This Day in History

1. CSE494 - Information Retrieval, Mining and Integration on the InternetDatabase Concepts - A Refresher30th March 2004

2. This Day in History • 1867 – US purchases Alaska from Russia for $7.2 million (2 cents/acre) • 1953 – Einstein announces revised unified field theory • 1954 – Test Cricket debut of Sir Garry Sobers vs. England • 1981 – President Reagan shot & wounded by John W Hinckley Jr • 2004 – The “first ever” regular class of Rao taught by someone other than Rao Slides adapted from Rao (ASU) & Franklin (Berkeley)

3. Concepts covered so far … • Information Retrieval • Text retrieval • Hyper-linked text retrieval • Improvements… • Information Mining • Clustering techniques to improve result presentation • Classification and filtering techniques Slides adapted from Rao (ASU) & Franklin (Berkeley)

4. Structured data.. • Focus on “text” data till date. • However, a lot of the data available on the web is actually from (semi-)structured databases !!!! • They do their best to look like they are text sources • What are the issues and opportunities brought up by the presence of such sources on the web? Slides adapted from Rao (ASU) & Franklin (Berkeley)

5. Databases !!!??? you may have used Slides adapted from Rao (ASU) & Franklin (Berkeley)

6. Is the a DBMS? Skeptic’s corner • Fairly sophisticated search available • crawler indexes pages on the web • Keyword-based search for pages • But, currently • data is mostly unstructured and untyped • search only: • can’t modify the data • can’t get summaries, complex combinations of data • Web sites typically have a DBMS in the background to provide these functions. • They dynamically convert (wrap) the structured data into readable English • <India, New Delhi> => The capital of India is New Delhi. • So, if we can “unwrap” the text, we have structured data! • Note also that such dynamic pages cannot be crawled... • The (coming) Semi-structured web • Most pages are at least “semi”-structured • XML standard is expected to ease the presentation/on-the-wire transfer of such pages. (BUT…..) • The Services • Travel services, mapping services • The Sensors Stock quotes, current temperatures, ticket prices… Slides adapted from Rao (ASU) & Franklin (Berkeley)

7. Structure [SQL] [English] [XML] • How will search and querying on these three types of data differ? An employee record A generic web page containing text A movie review Semi-Structured Slides adapted from Rao (ASU) & Franklin (Berkeley)

8. “Search” vs. Query • What if you wanted to find out which actors donated to Al Gore’s presidential campaign? • Try “actors donated to gore” in your favorite search engine. Slides adapted from Rao (ASU) & Franklin (Berkeley)

9. Structure helps querying • Expressive queries • Give me all pages that have key words “Get Rich Quick” • Give me the social security numbers of all the employees who have stayed with the company for more than 5 years, and whose yearly salaries are three standard deviations away from the average salary • Give me all mails from people from ASU written this year, which are relevant to “get rich quick” • Efficient searching • equality vs. “similarity” • range-limited search Slides adapted from Rao (ASU) & Franklin (Berkeley)

10. Why use a DBMS in your website? Suppose we are building web-based music distribution site. Several questions arise: • How do westore the data? (file organization, etc.) • How do we query the data? (write programs…) • Make sure that updates don’t mess things up? • Provide different views on the data? (registrar versus students) • How do we deal with crashes? Way too complicated! Buy a database system! Slides adapted from Rao (ASU) & Franklin (Berkeley)

11. What Is a Database System? • Database: a very large, integrated collection of data. • Models a real-world enterprise • Entities (e.g., teams, games) • Relationships (e.g., The Patriotsare playing inThe Superbowl) • More recently, also includes active components , often called “business logic”. (e.g., the BCS ranking system) • A Database Management System (DBMS)is a software system designed to store, manage, and facilitate access to databases. Slides adapted from Rao (ASU) & Franklin (Berkeley)

12. Functionality of a DBMS • Data Dictionary Management • Storage management • Data storage Definition Language (DDL) • High level query and data manipulation language • SQL/XQuery etc. • May tell us what we are missing in text-based search • Efficient query processing • May change in the internet scenario • Transaction processing • Resiliency: recovery from crashes, • Different views of the data, security • May be useful to model a collection of databases together • Interface with programming languages Slides adapted from Rao (ASU) & Franklin (Berkeley)

13. Query (SQL) Database Manager (DBMS) -Storage mgmt -Query processing -View management -(Transaction processing) Database (relational) Answer (relation) Traditional Database Architecture Slides adapted from Rao (ASU) & Franklin (Berkeley)

