Relational Algebra and Relational Calculus
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Tuple Relational Calculus Formula Defines Relation
COS 425: Database and Information Management Systems Relational model: Relational calculus Tuple Relational Calculus Queries are formulae, which define sets using: 1. Constants 2. Predicates (like select of algebra ) 3. BooleanE and, or, not 4.A there exists 5. for all • Variables range over tuples • Attributes of a tuple T can be referred to in predicates using T.attribute_name Example: { T | T ε tp and T.rank > 100 } |__formula, T free ______| tp: (name, rank); base relation of database Formula defines relation • Free variables in a formula take on the values of tuples •A tuple is in the defined relation if and only if when substituted for a free variable, it satisfies (makes true) the formula Free variable: A E x, x bind x – truth or falsehood no longer depends on a specific value of x If x is not bound it is free 1 Quantifiers There exists: E x (f(x)) for formula f with free variable x • Is true if there is some tuple which when substituted for x makes f true For all: A x (f(x)) for formula f with free variable x • Is true if any tuple substituted for x makes f true i.e. all tuples when substituted for x make f true Example E {T | E A B (A ε tp and B ε tp and A.name = T.name and A.rank = T.rank and B.rank =T.rank and T.name2= B.name ) } • T not constrained to be element of a named relation • T has attributes defined by naming them in the formula: T.name, T.rank, T.name2 – so schema for T is (name, rank, name2) unordered • Tuples T in result have values for (name, rank, name2) that satisfy the formula • What is the resulting relation? Formal definition: formula • A tuple relational calculus formula is – An atomic formula (uses predicate and constants): •T ε R where – T is a variable ranging over tuples – R is a named relation in the database – a base relation •T.a op W.b where – a and b are names of attributes of T and W, respectively, – op is one of < > = ≠≤≥ •T.a op constant • constant op T.a 2 Formal definition: formula cont. -
A Combinatorial Analysis of Finite Boolean Algebras
A Combinatorial Analysis of Finite Boolean Algebras Kevin Halasz [email protected] May 1, 2013 Copyright c Kevin Halasz. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license can be found at http://www.gnu.org/copyleft/fdl.html. 1 Contents 1 Introduction 3 2 Basic Concepts 3 2.1 Chains . .3 2.2 Antichains . .6 3 Dilworth's Chain Decomposition Theorem 6 4 Boolean Algebras 8 5 Sperner's Theorem 9 5.1 The Sperner Property . .9 5.2 Sperner's Theorem . 10 6 Extensions 12 6.1 Maximally Sized Antichains . 12 6.2 The Erdos-Ko-Rado Theorem . 13 7 Conclusion 14 2 1 Introduction Boolean algebras serve an important purpose in the study of algebraic systems, providing algebraic structure to the notions of order, inequality, and inclusion. The algebraist is always trying to understand some structured set using symbol manipulation. Boolean algebras are then used to study the relationships that hold between such algebraic structures while still using basic techniques of symbol manipulation. In this paper we will take a step back from the standard algebraic practices, and analyze these fascinating algebraic structures from a different point of view. Using combinatorial tools, we will provide an in-depth analysis of the structure of finite Boolean algebras. We will start by introducing several ways of analyzing poset substructure from a com- binatorial point of view. -
A Boolean Algebra and K-Map Approach
Proceedings of the International Conference on Industrial Engineering and Operations Management Washington DC, USA, September 27-29, 2018 Reliability Assessment of Bufferless Production System: A Boolean Algebra and K-Map Approach Firas Sallumi ([email protected]) and Walid Abdul-Kader ([email protected]) Industrial and Manufacturing Systems Engineering University of Windsor, Windsor (ON) Canada Abstract In this paper, system reliability is determined based on a K-map (Karnaugh map) and the Boolean algebra theory. The proposed methodology incorporates the K-map technique for system level reliability, and Boolean analysis for interactions. It considers not only the configuration of the production line, but also effectively incorporates the interactions