SlideShare une entreprise Scribd logo
1  sur  25
Télécharger pour lire hors ligne
Trio:  A System for Data, Uncertainty, and Lineage Search “stanford trio” http://i.stanford.edu/trio DATA UNCERTAINTY LINEAGE
People ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why Uncertainty + Lineage? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Trio Components ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Running Example: Crime-Solving ,[object Object],[object Object],[object Object]
Our Model for Uncertainty ,[object Object],[object Object],[object Object]
Our Model for Uncertainty ,[object Object],[object Object],[object Object],= Three possible instances (Amy, Honda)  ∥  (Amy, Toyota)  ∥  (Amy, Mazda) Saw (witness,car) { Honda, Toyota, Mazda } car Amy witness
Our Model for Uncertainty ,[object Object],[object Object],[object Object],Six possible instances ? (Amy, Honda)  ∥  (Amy, Toyota)  ∥  (Amy, Mazda) (Betty, Acura) Saw (witness,car)
Our Model for Uncertainty ,[object Object],[object Object],[object Object],? Six possible instances, each with a probability (Amy, Honda): 0.5  ∥  (Amy,Toyota): 0.3  ∥  (Amy, Mazda): 0.2 (Betty, Acura): 0.6 Saw (witness,car)
Models for Uncertainty ,[object Object],[object Object],[object Object],[object Object],[object Object]
Our Model is Not Closed Suspects   =  π person (Saw  ⋈  Drives) ? ? ? Does not correctly capture possible instances in the result CANNOT (Cathy, Honda)  ∥  (Cathy, Mazda) Saw (witness,car) (Billy, Honda)  ∥ (Frank, Honda) (Hank, Honda) (Jimmy, Toyota)  ∥ (Jimmy, Mazda) Drives (person,car) Jimmy Billy  ∥ Frank Hank Suspects
Lineage to the Rescue ,[object Object],[object Object],[object Object]
Example with Lineage ? ? ? Suspects   =  π person (Saw  ⋈  Drives) λ (31) = (11,2),(21,2) λ (32,1) = (11,1),(22,1);  λ (32,2) = (11,1),(22,2) λ (33) = (11,1), 23 11 ID (Cathy, Honda)  ∥  (Cathy, Mazda) Saw (witness,car) 23 22 21 ID (Billy, Honda)  ∥ (Frank, Honda) (Hank, Honda) (Jimmy, Toyota)  ∥ (Jimmy, Mazda) Drives (person,car) 33 32 31 ID Jimmy Billy  ∥ Frank Hank Suspects Correctly captures possible instances in the result
Uncertainty-Lineage Databases (ULDBs) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
ULDBs: Lineage ,[object Object],[object Object],[object Object],[object Object]
ULDBs: Interesting Questions ,[object Object],[object Object],[object Object],[object Object],[object Object]
Example: Extraneous Data extraneous ? ? ? (Diane, Mazda)  ∥ (Diane, Acura) Diane (Diane, Mazda) (Diane, Acura)
Example: Coexistence ? ? ? ? Can’t coexist Mazda Acura (Diane, Mazda)  ∥ (Diane, Acura) (Diane, Mazda) (Diane, Acura)
Querying ULDBs: Semantics ,[object Object],D D 1 ,  D 2 , …,  D n possible instances Q   on each instance representation of instances Q(D 1 ),  Q(D 2 ), …,  Q ( D n ) D’ implementation of  Q operational semantics D + Result
Querying ULDBs: TriQL ,[object Object],[object Object],[object Object],[object Object],[object Object]
Additional TriQL Constructs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Confidence Computation ,[object Object],[object Object],[object Object],[object Object],SELECT person,  min(conf(Saw),conf(Drives)) as conf FROM Saw, Drives WHERE Saw.car = Drives.car
Trio System: Version 1 Standard relational DBMS Trio API and translator (Python) Command-line client Trio Metadata TrioExplorer (GUI client) Trio Stored Procedures Encoded Data Tables Lineage Tables Standard SQL ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Current & Future Topics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Current & Future Topics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Contenu connexe

En vedette

Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...
Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...
Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...DATAVERSITY
 
The art of implementing data lineage
The art of implementing data lineageThe art of implementing data lineage
The art of implementing data lineageLeigh Hill
 
Meta Data Presentation 2013
Meta Data Presentation 2013Meta Data Presentation 2013
Meta Data Presentation 2013Angela Boyd
 
