Wed 1130 aasman_jans_color
-
Upload
dataversity -
Category
Education
-
view
234 -
download
0
Transcript of Wed 1130 aasman_jans_color
![Page 1: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/1.jpg)
When a relational database doesn’t work
And why a graph database might help
![Page 2: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/2.jpg)
ContentsContents
• Franz and customers• Two Use Cases
– Amdocs: a real time semantic platform for telecom that knows everything about everyone in real time
– Real time news and social network analysis using the Linked Open Data CloudLinked Open Data Cloud
• Scalability?• Integration with other NoSQL databases – Solr, MongoDBg , g
![Page 3: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/3.jpg)
Franz Inc – Who We AreFranz Inc Who We Are
• Private, founded 1984 • We are an AI and
Semantic Technology company• Out of BerkeleyOut of Berkeley
![Page 4: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/4.jpg)
![Page 5: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/5.jpg)
(1 (2 3) (4 5) (6 7) (8 9) (10 11) (12 13) (14 15)(16 17) (18 19 20 21 22 23 24 27 28) (29 30))
![Page 6: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/6.jpg)
Bob
AliceCraig
Bill
![Page 7: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/7.jpg)
![Page 8: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/8.jpg)
How is it different from an RDB d h i i fl ibl ?and why is it more flexible?
• No Schema. – Say whatever you want to say but– ontologies may constrain what you put in triple store
• No Link Tables – because you can do one‐to‐many relationships directly
• No Indexing Choices– Can add new data attributes (predicates) on‐the‐fly that will be real‐time available for querying becausewill be real time available for querying, because everything is automatically indexed.
• Takes anything you give it: it is trivial to consume– Rows and columns from RDB, XML, RDF(S), OWL, Text and Extracted Entities, JSON
![Page 9: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/9.jpg)
AllegroGraph: RDF Graph StoreAllegroGraph: RDF Graph Store
RESTBackup/Restore
ReplicationRules Java
Warm FailoverSparql Prolog Rules
Clif++ Geo SNA Time RDFS+ Java‐Script
Session Management, Query Engine, FederationSecurity
ManagementStorage layer ( compression, indexing, freetext, transactions )
![Page 10: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/10.jpg)
![Page 11: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/11.jpg)
![Page 12: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/12.jpg)
Use Case AmdocsUse Case Amdocs
Build a semantic platformthat knows everything
babout everyonein real time.
![Page 13: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/13.jpg)
![Page 14: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/14.jpg)
![Page 15: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/15.jpg)
![Page 16: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/16.jpg)
![Page 17: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/17.jpg)
![Page 18: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/18.jpg)
![Page 19: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/19.jpg)
![Page 20: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/20.jpg)
![Page 21: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/21.jpg)
![Page 22: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/22.jpg)
Telco Call Center Volume QuadruplesQuadruples Since 2007
• On average, each call – Lasts 10 minutes– Go thru 68 screens
• One call costs 3 months’ profit from that customer• One call costs 3 months profit from that customer• It’s getting worse every day!
![Page 23: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/23.jpg)
Typical Interaction Begins in the Dark
Bill
Dark
PlanPast Payments The unknown – why
calling? How to help?
