ics 10 1 fall 2012 introduction to data management

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ICS 101 Fall 2012 Introduction to Data Management Asst. Prof. Lipyeow Lim Information & Computer Science Department University of Hawaii at Manoa 10/16/2012 1 Lipyeow Lim -- University of Hawaii at Manoa

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ICS 10 1 Fall 2012 Introduction to Data Management. Asst. Prof. Lipyeow Lim Information & Computer Science Department University of Hawaii at Manoa. The Data Management Problem. Where is the photo I took last C hristmas ?. User. Where did I read about “Turing Machines” ?. - PowerPoint PPT Presentation

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Page 1: ICS  10 1  Fall 2012 Introduction to  Data Management

1

ICS 101 Fall 2012

Introduction to Data Management

Asst. Prof. Lipyeow LimInformation & Computer Science Department

University of Hawaii at Manoa

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Page 2: ICS  10 1  Fall 2012 Introduction to  Data Management

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The Data Management Problem

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Where is the photo I took last Christmas ?

Where did I read about “Turing Machines” ?

Where is the invoice for this computer ?

Which product is the most profitable ?

User

Queries

Data

?

Page 3: ICS  10 1  Fall 2012 Introduction to  Data Management

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What is ``data’’ ?

Data are known facts that can be recorded and that have implicit meaning.

Three broad categories of data Structured data Semi-structured data Unstructured data

``Structure’’ of data refers to the organization within the data that is identifiable.

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Page 4: ICS  10 1  Fall 2012 Introduction to  Data Management

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What is a database ? A database : a collection of related data.

Represents some aspect of the real world (aka universe of discourse).

Logically coherent collection of data Designed and built for specific purpose

A data model is a collection of concepts for describing/organizing the data.

A schema is a description of a particular collection of data, using the a given data model.

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Page 5: ICS  10 1  Fall 2012 Introduction to  Data Management

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The Relational Data Model• Relational database: a set of relations• A relation is made up of 2 parts:

– Instance : a table, with rows and columns. #Rows = cardinality, #fields = degree / arity.

– Schema : specifies name of relation, plus name and domain/type of each column or attribute.

• E.G. Students(sid: string, name: string, login: string, age: integer, gpa: real).

• Can think of a relation as a set of rows or tuples (i.e., all rows are distinct).

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Page 6: ICS  10 1  Fall 2012 Introduction to  Data Management

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Example Instance of Students Relation

• Cardinality = 3, degree=5, all rows distinct

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

sid name login age gpa

53666 Jones jones@cs 18 3.4

53688 Smith smith@eecs 18 3.2

53650 Smith smith@math 19 3.8

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Why is the relational model useful ?• Supports simple and powerful query

capabilities!• Structured Query Language (SQL)

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

SELECT S.snameFROM Students SWHERE S.gpa>3.5

sid name login age gpa

53666 Jones jones@cs 18 3.4

53688 Smith smith@eecs 18 3.2

53650 Smith smith@math 19 3.8

Page 8: ICS  10 1  Fall 2012 Introduction to  Data Management

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What is a DBMS ?• A database management system (DBMS) is a

collection of programs that enables users to– Create new DBs and specify the structure using

data definition language (DDL)– Query data using a query language or data

manipulation language (DML)– Store very large amounts of data– Support durability in the face of failures, errors,

misuse– Control concurrent access to data from many

users10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Page 9: ICS  10 1  Fall 2012 Introduction to  Data Management

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Types of Databases On-line Transaction

Processing (OLTP) Banking Airline reservations Corporate records

On-line Analytical Processing (OLAP)

Data warehouses, data marts

Business intelligence (BI) Specialized databases

Multimedia

XML Geographical Information

Systems (GIS) Real-time databases

(telecom industry) Special Applications

Customer Relationship Management (CRM)

Enterprise Resource Planning (ERP)

Hosted DB Services Amazon, Salesforce

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Page 10: ICS  10 1  Fall 2012 Introduction to  Data Management

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A Bit of History 1970 Edgar F Codd (aka “Ted”) invented the

relational model in the seminal paper “A Relational Model of Data for Large Shared Data Banks” Main concept: relation = a table with rows and columns. Every relation has a schema, which describes the columns.

