* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Insert Title Here
Oracle Database wikipedia , lookup
Microsoft Access wikipedia , lookup
Global serializability wikipedia , lookup
Microsoft SQL Server wikipedia , lookup
Open Database Connectivity wikipedia , lookup
Encyclopedia of World Problems and Human Potential wikipedia , lookup
Extensible Storage Engine wikipedia , lookup
Microsoft Jet Database Engine wikipedia , lookup
Concurrency control wikipedia , lookup
Entity–attribute–value model wikipedia , lookup
Ingres (database) wikipedia , lookup
Functional Database Model wikipedia , lookup
Clusterpoint wikipedia , lookup
Versant Object Database wikipedia , lookup
Healthcare Cost and Utilization Project wikipedia , lookup
Relational algebra wikipedia , lookup
Introduction to Databases: Relational and XML Models and Languages Instructors: Bertram Ludaescher Kai Lin Overview • • • • • • • • • 09:15-10:20 10:20-10:30 10:30-11:50 11:50-13:15 13:15-13:45 13:45-15:10 15:10-15:30 15:30-16:30 16:30-17:00 Relational Databases (1h05’) BREAK (10’) Relational Databases (1h20’) LUNCH (1h25’) Demo & Hands-on (30’) XML: Basics (1h25’) BREAK (20’) XML: Querying (1h) Demo & Hands-on (30’) Introduction to Databases, B. Ludaescher & K. Lin 2 Scope • Today: Introduction to Databases, in particular – – – – – – Relational database model Relational Operations and Queries Constraints XML “data” model Querying and transforming XML Some demos & simple hands-on exercise • Tomorrow: – Introduction to Knowledge Representation and Ontologies • But first: … déjà vu … Introduction to Databases, B. Ludaescher & K. Lin 3 Introduction to Databases, B. Ludaescher & K. Lin 4 What is a Database System? • Database (system) = – Database Instance (set of tables of rows) – Database Management System (DBMS) • Origins in the commercial world: – to organize, query, and manipulate data more effectively, efficiently, and independently • Scientific databases – often special features: • spatial, temporal, spatiotemporal, GIS, units, uncertainty, raw & derived data, … Introduction to Databases, B. Ludaescher & K. Lin 5 Why not just use files as “databases”? • For some applications: yeah… why not? • But in general: – scanning & ’grep’ing large files can be very inefficient – no language support for selecting desired data, joining them, etc. • cannot express the kinds of questions/queries you’d like to ask • ‘grep’ is no substitute for a query language – redundant and/or inconsistent storage of data – no transaction management and concurrency control among multiple users – no security – no recovery – no data independence (application data) – no data modeling support – … Introduction to Databases, B. Ludaescher & K. Lin 6 Features of a Database System • A data model (relational, object-oriented, XML) prescribes how data can be organized: – as relations (tables) of tuples (rows) – as classes of (linked) objects – as XML trees • A (database) schema (stored in the “data dictionary”) defines the structure of a specific database instance: – Relational schema – OO schema – XML Schema (or XML DTD) Introduction to Databases, B. Ludaescher & K. Lin 7 Features of a Database System • Data is treated uniformly and separately from the application • Efficient data access • Queries and views are expressed over the schema • Integrity constraints (checking and enforcement) • Transactions combine sets of operations into logical units (all-or-nothing) • Synchronization of concurrent user transactions • Recovery (after system crash) – not to be confused w/ backup – instead: guarantee consistency by “roll-back” of partially executed transactions (how? Hint: logging) • … Introduction to Databases, B. Ludaescher & K. Lin 8 DB features, e.g., Concurrency Control • Concurrent execution of simultaneous requests – long before web servers where around... – transaction management guarantees consistency despite concurrent/interleaved execution • Transaction (= sequence of read/write operations) – Atomicity: a transaction is executed completely or not at all – Consistency: a transaction creates a new consistent DB state, i.e., in which all integrity constraints are maintained – Isolation: to the user, a transaction seems to run in isolation – Durability: the effect of a successful (“committed”) transaction remains even after system failure Introduction to Databases, B. Ludaescher & K. Lin 9 Levels of Abstraction: Architecture Overview