between the machines composing the line. Through the K-map, a binary argument is used for considering the interactions between the machines and a probability expression is found to assess the reliability of a bufferless production system. The paper covers three main system design configurations: series, parallel and hybrid (mix of series and parallel). In the parallel production system section, three strategies or methods are presented to find the system reliability. These are the Split, the Overlap, and the Zeros methods. A real-world case study from the automotive industry is presented to demonstrate the applicability of the approach used in this research work. Keywords: Series, Series-Parallel, Hybrid, Bufferless, Production Line, Reliability, K-Map, Boolean algebra 1. Introduction Presently, the global economy is forcing companies to have their production systems maintain low inventory and provide short delivery times. Therefore, production systems are required to be reliable and productive to make products at rates which are changing with the demand. -
Tuple Relational Calculus Formula Defines Relation Quantifiers Example
COS 597A: Tuple Relational Calculus Principles of Queries are formulae, which define sets using: Database and Information Systems 1. Constants 2. Predicates (like select of algebra ) 3. Boolean and, or, not Relational model: 4. ∃ there exists 5. ∀ for all Relational calculus Variables range over tuples Value of an attribute of a tuple T can be referred to in predicates using T[attribute_name] Example: { T | T ε Winners and T[year] > 2006 } |__formula, T free ______| Winners: (name, tournament, year); base relation of database Formula defines relation Quantifiers • Free variables in a formula take on the values of There exists: ∃x (f(x)) for formula f with free tuples variable x • A tuple is in the defined relation if and only if • Is true if there is some tuple which when substituted when substituted for a free variable, it satisfies for x makes f true (makes true) the formula For all: ∀x (f(x)) for formula f with free variable x Free variable: • Is true if any tuple substituted for x makes f true i.e. all tuples when substituted for x make f true ∃x, ∀x bind x – truth or falsehood no longer depends on a specific value of x If x is not bound it is free Example Formal definition: formula • A tuple relational calculus formula is {T |∃A ∃B (A ε Winners and B ε Winners and – An atomic formula (uses predicate and constants): A[name] = T[name] and A[tournament] = T[tournament] and • T ε R where B[tournament] =T[tournament] and T[name2] = B[name] ) } – T is a variable ranging over tuples – R is a named relation in the database – a base relation • T not constrained to be element of a named relation • T[a] op W[b] where • Result has attributes defined by naming them in the formula: – a and b are names of attributes of T and W, respectively, T[name], T[tournament], T[name2] – op is one of < > = ≠ ≤ ≥ – so schema for result: (name, tournament, name2) • T[a] op constant unordered • constant op T[a] • Tuples T in result have values for (name, tournament, name2) that satisfy the formula • What is the resulting relation? 1 Formal definition: formula cont. -
Sets and Boolean Algebra
MATHEMATICAL THEORY FOR SOCIAL SCIENTISTS SETS AND SUBSETS Denitions (1) A set A in any collection of objects which have a common characteristic. If an object x has the characteristic, then we say that it is an element or a member of the set and we write x ∈ A.Ifxis not a member of the set A, then we write x/∈A. It may be possible to specify a set by writing down all of its elements. If x, y, z are the only elements of the set A, then the set can be written as (2) A = {x, y, z}. Similarly, if A is a nite set comprising n elements, then it can be denoted by (3) A = {x1,x2,...,xn}. The three dots, which stand for the phase “and so on”, are called an ellipsis. The subscripts which are applied to the x’s place them in a one-to-one cor- respondence with set of integers {1, 2,...,n} which constitutes the so-called index set. However, this indexing imposes no necessary order on the elements of the set; and its only pupose is to make it easier to reference the elements. Sometimes we write an expression in the form of (4) A = {x1,x2,...}. Here the implication is that the set A may have an innite number of elements; in which case a correspondence is indicated between the elements of the set and the set of natural numbers or positive integers which we shall denote by (5) Z = {1, 2,...}. (Here the letter Z, which denotes the set of natural numbers, derives from the German verb zahlen: to count) An alternative way of denoting a set is to specify the characteristic which is common to its elements. -