Prior 1970, no standard data model. Network model used by Codasyl Hierarchical model used by IMS

After 1970, IBM built System R as proof-of-concept for relational model and used SQL as the query language. SQL eventually became a standard.

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Page 11: ICS  10 1  Fall 2012 Introduction to  Data Management

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Why use a DBMS ? Large datasets Concurrency/ multi-

user Crash recovery Declarative query

language No need to figure out

what low level data structure

Data independence and efficient access.

Reduced application development time.

Data integrity and security.

Uniform data administration.

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Page 12: ICS  10 1  Fall 2012 Introduction to  Data Management

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Transactions• A transaction is the DBMS’s abstract view of a

user program: a sequence of reads and writes.– Eg. User 1 views available seats and reserves seat

22A.• A DBMS supports multiple users, ie, multiple

transactions may be running concurrently.– Eg. User 2 views available seats and reserves seat

22A.– Eg. User 3 views available seats and reserves seat

23D.

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Page 13: ICS  10 1  Fall 2012 Introduction to  Data Management

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ACID Properties of Transactions

• Atomicity : all-or-nothing execution of transactions

• Consistency: constraints on data elements is preserved

• Isolation: each transaction executes as if no other transaction is executing concurrently

• Durability: effect of an executed transaction must never be lost

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Page 14: ICS  10 1  Fall 2012 Introduction to  Data Management

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Atomicity

• A transaction might commit after completing all its actions, or it could abort (or be aborted by the DBMS) after executing some actions.

• A very important property guaranteed by the DBMS for all transactions is that they are atomic. That is, a user can think of a Xact as always executing all its actions in one step, or not executing any actions at all.– DBMS logs all actions so that it can undo the actions of

aborted transactions.10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Page 15: ICS  10 1  Fall 2012 Introduction to  Data Management

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Example (Atomicity)

• The first transaction is transferring $100 from B’s account to A’s account.

• The second is crediting both accounts with a 6% interest payment

• There is no guarantee that T1 will execute before T2 or vice-versa, if both are submitted together. However, the net effect must be equivalent to these two transactions running serially in some order.

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

T1: BEGIN A=A+100B=B-100 END

T2: BEGIN A=1.06*AB=1.06*B END

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Ensuring Isolation Scheduling concurrent transactions DBMS ensures that execution of {T1, ... , Tn} is

equivalent to some serial execution T1’ ... Tn’. Idea: use locks to serialize access to shared objects Strict 2 Phase locking protocol:

Before reading/writing an object, a transaction requests a lock on the object, and waits till the DBMS gives it the lock.

All locks are released at the end of the transaction. What if Tj already has a lock on Y and Ti later requests a

lock on Y? (Deadlock!) Ti or Tj is aborted and restarted! 10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Page 17: ICS  10 1  Fall 2012 Introduction to  Data Management

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Files vs DBMS Swapping data

between memory and files

Difficult to add records to files

Security & access control

Do optimization manually

Good for small data/files

Run out of pointers (32bit)

Code your own search algorithm

Search on different fields is difficult

Must protect data from inconsistency due to concurrency

Fault tolerance – crash recovery

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Page 18: ICS  10 1  Fall 2012 Introduction to  Data Management

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The Data Management Problem

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Where is the photo I took last Christmas ?

Where did I read about “Turing Machines” ?

Where is the invoice for this computer ?

Which product is the most profitable ?

User

Queries

Data

?

Page 19: ICS  10 1  Fall 2012 Introduction to  Data Management

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Unstructured Data• What are some examples of unstructured

data? • How do we model unstructured data ?• How do we query unstructured data ?• How do we process queries on unstructured

data ?• How do we index unstructured data ?

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Page 20: ICS  10 1  Fall 2012 Introduction to  Data Management

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Unstructured Text Data• Field of “Information Retrieval”• Data Model

– Collection of documents– Each document is a bag of words (aka terms)

• Query Model– Keyword + Boolean Combinations– Eg. DBMS and SQL and tutorial

• Details:– Not all words are equal. “Stop words” (eg. “the”, “a”, “his” ...)

are ignored.– Stemming : convert words to their basic form. Eg. “Surfing”,

“surfed” becomes “surf”10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Page 21: ICS  10 1  Fall 2012 Introduction to  Data Management

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Inverted Indexes• Recall: an index is a mapping of search key to data entries

– What is the search key ?– What is the data entry ?