DataEd Webinar: Implementing Successful Data Strategies - Developing Organiza...
DataEd Webinar: Implementing Successful Data Strategies - Developing Organiza...DataEd Webinar: Implementing Successful Data Strategies - Developing Organiza...
DataEd Webinar: Implementing Successful Data Strategies - Developing Organiza...DATAVERSITY
 
How to establish a sustainable solution for data lineage
How to establish a sustainable solution for data lineageHow to establish a sustainable solution for data lineage
How to establish a sustainable solution for data lineageLeigh Hill
 
How to Search, Explore and Visualize Neo4j with Linkurious - Jean Villedieu @...
How to Search, Explore and Visualize Neo4j with Linkurious - Jean Villedieu @...How to Search, Explore and Visualize Neo4j with Linkurious - Jean Villedieu @...
How to Search, Explore and Visualize Neo4j with Linkurious - Jean Villedieu @...Neo4j
 
Graphically understand and interactively explore your Data Lineage
Graphically understand and interactively explore your Data LineageGraphically understand and interactively explore your Data Lineage
Graphically understand and interactively explore your Data LineageMohammad Ahmed
 
The Parts Of Plants
The Parts Of PlantsThe Parts Of Plants
The Parts Of Plantsehostetler
 
Parts of the plant and their functions
Parts of the plant and their functionsParts of the plant and their functions
Parts of the plant and their functionsGenedkin Charm Aquino
 

En vedette (11)

Lean Data Lineage v10
Lean Data Lineage v10Lean Data Lineage v10
Lean Data Lineage v10
 
Lean Data Lineage
Lean Data LineageLean Data Lineage
Lean Data Lineage
 
Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...
Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...
Subscribing to Your Critical Data Supply Chain - Getting Value from True Data...
 
The art of implementing data lineage
The art of implementing data lineageThe art of implementing data lineage
The art of implementing data lineage
 
Meta Data Presentation 2013
Meta Data Presentation 2013Meta Data Presentation 2013
Meta Data Presentation 2013
 
DataEd Webinar: Implementing Successful Data Strategies - Developing Organiza...
DataEd Webinar: Implementing Successful Data Strategies - Developing Organiza...DataEd Webinar: Implementing Successful Data Strategies - Developing Organiza...
DataEd Webinar: Implementing Successful Data Strategies - Developing Organiza...
 
How to establish a sustainable solution for data lineage
How to establish a sustainable solution for data lineageHow to establish a sustainable solution for data lineage
How to establish a sustainable solution for data lineage
 
How to Search, Explore and Visualize Neo4j with Linkurious - Jean Villedieu @...
How to Search, Explore and Visualize Neo4j with Linkurious - Jean Villedieu @...How to Search, Explore and Visualize Neo4j with Linkurious - Jean Villedieu @...
How to Search, Explore and Visualize Neo4j with Linkurious - Jean Villedieu @...
 
Graphically understand and interactively explore your Data Lineage
Graphically understand and interactively explore your Data LineageGraphically understand and interactively explore your Data Lineage
Graphically understand and interactively explore your Data Lineage
 
The Parts Of Plants
The Parts Of PlantsThe Parts Of Plants
The Parts Of Plants
 
Parts of the plant and their functions
Parts of the plant and their functionsParts of the plant and their functions
Parts of the plant and their functions
 

Similaire à Mazda Trio Meeting

VLDB 2015 Tutorial: On Uncertain Graph Modeling and Queries
VLDB 2015 Tutorial: On Uncertain Graph Modeling and QueriesVLDB 2015 Tutorial: On Uncertain Graph Modeling and Queries
VLDB 2015 Tutorial: On Uncertain Graph Modeling and QueriesArijit Khan
 
SQL is Dead; Long Live SQL: Lightweight Query Services for Long Tail Science
SQL is Dead; Long Live SQL: Lightweight Query Services for Long Tail ScienceSQL is Dead; Long Live SQL: Lightweight Query Services for Long Tail Science
SQL is Dead; Long Live SQL: Lightweight Query Services for Long Tail ScienceUniversity of Washington
 
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
Schema-Agnostic Queries (SAQ-2015): Semantic Web ChallengeSchema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
Schema-Agnostic Queries (SAQ-2015): Semantic Web ChallengeAndre Freitas
 
