Databases. Exercises on Relational Algebra

Size: px
Start display at page:

Download "Databases. Exercises on Relational Algebra"

Transcription

1 Databases Exercises on Relational Algebra

2 The Lab Sessions Giacomo Bergami bergami.co.nr 2016/10/07 Keys and Superkeys Relational Algebra (I) Negation Minimum 2016/10/14 Relational Algebra (II) At least 2 More exercises + Questions

3 Keys def. A superkey K in r(s) (K S) univocally identifies tuples in r. t 1 t 2 r. t 1 [K]=t 2 [K] t 1 t 2 r. t 1 [K] t 2 [K] Recap: within the relational model, each tuple is unique. t 1 r. t 2 r\{t 1 }. t 1 t 2 (HP) Hence, having the aforementioned condition is sufficient for unicity.

4 and Superkeys def. K S is minimal for a property P(.) if T K.P(T) This means that any other key smaller than K does not satisfy a predicate. def. A key K in r(s) (K S) is a minimal superkey. This means that a key is already minimal, since there is no other subset of K satisfying the property being a superkey. (A key is also a minimal key ) T K.( t 1 t 2 r. t 1 [K]=t 2 [K]) t 1 t 2 r. t 1 t 2 r. t 1 [K]=t 2 [K]

5 Recap: operators Set Operations: /,,, σ θ (R): Filter R by θ π L (R): Reduce R s schema to the attributes in L R S, R θ S: combining two relations using a predicate θ ρ B A (R): Renames A as B in R s schema

6 Why? Query expressions equivalences: query optimizations in current (relational) databases use algebras to rewrite rules into an equivalent expression that takes less time to compute. In this course correctness and readable results are preferred to quick and efficient ones (see above). A way to express queries in a logical framework. How to express not forall? How to express minimum? How to count (there exists at least two )?

7 Exercise 1 Given the following relation: Train(Code, Start, End, miles) Provide all the routes between Boston and Chicago with one switch. In order to switch train, you have to reach an end and then start again the journey. We have to join the Train relation. We have to rename the fields before joining them

8 Exercise 1 Solution A ρ C1 Code,S Start,Sw End,M1 miles (Train) Train(C1, S, Sw, M1) S= Boston AND E= Chicago ρ C2 Code,Sw Start,E End,M2 miles (Train) Train(C2, Sw, E, M2) Solution B ρ C1 Code,S Start,Sw End,M1 miles (σ Start= Boston (Train)) ρ C2 Code,Sw Start,E End,M2 miles (σ End= Chicago (Train)) Which is the most efficient solution?

9 Exercise 2(a) Given the following relation: Employee(Code, Name Surname) BelongsTo(Employee, Office) Associate to each employee its office, only if it exists. R θ S combines the tuples from both relations iff. both of them satisfy a predicate θ

10 Exercise 2(a) Employee Code Name Surname BelongsTo Employee Office 0123 Johann Sebastian Bach 4567 Wolfgang Amadeus Mozart 0571 Edvard Grieg 0123 Baroque 4567 Classical 3573 Impressionism 3573 Claude Debussy π Code,Name,Surname,Office (Employee Code=Employee BelongsTo) Code Name Surname Office 0123 Johann Sebastian Bach Baroque 4567 Wolfgang Amadeus Mozart Classical 3573 Claude Debussy Impressionism

11 Exercise 2(b) Given the following relation: Employee(Code, Name Surname) BelongsTo(Employee, Office) Return employees codes with with no office. In this case we have to use a left join in order to get the complete list of employees

12 Exercise 2(b) Employee Code Name Surname BelongsTo Employee Office 0123 Johann Sebastian Bach 4567 Wolfgang Amadeus Mozart 0571 Edvard Grieg 0123 Baroque 4567 Classical 3573 Impressionism 3573 Claude Debussy π Code,Name,Surname,Office (Employee Code=Employee BelongsTo) Code Name Surname Office 0123 Johann Sebastian Bach Baroque 4567 Wolfgang Amadeus Mozart Classical 0571 Edvard Grieg NULL 3573 Claude Debussy Impressionism

13 Exercise 2(b) π Code,Name,Surname,Office (Employee Code=Employee BelongsTo) Code Name Surname Office 0123 Johann Sebastian Bach Baroque 4567 Wolfgang Amadeus Mozart Classical 0571 Edvard Grieg NULL 3573 Claude Debussy Impressionism π Code,Name,Surname (σ Office is NULL (Employee Code=Employee BelongsTo)) Code Name Surname 0571 Edvard Grieg

14 Exercise 3(a) Given the following relations: State(Name, Area) City(Code, Name, Inhabitants) FormedOf(State, City) Return the U.S.A. States names having more than inhabitants. How to solve this query?

15 Exercise 3(a) Given the following relations: State(Name, Area) City(Code, Name, Inhabitants) FormedOf(State, City) Return the U.S.A. States names having more than inhabitants. This query requires the group by operator (Γ,γ), that is missing in the proposed relational algebra. Let s change the query.

16 Exercise 3(b) Given the following relations: State(Name, Area) City(Code, Name, Inhabitants) FormedOf(State, City) Return the U.S.A States names having cities with more than inhabitants.

17 Exercise 3(b) Given the following relations State(Name, Area) City(Code, Name, Inhabitants) FormedOf(State, City) State FormedOf City Name Area State City Code Name Inhabitants Rhode Island 1,545 New Jersey 8,723 California 163,696 Alaska 665,384 Rhode Island 401 New Jersey 862 California 213 Alaska 907 Alaska Providence 178, Newark 277, Los Angeles 4,030, Anchorage 291, North Pole 2,117

18 Exercise 3(b) State Name Return the U.S.A States names having cities with more than inhabitants (θ).? Area FormedOf State City City Code Name Inhabitants Rhode Island 1,545 Rhode Island Providence 178,042 New Jersey 8,723 Florida 65,758 New Jersey 862 California Newark 277, Los Angeles 4,030,904 Alaska 665,384 Alaska Anchorage 291,826 Alaska North Pole 2,117? FormedOf already has the State s name information. City and FormedOf are enough σ Inhabitants> (City) Code Name Inhabitants 213 Los Angeles 4,030,904

19 Exercise 3(b) FormedOf State City Rhode Island 401 New Jersey 862 California 213 City=Code σ Inhabitants> (City) Code Name Inhabitants 213 Los Angeles 4,030,904 Alaska 907 Alaska 908 π State (FormedOf City=Code σ Inhabitants> (City)) ρ Code City (FormedOf) State Code Rhode Island 401 New Jersey 862 California 213 City=Code σ Inhabitants> (City) Code Name Inhabitants 213 Los Angeles 4,030,904 Alaska 907 Alaska 908 π State (ρ Code City (FormedOf) σ Inhabitants> (City))

20 Exercise 4 Given the following relations: Person(ID, Name, Surname, Age) Registration(Person,Lecture) Lecture(Name,Price) Return the people s names which are registred for a lecture that costs more than 10 times their age. Let s just use natural joins, projection, selections and renamings.