Regulations & Syllabi for AMIETE Examination (Computer Science
Regulations & Syllabi For AMIETE Examination (Computer Science & Engineering) Published under the authority of the Governing Council of The Institution of Electronics and Telecommunication Engineers 2, Institutional Area, Lodi Road, New Delhi – 110 003 (India) (2013 Edition) Website : http://www.iete.org Toll Free No : 18001025488 Tel. : (011) 43538800-99 Fax : (011) 24649429 Email : [email protected] [email protected] Prospectus Containing Regulations & Syllabi For AMIETE Examination (Computer Science & Engineering) Published under the authority of the Governing Council of The Institution of Electronics and Telecommunication Engineers 2, Institutional Area, Lodi Road, New Delhi – 110 003 (India) (2013 Edition) Website : http://www.iete.org Toll Free No : 18001025488 Tel. : (011) 43538800-99 Fax : (011) 24649429 Email : [email protected] [email protected] CONTENTS 1. ABOUT THE INSTITUTION 1 2. ELIGIBILITY 4 3. ENROLMENT 5 4. AMIETE EXAMINATION 6 5. ELIGIBILITY FOR VARIOUS SUBJECTS 6 6. EXEMPTIONS 7 7. CGPA SYSTEM 8 8. EXAMINATION APPLICATION 8 9. EXAMINATION FEE 9 10. LAST DATE FOR RECEIPT OF EXAMINATION APPLICATION 9 11. EXAMINATION CENTRES 10 12. USE OF UNFAIR MEANS 10 13. RECOUNTING 11 14. IMPROVEMENT OF GRADES 11 15. COURSE CURRICULUM (Computer Science) (CS) (Appendix-‘A’) 13 16. OUTLINE SYLLABUS (CS) (Appendix-‘B’) 16 17. COURSE CURRICULUM (Information Technology) (IT) (Appendix-‘C”) 24 18. OUTLINE SYLLABUS (IT) (Appendix-‘D’) 27 19. COURSE CURRICULUM (Electronics & Telecommunication) (ET) 35 (Appendix-‘E’) 20. OUTLINE SYLLABUS (ET) (Appendix-‘F’) 38 21. DETAILED SYLLABUS (CS) (Appendix-’G’) 45 22. RECOGNITIONS GRANTED TO THE AMIETE EXAMINATION BY STATE 113 GOVERNMENTS / UNIVERSITIES / INSTITUTIONS (Appendix-‘I’) 23. RECOGNITIONS GRANTED TO THE AMIETE EXAMINATION BY 117 GOVERNMENT OF INDIA / OTHER AUTHORITIES ( Annexure I - V) 24. -
Boolean Algebras
CHAPTER 8 Boolean Algebras 8.1. Combinatorial Circuits 8.1.1. Introduction. At their lowest level digital computers han- dle only binary signals, represented with the symbols 0 and 1. The most elementary circuits that combine those signals are called gates. Figure 8.1 shows three gates: OR, AND and NOT. x1 OR GATE x1 x2 x2 x1 AND GATE x1 x2 x2 NOT GATE x x Figure 8.1. Gates. Their outputs can be expressed as a function of their inputs by the following logic tables: x1 x2 x1 x2 1 1 ∨1 1 0 1 0 1 1 0 0 0 OR GATE 118 8.1. COMBINATORIAL CIRCUITS 119 x1 x2 x1 x2 1 1 ∧1 1 0 0 0 1 0 0 0 0 AND GATE x x¯ 1 0 0 1 NOT GATE These are examples of combinatorial circuits. A combinatorial cir- cuit is a circuit whose output is uniquely defined by its inputs. They do not have memory, previous inputs do not affect their outputs. Some combinations of gates can be used to make more complicated combi- natorial circuits. For instance figure 8.2 is combinatorial circuit with the logic table shown below, representing the values of the Boolean expression y = (x x ) x . 1 ∨ 2 ∧ 3 x1 x2 y x3 Figure 8.2. A combinatorial circuit. x1 x2 x3 y = (x1 x2) x3 1 1 1 ∨0 ∧ 1 1 0 1 1 0 1 0 1 0 0 1 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 1 However the circuit in figure 8.3 is not a combinatorial circuit. -
Boolean Algebra.Pdf
Section 3 Boolean Algebra The Building Blocks of Digital Logic Design Section Overview Binary Operations (AND, OR, NOT), Basic laws, Proof by Perfect Induction, De Morgan’s Theorem, Canonical and Standard Forms (SOP, POS), Gates as SSI Building Blocks (Buffer, NAND, NOR, XOR) Source: UCI Lecture Series on Computer Design — Gates as Building Blocks, Digital Design Sections 1-9 and 2-1 to 2-7, Digital Logic Design CECS 201 Lecture Notes by Professor Allison Section II — Boolean Algebra and Logic Gates, Digital Computer Fundamentals Chapter 4 — Boolean Algebra and Gate Networks, Principles of Digital Computer Design Chapter 5 — Switching Algebra and