User Conceptual … View 1 View 2 logical data independence Level ER-Model (Entity-Relationship) OO Models (Classes…) View n Export schemas Logical (“conceptual”) level Tables part of DB design conceptual design … often lost in the process… Introduction to Databases, B. Ludaescher & K. Lin physical data independence Physical level Index structures DB instances 10 Database Design: Entity-Relationship (ER) Model Name Salary Employee • • • • Name works-for Entities: Relationships: Attributes: ER Model: Manager Department since – initial, high-level DB design (conceptual model) – easy to map to a relational schema (database tables) – comes with more constraints (cardinalities, aggregation) and extensions: EER (is-a => class hierarchies) – related: UML (Unified Modeling Language) class diagrams Introduction to Databases, B. Ludaescher & K. Lin 11 The Relational Model • Relation/Table Name: Employee Emp tom tim sally carol carol …. Salary 60k 57k 45k 30k 35k DNo 1 1 3 1 2 FK: foreign key, pointing to another key Department DNo 1 2 3 Name Toys Comp. Shoes Mgr carol carol sam Introduction to Databases, B. Ludaescher & K. Lin – employee, dept • Attributes = Column Names: – Emp, Salary, DeptNo, Name, Mgr • Relational Schema: – employee(Emp:string, Salary:integer, DeptNo:integer), ... • Tuple = Row of the table: – (“tom”, “60000”, “1”) • Relation = Set of tuples: – {(...), (...), ...} 12 Ex: Creating a Relational Database in SQL CREATE TABLE employee ( ssn CHAR(11), name VARCHAR(30), deptNo INTEGER, PRIMARY KEY (ssn), FOREIGN KEY (deptNo) REFERENCES department ) CREATE TABLE department ( deptNo INTEGER, name VARCHAR(20), manager CHAR(11), PRIMARY KEY (deptNo), FOREIGN KEY (manager) REFERENCES employee(ssn) ) Introduction to Databases, B. Ludaescher & K. Lin 13 What is a Query? • Intuitively: – An “executable question” in terms of a database schema – Evaluating a query Q against a database instance D yields a set of answer objects: • Relational tuples or XML elements • Example: – Who are the employees in the ‘Toys’ dept.? – Who is (are) the manager(s) of ‘Tom’? – Show all pairs (Employee, Mgr) • Technically: – A mapping from an input schema (the given table schemas) to a result schema (the new columns you are interested in) defined in some query language Introduction to Databases, B. Ludaescher & K. Lin 14 Why (Declarative) Query Languages? “If you have a hammer, everything looks like a nail.” ,,Die Grenzen meiner Sprache bedeuten die Grenzen meiner Welt.” “The limits of my language mean the limits of my world.” Ludwig Wittgenstein, Tractatus Logico-Philosophicus • Things we talk and think about in PLs and QLs … – Assembly languages: • registers, memory locations, jumps, ... – C and the likes: • if-then-else, for, while, memory (de-)allocation, pointers, ... – Object-oriented languages: • • • • C++: C plus objects, methods, classes, ... Java: objects, methods, classes, references, ... Smalltalk: objects, objects, objects, ... OQL: object-query language Introduction to Databases, B. Ludaescher & K. Lin 15 Why (Declarative) Query Languages? • Things we talk and think about in PLs and QLs … – Functional languages (Haskell, ML): • (higher-order) functions, fold(l|r), recursion, patterns, ... => Relational languages (SQL, Datalog) • relations (tables), tuples (rows); conceptual level: ER • relational operations: , , , , ..., ,,,,,..., , , |X| => Semistructured/XML (Tree) & Graph Query Languages • trees, graphs, nodes, edges, children nodes, siblings, … • XPath, XQuery, … • Also: – Focus on what, and not how! Introduction to Databases, B. Ludaescher & K. Lin 16 Example: Querying a Relational Database input tables Employee Department Emp anne john DeptNoMgr 1 anne 2 anne Salary DeptNo 62k 2 60k 1 join SQL query (or view def.) SELECT e.Emp, d.Mgr FROM Employee e, Department d we don’t say how to WHERE e.DeptNo = d.DeptNo evaluate this expression result answer (or view) Introduction to Databases, B. Ludaescher & K. Lin Emp john anne 17 Mgr anne anne Example Query: SQL vs DATALOG • “List all employees and their managers” • In SQL: SELECT e.name, d.manager FROM Employee e, Department d WHERE e.deptNo = d.deptNo a “join” operation • In DATALOG: q(E, M) :- employee(E, S, D), department(D, N, M). Introduction to Databases, B. Ludaescher & K. Lin 18 Important Relational Operations • select(R, Condition) – filter rows of a table wrt. a condition • project(R, Attr) – remove unwanted