21 Exercise 4 Given the following relations: ρ Person ID (Person) Registration ρ Lecture Name (Lecture) Person(Person, Name,, Age) Registration(Person,Lecture) Lecture(Lecture,Price) Return the people s names which are registred for a lecture that costs more than 10 times their age. Let s just use natural joins, projection, selections and renamings.

22 Exercise 4 π Name (σ Cost>10*Age ( ρ Person ID (Person) Registration ρ Lecture Name (Lecture) )) Return the people s names which are registred for a lecture that costs more than 10 times their age.

23 Exercise 5 Given the following relations: State(Name, Area) City(Code, Name, Inhabitants) FormedOf(State, City) return the City names belonging to states larger than squared miles

24 ) Exercise 5 ρ State Name (σ Area>1000 (State)) ρ City Code (City) State(State, Area) City(City, Name, Inhabitants) FormedOf FormedOf(State, City) return the City names belonging to states larger than squared miles π Name ( ρ City Code (City) FormedOf ρ State Name (σ Area>1000 (State))

25 Exercise 6(a) Given the following relations: Student(Number, Surname, Name, Dept) Exam(Student, Subject, Grade, Day) Provide the students surnames and names that passed at least one exam with grade A. Please note that, in this case, we could select all the exams with grade A, then grasp the student This time we re going to use theta-joins

26 Exercise 6(a) Student(Number, Surname, Name, Dept) Exam(Student, Subject, Grade, Day) π Surname,Name ( Student Number=Student π Student (σ Grade= A (Exam)) ) Using natural joins π Surname,Name ( Student π Number (ρ Number Student (σ Grade= A (Exam))) )

27 Exercise 6(b) Given the following relations: Student(Number, Surname, Name, Dept) Exam(Student, Subject, Grade, Day) Provide the students surnames and names that passed no exam with grade A.

28 Example 6(b) Remember that S =U\ S, where U is the universe relation U: Student (id) S: Students (id) that gave exams with grade A π Surname,Name (Student ( π Number (Student) \ π Number (ρ Number Student (σ Grade= A (Exam) ) ) U \ S

29 Exercise 6(c) Given the following relations: Student(Number, Surname, Name, Dept) Exam(Student, Subject, Grade, Day) Provide the students surnames, names and first exam (by date) passed with grade A. Is it possible to define a minimum operator in relational algebra?

30 Exercise 6(c) Student Number Surname Name Dept. M1 Rossi Ugo Computer Science M2 Bianchi Mario Computer Science Exam Student Subject Grade Day M1 DB A 08/05/2012 M1 Compl. DB A 10/05/2012 M1 Lambda Calc. A 06/06/2012 M1 ALGEBRA B 07/01/2011 M2 OS B 07/02/2012

31 Defining the min. operator Return the tuple in S having the minimum value of C. In this case we return the tuple t from S having the minimum C value. min C (S)={t t,t S. t[c] t [C]} ={t t S. t S. t[c]>t [C]} =S\{t t S. t S. t[c]>t [C]} =S (π C (S)\π C (π C (S) C>C π C (ρ C C (S))))

32 Defining the local min. operator Return the tuple in S having the minimum value of C within the entries with the same A value. By doing so, we perform the comparison C>C among all the tuples with the same A values locmin A,C (S)= =S (π A,C (S)\π A,C (π A,C (S) C>C π A,C (ρ C C (S))))

33 Warning! min and local min. operators are non standard operators When you do the exam, you have to provide the algebra expression associated with it. Problem: it is too difficult to keep in mind Any kind of cards, texts and notes are forbidden. Logical language is a way to obtain a safe expressions without risks. Use them!

34 Exercise 6(c): Final Result Provide the students surnames, names and first exam (by date) passed with grade A. S = σ Grade= A (Exam) T = π Student,Day (S)\π Student,Day (π Student,Day (S) Day>Day π Student,Day (ρ Day Day (S))) π Surname,Name,Day (Student Number=Student T)

35 Local minimum for student/day Provide the students surnames, names and first exam (by date) passed with grade A. Return the student (id) and exam day having the minimum value of exam date with grade A within the entries for the same student (id). S = σ Grade= A (Exam) π Student,Day (S)\π Student,Day (π Student,Day (S) Day>Day π Student,Day (ρ Day Day (S)))

36 Exercise 7 Given the following relations: User(Tax,Surname,Birth) Field(FCode,IsCovered) Bookings(FCode,Day,TimeStart,TimeEnd,Tax) return the Tax code of the users that have booked at least two times a non covered field and that have never booked a covered field. and = intersection never booked a covered field = negation at least two times = how to count?

37 Exercise 7(a) Given the following relations: User(Tax,Surname,Birth) Field(FCode,IsCovered) Bookings(FCode,Day,TimeStart,TimeEnd,Tax) return the Tax code of the users that have never booked a covered field Use the negated semantics.

38 Exercise 7(a) return the Tax code of the users that have never booked a covered field Use the negated semantics. π Tax (User)\π Tax ( User Bookings σ IsCovered=True (Field))

39 Exercise 7(b) Given the following relations: User(Tax,Surname,Birth) Field(FCode,IsCovered) Bookings(FCode,Day,TimeStart,TimeEnd,Tax) return the Tax code of the users that have booked at least two times a non covered field Is there a way to evaluate such function using the previoulsy defined functions?