DeviceCalculator (avg peak usage)
g p
Past Interactions (Memos)
Statements
No real‐time context ‐ insight & guidance
(Memos)g g
High AHT, poor FCR, low customer and agent satisfaction
![Page 24: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/24.jpg)
![Page 25: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/25.jpg)
AIDA Maps Events to C tConcepts
Events from many source systems are transformed into a set of related business concepts
Interactions
Bills
Orders
Many events Triple Store with business concepts
Bills
Payments
Collections
Charge disputeg p
Individual
Customer
Pay instructions Subjective "good payer"Patterns "always pays 2 days late"
Chronology of events
Device Activated
Device heartbeat
Subscriptions
D i h
a e s a ays pays days a eTrends “improving payer"Geospatial “within 5 miles of the tower"Time “within 5 minutes of an outage" Chronology of eventsDevice changes Probability “probably will call about the bill"Absence of occurrence “missed payment"Relationship between " friend of a friend"
![Page 26: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/26.jpg)
Events Decision Engine
Container
ActionsSBA Application Server
ContainerContainer
EventIngestion Inference
Amdocs Event Collector
Amdocs Integration Framework
Scheduled
Inference Engine(Business Rules)
Bayesian
EventsEvents
“Sesame”
ScheduledEvents
yBeliefNetwork
Operational SystemsOperational Systems
CRMCRMRM OMS
AllegroGraph
Operational SystemsOperational Systems
Event Data SourcesEvent Data Sources
NW Web 2.0
AllegroGraphTriple Store DB
![Page 27: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/27.jpg)
AIDA Event CollectionAIDA Event Collection
Amdocs Event CollectorInference & DecisionAmdocs Event Collector
Event Sources Collection Parsing Mapping Publishing
Decision
Ingestion
• Events are collected from many heterogeneous, configured event sources
Phone calls texting video upload roaming etc– Phone calls, texting, video upload, roaming, etc.– iTune download, web site interaction, media upload– Emails, support calls
Bill payment or non payment– Bill payment or non‐payment– Phones stop working or disconnect
• All fused and mapped into a single event knowledge base
![Page 28: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/28.jpg)
AIDA Semantic Inference
• Define rules to operate to create higher level concepts
AIDA Semantic Inference
– Event (mapping) rules ‐Map event data into the domain ontology– Automatic rules – Compute new properties defined by the ontology– On‐demand rules ‐ perform inference for the services
• Rules triggered upon event ingestion, service request or schedule• Semantic rule inference generates new triples from existing ones
Bills
Charges
P t
Amount
Payment P
Customer
Payments Due Date
“Timeliness”Make
Pattern
Good
Bad
Devices Model
StatusOnTime
Early
Late
Improving
Worsening
![Page 29: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/29.jpg)
Semantic Inference – Using Business R l hi h l l
• AIDA provides Workbench for business
Rules to generate high level concepts“Late Payment” defined in Workbench
rule construction• Utilizes a sophisticated
magnetic block GUI for b i lbusiness analysts
• Rules triggered to infer and generate newbusiness conceptsbusiness concepts
rule PaymentDetails.timeliness{
if date within EarlyPeriod days after customerBill.billDatethen timeliness = Early ;
Each business rule defines an attribute. This rule defines an attribute of the PaymentDetails class called timeliness
then timeliness = Early ;else if date not within LatePeriod days after customerBill.billDatethen timeliness = Late ;else timeliness = OnTime ;
}All classes and their attributes are defined in the application ontology
Java codeJava code
![Page 30: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/30.jpg)
Decisioning – Probabilistic
• AIDA incorporates also Bayesian Belief Networks (BBN)
Assessment
• These are graphical models for reasoning under uncertainty• Important part of decision making – the likelihood of something happenning
estimated by how often it occurred in the past (primarily used in medical research til tl )until recently)
• Evidence consists of observations on certain nodes leading to conclusions
Evidence Conclusions
Payment Pattern
Bill Expect Payment Arrangement
Setup
Payment
Expect Payment
![Page 31: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/31.jpg)
Presenting insight to the CSRese t g s g t to t e CS
Prediction on reason for the Process opens Prediction on reason for the call – ranked by probability relevant screen for
reference and action
Presentation of recent dinteractions and events
Prioritized Recommended treatment and script
![Page 32: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/32.jpg)
First application: CRMAmdocs Guided Interaction Advisor
First Call ResolutionFirst Call Resolution• Increase up to 15%
Average Handling Time• Reduce up to 30%
Training CostsR d 25%• Reduce up to 25%
![Page 33: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/33.jpg)
Triples all the way downTriples all the way down
![Page 34: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/34.jpg)
So why a triple storeSo why a triple store
• Flexibility, flexibility and flexibilityy, y y– Change the schema on a daily basis– Customers create new policies which in turn will create new schemas on the fly
• Needed to work with meaningRdf describes data– Rdf describes data
• Needed to be declarative for everything– Most RTBI is a combination of data in the DB and javaMost RTBI is a combination of data in the DB and java variables in the application.