ABSTAT: Ontology-driven Linked Data Summaries with Pattern Minimalization
ABSTAT: Ontology-driven Linked Data Summaries with Pattern MinimalizationABSTAT: Ontology-driven Linked Data Summaries with Pattern Minimalization
ABSTAT: Ontology-driven Linked Data Summaries with Pattern MinimalizationBlerina Spahiu
 
Changes and Bugs: Mining and Predicting Development Activities
Changes and Bugs: Mining and Predicting Development ActivitiesChanges and Bugs: Mining and Predicting Development Activities
Changes and Bugs: Mining and Predicting Development ActivitiesThomas Zimmermann
 
Fuzzy Logic Ppt
Fuzzy Logic PptFuzzy Logic Ppt
Fuzzy Logic Pptrafi
 
Introduction to Deep Learning
Introduction to Deep LearningIntroduction to Deep Learning
Introduction to Deep LearningOswald Campesato
 
Ontologies and Similarity
Ontologies and SimilarityOntologies and Similarity
Ontologies and SimilaritySteffen Staab
 
Types Working for You, Not Against You
Types Working for You, Not Against YouTypes Working for You, Not Against You
Types Working for You, Not Against YouC4Media
 
Predicting Preference Reversals via Gaussian Process Uncertainty Aversion
Predicting Preference Reversals via Gaussian Process Uncertainty AversionPredicting Preference Reversals via Gaussian Process Uncertainty Aversion
Predicting Preference Reversals via Gaussian Process Uncertainty AversionRikiya Takahashi
 
Rsqrd AI - ML Interpretability: Beyond Feature Importance
Rsqrd AI - ML Interpretability: Beyond Feature ImportanceRsqrd AI - ML Interpretability: Beyond Feature Importance
Rsqrd AI - ML Interpretability: Beyond Feature ImportanceAlessya Visnjic
 
Artificial Intelligence and Optimization with Parallelism
Artificial Intelligence and Optimization with ParallelismArtificial Intelligence and Optimization with Parallelism
Artificial Intelligence and Optimization with ParallelismOlivier Teytaud
 
Functional Programming with Immutable Data Structures
Functional Programming with Immutable Data StructuresFunctional Programming with Immutable Data Structures
Functional Programming with Immutable Data Structureselliando dias
 
Factorization Machines and Applications in Recommender Systems
Factorization Machines and Applications in Recommender SystemsFactorization Machines and Applications in Recommender Systems
Factorization Machines and Applications in Recommender SystemsEvgeniy Marinov
 
A Distributional Semantics Approach for Selective Reasoning on Commonsense Gr...
A Distributional Semantics Approach for Selective Reasoning on Commonsense Gr...A Distributional Semantics Approach for Selective Reasoning on Commonsense Gr...
A Distributional Semantics Approach for Selective Reasoning on Commonsense Gr...Andre Freitas
 
Evaluation Initiatives for Entity-oriented Search
Evaluation Initiatives for Entity-oriented SearchEvaluation Initiatives for Entity-oriented Search
Evaluation Initiatives for Entity-oriented Searchkrisztianbalog
 

Similaire à Mazda Trio Meeting (20)

Trio Notes
Trio NotesTrio Notes
Trio Notes
 
VLDB 2015 Tutorial: On Uncertain Graph Modeling and Queries
VLDB 2015 Tutorial: On Uncertain Graph Modeling and QueriesVLDB 2015 Tutorial: On Uncertain Graph Modeling and Queries
VLDB 2015 Tutorial: On Uncertain Graph Modeling and Queries
 
SQL is Dead; Long Live SQL: Lightweight Query Services for Long Tail Science
SQL is Dead; Long Live SQL: Lightweight Query Services for Long Tail ScienceSQL is Dead; Long Live SQL: Lightweight Query Services for Long Tail Science
SQL is Dead; Long Live SQL: Lightweight Query Services for Long Tail Science
 
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
Schema-Agnostic Queries (SAQ-2015): Semantic Web ChallengeSchema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
 
ABSTAT: Ontology-driven Linked Data Summaries with Pattern Minimalization
ABSTAT: Ontology-driven Linked Data Summaries with Pattern MinimalizationABSTAT: Ontology-driven Linked Data Summaries with Pattern Minimalization
ABSTAT: Ontology-driven Linked Data Summaries with Pattern Minimalization
 
Changes and Bugs: Mining and Predicting Development Activities
Changes and Bugs: Mining and Predicting Development ActivitiesChanges and Bugs: Mining and Predicting Development Activities
Changes and Bugs: Mining and Predicting Development Activities
 