40 At least one, at least two First: if there exists a minimum, then it means that there exists at least one element (that is the minimum). ex C (S)=min C (S) Second: if there exsits another element immediately following the minimum, then there exists two elements (the minimum and the element immediately following it) ex2 C (S)=ex C (S\ex C (S))=min C (S\min C (S))

41 At least two locmin (1) locmin A,C (S\locmin A,C (S))=? First of all, exclude the minimum from S: S\locmin A,C (S)= = S\(S (π A,C (S)\π A,C (π A,C (S) C>C π A,C (ρ C C (S)) ) )) = S π A,C (π A,C (S) C>C π A,C (ρ C C (S))) = S π A,C ( S)

42 At least two minloc (2) locmin A,C (S\locmin A,C (S))=? T = S π A,C ( S) = S\locmin A,C (S) locmin A,C (S)= =S (π A,C (S)\π A,C (π A,C (S) C>C π A,C (ρ C C (S)))) locmin A,C (S\locmin A,C (S))= = locmin A,C (S π A,C ( S)) = (S π A,C ( S)) (π A,C (T)\π A,C (π A,C (T) C>C π A,C (ρ C C (T)))) = S (π A,C (T)\π A,C (π A,C (T) C>C π A,C (ρ C C (T))))

43 At least two minloc (3) locmin A,C (S\locmin A,C (S))= = S (π A,C (T)\π A,C (π A,C (T) C>C π A,C (ρ C C (T)))) Recall: π A,C (ρ C C (T))=ρ C C (π A,C (T)) = S (π A,C (T)\π A,C (π A,C (T) C>C ρ C C (π A,C (T))))

44 At least two minloc (4) locmin A,C (S\locmin A,C (S))= = S (π A,C (T)\π A,C (π A,C (T) C>C ρ C C (π A,C (T)))) Recall: π A,C (S π A,C ( S))=π A,C ( S) Recall: S = π A,C (S) C>C π A,C (ρ C C (S)) = S (π A,C ( S)\π A,C (π A,C ( S) C>C ρ C C (π A,C ( S)))).

45 Exercise 7(b) [ ] Return the Tax code of the users that have booked at least two times a non covered field S=π FCode,,Tax (Bookings σ IsCovered=True (Field)) π Tax (ex2 Tax,(FCode,Day,timeStart) (S)) For ordering multiple attributes, use the lexicographical order: (FCode<FCode ) OR (FCode=FCode AND Day<Day ) OR (FCode=FCode AND Day=Day AND timestart<timestart )

46 Exercise 7 (finally!) [ ] Return the Tax code of the users that have booked at least two times a non covered field and that have never booked a covered field. Use the intersection. A = π Tax (User)\π Tax (User Bookings σ IsCovered=True (Field)) S = π FCode,,Tax (Bookings σ IsCovered=True (Field)) A π Tax (ex2 Tax,(FCode,Day,timeStart) (S))

47 Warning! If logic is used as a formal specification, you could derive a correct relational algebra expression from it. Abstract reasoning (see functional programming) allows to reason on the result and not on how to evaluate the solution. Intermediate variables and operators allow to have a more readable code. Any non standard operator (min, locmin, ex, ex2, ) shall be defined.

Database Applications (15-415)

Database Applications (15-415) Database Applications (15-415) Relational Calculus Lecture 5, January 27, 2014 Mohammad Hammoud Today Last Session: Relational Algebra Today s Session: Relational algebra The division operator and summary

More information

Databases 2011 The Relational Algebra

Databases 2011 The Relational Algebra Databases 2011 Christian S. Jensen Computer Science, Aarhus University What is an Algebra? An algebra consists of values operators rules Closure: operations yield values Examples integers with +,, sets

More information

Database Applications (15-415)

Database Applications (15-415) Database Applications (15-415) Relational Calculus Lecture 6, January 26, 2016 Mohammad Hammoud Today Last Session: Relational Algebra Today s Session: Relational calculus Relational tuple calculus Announcements:

More information

Mathematics Review for Business PhD Students Lecture Notes

Mathematics Review for Business PhD Students Lecture Notes Mathematics Review for Business PhD Students Lecture Notes Anthony M. Marino Department of Finance and Business Economics Marshall School of Business University of Southern California Los Angeles, CA 90089-0804

More information

CSE 311: Foundations of Computing I Autumn 2014 Practice Final: Section X. Closed book, closed notes, no cell phones, no calculators.

CSE 311: Foundations of Computing I Autumn 2014 Practice Final: Section X. Closed book, closed notes, no cell phones, no calculators. CSE 311: Foundations of Computing I Autumn 014 Practice Final: Section X YY ZZ Name: UW ID: Instructions: Closed book, closed notes, no cell phones, no calculators. You have 110 minutes to complete the

More information

A Knowledge-Based Approach to Entity Resolution

A Knowledge-Based Approach to Entity Resolution A Knowledge-Based Approach to Entity Resolution Klaus-Dieter Schewe Software Competence Center Hagenberg and Johannes-Kepler-University Linz Austria kd.schewe@scch.at, kd.schewe@faw.at Qing Wang Research

More information

UVA UVA UVA UVA. Database Design. Relational Database Design. Functional Dependency. Loss of Information

UVA UVA UVA UVA. Database Design. Relational Database Design. Functional Dependency. Loss of Information Relational Database Design Database Design To generate a set of relation schemas that allows - to store information without unnecessary redundancy - to retrieve desired information easily Approach - design

More information

CS 4604: Introduc0on to Database Management Systems. B. Aditya Prakash Lecture #3: SQL---Part 1

CS 4604: Introduc0on to Database Management Systems. B. Aditya Prakash Lecture #3: SQL---Part 1 CS 4604: Introduc0on to Database Management Systems B. Aditya Prakash Lecture #3: SQL---Part 1 Announcements---Project Goal: design a database system applica=on with a web front-end Project Assignment

More information

Tutorial Exercises 1 (mjs)

Tutorial Exercises 1 (mjs) 499 Modal and Temporal Logic Autumn 2008 Tutorial Exercises 1 (mjs) 1. Suppose that Σ is closed under RM. SOLUTIONS Suppose first that Σ contains C. A derivation of K: 1. Σ (A (A B) ) B PL 2. Σ (A (A B)

More information

CSE 562 Database Systems

CSE 562 Database Systems Outline Query Optimization CSE 562 Database Systems Query Processing: Algebraic Optimization Some slides are based or modified from originals by Database Systems: The Complete Book, Pearson Prentice Hall

More information

EECS-3421a: Test #2 Electrical Engineering & Computer Science York University

EECS-3421a: Test #2 Electrical Engineering & Computer Science York University 18 November 2015 EECS-3421a: Test #2 1 of 16 EECS-3421a: Test #2 Electrical Engineering & Computer Science York University Family Name: Given Name: Student#: CSE Account: Instructor: Parke Godfrey Exam

More information

7 RC Simulates RA. Lemma: For every RA expression E(A 1... A k ) there exists a DRC formula F with F V (F ) = {A 1,..., A k } and

7 RC Simulates RA. Lemma: For every RA expression E(A 1... A k ) there exists a DRC formula F with F V (F ) = {A 1,..., A k } and 7 RC Simulates RA. We now show that DRC (and hence TRC) is at least as expressive as RA. That is, given an RA expression E that mentions at most C, there is an equivalent DRC expression E that mentions