Logic Gates, Computer Hardware Theory Section 6.3 — Remarks about Boolean Algebra, An Introduction To Microcomputers pp. 2-7 to 2-10 — Boolean Algebra and Computer Logic. Sessions: Four(4) Topics: 1) Binary Operations and Their Representation 2) Basic Laws and Theorems of Boolean Algebra 3) Derivation of Boolean Expressions (Sum-of-products and Product-of-sums) 4) Reducing Algebraic Expressions 5) Converting an Algebraic Expression into Logic Gates 6) From Logic Gates to SSI Circuits Binary Operations and Their Representation The Problem Modern digital computers are designed using techniques and symbology from a field of mathematics called modern algebra. Algebraists have studied for a period of over a hundred years mathematical systems called Boolean algebras. Nothing could be more simple and normal to human reasoning than the rules of a Boolean algebra, for these originated in studies of how we reason, what lines of reasoning are valid, what constitutes proof, and other allied subjects. The name Boolean algebra honors a fascinating English mathematician, George Boole, who in 1854 published a classic book, "An Investigation of the Laws of Thought, on Which Are Founded the Mathematical Theories of Logic and Probabilities." Boole's stated intention was to perform a mathematical analysis of logic. -
Boolean Algebra
Boolean Algebra ECE 152A – Winter 2012 Reading Assignment Brown and Vranesic 2 Introduction to Logic Circuits 2.5 Boolean Algebra 2.5.1 The Venn Diagram 2.5.2 Notation and Terminology 2.5.3 Precedence of Operations 2.6 Synthesis Using AND, OR and NOT Gates 2.6.1 Sum-of-Products and Product of Sums Forms January 11, 2012 ECE 152A - Digital Design Principles 2 Reading Assignment Brown and Vranesic (cont) 2 Introduction to Logic Circuits (cont) 2.7 NAND and NOR Logic Networks 2.8 Design Examples 2.8.1 Three-Way Light Control 2.8.2 Multiplexer Circuit January 11, 2012 ECE 152A - Digital Design Principles 3 Reading Assignment Roth 2 Boolean Algebra 2.3 Boolean Expressions and Truth Tables 2.4 Basic Theorems 2.5 Commutative, Associative, and Distributive Laws 2.6 Simplification Theorems 2.7 Multiplying Out and Factoring 2.8 DeMorgan’s Laws January 11, 2012 ECE 152A - Digital Design Principles 4 Reading Assignment Roth (cont) 3 Boolean Algebra (Continued) 3.1 Multiplying Out and Factoring Expressions 3.2 Exclusive-OR and Equivalence Operation 3.3 The Consensus Theorem 3.4 Algebraic Simplification of Switching Expressions January 11, 2012 ECE 152A - Digital Design Principles 5 Reading Assignment Roth (cont) 4 Applications of Boolean Algebra Minterm and Maxterm Expressions 4.3 Minterm and Maxterm Expansions 7 Multi-Level Gate Circuits NAND and NOR Gates 7.2 NAND and NOR Gates 7.3 Design of Two-Level Circuits Using NAND and NOR Gates 7.5 Circuit Conversion Using Alternative Gate Symbols January 11, 2012 ECE 152A - Digital Design Principles 6 Boolean Algebra Axioms of Boolean Algebra Axioms generally presented without proof 0 · 0 = 0 1 + 1 = 1 1 · 1 = 1 0 + 0 = 0 0 · 1 = 1 · 0 = 0 1 + 0 = 0 + 1 = 1 if X = 0, then X’ = 1 if X = 1, then X’ = 0 January 11, 2012 ECE 152A - Digital Design Principles 7 Boolean Algebra The Principle of Duality from Zvi Kohavi, Switching and Finite Automata Theory “We observe that all the preceding properties are grouped in pairs. -
Boolean Algebra Computer Fundamentals
CCoommppuutterer FFununddaammenenttaallss:: PPrradadeeepep KK.. SSiinhanha && PPrriititi SSiinhanha Ref. Page Chapter 6: Boolean Algebra and Logic Circuits Slide 1/78 CCoommppuutterer FFununddaammenenttaallss:: PPrradadeeepep KK.. SSiinhanha && PPrriititi SSiinhanha LLeeararnniinngg ObObjjeeccttiivveses In this chapter you will learn about: § Boolean algebra § Fundamental concepts and basic laws of Boolean algebra § Boolean function and minimization § Logic gates § Logic circuits and Boolean expressions § Combinational circuits and design Ref. Page 60 Chapter 6: Boolean Algebra and Logic Circuits Slide 2/78 CCoommppuutterer FFununddaammenenttaallss:: PPrradadeeepep KK.. SSiinhanha && PPrriititi SSiinhanha BBooooleleaann AAllggeebrabra § An algebra that deals with binary number system § George Boole (1815-1864), an English mathematician, developed it for: § Simplifying representation § Manipulation of propositional logic § In 1938, Claude E. Shannon proposed using Boolean algebra in design of relay switching circuits § Provides economical and straightforward approach § Used extensively in designing electronic circuits used in computers Ref. Page 60 Chapter 6: Boolean Algebra and Logic Circuits Slide 3/78 CCoommppuutterer FFununddaammenenttaallss:: PPrradadeeepep KK.. SSiinhanha && PPrriititi SSiinhanha FundamFundameennttalal CCoonncceeppttss ooffBBoooolleeaann AAlglgeebbrara § Use of Binary Digit § Boolean equations can have either of two possible values, 0 and 1 § Logical Addition § Symbol ‘+’, also known as ‘OR’operator, used for logical -