![Page 35: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/35.jpg)
![Page 36: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/36.jpg)
Text Intelligence for DOD/ISText Intelligence for DOD/IS
![Page 37: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/37.jpg)
How would you do this with d d h iyour standard search engine
• Give me a newspaper text with a republican and a democrat that serve on two subcommittees that have the same parent committee.
• Which [democrat|republican] is most vocal in the oil spill disaster[ | p ] p
• Given this text, find all the other texts that have the same people and the same main topics but not democrats in the textsame main topics but not democrats in the text.
• Which newspaper favors [democrats|republicans]
• Which [democrate|republican|senator|representative] get most of the attention in the last week.
• Give me the distribution of the most important topics yesterday
![Page 38: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/38.jpg)
The processThe process
• We spider daily > 300 on‐line newspapers and thousands of p y p pblogs
• And search specifically for all the member of the senate and house of representatives and the executive branch
• Apply entity extractor to the text and extract main concepts – About 150 triples per text…p p
• Hook up these concepts with a detailed database of each politician and with information from the linked open data cloud
![Page 39: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/39.jpg)
![Page 40: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/40.jpg)
![Page 41: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/41.jpg)
![Page 42: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/42.jpg)
From News Article toFrom News Article to
• People (has‐people)p ( p p )– And their roles
• Places (has‐places)– And the county, state, country they are in
• Organizations (has‐organizations)– Government departments, company names, etc.
• Main Categories (has‐domains)Politics sports ministries energy finance economics– Politics, sports, ministries, energy, finance, economics, ecology, oil, mining industry, etc..
• Main Concepts (has‐main‐groups)– Other important nouns and phrases in a text
![Page 43: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/43.jpg)
![Page 44: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/44.jpg)
LOD cloud – Sept 22 2010LOD cloud Sept 22 2010
latest LOD cloud
![Page 45: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/45.jpg)
![Page 46: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/46.jpg)
AllegroTextAllegroText
![Page 47: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/47.jpg)
![Page 48: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/48.jpg)
• A little demo?
![Page 49: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/49.jpg)
How scalable is this?How scalable is this?
![Page 50: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/50.jpg)
![Page 51: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/51.jpg)
![Page 52: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/52.jpg)
![Page 53: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/53.jpg)
![Page 54: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/54.jpg)
![Page 55: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/55.jpg)
![Page 56: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/56.jpg)
LoadingLoading
![Page 57: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/57.jpg)
QueriesQueries
• Query planner now takes 99% of SPARQL 1.0, automatically Q y p Q , ycompiles it into query graph flow language…
![Page 58: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/58.jpg)
![Page 59: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/59.jpg)
![Page 60: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/60.jpg)
You can write this by hand if you i i lfwant to optimize yourself.
![Page 61: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/61.jpg)
![Page 62: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/62.jpg)
![Page 63: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/63.jpg)
![Page 64: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/64.jpg)
![Page 65: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/65.jpg)
![Page 66: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/66.jpg)
![Page 67: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/67.jpg)
This will actually work on Prolog i h l !with rules too!
![Page 68: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/68.jpg)
![Page 69: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/69.jpg)
Query performance notes:iWins
• Indices are small enough to fit in memory of conventialg ymachines
• Simultaneous access to indices (see next slide)
• Pipe line architecture• Pipe line architecture– Stream based processing (all nodes can be active in parallel. Most nodes can begin before the end of data is p greached.)
![Page 70: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/70.jpg)
![Page 71: Wed 1130 aasman_jans_color](https://reader038.fdocuments.in/reader038/viewer/2022102905/55d5031fbb61eb497e8b4709/html5/thumbnails/71.jpg)
The endThe end