columns; keep rest • join(R1, A2, R2, A2, Condition) – find “matches” in a “related” table – e.g. match R1.foreign key = R2.primary key • cartesian product(R1, R2) • union (“OR”), intersection (“AND”) • set-difference (“NOT IN”) Introduction to Databases, B. Ludaescher & K. Lin 19 Relational Operations (in DATALOG) (query) output :– (query) input condition Y1=Y2Y independent same multiple rules union results Introduction to Databases, B. Ludaescher & K. Lin 20 Demo Relational Queries in DATALOG Introduction to Databases, B. Ludaescher & K. Lin 21 Queries, Views, Integrity Constraints • … can all be seen as “special queries” • Query q(…) :- … ad-hoc queries • View v(…) :- … exported views; • Integrity Constraints – ic (…) :- …. MgrSal < EmpSal … – say what shouldn’t happen – if it does: alert the user (or refuse an update, …) Introduction to Databases, B. Ludaescher & K. Lin 22 Query Evaluation vs Reasoning • Query evaluation – Given a database instance D and a query Q, run Q(D) – What databases do all the time • Reasoning (aka “Semantic Query Optimization”) – Given a query Q and a constraint C, “optimize” Q&C (e.g., given C, Q might be unsatisfiable) – Given Q1 and Q2 decide whether Q1 Q2 – Given Q1,Q2, C decide whether Q1 Q2 | C – Note: we are NOT given a database instance D here; just the schema and the query/IC expressions Introduction to Databases, B. Ludaescher & K. Lin 23 Summary QLs for Relational Databases Natural Hoin: same attribute name add condition that values must match Introduction to Databases, B. Ludaescher & K. Lin 24 Relational Algebra Introduction to Databases, B. Ludaescher & K. Lin 25 Relational Algebra Introduction to Databases, B. Ludaescher & K. Lin 26 Relational Algebra Introduction to Databases, B. Ludaescher & K. Lin 27 Relational Algebra Introduction to Databases, B. Ludaescher & K. Lin 28 Relational Algebra Introduction to Databases, B. Ludaescher & K. Lin 29 Hands-on Part DBDesigner 4 Introduction to Databases, B. Ludaescher & K. Lin 30 DBDesigner 4 • 1. 2. 3. 4. 5. 6. 7. 8. An open source (GPL) database design tool Goto http://www.fabforce.net/dbdesigner4/ Download and install (5 min) Open the example schema Order (File -> Open -> Order), and examine the relations between the tables forumtopic and forumpost. Find the foreign key used in the relation postHasTopic (5 min) Connect to a sample MySQL database host: geon07.sdsc.edu port: 3306 database: summer_institute username: root password: [blank] (5 min) Select all records in the table forumtopic (2 min) Select all records in the table forumpost, and sort the result according to their idforumpost (3min) Find all forum posts with the topic Cars (5 min) Insert a record into the table forumpost (5 min) Introduction to Databases, B. Ludaescher & K. Lin 31 Additional Material (not presented) Introduction to Databases, B. Ludaescher & K. Lin 32 Non-Relational Data Models • Relational model is “flat”: atomic data values – nesting is modeled via “pointers” (foreign keys) and “Skolem-ids” – extension: nested relational model (“tables within tables”, cf. nested HTML tables) – values can be nested lists {...}, tuples (...), sets [...] – ISO standard(s): SQL – identity is value based • Object-oriented data model: – complex (structured) objects with object-identity (oid) – class and type hierarchies (sub-/superclass, sub-/supertype) – OODB schema may be very close to “world model” (no translation into tables) (+) queries fit your OO schema (-) (new) queries that don’t fit nicely – ODMG standard, OQL (Object Query Language) Introduction to Databases, B. Ludaescher & K. Lin 33 Example: Object Query Language (OQL) SELECT DISTINCT STRUCT( E: e.name, C: e.manager.name, M: ( SELECT c.name FROM c IN e.children WHERE FOR ALL d IN e.manager.children: c.age > d.age ) ) FROM e IN Employees; • Q: what does this OQL query compute? • Note the use of path expressions like e.manager.children => Semistructured/Graph Databases Introduction to Databases, B. Ludaescher & K. Lin 34 A Graph Database Introduction to Databases, B. Ludaescher & K. Lin 35 Querying Graphs with OO-Path Expressions ?- dblp."Inf. Systems".L."Michael E. Senko". Answer: L="Volume 1, 1975”; L="Volume 5, 1980". ?- dblp."Inf. Systems".L.P, substr("Volume",L), P : person.spouse[lives_in = P.lives_in]. Introduction to Databases, B. Ludaescher & K. Lin 36 Constructs for Querying Graphs Example: ?