Purpose of Data Structures

Transcription

1 Purpose of Data Structures Primary goal of computer programs: to store and retrieve information. How to structure information to support efficient processing. Study of data structures and algorithms is fundamental to computer science. How to measure the effectiveness of a data structure or algorithm is key to the use of data structures. Data Structures 1 Introduction

2 Why Data Structures? More powerful computers = more complex applications. More complex applications demand more calculations, eg., scientific computing, weather/oceanography modeling. Complex computing tasks are unlike our everyday experience. Complex applications require careful and effficient program design Data Structures 2 Introduction

3 Data Structures and Efficiency Data Structure: A data representation and its associated operations, eg. integer, floating point numbers. More typically, data structures are aggregates, or collections of data items. Efficiency: Choice of a data structure can mean running times between seconds and days. Efficient Solution: Solves the problem within the required resource contraints. Cost: the amount of resources (running time, RAM, disk storage) Data Structures 3 Introduction

4 Choosing a Data Structure Analyze problem to determine resource constraints. Determine the basic operations that must be supported. Quantify the resource constraints for each operation. Select the data structure that best meets these requirements. Typical Operations Insert, Delete, Search for an element. Ordered vs Unordered structures. Data Structures 4 Introduction

5 Data Structure Costs and Benefits Data Structures require Space to store data items Time to perform each operation Programming effort Need a careful analysis of problem characteristics to determine the best data structure. Frequently, time/space tradeoffs are possible. Data Structures 5 Introduction

6 Abstract Data Type and Data Structures Definitions Type: Collection of values, eg., boolean (true, false) Data Type: Type with a collection of operations Data Item: A value drawn from its type. Simple Data Item: No sub-parts, atomic. Aggregate: Records, containing multiple items/fields. Data Type: Logical definition vs. Implementation List Array. List Linked List Array Array (contiguous memory locations) Array Array (set of homogeneous items) Data Structures 6 Introduction

7 Abstract Data Type (ADT) Definitions Defines a data type in terms of type and operations on the type. No implementation is part of an ADT. Encapsulation: Hiding implementation details from a user. Data Structure: A physical implementation of an ADT. File Structure: Data stored in secondary storage, such as a hard disk, magnetic tape. Role of ADT Managing Complexity via Abstraction. Similar in spirit to metaphors, icons Hierarchy of concepts, labels. Data Structures 7 Introduction

8 Problems, Algorithms, Programs Problems A task to be performed, in terms of inputs and outputs. Must include constraints in its definition. Can be mathematically viewed as functions, eg., y = f(x 1, x 2, x 3,...x n ) In other words, it produces the same output for a particular set of inputs. Data Structures 8 Introduction

9 Algorithms A method followed to solve a problem. Multiple algorithms can solve the same probem. An algorithm computes a function, i.e. solves a single problem Properties Must be correct. Sequence of concrete steps. No ambiguity. Finite number of steps. Must terminate. Data Structures 9 Introduction

10 Programs Computer Program is an instance or concrete implementation of an algorithm. Multiple programs are instances of a single algorithm. Not all programs are algorithms, eg., an operating system. Data Structures 10 Introduction

11 Algorithm Efficiency Two conflicting Goals: Design an algorithm that is easy to understand, code, debug. Makes efficient use of computer s resources. Measuring Efficieny Using Algorithm or Complexity Analysis Useful in comparing algorithms Data Structures 11 Introduction

12 Data Structures 12 Introduction