14. Building an Application with a Database System • Requirements modeling (conceptual, pictures) • Decide what entities should be part of the application and how they should be linked. • Schema design and implementation • Decide on a set of tables, attributes. • Define the tables in the database system. • Populate database (insert tuples). • Write application programs using the DBMS • Now much easier, with data management API Slides adapted from Rao (ASU) & Franklin (Berkeley)

15. name category name Takes Course Student quarter Advises Teaches Professor name field address Conceptual Modeling ssn Slides adapted from Rao (ASU) & Franklin (Berkeley)

16. Data Models • A data model is a collection of concepts for describing data. • A schemais a description of a particular collection of data, using a given data model. • The relational model of datais the most widely used model today. • Main concept: relation, basically a table with rows and columns. • Every relation has a schema, which describes the columns, or fields. Slides adapted from Rao (ASU) & Franklin (Berkeley)

17. Levels of Abstraction View 1 View 2 View 3 Conceptual Schema Physical Schema DB • Views describe how users see the data. • Conceptual schemadefines logical structure • Physical schema describes the files and indexes used. Slides adapted from Rao (ASU) & Franklin (Berkeley)

18. Example: University Database View 1 View 2 View 3 Conceptual Schema Physical Schema DB • Conceptual schema: • Students(sid: string, name: string, login: string, age: integer, gpa:real) • Courses(cid: string, cname:string, credits:integer) • External Schema (View): • Course_info(cid:string,enrollment:integer) • Physical schema: • Relations stored as unordered files. • Index on first column of Students. Slides adapted from Rao (ASU) & Franklin (Berkeley)

19. Data Independence View 1 View 2 View 3 Conceptual Schema Physical Schema DB • Applications insulated from how data is structured and stored. • Logical data independence: Protection from changes in logical structure of data. • Physical data independence: Protection from changes in physical structure of data. • Q: Why are these particularly important for DBMS? Slides adapted from Rao (ASU) & Franklin (Berkeley)

20. Schema Design & Implementation • Table Students • Separates the logical view from the physical view of the data. Slides adapted from Rao (ASU) & Franklin (Berkeley)

21. Terminology Attribute names Students tuples (Arity=3) Slides adapted from Rao (ASU) & Franklin (Berkeley)

22. Querying a Database • Find all the students taking CSE594 in Q1, 2004 • S(tructured) Q(uery) L(anguage) select E.name from Enroll E where E.course=CS490i and E.quarter=“Winter, 2000” • Query processor figures out how to answer the query efficiently. Slides adapted from Rao (ASU) & Franklin (Berkeley)

23. Relational Algebra • Operators • tuple sets as input, new set as output • Basic Binary Set Operators • Result is table (set) with same attributes • Sets must be compatible! • R1(A1,A2,A3)  R2(B1,B2,B3) •  Domain(Ai) = Domain(Bi) • Union • All tuples in either R1 or in R2 • Intersection • All tuples in both R1 and R2 • Difference • All tuples in R1 but not in R2 • Complement • All tuples not in R1 • Selection, Projection, Cartesian Product, Join what’s the universe? Slides adapted from Rao (ASU) & Franklin (Berkeley)

24. Selection s • Grab a subset of the tuples in a relation that satisfy a given condition • Use and, or, not, >, <… to build condition • Unary operation… returns set with same attributes, but ‘selects’ rows Slides adapted from Rao (ASU) & Franklin (Berkeley)

25. Projection p • Unary operation, selects columns • Returned schema is different, • So returned tuples are not subset of original set • Contrast with selection • Eliminates duplicate tuples Slides adapted from Rao (ASU) & Franklin (Berkeley)

26. Cartesian Product X • Binary Operation • Result is set of tuples combining all elements of R1 with all elements of R2, for R1  R2 • Schema is union of Schema(R1) & Schema(R2) • Notice we could do selection on result to get meaningful info! Slides adapted from Rao (ASU) & Franklin (Berkeley)

27. Join • Most common (and exciting!) operator… • Combines 2 relations • Selecting only related tuples • Result has all attributes of the two relations • Equivalent to • Cross product followed by selection followed by Projection • Equijoin • Join condition is equality between two attributes • Natural join • Equijoin on attributes of same name • result has only one copy of join condition attribute Slides adapted from Rao (ASU) & Franklin (Berkeley)