Session 5 – Main Theme
Database Systems Session 5 – Main Theme Relational Algebra, Relational Calculus, and SQL Dr. Jean-Claude Franchitti New York University Computer Science Department Courant Institute of Mathematical Sciences Presentation material partially based on textbook slides Fundamentals of Database Systems (6th Edition) by Ramez Elmasri and Shamkant Navathe Slides copyright © 2011 and on slides produced by Zvi Kedem copyight © 2014 1 Agenda 1 Session Overview 2 Relational Algebra and Relational Calculus 3 Relational Algebra Using SQL Syntax 5 Summary and Conclusion 2 Session Agenda . Session Overview . Relational Algebra and Relational Calculus . Relational Algebra Using SQL Syntax . Summary & Conclusion 3 What is the class about? . Course description and syllabus: » http://www.nyu.edu/classes/jcf/CSCI-GA.2433-001 » http://cs.nyu.edu/courses/fall11/CSCI-GA.2433-001/ . Textbooks: » Fundamentals of Database Systems (6th Edition) Ramez Elmasri and Shamkant Navathe Addition Wesley ISBN-10: 0-1360-8620-9, ISBN-13: 978-0136086208 6th Edition (04/10) 4 Icons / Metaphors Information Common Realization Knowledge/Competency Pattern Governance Alignment Solution Approach 55 Agenda 1 Session Overview 2 Relational Algebra and Relational Calculus 3 Relational Algebra Using SQL Syntax 5 Summary and Conclusion 6 Agenda . Unary Relational Operations: SELECT and PROJECT . Relational Algebra Operations from Set Theory . Binary Relational Operations: JOIN and DIVISION . Additional Relational Operations . Examples of Queries in Relational Algebra . The Tuple Relational Calculus . The Domain Relational Calculus 7 The Relational Algebra and Relational Calculus . Relational algebra . Basic set of operations for the relational model . Relational algebra expression . Sequence of relational algebra operations . Relational calculus . Higher-level declarative language for specifying relational queries 8 Unary Relational Operations: SELECT and PROJECT (1/3) . -
Ch06-The Relational Algebra and Calculus.Pdf
Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide 6- 1 Chapter 6 The Relational Algebra and Calculus Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Chapter Outline Relational Algebra Unary Relational Operations Relational Algebra Operations From Set Theory Binary Relational Operations Additional Relational Operations Examples of Queries in Relational Algebra Relational Calculus Tuple Relational Calculus Domain Relational Calculus Example Database Application (COMPANY) Overview of the QBE language (appendix D) Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide 6- 3 Relational Algebra Overview Relational algebra is the basic set of operations for the relational model These operations enable a user to specify basic retrieval requests (or queries) The result of an operation is a new relation, which may have been formed from one or more input relations This property makes the algebra “closed” (all objects in relational algebra are relations) Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide 6- 4 Relational Algebra Overview (continued) The algebra operations thus produce new relations These can be further manipulated using operations of the same algebra A sequence of relational algebra operations forms a relational algebra expression The result of a relational algebra expression is also a relation that represents the result of a database query (or retrieval request) Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide 6- 5 Brief History of Origins of Algebra Muhammad ibn Musa al-Khwarizmi (800-847 CE) wrote a book titled al-jabr about arithmetic of variables Book was translated into Latin. Its title (al-jabr) gave Algebra its name. Al-Khwarizmi called variables “shay” “Shay” is Arabic for “thing”.