- dblp . any* . (if(vldb)| if(sigmod)) Introduction to Databases, B. Ludaescher & K. Lin 37 Keys, Keys, and more Keys • A key is a minimal set of attributes that: – uniquely identify a tuple – determine every other attribute value in a tuple • There may be many keys for a relation; we designate one as the primary key • The phrase candidate key is used in place of “key” where “the key” denotes the primary key • A superkey is a superset of a key (i.e., not necessarily minimal) Introduction to Databases, B. Ludaescher & K. Lin 38 Normalization of Relations Example of “good” design Employee(EName, SSN, BDate, Address, DNumber) Department(DName, DNumber, DMgrSSN) Example of “bad” design (why is it bad?) Emp(EName, SSN, BDate, Address, DNum, DName, DMgrSSN) Introduction to Databases, B. Ludaescher & K. Lin 39 What’s Wrong? Emp(EName, SSN, BDate, Address, DNum, DName, DMgrSSN) The description of the department (DName, DMgrSSN) is repeated for every employee that works in that department. Redundancy! The department is described redundantly. This leads to update anomalies! (… and wastes space) Digression (for experts only): • redundancy can be used to increase performance; e.g. “materialized views”) Introduction to Databases, B. Ludaescher & K. Lin 40 Update Anomalies (caused by redundancy) Insertion Anomalies If you insert an employee Need to know which department he/she works Need to know the description information for that department If you want to insert a department, you can’t … until there is at least one employee Deletion Anomalies: if you delete an employee, is that dept. gone? was this the last employee in that dept? * Modification Anomalies: if you change DName, for example, it needs to be changed everywhere! Introduction to Databases, B. Ludaescher & K. Lin 41 Null values also cause problems Null values might help in special cases, but are not a general solution to update anomalies For example, they may: – – – – Waste space Make it hard to specify and understand joins Make it hard to aggregate (count, sum, etc.) Have different meanings: • Attribute does not apply to this tuple • Attribute value is unkown • Value is known but absent (not yet recorded) – Causes problems with queries (can’t interpret query answers) Introduction to Databases, B. Ludaescher & K. Lin 42 Why worry about normalization? • To … • … reduce redundancy & update anomalies • … reduce the need for null values • Last not least: it’s a solved problem: •all algorithms & proofs have been worked out Here: Normalization based on FDs (there’s more…) Introduction to Databases, B. Ludaescher & K. Lin 43 Functional Dependencies Statement that if two tuples agree on attributes A they must agree on attributes B AB If the value of the first attribute(s), A, is known, then the value of the second attribute(s), B, is known (read “A determines B”) We want to know if it is always true in the application Introduction to Databases, B. Ludaescher & K. Lin 44 Functional Dependencies Examples of functional dependencies: social-security-number employee-name course-number course-title Examples that are NOT functional dependencies course-number - book course-number - car-color Introduction to Databases, B. Ludaescher & K. Lin 45 What is a functional dependency? Remember what it means to be a function: x f(x) 1 2 1 3 2 5 3 5 x g(x) 1 2 2 2 3 5 x h(x) 1 10 2 20 3 30 f is not a function for x=1, f(x) is not unique we are looking for functional relationships (that must occur in a relation) among attribute values Introduction to Databases, B. Ludaescher & K. Lin 46 What are the FDs? EMP(ENAME, SSN, BDATE, ADDRESS, DNUM, DNAME, DMGRSSN) EMP_PROJ(SSN, PNUM, HOURS, ENAME, PNAME, PLOCATION) Introduction to Databases, B. Ludaescher & K. Lin 47 What are the FDs? EMP(ENAME, SSN, BDATE, ADDRESS, DNUM, DNAME, DMGRSSN) 1 2 5 3 6 4 9 10 EMP_PROJ(SSN, PNUM, HOURS, ENAME, PNAME, PLOCATION) 7 8 Introduction to Databases, B. Ludaescher & K. Lin 48 Why do we care? EMPLOYEE (SSN, NAME, SALARY, JOB_DESC) If all FDs are “implied by the key” it means that the DBMS only enforces keys (not FDs) and the DBMS is going to enforce the keys anyway otherwise, we have to perform expensive operations to maintain consistency (code or check statements) Introduction to Databases, B. Ludaescher & K. Lin 49 Decomposition Main refinement technique: decomposition (replacing ABCD with, say, AB and BCD, or ACD and ABD) based on the projection operator. Decomposition should be used judiciously: – Is