Fuzzy Logic Ppt
Fuzzy Logic PptFuzzy Logic Ppt
Fuzzy Logic Ppt
 
Fusing semantic data
Fusing semantic dataFusing semantic data
Fusing semantic data
 
Introduction to Deep Learning
Introduction to Deep LearningIntroduction to Deep Learning
Introduction to Deep Learning
 
Ontologies and Similarity
Ontologies and SimilarityOntologies and Similarity
Ontologies and Similarity
 
Types Working for You, Not Against You
Types Working for You, Not Against YouTypes Working for You, Not Against You
Types Working for You, Not Against You
 
Predicting Preference Reversals via Gaussian Process Uncertainty Aversion
Predicting Preference Reversals via Gaussian Process Uncertainty AversionPredicting Preference Reversals via Gaussian Process Uncertainty Aversion
Predicting Preference Reversals via Gaussian Process Uncertainty Aversion
 
Rsqrd AI - ML Interpretability: Beyond Feature Importance
Rsqrd AI - ML Interpretability: Beyond Feature ImportanceRsqrd AI - ML Interpretability: Beyond Feature Importance
Rsqrd AI - ML Interpretability: Beyond Feature Importance
 
Artificial Intelligence and Optimization with Parallelism
Artificial Intelligence and Optimization with ParallelismArtificial Intelligence and Optimization with Parallelism
Artificial Intelligence and Optimization with Parallelism
 
Functional Programming with Immutable Data Structures
Functional Programming with Immutable Data StructuresFunctional Programming with Immutable Data Structures
Functional Programming with Immutable Data Structures
 
Blinkdb
BlinkdbBlinkdb
Blinkdb
 
Factorization Machines and Applications in Recommender Systems
Factorization Machines and Applications in Recommender SystemsFactorization Machines and Applications in Recommender Systems
Factorization Machines and Applications in Recommender Systems
 
A Distributional Semantics Approach for Selective Reasoning on Commonsense Gr...
A Distributional Semantics Approach for Selective Reasoning on Commonsense Gr...A Distributional Semantics Approach for Selective Reasoning on Commonsense Gr...
A Distributional Semantics Approach for Selective Reasoning on Commonsense Gr...
 
Android and Deep Learning
Android and Deep LearningAndroid and Deep Learning
Android and Deep Learning
 
Evaluation Initiatives for Entity-oriented Search
Evaluation Initiatives for Entity-oriented SearchEvaluation Initiatives for Entity-oriented Search
Evaluation Initiatives for Entity-oriented Search
 

Plus de CardinaleWay Mazda (20)

Playboy Mazda Cup Racing Schedule
Playboy Mazda Cup Racing SchedulePlayboy Mazda Cup Racing Schedule
Playboy Mazda Cup Racing Schedule
 
Mazda Tribute Ford Escape Gear Product List
Mazda Tribute Ford Escape Gear Product ListMazda Tribute Ford Escape Gear Product List
Mazda Tribute Ford Escape Gear Product List
 
Mazda Rx 8 Compressor
Mazda Rx 8 CompressorMazda Rx 8 Compressor
Mazda Rx 8 Compressor
 
Mazda Rx7 Ignition Operation
Mazda Rx7 Ignition OperationMazda Rx7 Ignition Operation
Mazda Rx7 Ignition Operation
 
Mazda Foundation Form
Mazda Foundation FormMazda Foundation Form
Mazda Foundation Form
 
Mazda Domain Name Dispute Wiho
Mazda Domain Name Dispute WihoMazda Domain Name Dispute Wiho
Mazda Domain Name Dispute Wiho
 
Mazda Catalog
Mazda CatalogMazda Catalog
Mazda Catalog
 
Mazda Case
Mazda CaseMazda Case
Mazda Case
 
Mazda3
Mazda3Mazda3
Mazda3
 
Mazda Price List 2007
Mazda Price List 2007Mazda Price List 2007
Mazda Price List 2007
 
Mazda Challenge Rules
Mazda Challenge RulesMazda Challenge Rules
Mazda Challenge Rules
 
Haltech Mazda3
Haltech Mazda3Haltech Mazda3
Haltech Mazda3
 
Mazda Zoro Talk
Mazda Zoro TalkMazda Zoro Talk
Mazda Zoro Talk
 
Mazda Rotary Engine Conversion Prototype
Mazda Rotary Engine Conversion PrototypeMazda Rotary Engine Conversion Prototype
Mazda Rotary Engine Conversion Prototype
 