More information

Information Systems for Engineers. Exercise 5. ETH Zurich, Fall Semester Hand-out Due

Information Systems for Engineers. Exercise 5. ETH Zurich, Fall Semester Hand-out Due Information Systems for Engineers Exercise 5 ETH Zurich, Fall Semester 2017 Hand-out 27.10.2017 Due 03.11.2017 Reading material: Chapter 2.4 in [1]. Lecture slides 4. 1. Given the two tables below, write

More information

General Overview - rel. model. Carnegie Mellon Univ. Dept. of Computer Science /615 DB Applications. Motivation. Overview - detailed

General Overview - rel. model. Carnegie Mellon Univ. Dept. of Computer Science /615 DB Applications. Motivation. Overview - detailed Carnegie Mellon Univ. Dep of Computer Science 15-415/615 DB Applications C. Faloutsos & A. Pavlo Lecture#5: Relational calculus General Overview - rel. model history concepts Formal query languages relational

More information

Schema Refinement. Feb 4, 2010

Schema Refinement. Feb 4, 2010 Schema Refinement Feb 4, 2010 1 Relational Schema Design Conceptual Design name Product buys Person price name ssn ER Model Logical design Relational Schema plus Integrity Constraints Schema Refinement

More information

Relational Algebra & Calculus

Relational Algebra & Calculus Relational Algebra & Calculus Yanlei Diao UMass Amherst Slides Courtesy of R. Ramakrishnan and J. Gehrke 1 Outline v Conceptual Design: ER model v Logical Design: ER to relational model v Querying and

More information

Section 2.3: Statements Containing Multiple Quantifiers

Section 2.3: Statements Containing Multiple Quantifiers Section 2.3: Statements Containing Multiple Quantifiers In this section, we consider statements such as there is a person in this company who is in charge of all the paperwork where more than one quantifier

More information

Plan of the lecture. G53RDB: Theory of Relational Databases Lecture 2. More operations: renaming. Previous lecture. Renaming.

Plan of the lecture. G53RDB: Theory of Relational Databases Lecture 2. More operations: renaming. Previous lecture. Renaming. Plan of the lecture G53RDB: Theory of Relational Lecture 2 Natasha Alechina chool of Computer cience & IT nza@cs.nott.ac.uk Renaming Joins Definability of intersection Division ome properties of relational

More information

Propositional logic (revision) & semantic entailment. p. 1/34

Propositional logic (revision) & semantic entailment. p. 1/34 Propositional logic (revision) & semantic entailment p. 1/34 Reading The background reading for propositional logic is Chapter 1 of Huth/Ryan. (This will cover approximately the first three lectures.)

More information

Information Systems for Engineers. Exercise 5. ETH Zurich, Fall Semester Hand-out Due

Information Systems for Engineers. Exercise 5. ETH Zurich, Fall Semester Hand-out Due Information Systems for Engineers Exercise 5 ETH Zurich, Fall Semester 2017 Hand-out 27.10.2017 Due 03.11.2017 Reading material: Chapter 2.4 in [1]. Lecture slides 4. 1. Given the two tables below, write

More information

Lectures 6. Lecture 6: Design Theory

Lectures 6. Lecture 6: Design Theory Lectures 6 Lecture 6: Design Theory Lecture 6 Announcements Solutions to PS1 are posted online. Grades coming soon! Project part 1 is out. Check your groups and let us know if you have any issues. We have

More information

Databases Exam HT2016 Solution

Databases Exam HT2016 Solution Databases Exam HT2016 Solution Solution 1a Solution 1b Trainer ( ssn ) Pokemon ( ssn, name ) ssn - > Trainer. ssn Club ( name, city, street, streetnumber ) MemberOf ( ssn, name, city ) ssn - > Trainer.

More information

Set Theory Basics of Set Theory. 6.2 Properties of Sets and Element Argument. 6.3 Algebraic Proofs 6.4 Boolean Algebras.

Set Theory Basics of Set Theory. 6.2 Properties of Sets and Element Argument. 6.3 Algebraic Proofs 6.4 Boolean Algebras. 9/6/17 Mustafa Jarrar: Lecture Notes in Discrete Mathematics. Birzeit University Palestine 2015 Set Theory 6.1. Basics of Set Theory 6.2 Properties of Sets and Element Argument 6.3 Algebraic Proofs 6.4

More information

INTRODUCTION TO RELATIONAL DATABASE SYSTEMS

INTRODUCTION TO RELATIONAL DATABASE SYSTEMS INTRODUCTION TO RELATIONAL DATABASE SYSTEMS DATENBANKSYSTEME 1 (INF 3131) Torsten Grust Universität Tübingen Winter 2017/18 1 THE RELATIONAL ALGEBRA The Relational Algebra (RA) is a query language for

More information

RYERSON UNIVERSITY DEPARTMENT OF MATHEMATICS

RYERSON UNIVERSITY DEPARTMENT OF MATHEMATICS RYERSON UNIVERSITY DEPARTMENT OF MATHEMATICS MTH 110 Final Exam December 6, 2008 Total marks: 100 Time allowed: 3 Hours. NAME (Print): STUDENT #: SIGNATURE: Circle your Lab Section: Section 1 Section 2

More information

Provenance Semirings. Todd Green Grigoris Karvounarakis Val Tannen. presented by Clemens Ley

Provenance Semirings. Todd Green Grigoris Karvounarakis Val Tannen. presented by Clemens Ley Provenance Semirings Todd Green Grigoris Karvounarakis Val Tannen presented by Clemens Ley place of origin Provenance Semirings Todd Green Grigoris Karvounarakis Val Tannen presented by Clemens Ley place

More information

Methods of AI. Practice Session 7 Kai-Uwe Kühnberger Dec. 13 th, 2002

Methods of AI. Practice Session 7 Kai-Uwe Kühnberger Dec. 13 th, 2002 Methods of AI Practice Session 7 Kai-Uwe Kühnberger Dec. 13 th, 2002 Today! As usual: Organizational issues! Midterm! Further plan! Proof Methods! Fourth part: Cases in direct proof! Some invalid proof

More information

Chapter 3 Relational Model

Chapter 3 Relational Model Chapter 3 Relational Model Table of Contents 1. Structure of Relational Databases 2. Relational Algebra 3. Tuple Relational Calculus 4. Domain Relational Calculus Chapter 3-1 1 1. Structure of Relational