28. Example: Natural Join Employee Dependents Slides adapted from Rao (ASU) & Franklin (Berkeley)

29. Exercises Product ( pname, price, category, maker) Purchase (buyer, seller, store, prodname) Company (cname, stock price, country) Person( per-name, phone number, city) Ex #1: Find people who bought telephony products. Ex #2: Find names of people who bought American products Ex #3: Find names of people who bought American products and did not buy French products Ex #4: Find names of people who bought American products and they live in Seattle. Ex #5: Find people who bought stuff from Joe or bought products from a company whose stock prices is more than $50. Slides adapted from Rao (ASU) & Franklin (Berkeley)

30. SQL Introduction Standard language for querying and manipulating data Structured Query Language Many standards out there: SQL92, SQL2, SQL3, SQL99 Vendors support various subsets of these (but we’ll only discuss a subset of what they support) Basic form = syntax on relational algebra (but many other features too) Select attributes From relations (possibly multiple, joined) Where conditions (selections) Slides adapted from Rao (ASU) & Franklin (Berkeley)

31. Selections s SELECT * FROM Company WHERE country=“USA” AND stockPrice > 50 You can use: Attribute names of the relation(s) used in the FROM. Comparison operators: =, <>, <, >, <=, >= Apply arithmetic operations: stockprice*2 Operations on strings (e.g., “||” for concatenation). Lexicographic order on strings. Pattern matching: s LIKE p Special stuff for comparing dates and times. Slides adapted from Rao (ASU) & Franklin (Berkeley)

32. Projection p Select only a subset of the attributes SELECTname, stock price FROM Company WHERE country=“USA” AND stockPrice > 50 Rename the attributes in the resulting table SELECTname AS company, stockprice AS price FROM Company WHERE country=“USA” AND stockPrice > 50 Slides adapted from Rao (ASU) & Franklin (Berkeley)

33. Ordering the Results SELECT name, stock price FROM Company WHERE country=“USA” AND stockPrice > 50 ORDERBY country, name Ordering is ascending, unless you specify the DESC keyword. Ties are broken by the second attribute on the ORDERBY list, etc. Slides adapted from Rao (ASU) & Franklin (Berkeley)

34. Join SELECT name, store FROM Person, Purchase WHERE per-name=buyer AND city=“Seattle” AND product=“gizmo” Product ( pname, price, category, maker) Purchase (buyer, seller, store, product) Company (cname, stock price, country) Person( per-name, phone number, city) Slides adapted from Rao (ASU) & Franklin (Berkeley)

35. Disambiguating Attributes Find names of people buying telephony products: SELECT Person.name FROM Person, Purchase, Product WHERE Person.name=buyer AND product=Product.name AND Product.category=“telephony” Product ( name, price, category, maker) Purchase (buyer, seller, store, product) Person( name, phone number, city) Slides adapted from Rao (ASU) & Franklin (Berkeley)

36. Tuple Variables Find pairs of companies making products in the same category SELECT product1.maker, product2.maker FROM Product AS product1, Product AS product2 WHERE product1.category = product2.category AND product1.maker <> product2.maker Product ( name, price, category, maker) Slides adapted from Rao (ASU) & Franklin (Berkeley)

37. Views

38. Defining Views (Virtual) Views are relations, except that they are not physically stored. They are used mostly in order to simplify complex queries and to define conceptually different views of the database to different classes of users. View: purchases of telephony products: CREATE VIEW telephony-purchases AS SELECT product, buyer, seller, store FROM Purchase, Product WHERE Purchase.product = Product.name AND Product.category = “telephony” Slides adapted from Rao (ASU) & Franklin (Berkeley)

39. A Different View CREATE VIEW Seattle-view AS SELECT buyer, seller, product, store FROM Person, Purchase WHERE Person.city = “Seattle” AND Person.name = Purchase.buyer We can later use the views: SELECT name, store FROM Seattle-view, Product WHERE Seattle-view.product = Product.name AND Product.category = “shoes” What’s really happening when we query a view?? Slides adapted from Rao (ASU) & Franklin (Berkeley)

40. Updating Views How can I insert a tuple into a table that doesn’t exist? CREATE VIEW bon-purchase AS SELECT store, seller, product FROM Purchase WHERE store = “The Bon Marche” If we make the following insertion: INSERT INTO bon-purchase VALUES (“the Bon Marche”, Joe, “Denby Mug”) We can simply add a tuple (“the Bon Marche”, Joe, NULL, “Denby Mug”) to relation Purchase. Slides adapted from Rao (ASU) & Franklin (Berkeley)