Xarsxthroughxthexages 002
Xarsxthroughxthexages 002Xarsxthroughxthexages 002
Xarsxthroughxthexages 002
 
Mazda versus Vette Performance
Mazda versus Vette PerformanceMazda versus Vette Performance
Mazda versus Vette Performance
 
Weighted Score And Topsis
Weighted Score And TopsisWeighted Score And Topsis
Weighted Score And Topsis
 
What Can Be Done Ip Litigation Prall
What Can Be Done Ip Litigation PrallWhat Can Be Done Ip Litigation Prall
What Can Be Done Ip Litigation Prall
 
Mazda T21040000010021 Ppte
Mazda T21040000010021 PpteMazda T21040000010021 Ppte
Mazda T21040000010021 Ppte
 
Mazda Use of Third Generation Xml Tools
Mazda Use of Third Generation Xml ToolsMazda Use of Third Generation Xml Tools
Mazda Use of Third Generation Xml Tools
 

Dernier

Welcome to Auto Know University Orientation
Welcome to Auto Know University OrientationWelcome to Auto Know University Orientation
Welcome to Auto Know University Orientationxlr8sales
 
Lighting the Way Understanding Jaguar Car Check Engine Light Service
Lighting the Way Understanding Jaguar Car Check Engine Light ServiceLighting the Way Understanding Jaguar Car Check Engine Light Service
Lighting the Way Understanding Jaguar Car Check Engine Light ServiceImport Car Center
 
Mastering Mercedes Engine Care Top Tips for Rowlett, TX Residents
Mastering Mercedes Engine Care Top Tips for Rowlett, TX ResidentsMastering Mercedes Engine Care Top Tips for Rowlett, TX Residents
Mastering Mercedes Engine Care Top Tips for Rowlett, TX ResidentsRowlett Motorwerks
 
怎么办理美国UCONN毕业证康涅狄格大学学位证书一手渠道
怎么办理美国UCONN毕业证康涅狄格大学学位证书一手渠道怎么办理美国UCONN毕业证康涅狄格大学学位证书一手渠道
怎么办理美国UCONN毕业证康涅狄格大学学位证书一手渠道7283h7lh
 
Can't Roll Up Your Audi A4 Power Window Let's Uncover the Issue!
Can't Roll Up Your Audi A4 Power Window Let's Uncover the Issue!Can't Roll Up Your Audi A4 Power Window Let's Uncover the Issue!
Can't Roll Up Your Audi A4 Power Window Let's Uncover the Issue!Mint Automotive
 
A Comprehensive Exploration of the Components and Parts Found in Diesel Engines
A Comprehensive Exploration of the Components and Parts Found in Diesel EnginesA Comprehensive Exploration of the Components and Parts Found in Diesel Engines
A Comprehensive Exploration of the Components and Parts Found in Diesel EnginesROJANE BERNAS, PhD.
 
Pros and cons of buying used fleet vehicles.pptx
Pros and cons of buying used fleet vehicles.pptxPros and cons of buying used fleet vehicles.pptx
Pros and cons of buying used fleet vehicles.pptxjennifermiller8137
 
Control-Plan-Training.pptx for the Automotive standard AIAG
Control-Plan-Training.pptx for the Automotive standard AIAGControl-Plan-Training.pptx for the Automotive standard AIAG
Control-Plan-Training.pptx for the Automotive standard AIAGVikrantPawar37
 
ABOUT REGENERATIVE BRAKING SYSTEM ON AUTOMOBILES
ABOUT REGENERATIVE BRAKING SYSTEM ON AUTOMOBILESABOUT REGENERATIVE BRAKING SYSTEM ON AUTOMOBILES
ABOUT REGENERATIVE BRAKING SYSTEM ON AUTOMOBILESsriharshaganjam1
 
Human Resource Practices TATA MOTORS.pdf
Human Resource Practices TATA MOTORS.pdfHuman Resource Practices TATA MOTORS.pdf
Human Resource Practices TATA MOTORS.pdfAditiMishra247289
 

Dernier (10)

Welcome to Auto Know University Orientation
Welcome to Auto Know University OrientationWelcome to Auto Know University Orientation
Welcome to Auto Know University Orientation
 