More information

1 Predicates and Quantifiers

1 Predicates and Quantifiers 1 Predicates and Quantifiers We have seen how to represent properties of objects. For example, B(x) may represent that x is a student at Bryn Mawr College. Here B stands for is a student at Bryn Mawr College

More information

From Constructibility and Absoluteness to Computability and Domain Independence

From Constructibility and Absoluteness to Computability and Domain Independence From Constructibility and Absoluteness to Computability and Domain Independence Arnon Avron School of Computer Science Tel Aviv University, Tel Aviv 69978, Israel aa@math.tau.ac.il Abstract. Gödel s main

More information

Data Dependencies in the Presence of Difference

Data Dependencies in the Presence of Difference Data Dependencies in the Presence of Difference Tsinghua University sxsong@tsinghua.edu.cn Outline Introduction Application Foundation Discovery Conclusion and Future Work Data Dependencies in the Presence

More information

Logical Structures in Natural Language: First order Logic (FoL)

Logical Structures in Natural Language: First order Logic (FoL) Logical Structures in Natural Language: First order Logic (FoL) Raffaella Bernardi Università degli Studi di Trento e-mail: bernardi@disi.unitn.it Contents 1 How far can we go with PL?................................

More information

Review: Keys. What is a Functional Dependency? Why use Functional Dependencies? Functional Dependency Properties

Review: Keys. What is a Functional Dependency? Why use Functional Dependencies? Functional Dependency Properties Review: Keys Superkey: set of attributes whose values are unique for each tuple Note: a superkey isn t necessarily minimal. For example, for any relation, the entire set of attributes is always a superkey.

More information

2.5 Compound Inequalities

2.5 Compound Inequalities Section.5 Compound Inequalities 89.5 Compound Inequalities S 1 Find the Intersection of Two Sets. Solve Compound Inequalities Containing and. Find the Union of Two Sets. 4 Solve Compound Inequalities Containing

More information

Review of Lecture 1. Across records. Within records. Classification, Clustering, Outlier detection. Associations

Review of Lecture 1. Across records. Within records. Classification, Clustering, Outlier detection. Associations Review of Lecture 1 This course is about finding novel actionable patterns in data. We can divide data mining algorithms (and the patterns they find) into five groups Across records Classification, Clustering,

More information

Introduction to Data Management. Lecture #12 (Relational Algebra II)

Introduction to Data Management. Lecture #12 (Relational Algebra II) Introduction to Data Management Lecture #12 (Relational Algebra II) Instructor: Mike Carey mjcarey@ics.uci.edu Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke 1 Announcements v HW and exams:

More information

Definite Logic Programs

Definite Logic Programs Chapter 2 Definite Logic Programs 2.1 Definite Clauses The idea of logic programming is to use a computer for drawing conclusions from declarative descriptions. Such descriptions called logic programs

More information

MATH 341, Section 001 FALL 2014 Introduction to the Language and Practice of Mathematics

MATH 341, Section 001 FALL 2014 Introduction to the Language and Practice of Mathematics MATH 341, Section 001 FALL 2014 Introduction to the Language and Practice of Mathematics Class Meetings: MW 9:30-10:45 am in EMS E424A, September 3 to December 10 [Thanksgiving break November 26 30; final

More information

Lecture 2: Syntax. January 24, 2018

Lecture 2: Syntax. January 24, 2018 Lecture 2: Syntax January 24, 2018 We now review the basic definitions of first-order logic in more detail. Recall that a language consists of a collection of symbols {P i }, each of which has some specified

More information

ER Modelling: Summary

ER Modelling: Summary ER Modelling: Summary ER describes the world (the set of possible worlds) what it is and the laws of this world ER is static: it does not describe (legal) transitions Useful for two purposes build software

More information

Name: Matriculation Number: Tutorial Group: A B C D E

Name: Matriculation Number: Tutorial Group: A B C D E Name: Matriculation Number: Tutorial Group: A B C D E Question: 1 (5 Points) 2 (6 Points) 3 (5 Points) 4 (5 Points) Total (21 points) Score: General instructions: The written test contains 4 questions

More information

Lecture 7: Sections 2.3 and 2.4 Rational and Exponential Functions. Recall that a power function has the form f(x) = x r where r is a real number.

Lecture 7: Sections 2.3 and 2.4 Rational and Exponential Functions. Recall that a power function has the form f(x) = x r where r is a real number. L7-1 Lecture 7: Sections 2.3 and 2.4 Rational and Exponential Functions Recall that a power function has the form f(x) = x r where r is a real number. f(x) = x 1/2 f(x) = x 1/3 ex. Sketch the graph of

More information

Translatable Updates of Selection Views under Constant Complement

Translatable Updates of Selection Views under Constant Complement Translatable Updates of Selection Views under Constant Complement Enrico Franconi and Paolo Guagliardo Free University of Bozen-Bolzano, Italy 4 th September 2014 DEXA 2014, Munich (Germany) KRDB Research

More information

Methods of Proof. Zach Wagner Department of Mathematics University of California, Los Angeles Los Angeles, California USA.

Methods of Proof. Zach Wagner Department of Mathematics University of California, Los Angeles Los Angeles, California USA. Methods of Proof Zach Wagner Department of Mathematics University of California, Los Angeles Los Angeles, California 9004 USA August 15, 01 Abstract Of all of the subjects encountered when studying mathematics,

More information

MAT 1302B Mathematical Methods II

MAT 1302B Mathematical Methods II MAT 1302B Mathematical Methods II Alistair Savage Mathematics and Statistics University of Ottawa Winter 2015 Lecture 19 Alistair Savage (uottawa) MAT 1302B Mathematical Methods II Winter 2015 Lecture

More information

Relational Algebra and Calculus

Relational Algebra and Calculus Topics Relational Algebra and Calculus Linda Wu Formal query languages Preliminaries Relational algebra Relational calculus Expressive power of algebra and calculus (CMPT 354 2004-2) Chapter 4 CMPT 354

More information

Query answering using views

Query answering using views Query answering using views General setting: database relations R 1,...,R n. Several views V 1,...,V k are defined as results of queries over the R i s. We have a query Q over R 1,...,R n. Question: Can

More information

Tuple Relational Calculus

Tuple Relational Calculus Tuple Relational Calculus Université de Mons (UMONS) May 14, 2018 Motivation S[S#, SNAME, STATUS, CITY] P[P#, PNAME, COLOR, WEIGHT, CITY] SP[S#, P#, QTY)] Get all pairs of city names such that a supplier

