Bab-001_Pengantar_ADT

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Semester Genap 2010/2011

Beni Suranto, ST.

[email protected]


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Data Types

We type data--classify it into various categories--such as int,

boolean, String, Applet A data type represents a set of possible values, such as

{..., -2, -1, 0, 1, 2, ...}, or {true, false}

By typing our variables, we allow the computer to find some of our

errors Some operations only make sense when applied to certain kinds

of data--multiplication, searching

Typing simplifies internal representation

A String requires more and different storagethan a boolean


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A data type is characterized by:

a set ofvalues

a data representation, which is common to all these values, and

a set ofoperations, which can be applied uniformly to all thesevalues

Data Types


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Primitive Types in Java

Java provides eight primitive types:

boolean

char, byte, short, int, long

float, double

Each primitive type has

a set ofvalues

a data representation

a set ofoperations

These are set in stonethere is nothing

the programmer can do to change anything

about them


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Primitive Types as Data Types

Type Values Representation Operations

boolean true, false Single byte &&, ||, !

char, byte,short, int,long

Integers ofvarying sizes

Twos complement +, -, *, /,others

float,double

Floating pointnumbers ofvarying sizesandprecisions

Twos complementwith exponent andmantissa

+, -, *, /,others


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The Need for Data Structures

Data structures organize data

more efficient programs.

More powerful computersmore complex applications.

More complex applications demand more calculations.

Complex computing tasks are unlike our everyday experience.


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What is a data structure?

In a general sense, any data representation is a data structure.Example: An integer

More typically, a data structure is meant to be an organizationfor a collection of data items.


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Organizing Data

Any organization for a collection of records can be searched,

processed in any order, or modified.

The choice of data structure and algorithm can make the

difference between a program running in a few seconds or

many days.


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Efficiency

A solution is said to be efficient if it solves the problem within its

resource constraints.

Space

Time

The cost of a solution is the amount of resources that thesolution consumes.


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Costs and Benefits

A data structure requires a certain amount of:

space for each data item it stores

time to perform a single basic operation

programming effort.


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Example: Banking Application

Operations are (typically):

Open accounts (far less often than access)

Close accounts (far less often than access)

Access account to Add money

Access account to Withdraw money


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Teller and ATM transactions are expected to take little time.

Opening or closing an account can take much longer (perhapsup to an hour).



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When considering the choice of data structure to use in thedatabase system that manages the accounts, we are looking

for a data structure that:

Is inefficient for deletion

Highly efficient for search

Moderately efficient for insertion



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One data structure that meets these requirements is the hashtable.

Records are accessible by account number (called an exact-match query)

Hash tables allow for extremely fast exact-match search. Hash tables also support efficient insertion of new records. Deletions can also be supported efficiently (but too many

deletions lead to some degradation in performance requiring the hash table to be reorganized).



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Example: City Database

Database system for cities and towns.

Users find information about a particular place by name(exact-match query)

Users also find all places that match a particular value (orrange of values), such as location or population size (called arange query).


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The database must answer queries quickly enough to satisfythe patience of a typical user.

For an exact-match query, a few seconds is satisfactory

For a range queries, the entire operation may be allowed totake longer, perhaps on the order of a minute.



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The hash table is inappropriate for implementing the citydatabase because: It cannot perform efficient range queries

The B+ tree supports large databases:

InsertionDeletionRange queries

If the database is created once and then never changed, a

simple linear index would be moreappropriate.



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Selecting a Data Structure

Select a data structure as follows:

1. Analyze the problem to determine the resource constraints

a solution must meet.

2. Determine the basic operations that must be supported.

Quantify the resource constraints for each operation.

3. Select the data structure that best meets these

requirements.


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Some Questions to Ask

Are all data inserted into the data structure at the beginning,or are insertions intersparsed with other operations?

Can data be deleted?

Are all data processed in some well-defined order, or israndom access allowed?


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Data Structure Philosophy

Each data structure has costs and benefits.

Rarely is one data structure better than another in all situations.

A data structure requires:

space for each data item it stores,

time to perform each basic operation,

programming effort.


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Each problem has constraints on available space and time.

Only after a careful analysis of problem characteristics can weknow the best data structure for the task.

Bank example:

Start account : a few minutesTransactions : a few secondsClose account : overnight

Data Structure Philosophy


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Abstract Data Types

Abstract Data Type (ADT): a definition for a data type solely interms of a set of values and a set of operations on that datatype.

Each ADT operation is defined by its inputs and outputs.Encapsulation: Hide implementation details.


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A data structure is the physical implementation of an ADT.Each operation associated with the ADT is implemented by

one or more subroutines in the implementation.

In a OO language such as Java, an ADT and its implementationtogether make up a class.

Data structure usually refers to an organization for data inmain memory.

File structure: an organization for data on peripheral storage,such as a disk drive.

Abstract Data Types


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Labeling collections of objects

Humans deal with complexity by assigning a label to anassembly of objects. An ADT manages complexity through

abstraction.

Hierarchies of labels

Example : transistors gates CPU.

In a program, implement an ADT, then think only about the ADT,

not its implementation.


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Logical vs. Physical Form

Data items have both a logical and a physical form.

Logical form: definition of the data item within an ADT.

Ex: Integers in mathematical sense: +, -

Physical form: implementation of the data item within a datastructure.

Ex: 16/32 bit integers, overflow.


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Data Type

ADT:TypeOperations

Data Items:Logical Form

Data Items:Physical Form

Data Structure:Storage Space

Subroutines


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