java-Script
Session Management, Query Engine, Federation
Storage layer ( compression, indexing, freetext, transactions )

Thanks…

Allegograph

by University of New South Wales

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Allegograph

by University of New South Wales

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Allegograph Presentation Transcript

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