More information

GAV-sound with conjunctive queries

GAV-sound with conjunctive queries GAV-sound with conjunctive queries Source and global schema as before: source R 1 (A, B),R 2 (B,C) Global schema: T 1 (A, C), T 2 (B,C) GAV mappings become sound: T 1 {x, y, z R 1 (x,y) R 2 (y,z)} T 2

More information

Relational-Database Design

Relational-Database Design C H A P T E R 7 Relational-Database Design Exercises 7.2 Answer: A decomposition {R 1, R 2 } is a lossless-join decomposition if R 1 R 2 R 1 or R 1 R 2 R 2. Let R 1 =(A, B, C), R 2 =(A, D, E), and R 1

More information

CS5300 Database Systems

CS5300 Database Systems CS5300 Database Systems Relational Algebra A.R. Hurson 323 CS Building hurson@mst.edu This module is intended to introduce: relational algebra as the backbone of relational model, and set of operations

More information

10/12/10. Outline. Schema Refinements = Normal Forms. First Normal Form (1NF) Data Anomalies. Relational Schema Design

10/12/10. Outline. Schema Refinements = Normal Forms. First Normal Form (1NF) Data Anomalies. Relational Schema Design Outline Introduction to Database Systems CSE 444 Design theory: 3.1-3.4 [Old edition: 3.4-3.6] Lectures 6-7: Database Design 1 2 Schema Refinements = Normal Forms 1st Normal Form = all tables are flat

More information

Sets are one of the basic building blocks for the types of objects considered in discrete mathematics.

Sets are one of the basic building blocks for the types of objects considered in discrete mathematics. Section 2.1 Introduction Sets are one of the basic building blocks for the types of objects considered in discrete mathematics. Important for counting. Programming languages have set operations. Set theory

More information

Propositional Logic: Semantics and an Example

Propositional Logic: Semantics and an Example Propositional Logic: Semantics and an Example CPSC 322 Logic 2 Textbook 5.2 Propositional Logic: Semantics and an Example CPSC 322 Logic 2, Slide 1 Lecture Overview 1 Recap: Syntax 2 Propositional Definite

More information

Section 3.1 Statements, Negations, and Quantified Statements

Section 3.1 Statements, Negations, and Quantified Statements Section 3.1 Statements, Negations, and Quantified Statements Objectives 1. Identify English sentences that are statements. 2. Express statements using symbols. 3. Form the negation of a statement 4. Express

More information

SCHEMA NORMALIZATION. CS 564- Fall 2015

SCHEMA NORMALIZATION. CS 564- Fall 2015 SCHEMA NORMALIZATION CS 564- Fall 2015 HOW TO BUILD A DB APPLICATION Pick an application Figure out what to model (ER model) Output: ER diagram Transform the ER diagram to a relational schema Refine the

More information

CSC 261/461 Database Systems Lecture 10 (part 2) Spring 2018

CSC 261/461 Database Systems Lecture 10 (part 2) Spring 2018 CSC 261/461 Database Systems Lecture 10 (part 2) Spring 2018 Announcement Read Chapter 14 and 15 You must self-study these chapters Too huge to cover in Lectures Project 2 Part 1 due tonight Agenda 1.

More information

Relational Calculus. Dr Paolo Guagliardo. University of Edinburgh. Fall 2016

Relational Calculus. Dr Paolo Guagliardo. University of Edinburgh. Fall 2016 Relational Calculus Dr Paolo Guagliardo University of Edinburgh Fall 2016 First-order logic term t := x (variable) c (constant) f(t 1,..., t n ) (function application) formula ϕ := P (t 1,..., t n ) t

More information

Constraints: Functional Dependencies

Constraints: Functional Dependencies Constraints: Functional Dependencies Fall 2017 School of Computer Science University of Waterloo Databases CS348 (University of Waterloo) Functional Dependencies 1 / 42 Schema Design When we get a relational

More information

Welcome to PHYS-131 Physics-I with Algebra. Please turn off your cell phones

Welcome to PHYS-131 Physics-I with Algebra. Please turn off your cell phones Welcome to PHYS-131 Physics-I with Algebra Please turn off your cell phones TARIQ H. GILANI PhD Kyoto University, Japan (1997) ASSOCIATE PROFESSOR since 2002 Office: R 236 (Caputo) Ph. 871-7449 E-mail:

More information

APPM 2350 Section Exam points Wednesday September 26, 6:00pm 7:30pm, 2018

APPM 2350 Section Exam points Wednesday September 26, 6:00pm 7:30pm, 2018 APPM 2350 Section Exam 1 140 points Wednesday September 26, 6:00pm 7:30pm, 2018 ON THE FRONT OF YOUR BLUEBOOK write: (1) your name, (2) your student ID number, (3) lecture section/time (4) your instructor

More information

ICS141: Discrete Mathematics for Computer Science I

ICS141: Discrete Mathematics for Computer Science I ICS141: Discrete Mathematics for Computer Science I Dept. Information & Computer Sci., Originals slides by Dr. Baek and Dr. Still, adapted by J. Stelovsky Based on slides Dr. M. P. Frank and Dr. J.L. Gross

More information

Computational Logic. Relational Query Languages with Negation. Free University of Bozen-Bolzano, Werner Nutt

Computational Logic. Relational Query Languages with Negation. Free University of Bozen-Bolzano, Werner Nutt Computational Logic Free University of Bozen-Bolzano, 2010 Werner Nutt (Slides adapted from Thomas Eiter and Leonid Libkin) Computational Logic 1 Queries with All Who are the directors whose movies are

More information

Department of Computer and Information Science and Engineering. CAP4770/CAP5771 Fall Midterm Exam. Instructor: Prof.

