By Michael Stonebraker
Bankruptcy 1 The DBMS Matrix 1.1 A DBMS category Matrix 1.2 Quadrant 1: easy info with no Queries 1.3 Quadrant 2: easy facts with Queries 1.4 Quadrant three: complicated information with out Queries 1.5 Quadrant four: complicated facts with Queries 1.6 common functions 1.7 Technological Drivers within the DBMS marketplace 1.8 precis bankruptcy 2 attribute 1: Base info sort Extension 2.1 desire for Base sort Extension 2.2 a greater approach: Extensible information varieties 2.3 User-Defined capabilities and Operators 2.4 absolutely Object-Relational sort Extension 2.5 precis bankruptcy three Examples of Base kind Extension 3.1 Geographic info structures purposes 3.2 photograph sort Library purposes 3.3 the volume facts kind bankruptcy four attribute 2: advanced items 4.1 style Constructors 4.2 utilizing variety Constructors 4.3 Collections and Client-Server conversation 4.4 Base kinds and complicated gadgets 4.5 precis bankruptcy five different style Constructors? 5.1 A Time sequence instance 5.2 An Array instance 5.3 precis 86 bankruptcy 6 attribute three: Inheritance? 6.1 info Inheritance 6.2 Inheritance of services 6.3 precis bankruptcy 7 attribute four: principles 7.1 Update-Update ideas 7.2 Query-Update principles 7.3 Update-Query ideas 7.4 Query-Query ideas 7.5 Semantics: The darkish facet of ideas 7.6 precis bankruptcy eight Object-Relational Parsing 8.1 How an Object-Relational Parser Works 8.2 precis bankruptcy nine conventional Relational Optimizers 9.1 How Relational Optimizers paintings 9.2 acting the limit at the desk 9.3 equipment for Processing the sign up for bankruptcy 10 Object-Relational Optimizers 10.1 Extension 1: Operator and serve as Notation 10.2 Extensions 2 and three: B-Trees and User-Defined comparability Operators 10.3 Extension four: User-Defined Selectivity services 10.4 Extension five: User-Defined Negators 10.5 Extension 6: User-Defined Commutators 10.6 Extension 7: entry equipment on a functionality of the information 10.7 Extension eight: clever Ordering of the Clauses in a Predicate 10.8 Extension nine: Optimization of pricey capabilities 10.9 Extension 10: User-Defined entry equipment 10.10 Extension eleven: "Flattening" advanced item Queries 10.11 Extension 12: "In-Line" units 10.12 Extension thirteen: Indexes on Attributes of units 10.13 Extension 14: Optimization of Scans of Inheritance Hierarchies 10.14 Extension 15: Optimization of sign up for over Inheritance Hierarchies 10.15 Extension sixteen: aid for User-Defined Aggregates 10.16 precis bankruptcy eleven Implementation of Rule structures 11.1 aid for Triggers 11.2 Extension to extra common principles 11.3 Scalability 11.4 precis bankruptcy 12 Architectural ideas for advertisement owners 12.1 method 1: Do not anything 12.2 procedure 2: Rewrite a Relational Engine from Scratch 12.3 approach three: promote structures 12.4 procedure four: An Object-Relational most sensible on a Relational garage supervisor 12.5 process five: Incremental Evolution 12.6 technique 6: Write a Wrapper one hundred seventy 12.7 process 7: Write a Gateway a hundred seventy five 12.8 procedure eight: expand an Object-Oriented DBMS 181 12.9 procedure nine: Glue an Object-Relational Engine onto a chronic Language 12.10 precis bankruptcy thirteen extra on Object-Relational structure 13.1 Calling Extensions 185 13.2 Middleware 193 13.3 precis two hundred bankruptcy 14 Extension functionality: Implementation Makes a distinction 14.1 Examples 14.2 An SQL-92 answer 14.3 The Informix 2-D Spatial DataBlade strategy 14.4 A Z rework resolution 14.5 a last Spatial Extension 14.6 A comparability of the choices 14.7 precis bankruptcy 15 Object-Relational DBMS and alertness Servers 15.1 Implications of a Thick Database on conventional software Servers 15.2 Two-Tier Thick Database version 15.3 Three-Tier Thick Database version 15.4 precis bankruptcy sixteen Multiquadrant difficulties 16.1 A Video carrier program 16.2 An assurance software 16.3 precis bankruptcy 17 strategies to Multiquadrant difficulties 17.1 aiding dossier approach elements of an program 17.2 aiding Lower-Right Quadrant purposes 17.3 precis bankruptcy 18 Database layout for Object-Relational DBMSs 18.1 Relational Database layout 18.2 purposes for difficulties 18.3 demanding situations in Object-Relational Database layout 18.4 precis bankruptcy 19 the following nice Wave in DBMS expertise 19.1 evaluation of the booklet 19.2 evaluation of the Object-Relational market 19.3 Integration of Object-Relational positive factors with Database companies 19.4 a quick historic standpoint 19.5 precis References Index
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Additional info for Object-Relational DBMSs, Second Edition
Prove Newman’s Lemma, which states that if a relation is weakly Church-Rosser and obeys strong normalization, then −→∗ is Church-Rosser. )10 9 10 See Mairson (1991) for more on this question. If you get stuck, there is a nice proof in Huet (1980). Chapter 5 A taste of ML In the last few chapters, we have started with pure lambda calculus and systematically extended it with new primitive features. For example we added the ‘let’ construct as primitive for the sake of making the polymorphic typing more useful, and a recursion operation in order to restore the computational power that was lost when adding types.
T in (λx. y) t. Of course, eager evaluation means that some expressions may loop that would terminate in a lazy regime. But this is considered acceptable, since these instances are generally easy to avoid in practice. In any case, an eager evaluation strategy is the standard in many programming languages like C, where it is referred to as call by value. ML adopts eager evaluation, for two main reasons. Choreographing the reductions and sharings that occur in lazy evaluation is quite tricky, and implementations tend to be relatively inefficient and complicated.
F p (λg. g q (λh. h r s)) where in the last line we have performed an alpha-conversion for the sake of clarity. Although tuples are built up in a flat manner, it is easy to create arbitrary finitely-branching tree structures by using tupling repeatedly. Finally, if one prefers conventional functions over Cartesian products to our ‘curried’ functions, one can convert between the two using the following: CURRY f = λx y. f (x, y) UNCURRY g = λp. 1. REPRESENTING DATA IN LAMBDA CALCULUS 29 These special operations for pairs can easily be generalized to arbitrary ntuples.
Object-Relational DBMSs, Second Edition by Michael Stonebraker