41. Materialized Views • Views whose corresponding queries have been executed and the data is stored in a separate database • Uses: Caching • Issues • Using views in answering queries • Normally, the views are available in addition to database • (so, views are local caches) • In information integration, views may be the only things we have access to. • An internet source that specializes in woody allen movies can be seen as a view on a database of all movies. Except, there is no database out there which contains all movies.. • Maintaining consistency of materialized views Slides adapted from Rao (ASU) & Franklin (Berkeley)

42. Non-Updatable Views Given Purchase (buyer, seller, store, product) Person( name, phone-num, city) CREATE VIEW Seattle-view AS SELECT seller, product, store FROM Person, Purchase WHERE Person.city = “Seattle” AND Person.name = Purchase.buyer Why non-updatable? How can we add the following tuple to the view? (Joe, “Shoe Model 12345”, “Nine West”) Slides adapted from Rao (ASU) & Franklin (Berkeley)

43. Issues w.r.t. Databases on the Web • Information Extraction (invert the tuple to text transformation) • Support lay user queries • More flexible queries • Exact (SQL) vs Approximate/Similar (Text search?) • On “semi-structured” databases • Joins over text attributes? • Exact (SQL) vs Approximate/Similar !!!!! • Support integration/aggregation of multiple databases • Take a query from the user and send it to all relevant databases… • TONS of challenges… Slides adapted from Rao (ASU) & Franklin (Berkeley)

44. Imprecise Queries • Increasing number of Web accessible databases • E.g. bibliographies, reservation systems, department catalogs etc • Support for precise queries only – exactly matching tuples • Difficulty in extracting desired information • Limited query capabilities provided by form based query interface • Lack of schema/domain information • Increasing complexity of types of data e.g. hyptertext, images etc • Often times user wants ‘about the same’ instead of ‘exact’ • Bibliography search — find similar publications Solution: Provide answers closely matching query constraints Slides adapted from Rao (ASU) & Franklin (Berkeley)

45. Query Optimization

46. Query Optimization buyer  City=‘seattle’ phone>’5430000’ Buyer=name (Simple Nested Loops) Person Purchase (Table scan) (Index scan) Goal: Declarative SQL query Imperative query execution plan: SELECT S.buyer FROM Purchase P, Person Q WHERE P.buyer=Q.name AND Q.city=‘seattle’ AND Q.phone > ‘5430000’ • Inputs: • the query • statistics about the data (indexes, cardinalities, selectivity factors) • available memory Ideally: Want to find best plan. Practically: Avoid worst plans! Slides adapted from Rao (ASU) & Franklin (Berkeley)

47. Optimizing Joins • Q(u,x) :- R(u,v), S(v,w), T(w,x) • R S T • Many ways of doing a single join R S • Symmetric vs. asymmetric join operations • Nested join, hash join, double pipe-lined hash join etc. • Processing costs alone vs. processing + transfer costs • Get R and S together vs, get R, get just the tuples of S that will join with R (“semi-join”) • Many orders in which to do the join • (R join S) join T • (S join R) join T • (T join S) join R etc. • All with different costs Slides adapted from Rao (ASU) & Franklin (Berkeley)

48. Determining Join Order D D C C D B A C B A B A • In principle, we need to consider all possible join orderings: • As the number of joins increases, the number of alternative plans grows rapidly; we need to restrict the search space. • System-R: consider only left-deep join trees. • Left-deep trees allow us to generate all fully pipelined plans:Intermediate results not written to temporary files. • Not all left-deep trees are fully pipelined (e.g., SM join). Slides adapted from Rao (ASU) & Franklin (Berkeley)

49. Query Optimization Process(simplified a bit) • Parse the SQL query into a logical tree: • identify distinct blocks (corresponding to nested sub-queries or views). • Query rewrite phase: • apply algebraic transformations to yield a cheaper plan. • Merge blocks and move predicates between blocks. • Optimize each block: join ordering. • Complete the optimization: select scheduling (pipelining strategy). Slides adapted from Rao (ASU) & Franklin (Berkeley)

50. Cost Estimation • For each plan considered, must estimate cost: • Must estimate costof each operation in plan tree. • Depends on input cardinalities. • Must estimate size of result for each operation in tree! • Use information about the input relations. • For selections and joins, assume independence of predicates. • System R cost estimation approach. • Very inexact, but works ok in practice. • More sophisticated techniques known now. Slides adapted from Rao (ASU) & Franklin (Berkeley)