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TECHNICAL UNIVERSITY OF CRETE
DEPT OF ELECTRONIC AND COMPUTER ENGINEERING
DATA STRUCTURES
AND
FILE STRUCTURES
Euripides G.M. Petrakis
http://www.intelligence.tuc.gr/~petrakis
Chania, 2007
E.G.M. Petrakis
Abstract Data Types (ADT)
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Introduction
 We study data structures and we
learn how to write efficient programs
 this hasn’t to do with programming tricks
but rather with
 good organization of information and
good algorithms that save
 computer memory and running time
E.G.M. Petrakis
Abstract Data Types (ADT)
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Data Structures
 Representation of data in the memory
 file structure: representation of data on
the disk
 e.g., collection of records (list, tree, etc)
 Efficient programs require efficient
data structures
 a problem has to be solved within the
given time and space constraints
E.G.M. Petrakis
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Problem Constraints
 Each problem puts constraints on time and
space
 e.g., bank example:
 start account: a few minutes
 transactions: a few seconds
 close account: overnight
 A solution is efficient if it solves the
problem within its space and time
constraints
 Cost of solution: amount of resources
consumed
E.G.M. Petrakis
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Goals of this course
 Teach data structures for main memory
and disk
 Teach algorithms for different tasks and
data structures
 Teach the idea of trade-offs
 there are costs and benefits associated with
each data structure and each algorithm
 Teach how to measure effectiveness of
each algorithm and data structure
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Selecting Data Structures
1.
Analyze the problem to determine the
resource constraints a solution must meet
2. Determine the operations that must be
supported
•
e.g., record search, insertion, deletion etc.
•
e.g., search operations must be very fast
3. Quantify the constraints for each
operation
4. Select data structure that best meet
these requirements
E.G.M. Petrakis
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Costs & Benefits
 Each data structure requires:
 space for each data item it stores
 time to perform each operation
 programming effort to implement it
 Each data structure has costs and benefits
 rarely one data structure is better than
another in all situations
 one may permit faster search (or insertion or
deletion) operations than another
 are all operations the same important?
E.G.M. Petrakis
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Abstract Data Type (ADT)
 ADT: definition of a data type in terms of
 a set of values and
 a set of operations allowed on that data type.
 Each ADT operation is defined by its
inputs and outputs
 ADTs hide implementation details
 A data structure is the implementation of
an ADT
 operations associated with the ADT are
implemented by one or more functions
E.G.M. Petrakis
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Logical and Physical forms

Data items have both a logical and a
physical form
1. Logical form: definition of the data
item within an ADT

e.g., integers in mathematical sense: +, -
2. Physical form: implementation of the
data item

E.G.M. Petrakis
e.g., 16 or 32 bit integers
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Data Type
ADT:Type +
Operations
Data Structure:
Storage Space +
functions
E.G.M. Petrakis
Data Items:
Logical Form
Data Items:
Physical Form
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ADT String: Sequence of chars
• ADT function length (s: string): integer;
post condition : length = len(s);
•
ADT function concat (s1,s2: string): string;
post condition: concat = s1 + s2;
 ADT function substr (s: string, i, j: integer): string;
precondition: 0 < i < len(s), 0 < j < len(s) – i + 1
post condition: substr(s, i, j);

ADT function pos (s1, s2): integer;
precondition …
post condition …
E.G.M. Petrakis
Abstract Data Types (ADT)
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Definition of an ADT
 Depends on the application
 Different definitions for the same
application
 An ADT hides implementation details
 different implementations for the same ADT
 When the ADT is given, its data type can
be used by the programmer
 e.g., string, math libraries in C
 when the implementation changes the programs
need not be changed
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Algorithms
 The method that solves a problem
 An algorithm takes the input to a problem
and transforms it to the output
 a mapping of input to output
 a problem can have many algorithms
 A program is the implementation of an
algorithm in some programming language
E.G.M. Petrakis
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Properties of Algorithms
 Effectiveness: the algorithm can be written as a
program
 there are problems for which no algorithm
exists
 Correctness: finds the correct solution for every
input
 Termination: terminates after a finite number of
steps
 each step requires a finite amount of time
 Efficiency: makes efficient use of the computer’s
resources
 Complexity: it must be easy to implement, code
and debug
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Tiling Problem
 The algorithm inputs a finite set T of tiles
 it is assumed that an unlimited number of cards
of each type is available
 asks whether any finite area, of any size, can
be covered using tiles in T so that
 the colors in any two touching edges are the
same
 For any algorithm there can be inputs T for
which the algorithm never terminates or
finds a wrong answer
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Tile tile types that
can tile any area
Tile tile types that
cannot tile any area
From “Algorithmics”, David Harel,
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A Termination Problem
 An algorithm must terminate with the
correct solution for any input
int OddEven( int n ) {
while ( n > 1 )
if (( n % 2 ) == 0) n = n / 2;
else n = 3n + 1;
return n;
}
 No one has been able to prove that the
algorithm terminates for any positive n
although most people believe that it does!!
E.G.M. Petrakis
Abstract Data Types (ADT)
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Taxonomy of Algorithms
 An algorithmic
problem that
admits no algorithm
is termed “noncomputable”
 If it is a decision
problem it is
termed
“undecidable”
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Abstract Data Types (ADT)
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Disk Model
 T = Taccess +
Trotation + Tread
 Block: unit memory
size for disk
 size of data
transferred in main
memory per disk
access
 In most cases
page=block=track
 e.g., 1, 2, 4, 8Kbytes
E.G.M. Petrakis
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Disk Model (cont.)
 Taccess > Trotation > Tread  increase the amount of data which
is transferred to the main memory per disk access
 large blocks, compression, data in memory
 in multi-user systems, the disk head can be anywhere
time
distance covered by disk head
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