there reason to decompose a relation? (via Normal Forms) – What problems (if any) does the decomposition cause? (lost information or dependencies?) Introduction to Databases, B. Ludaescher & K. Lin 50 How can we decompose using the Project Operator? EMP(ENAME, SSN, BDATE, ADDRESS, DNUM, DNAME, DMGRSSN) 2 1 EMP1(SSN, ENAME, BDATE, ADDRESS, DNUM) 3 4 X(DNUM, DNAME, DMGRSSN) 5 6 EMP_PROJ(SSN, PNUM, HOURS, ENAME, PNAME, PLOCATION) EMP2(SSN,8ENAME) 9 X(PNUM, PNAME, PLOCATION) 10 Y(SSN, PNUM, HOURS) 7 Introduction to Databases, B. Ludaescher & K. Lin 51 Correct Decompositions How do we know if a decomposition is correct? That we haven’t lost anything? • We have three goals: lossless-join decomposition (don’t throw any information away) (be able to reconstruct the original relation) dependency preservation all of the FDs end up in just one relation (not split across two or more relations) Boyce-Codd Normal Form (BCNF) - no redundancy Introduction to Databases, B. Ludaescher & K. Lin 52 Lossless Decompositions • What is a lossless decomposition? • What is a lossy decomposition? When R is decomposed into R1 and R2 Check to see if (R1 R2) = R if it is a lossy decomposition, then R1 gives you TOO MANY tuples. Note: we are doing a natural join Introduction to Databases, B. Ludaescher & K. Lin 53 R2 Example: a lossy decomposition Original: Employee SSN 1 2 3 Name Smith Jones Smith Decomposition: Employee SSN 1 2 3 Name Smith Jones Smith Project p1 p1 p2 Ptitle accounting accounting billing Project PID p1 p1 p2 Ptitle accounting accounting billing Name Smith Jones Smith Now join them using natural join: we get extra tuples!!! 1 smith Introduction to Databases, B. Ludaescher & K. Lin p2 billing 54 Testing for a Lossless Decomposition For decomposition into two relations Let R1 and R2 form a decomposition of R. R1 and R2 are both sets of attributes from R. For the decomposition to be lossless ... The attributes in common must be a key for 1 of the relations! Note: You are joining a key and a foreign key Introduction to Databases, B. Ludaescher & K. Lin 55 Example: test for a lossless decomposition Employee(SSN, name, project, p-title) decomposition: Employee (SSN, name) Project (project, p-title, name) Which attribute is in common? Employee.Name and Project.Name Is name a key for either of these two tables? NO! We have a problem. Introduction to Databases, B. Ludaescher & K. Lin 56 Example: test for a lossless decomposition Employee(SSN, name, project, p-title) decomposition: Employee (SSN, name) Project (project, p-title) Which attribute is in common? None Is this decomposition lossless? NO! We have a problem. Introduction to Databases, B. Ludaescher & K. Lin 57 Example: test for a lossless decomposition Employee(SSN, name, project, p-title) decomposition: Employee (SSN, name, project) Project (project, p-title) Which attribute is in common? Employee.project and Project.project Is project a key for either of these two tables? YES! We have a lossless decomposition. Introduction to Databases, B. Ludaescher & K. Lin 58 Some Preliminary Normal Form Definitions Prime Attribute - an attribute A is prime if it is a member of a key, otherwise it is nonprime Partial Dependency - given an FD XY, Y is partially dependent on X if there is a proper subset X of X such that XY. Transitive Dependency - A is transitively dependent upon X if XY, Y-/X, and YA and AXY. Introduction to Databases, B. Ludaescher & K. Lin 59 What are the FDs? EMP(ENAME, SSN, BDATE, ADDRESS, DNUM, DNAME, DMGRSSN) 5 2 1 3 6 4 9 10 EMP_PROJ(SSN, PNUM, HOURS, ENAME, PNAME, PLOCATION) 7 8 Introduction to Databases, B. Ludaescher & K. Lin 60 Normal Forms Based on FDs 1NF - all attribute values (domain values) are atomic (part of the definition of the relational model) 2NF - 1NF + no key partial dependencies (every nonprime attribute fully depends on every key of R) R(A B C D) BC (not allowed) 3NF - 2NF + no nonprime attribute is transitively dependent upon any key R(A B C D) AB C, C D (not allowed) BCNF - 3NF + no attribute is transitively dependent upon any key (all FDs determined by a key) R(A B C D) Introduction to Databases, B. Ludaescher & K. Lin AB C, C B 61 (not allowed) What’s the Goal? BCNF, Lossless, and Dependency-Preserving (first choice) 3NF, Lossless and Dependency-Preserving (second choice) because sometimes you can’t preserve all dependencies Introduction to Databases, B. Ludaescher & K. Lin 62 Finding