Zinovy Diskin School of Computing, Queen’s University Kingston, Canada

Slides:



Advertisements
Similar presentations
Three-Step Database Design
Advertisements

D ATABASE S YSTEMS I R ELATIONAL A LGEBRA. 22 R ELATIONAL Q UERY L ANGUAGES Query languages (QL): Allow manipulation and retrieval of data from a database.
INFS614, Fall 08 1 Relational Algebra Lecture 4. INFS614, Fall 08 2 Relational Query Languages v Query languages: Allow manipulation and retrieval of.
Reducing the Cost of Validating Mapping Compositions by Exploiting Semantic Relationships Eduard C. Dragut Ramon Lawrence Eduard C. Dragut Ramon Lawrence.
By relieving the brain of all unnecessary work, a good notation sets it free to concentrate on more advanced problems, and, in effect, increases the mental.
CMPT 354, Simon Fraser University, Fall 2008, Martin Ester 52 Database Systems I Relational Algebra.
1 Software Testing and Quality Assurance Lecture 12 - The Testing Perspective (Chapter 2, A Practical Guide to Testing Object-Oriented Software)
Automated Analysis and Code Generation for Domain-Specific Models George Edwards Center for Systems and Software Engineering University of Southern California.
By relieving the brain of all unnecessary work, a good notation sets it free to concentrate on more advanced problems, and, in effect, increases the mental.
By relieving the brain of all unnecessary work, a good notation sets it free to concentrate on more advanced problems, and, in effect, increases the mental.
A First Attempt towards a Logical Model for the PBMS PANDA Meeting, Milano, 18 April 2002 National Technical University of Athens Patterns for Next-Generation.
By relieving the brain of all unnecessary work, a good notation sets it free to concentrate on more advanced problems, and, in effect, increases the mental.
Database Management Systems, R. Ramakrishnan and J. Gehrke1 Relational Algebra Chapter 4, Part A.
Automatic Data Ramon Lawrence University of Manitoba
By relieving the brain of all unnecessary work, a good notation sets it free to concentrate on more advanced problems, and, in effect, increases the mental.
1 Relational Algebra and Calculus Yanlei Diao UMass Amherst Feb 1, 2007 Slides Courtesy of R. Ramakrishnan and J. Gehrke.
Rutgers University Relational Algebra 198:541 Rutgers University.
CIS607, Fall 2005 Semantic Information Integration Article Name: Clio Grows Up: From Research Prototype to Industrial Tool Name: DH(Dong Hwi) kwak Date:
ANHAI DOAN ALON HALEVY ZACHARY IVES Chapter 6: General Schema Manipulation Operators PRINCIPLES OF DATA INTEGRATION.
CSE 590DB: Database Seminar Autumn 2002: Meta Data Management Phil Bernstein Microsoft Research.
10 December, 2013 Katrin Heinze, Bundesbank CEN/WS XBRL CWA1: DPM Meta model CWA1Page 1.
Zinovy Diskin and Juergen Dingel Queen’s University Kingston, Ontario, Canada Mappings, maps and tables: Towards formal semantics for associations in UML.
Intermodeling, Queries and Kleisli categories Zinovy Diskin, Tom Maibaum, Krzysztof Czarnecki McMaster University, University of Waterloo.
1 Relational Algebra and Calculus Chapter 4. 2 Relational Query Languages  Query languages: Allow manipulation and retrieval of data from a database.
Understanding PML Paulo Pinheiro da Silva. PML PML is a provenance language (a language used to encode provenance knowledge) that has been proudly derived.
Formal Models in AGI Research Pei Wang Temple University Philadelphia, USA.
A language to describe software texture in abstract design models and implementation.
Managing multiple client systems and building a shared interoperability vision in the Health Sector Dennis Wollersheim Health Information Management.
Database Management Systems, R. Ramakrishnan and J. Gehrke1 Relational Algebra.
1 © 1999 Microsoft Corp.. Microsoft Repository Phil Bernstein Microsoft Corp.
NECSIS Workshop March 10 NECSIS Workshops1. Source-to-target model transf. B … A T(B) … Space of M’s instances (models), [[ M]] T(A) … Space of N’s instances.
1 Relational Algebra and Calculas Chapter 4, Part A.
1 Relational Algebra Chapter 4, Sections 4.1 – 4.2.
Automata Based Method for Domain Specific Languages Definition Ulyana Tikhonova PhD student at St. Petersburg State Politechnical University, supervised.
CMPT 258 Database Systems Relational Algebra (Chapter 4)
Class Diagrams. Terms and Concepts A class diagram is a diagram that shows a set of classes, interfaces, and collaborations and their relationships.
Lecture 15: Query Optimization. Very Big Picture Usually, there are many possible query execution plans. The optimizer is trying to chose a good one.
Department of Mathematics Computer and Information Science1 CS 351: Database Management Systems Christopher I. G. Lanclos Chapter 4.
© 2014 IBM Corporation The BE 2 model: When Business Events meet Business Entities Fabiana Fournier and Lior Limonad 8 September 2014.
Of 24 lecture 11: ontology – mediation, merging & aligning.
Defects of UML Yang Yichuan. For the Presentation Something you know Instead of lots of new stuff. Cases Instead of Concepts. Methodology instead of the.
Mathematical Practice Standards
Database Systems: Design, Implementation, and Management Tenth Edition
Definition CASE tools are software systems that are intended to provide automated support for routine activities in the software process such as editing.
SysML 2.0 Formalism: Requirement Benefits, Use Cases, and Potential Language Architectures Formalism WG December 6, 2016.
SysML v2 Formalism: Requirements & Benefits
Relational Algebra Chapter 4 1.
Artificial Intelligence
Business Process Measures
Phil Bernstein Microsoft Corp.
Relational Algebra Chapter 4, Part A
Relational Algebra 461 The slides for this text are organized into chapters. This lecture covers relational algebra, from Chapter 4. The relational calculus.
Scenario Integration via Higher-Order Graphs *)
Chapter 2 Database Environment.
Relational Algebra 1.
Relational Algebra Chapter 4 1.
Patterns.
Implementing Mapping Composition
Relational Algebra Chapter 4, Sections 4.1 – 4.2
Metadata Framework as the basis for Metadata-driven Architecture
1/16/2019 A metamodel independent framework for model transformation: Towards generic model management patterns in reverse engineering Zinovy Diskin and.
Automated Analysis and Code Generation for Domain-Specific Models
Applying Use Cases (Chapters 25,26)
Applying Use Cases (Chapters 25,26)
SQL-Views and External Schemas
ONTOMERGE Ontology translations by merging ontologies Paper: Ontology Translation on the Semantic Web by Dejing Dou, Drew McDermott and Peishen Qi 2003.
Software Development Process Using UML Recap
Software Architecture & Design
Presentation transcript:

Zinovy Diskin School of Computing, Queen’s University Kingston, Canada Do model mappings really map? On derived information in model management Zinovy Diskin School of Computing, Queen’s University Kingston, Canada

Q: What is model mapping? Schema mappings are specifications that describe the relationships between schemas at a high level. These specifications are typically given in a logical formalism that captures the interaction between schemas at a logical level without spelling out implementation details relevant to the physical level. - Phokion Kolaitis. Invited Talk at ACM PODS’2005 Mapping (in the math sense) between models? (early MMt papers, 2000-02) Relation (math) between models? (2003-current) Relation (math) between sets of instances of the models? (05-current) Correspondence between models? Anything talking about anything in-between models.

Lewis Carroll about the issue: When I use a word, Humpty Dumpty said, in a rather scornful tone, it means just what I choose it to mean, neither more nor less. The question is, said Alice, whether you can make words mean so many different things. The question is, said Humpty Dumpty, which is to be master - that's all. <….> When I make a word do a lot of work like that, said Humpty Dumpty, I always pay it extra.

“Humpty Dumpty” Syndrome Patients: model mapping, model management, association (in UML), semantics, formalization, … Etiology: informal/semi-formal interpretations with some elementary concepts missing. Treatment: build an adequate formal framework, disassemble complex notions into elementary units and accurately re-assemble them again

Modeling: engineering and mathematical Structure of Engineering Models (models and operations/relations over them) M-modeling Structure of Mathematical Models E-modeling Model11 Model12 Model 1 Model13 Reality (say, a bridge) Model 2 Model21 Model22 … … E-mechanics M-mechanics

Back to model mappings Practical situations of correspondence between models often involve derived info: elements of one model correspond to elements that can be derived over another model rather than immediately belong to it. How to model and manage such situation in an intelligent way?

An early attempt to work with derived info (Bernstein, 2003) {name=l-name + f-name} { age= currentYear - b-date.year } The problem is how to compose mappings with annotated expressions

Solution suggested by categorical algebra: Place operation expressions in models rather than in mappings. In more detail, augment models with required derived elements and consider mappings between models augmented with derived elements (so called Kleisly mappings). We will consider two examples. One is from model merge and the other is model translation.

Model merge: a generic pattern Corresp. Model, R r1 (e1  f1) r12 (/e14  f12) Model A Model B r2 (/e4  f2) f1 e3 f3 e1 f12 f23 e2 [q] f2 /e14 /e4 Model derQAB Model derQBA e3 r1 /r12 f3 e2 f23 /r2 Model derQBA R derQAB

Meta-schema of merge match matching merge merge normalization [ join ] merge merge normalization normalization Color Legend: green means ‘heuristic’, blue means ‘automatic’

Example: extracting ER-diagrams from SQL-tables (simplified)

Model translation (MT): MT-programming (on the left) via PB (pull-back) (right) Source model S; Source metamodel MS; Target metamodel MT; Source model; Metamodel mapping, MT  MS Transformation Spec (rules), Transformation Engine PB-algorithm Trace mapping Target model Trace mapping Target model

Th. (2) gives rise to a procedure implementing specification (1) MT in universal (not elementwise) terms (specification vs. implementation) m*’ T’ u!  ’ m* S T (1) Definition: (T,,m*) = PB(, m)   [ = ] MS MT Talk about drawbacks of ordinary MT: algorithm IS the definition m (2) Theorem [an elementwise implementation of def(1)] : T = {(e,y) S x MT | e.  = y.m } Th. (2) gives rise to a procedure implementing specification (1) 7/14/2019

MT-via-PB: separation of concerns Procedural part Declarative part m* S derQS T  [ algExp] (query exec) [PB] (retyping)  Q MS derQMS MT Talk about drawbacks of ordinary MT: algorithm IS the definition m 7/14/2019

Does PB works? Yes, if we use proper (Kleisly) mappings to derived elements.

How essential are derived elements? Relational metamodel augmented with derived elements to interpret ER-metamodel. Semantics of data is hidden in the application code.

Delicate issues to be addressed: automation (algebra) and heuristics in model management; does algebra do what we really need? basic-vs.-derived: a pseudo-conflict between views; derived information and normalization.

Summary Kleisly mappings provide a convenient way to handle derived information issues in MMt They appear quite naturally in model match, merge and translation tasks They allow us to consider many seemingly different notions of model mapping in a unified and mathematically justified way.