Lighting the Way Understanding Jaguar Car Check Engine Light Service
Lighting the Way Understanding Jaguar Car Check Engine Light ServiceLighting the Way Understanding Jaguar Car Check Engine Light Service
Lighting the Way Understanding Jaguar Car Check Engine Light Service
 
Mastering Mercedes Engine Care Top Tips for Rowlett, TX Residents
Mastering Mercedes Engine Care Top Tips for Rowlett, TX ResidentsMastering Mercedes Engine Care Top Tips for Rowlett, TX Residents
Mastering Mercedes Engine Care Top Tips for Rowlett, TX Residents
 
怎么办理美国UCONN毕业证康涅狄格大学学位证书一手渠道
怎么办理美国UCONN毕业证康涅狄格大学学位证书一手渠道怎么办理美国UCONN毕业证康涅狄格大学学位证书一手渠道
怎么办理美国UCONN毕业证康涅狄格大学学位证书一手渠道
 
Can't Roll Up Your Audi A4 Power Window Let's Uncover the Issue!
Can't Roll Up Your Audi A4 Power Window Let's Uncover the Issue!Can't Roll Up Your Audi A4 Power Window Let's Uncover the Issue!
Can't Roll Up Your Audi A4 Power Window Let's Uncover the Issue!
 
A Comprehensive Exploration of the Components and Parts Found in Diesel Engines
A Comprehensive Exploration of the Components and Parts Found in Diesel EnginesA Comprehensive Exploration of the Components and Parts Found in Diesel Engines
A Comprehensive Exploration of the Components and Parts Found in Diesel Engines
 
Pros and cons of buying used fleet vehicles.pptx
Pros and cons of buying used fleet vehicles.pptxPros and cons of buying used fleet vehicles.pptx
Pros and cons of buying used fleet vehicles.pptx
 
Control-Plan-Training.pptx for the Automotive standard AIAG
Control-Plan-Training.pptx for the Automotive standard AIAGControl-Plan-Training.pptx for the Automotive standard AIAG
Control-Plan-Training.pptx for the Automotive standard AIAG
 
ABOUT REGENERATIVE BRAKING SYSTEM ON AUTOMOBILES
ABOUT REGENERATIVE BRAKING SYSTEM ON AUTOMOBILESABOUT REGENERATIVE BRAKING SYSTEM ON AUTOMOBILES
ABOUT REGENERATIVE BRAKING SYSTEM ON AUTOMOBILES
 
Human Resource Practices TATA MOTORS.pdf
Human Resource Practices TATA MOTORS.pdfHuman Resource Practices TATA MOTORS.pdf
Human Resource Practices TATA MOTORS.pdf
 

Mazda Trio Meeting

  • 1. Trio: A System for Data, Uncertainty, and Lineage Search “stanford trio” http://i.stanford.edu/trio DATA UNCERTAINTY LINEAGE
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11. Our Model is Not Closed Suspects = π person (Saw ⋈ Drives) ? ? ? Does not correctly capture possible instances in the result CANNOT (Cathy, Honda) ∥ (Cathy, Mazda) Saw (witness,car) (Billy, Honda) ∥ (Frank, Honda) (Hank, Honda) (Jimmy, Toyota) ∥ (Jimmy, Mazda) Drives (person,car) Jimmy Billy ∥ Frank Hank Suspects
  • 12.
  • 13. Example with Lineage ? ? ? Suspects = π person (Saw ⋈ Drives) λ (31) = (11,2),(21,2) λ (32,1) = (11,1),(22,1); λ (32,2) = (11,1),(22,2) λ (33) = (11,1), 23 11 ID (Cathy, Honda) ∥ (Cathy, Mazda) Saw (witness,car) 23 22 21 ID (Billy, Honda) ∥ (Frank, Honda) (Hank, Honda) (Jimmy, Toyota) ∥ (Jimmy, Mazda) Drives (person,car) 33 32 31 ID Jimmy Billy ∥ Frank Hank Suspects Correctly captures possible instances in the result
  • 14.
  • 15.
  • 16.
  • 17. Example: Extraneous Data extraneous ? ? ? (Diane, Mazda) ∥ (Diane, Acura) Diane (Diane, Mazda) (Diane, Acura)
  • 18. Example: Coexistence ? ? ? ? Can’t coexist Mazda Acura (Diane, Mazda) ∥ (Diane, Acura) (Diane, Mazda) (Diane, Acura)
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.