• Inverted Index: – For each term store a list of postings– A posting consists of <docid,position> pairs

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

DBMS doc01 10 18 20 doc02 5 38 doc03 13

SQL doc06 1 12 doc09 4 9 doc20 12

trigger doc01 12 15 doc09 14 21 doc10 1125 55

... ...

lexicon Posting lists

What is the data in an inverted index sorted on ?

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Lookups using Inverted Indexes

• Given a single keyword query “k” (eg. SQL)– Find k in the lexicon– Retrieve the posting list for k– Scan posting list for document IDs [and positions]

• What if the query is “k1 and k2” ?– Retrieve document IDs for k1 and k2– Perform intersection

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

DBMS doc01 10 18 20 doc02 5 38 doc01 13

SQL doc06 1 12 doc09 4 9 doc20 12

trigger doc01 12 15 doc09 14 21 doc10 1125 55

... ...

lexicon Posting lists

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Too Many Matching Documents• Rank the results by “relevance”!• Vector-Space Model

– Documents are vectors in hi-dimensional space

– Each dimension in the vector represents a term

– Queries are represented as vectors similarly

– Vector distance (dot product) between query vector and document vector gives ranking criteria

– Weights can be used to tweak relevance• PageRank (later)

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Star

Diet

Doc about astronomy

Doc about movie stars

Doc about behavior

Page 24: ICS  10 1  Fall 2012 Introduction to  Data Management

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Internet Search Engines

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

World Wide Web

Web Page Repository

Inverted Index

Web Crawler

Search Engine Web Server

Keyword Query

Query

Indexer

Ranked Results

Postings etcDoc IDs

Snipplets

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Basic Web Search• http://www.googleguide.com/

advanced_operators_reference.html

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Query Expression What it means

Biking italy Biking AND italy

Recycle steel OR iron Recycle AND (steel OR iron)

“I have a dream” “I have a dream” treated as one term

Salsa -dance Salsa AND NOT dance

Other nifty expressions What it means

12 + 34 - 56 * 7 / 8 Evaluates the arithmetic expression

300 Euros in USD Converts 300 euros to US currency

Page 26: ICS  10 1  Fall 2012 Introduction to  Data Management

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Ranking Web Pages• Google’s PageRank

– Links in web pages provide clues to how important a webpage is.

• Take a random walk– Start at some webpage p– Randomly pick one of the links

and go to that webpage– Repeat for all eternity

• The number of times the walker visits a page is an indication of how important the page is.

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

1

3

2

4

5

6

Vertices represent web pages.Edges represent web links.

Page 27: ICS  10 1  Fall 2012 Introduction to  Data Management

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Semi-structured Search

Query Expression What it means

define:imbroglio Find definitions of “imbroglio”

Halloween site:www.census.gov Restrict search for “halloween” to US census website

Form 1098-T IRS filetype:pdf Find the US tax form 1098-T in PDF format

link:warriorlibrarian.com Find pages that link to Warrior Librarian's website

Dan Shugar intext:Powerlight Find pages mentioning Dan Shugar where his company, Powerlight, is included in the text of the page, i.e., less likely to be from the corporate website.

allintitle: Google Advanced Operators Search for pages with titles containing "Google," "Advanced,", and "Operators"

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa

Web pages are not really unstructured! Click “view source” to view HTML.

Page 28: ICS  10 1  Fall 2012 Introduction to  Data Management

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Summary• Data Management Problem

– How do we pose and answer queries on data?• Structured data

– Relational Data Model – SQL– Relational DBMS– Transactions

• Unstructured data– Bag of terms– Boolean combination of keyword queries– Inverted Indexes (Web Search Engines)

• Semi-structured data– Could use techniques from either structured or unstructured– More sophisticated keyword queries

10/16/2012 Lipyeow Lim -- University of Hawaii at Manoa