all of the FDs Armstrong’s Axioms Reflexivity: If X Y, then X Y Trivially, all attributes A A, e.g., name name and gender gender Augmentation: If X Y, then XZ YZ for any Z Transitivity: If X Y and Y Z, then X Z X, Y, and Z are sets of attributes in R Armstrong’s Axioms are a sound & complete set of inference rules Union: If X Y and X Z, then X YZ Decomposition: If X YZ, then X Y and X Z Introduction to Databases, B. Ludaescher & K. Lin 63 Finding all FDs: the closure of a set of FDs The closure of a set of FDs: Let F be a set of FDs and F+ the closure of F. F+ is the set of all FDs implied (or derivable) from F using Armstrong’s Axioms F+ is computed by applying the inference rules until no new FDs can be found Introduction to Databases, B. Ludaescher & K. Lin 64 What is “Dependency Preserving” Suppose F is the original set of FDs and G is the set of FDs after decomposition If we compute F+ and G+, and F+ = G+ then the decomposition is dependency preserving Introduction to Databases, B. Ludaescher & K. Lin 65 Other Results (textbook) Algorithms To: – Compute F+ – Compute the Minimal Cover for F – Find a dependency-preserving decomposition into 3NF – Find a lossless-join decomposition into BCNF – Find a lossless-join & dependency preserving decomposition into 3NF Introduction to Databases, B. Ludaescher & K. Lin 66 Example: Not dependency preserving addr(number, street, city, state, zip) number, street, city, state zip zip state If we decompose, this FD won’t occur within one relation. Thus, since the DBMS only enforces keys (and not FDs directly), this can’t be enforced. (If we leave it alone, it is in 3NF) Introduction to Databases, B. Ludaescher & K. Lin 67 Lossless join decomposition algorithm 1. Set D = {R} (the current set of relations) 2. While there is a relation in D that’s not in BCNF Choose a relation scheme Q that is not in BCNF Find a FD X Y in Q that violates BCNF Replace Q in D by (Q – Y) and (X Y) End While; 3. Identify dependences that are not preserved (X A). 4. Add XA as a table to the set D Introduction to Databases, B. Ludaescher & K. Lin 68 Tuning the Conceptual Schema The choice of conceptual schema should be guided by the workload, in addition to redundancy issues: – We may settle for a 3NF schema rather than BCNF. – E.g., the workload may influence our choice to decompose a relation into 3NF or BCNF. – We may further decompose a BCNF schema! – We might denormalize (i.e., undo a decomposition step), or add fields to a relation. We must take care to avoid the problems caused by the redundancy! Introduction to Databases, B. Ludaescher & K. Lin 69 Decomposition of one table into several A relation is replaced by a collection of relations that are projections. Most important case. (Vertical partitioning) • Sometimes, we might want to replace a relation by a collection of relations that are selections. (Horizontal partitioning) – Each new relation has the same schema as the original, but a subset of the rows. – Collectively, new relations contain all rows of the original. Typically, the new relations are disjoint. Why might we do this? Introduction to Databases, B. Ludaescher & K. Lin 70 Tuning the Conceptual Schema Vertical decomposition using the project operator Course c# cname Course1 c# room instructor Course3 c# instructor days Course2 c# cname Introduction to Databases, B. Ludaescher & K. Lin room days 71 Tuning the Conceptual Schema Horizontal partition/decomposition using the select operator Course c# cname instructor room days Undergraduate-Course c# cname instructor room days Graduate-Course c# cname room days Introduction to Databases, B. Ludaescher & K. Lin instructor 72 Tuning the Conceptual Schema Denormalizing……always introduces redundancy! Course-Offering (offering#, quarter, c#, instructor, time, days) Course (c#, cname) Instructor-Room (instructor, room) Course-Offering (offering#, quarter, c#, cname, instructor, room, time, days) This introduces redundancy - which must be managed. Introduction to Databases, B. Ludaescher & K. Lin 73 Denormalization Denormalization interferes with dependency preservation Course-Offering (offering#, quarter, c#, cname, instructor, room, time, days) Note that having the FD instructor room (and nothing else) in a single table allows the FD to be enforced (by enforcing the candidate key of instructor). Instructor-Room (instructor, room) Introduction to Databases, B. Ludaescher & K. Lin 74