Department of Computer and Information Science and Engineering. CAP4770/CAP5771 Fall Midterm Exam. Instructor: Prof. Department of Computer and Information Science and Engineering UNIVERSITY OF FLORIDA CAP4770/CAP5771 Fall 2016 Midterm Exam Instructor: Prof. Daisy Zhe Wang This is a in-class, closed-book exam. This exam

More information

Functional Dependencies. Chapter 3

Functional Dependencies. Chapter 3 Functional Dependencies Chapter 3 1 Halfway done! So far: Relational algebra (theory) SQL (practice) E/R modeling (theory) HTML/Flask/Python (practice) Midterm (super fun!) Next up: Database design theory

More information

6.830 Lecture 11. Recap 10/15/2018

6.830 Lecture 11. Recap 10/15/2018 6.830 Lecture 11 Recap 10/15/2018 Celebration of Knowledge 1.5h No phones, No laptops Bring your Student-ID The 5 things allowed on your desk Calculator allowed 4 pages (2 pages double sided) of your liking

More information

A 2. =... = c c N. 's arise from the three types of elementary row operations. If rref A = I its determinant is 1, and A = c 1

A 2. =... = c c N. 's arise from the three types of elementary row operations. If rref A = I its determinant is 1, and A = c 1 Theorem: Let A n n Then A 1 exists if and only if det A 0 proof: We already know that A 1 exists if and only if the reduced row echelon form of A is the identity matrix Now, consider reducing A to its

More information

MACM 101 Discrete Mathematics I. Exercises on Predicates and Quantifiers. Due: Tuesday, October 13th (at the beginning of the class)

MACM 101 Discrete Mathematics I. Exercises on Predicates and Quantifiers. Due: Tuesday, October 13th (at the beginning of the class) MACM 101 Discrete Mathematics I Exercises on Predicates and Quantifiers. Due: Tuesday, October 13th (at the beginning of the class) Reminder: the work you submit must be your own. Any collaboration and

More information

Practice Midterm Exam Solutions

Practice Midterm Exam Solutions CSE 311: Foundations of Computing I Practice Midterm Exam Solutions Name: Sample Solutions ID: TA: Section: INSTRUCTIONS: You have 50 minutes to complete the exam. The exam is closed book. You may not

More information

CSE 303: Database. Outline. Lecture 10. First Normal Form (1NF) First Normal Form (1NF) 10/1/2016. Chapter 3: Design Theory of Relational Database

CSE 303: Database. Outline. Lecture 10. First Normal Form (1NF) First Normal Form (1NF) 10/1/2016. Chapter 3: Design Theory of Relational Database CSE 303: Database Lecture 10 Chapter 3: Design Theory of Relational Database Outline 1st Normal Form = all tables attributes are atomic 2nd Normal Form = obsolete Boyce Codd Normal Form = will study 3rd

More information

Query Processing. 3 steps: Parsing & Translation Optimization Evaluation

Query Processing. 3 steps: Parsing & Translation Optimization Evaluation rela%onal algebra Query Processing 3 steps: Parsing & Translation Optimization Evaluation 30 Simple set of algebraic operations on relations Journey of a query SQL select from where Rela%onal algebra π

More information

Relationships between elements of sets occur in many contexts. Every day we deal with

Relationships between elements of sets occur in many contexts. Every day we deal with C H A P T E R 9 Relations 9.1 Relations and Their Properties 9.2 n-ary Relations and Their Applications 9.3 Representing Relations 9.4 Closures of Relations 9.5 Equivalence Relations 9.6 Partial Orderings

More information

CS 581: Introduction to the Theory of Computation! Lecture 1!

CS 581: Introduction to the Theory of Computation! Lecture 1! CS 581: Introduction to the Theory of Computation! Lecture 1! James Hook! Portland State University! hook@cs.pdx.edu! http://www.cs.pdx.edu/~hook/cs581f10/! Welcome!! Contact Information! Jim Hook! Office:

More information

Also, course highlights the importance of discrete structures towards simulation of a problem in computer science and engineering.

Also, course highlights the importance of discrete structures towards simulation of a problem in computer science and engineering. The objectives of Discrete Mathematical Structures are: To introduce a number of Discrete Mathematical Structures (DMS) found to be serving as tools even today in the development of theoretical computer

More information

) ( ) Thus, (, 4.5] [ 7, 6) Thus, (, 3) ( 5, ) = (, 6). = ( 5, 3).

) ( ) Thus, (, 4.5] [ 7, 6) Thus, (, 3) ( 5, ) = (, 6). = ( 5, 3). 152 Sect 9.1 - Compound Inequalities Concept #1 Union and Intersection To understand the Union and Intersection of two sets, let s begin with an example. Let A = {1, 2,,, 5} and B = {2,, 6, 8}. Union of

More information

Lecture 2: A Las Vegas Algorithm for finding the closest pair of points in the plane

Lecture 2: A Las Vegas Algorithm for finding the closest pair of points in the plane Randomized Algorithms Lecture 2: A Las Vegas Algorithm for finding the closest pair of points in the plane Sotiris Nikoletseas Professor CEID - ETY Course 2017-2018 Sotiris Nikoletseas, Professor Randomized

More information

Relational Algebra Part 1. Definitions.

Relational Algebra Part 1. Definitions. .. Cal Poly pring 2016 CPE/CC 365 Introduction to Database ystems Alexander Dekhtyar Eriq Augustine.. elational Algebra Notation, T,,... relations. t, t 1, t 2,... tuples of relations. t (n tuple with

More information

Design Theory: Functional Dependencies and Normal Forms, Part I Instructor: Shel Finkelstein

Design Theory: Functional Dependencies and Normal Forms, Part I Instructor: Shel Finkelstein Design Theory: Functional Dependencies and Normal Forms, Part I Instructor: Shel Finkelstein Reference: A First Course in Database Systems, 3 rd edition, Chapter 3 Important Notices CMPS 180 Final Exam

More information

Lineage implementation in PostgreSQL

Lineage implementation in PostgreSQL Lineage implementation in PostgreSQL Andrin Betschart, 09-714-882 Martin Leimer, 09-728-569 3. Oktober 2013 Contents Contents 1. Introduction 3 2. Lineage computation in TPDBs 4 2.1. Lineage......................................

More information

With Question/Answer Animations. Chapter 2

With Question/Answer Animations. Chapter 2 With Question/Answer Animations Chapter 2 Chapter Summary Sets The Language of Sets Set Operations Set Identities Functions Types of Functions Operations on Functions Sequences and Summations Types of

More information

a b c d e GOOD LUCK! 3. a b c d e 12. a b c d e 4. a b c d e 13. a b c d e 5. a b c d e 14. a b c d e 6. a b c d e 15. a b c d e

a b c d e GOOD LUCK! 3. a b c d e 12. a b c d e 4. a b c d e 13. a b c d e 5. a b c d e 14. a b c d e 6. a b c d e 15. a b c d e MA3 Elem. Calculus Spring 06 Exam 06-0- Name: Sec.: Do not remove this answer page you will turn in the entire exam. No books or notes may be used. You may use an ACT-approved calculator during the exam,

More information

Logic, Sets, and Proofs

Logic, Sets, and Proofs Logic, Sets, and Proofs David A. Cox and Catherine C. McGeoch Amherst College 1 Logic Logical Operators. A logical statement is a mathematical statement that can be assigned a value either true or false.

More information

Discrete Structures for Computer Science

Discrete Structures for Computer Science Discrete Structures for Computer Science William Garrison bill@cs.pitt.edu 6311 Sennott Square Lecture #4: Predicates and Quantifiers Based on materials developed by Dr. Adam Lee Topics n Predicates n

More information

COSC 430 Advanced Database Topics. Lecture 2: Relational Theory Haibo Zhang Computer Science, University of Otago

COSC 430 Advanced Database Topics. Lecture 2: Relational Theory Haibo Zhang Computer Science, University of Otago COSC 430 Advanced Database Topics Lecture 2: Relational Theory Haibo Zhang Computer Science, University of Otago Learning objectives and references You should be able to: define the elements of the relational

More information

(a) Draw the coordinate system you are using and draw the free body diagram of the block during rotation with constant speed.

(a) Draw the coordinate system you are using and draw the free body diagram of the block during rotation with constant speed. 4-[25 pts.] A block of mass m is placed at the side surface of a cone. The cone can rotate about an axis through its center so that the block can make circular motion. The static friction coefficient between

More information

CSC 261/461 Database Systems Lecture 8. Spring 2017 MW 3:25 pm 4:40 pm January 18 May 3 Dewey 1101

CSC 261/461 Database Systems Lecture 8. Spring 2017 MW 3:25 pm 4:40 pm January 18 May 3 Dewey 1101 CSC 261/461 Database Systems Lecture 8 Spring 2017 MW 3:25 pm 4:40 pm January 18 May 3 Dewey 1101 Agenda 1. Database Design 2. Normal forms & functional dependencies 3. Finding functional dependencies

More information

Sec$on Summary. Definition of sets Describing Sets

Sec$on Summary. Definition of sets Describing Sets Section 2.1 Sec$on Summary Definition of sets Describing Sets Roster Method Set-Builder Notation Some Important Sets in Mathematics Empty Set and Universal Set Subsets and Set Equality Cardinality of Sets

More information

Welcome to MAT 137! Course website:

Welcome to MAT 137! Course website: Welcome to MAT 137! Course website: http://uoft.me/ Read the course outline Office hours to be posted here Online forum: Piazza Precalculus review: http://uoft.me/precalc If you haven t gotten an email

More information

α-acyclic Joins Jef Wijsen May 4, 2017

α-acyclic Joins Jef Wijsen May 4, 2017 α-acyclic Joins Jef Wijsen May 4, 2017 1 Motivation Joins in a Distributed Environment Assume the following relations. 1 M[NN, Field of Study, Year] stores data about students of UMONS. For example, (19950423158,

More information

Homework every week. Keep up to date or you risk falling behind. Quizzes and Final exam are based on homework questions.

Homework every week. Keep up to date or you risk falling behind. Quizzes and Final exam are based on homework questions. Week 1 Fall 2016 1 of 25 CISC-102 Fall 2016 Week 1 David Rappaport daver@cs.queensu.ca Goodwin G-532 Office Hours: Monday 1:00-3:00 (or by appointment) Homework Homework every week. Keep up to date or

More information

1 FUNDAMENTALS OF LOGIC NO.10 HERBRAND THEOREM Tatsuya Hagino hagino@sfc.keio.ac.jp lecture URL https://vu5.sfc.keio.ac.jp/slide/ 2 So Far Propositional Logic Logical connectives (,,, ) Truth table Tautology

More information

Chapter 7: Relational Database Design

Chapter 7: Relational Database Design Chapter 7: Relational Database Design Chapter 7: Relational Database Design! First Normal Form! Pitfalls in Relational Database Design! Functional Dependencies! Decomposition! Boyce-Codd Normal Form! Third

More information

Parts 3-6 are EXAMPLES for cse634

Parts 3-6 are EXAMPLES for cse634 1 Parts 3-6 are EXAMPLES for cse634 FINAL TEST CSE 352 ARTIFICIAL INTELLIGENCE Fall 2008 There are 6 pages in this exam. Please make sure you have all of them INTRODUCTION Philosophical AI Questions Q1.

More information

CISC-102 Fall 2017 Week 1 David Rappaport Goodwin G-532 Office Hours: Tuesday 1:30-3:30

CISC-102 Fall 2017 Week 1 David Rappaport Goodwin G-532 Office Hours: Tuesday 1:30-3:30 Week 1 Fall 2017 1 of 42 CISC-102 Fall 2017 Week 1 David Rappaport daver@cs.queensu.ca Goodwin G-532 Office Hours: Tuesday 1:30-3:30 Homework Homework every week. Keep up to date or you risk falling behind.

More information

Semantics and Generative Grammar. Formal Foundations: A Basic Review of Sets and Functions 1

Semantics and Generative Grammar. Formal Foundations: A Basic Review of Sets and Functions 1 Formal Foundations: A Basic Review of Sets and Functions 1 1. Naïve Set Theory 1.1 Basic Properties of Sets A set is a group of objects. Any group of objects a, b, c forms a set. (1) Representation of

More information

A Safe Relational Calculus for Functional Logic Deductive Databases 1

A Safe Relational Calculus for Functional Logic Deductive Databases 1 Electronic Notes in Theoretical Computer Science 86 No. 3 (2003) URL: http://www.elsevier.nl/locate/entcs/volume86.html 37 pages A Safe Relational Calculus for Functional Logic Deductive Databases 1 Jesús

More information

KRIPKE S THEORY OF TRUTH 1. INTRODUCTION

KRIPKE S THEORY OF TRUTH 1. INTRODUCTION KRIPKE S THEORY OF TRUTH RICHARD G HECK, JR 1. INTRODUCTION The purpose of this note is to give a simple, easily accessible proof of the existence of the minimal fixed point, and of various maximal fixed

More information

Database design and implementation CMPSCI 645

Database design and implementation CMPSCI 645 Database design and implementation CMPSCI 645 Lectures 20: Probabilistic Databases *based on a tutorial by Dan Suciu Have we seen uncertainty in DB yet? } NULL values Age Height Weight 20 NULL 200 NULL

More information

Proofs Propositions and Calculuses

Proofs Propositions and Calculuses Lecture 2 CS 1813 Discrete Mathematics Proofs Propositions and Calculuses 1 City of Königsberg (Kaliningrad) 2 Bridges of Königsberg Problem Find a route that crosses each bridge exactly once Must the

More information

COMP 409: Logic Homework 5

COMP 409: Logic Homework 5 COMP 409: Logic Homework 5 Note: The pages below refer to the text from the book by Enderton. 1. Exercises 1-6 on p. 78. 1. Translate into this language the English sentences listed below. If the English

More information