1. Data & Storage Structures: Introductions & Overview Introduction to Data Structures Introduction to Data Structures Introduction to Storage structures Introduction to Storage structures Coverage Overview Coverage Overview Fundamental Building BlocksFundamental Building Blocks Linear Data StructuresLinear Data Structures Non-Linear Information StructuresNon-Linear Information Structures ApplicationsApplications Some Skills you should Acquire Through The course Some Skills you should Acquire Through The course

2. Motivation for Data Structures In a beginning programming course, variables are introduced that store a single datum. In a beginning programming course, variables are introduced that store a single datum. In all but trivial programs, however, we need to store multiple related data items for convenience. In all but trivial programs, however, we need to store multiple related data items for convenience. For example, data items like students records many need to be stored under a single name for ease of processing. For example, data items like students records many need to be stored under a single name for ease of processing. Such convenient structuring of data is called data organization a container for the data is called data structure. Such convenient structuring of data is called data organization a container for the data is called data structure. A data structure is a means of organizing data in primary memory in a form is convenient to process by a program. A data structure is a means of organizing data in primary memory in a form is convenient to process by a program.

3. Motivation for Data Structures (contd.) A data structure could be a programming construct provided in a language or it can be defined by the programmer. A data structure could be a programming construct provided in a language or it can be defined by the programmer. Example of data structure include: Example of data structure include: Arrays, Linked lists, Stacks, Queues, Trees, Graphs Arrays, Linked lists, Stacks, Queues, Trees, Graphs Data structures are applied in sorting, searching, hash tables, graph algorithms, pattern matching, data compressing etc. Data structures are applied in sorting, searching, hash tables, graph algorithms, pattern matching, data compressing etc. A course in data structure is a core course in most undergraduate Computer Science degree program. A course in data structure is a core course in most undergraduate Computer Science degree program. The contents of this course has not changed much in the last two decades except for inclusion of some new algorithms. The contents of this course has not changed much in the last two decades except for inclusion of some new algorithms.

4. Need for Knowledge of Data Structures Good programming and problem solving requires knowledge of data structures. Good programming and problem solving requires knowledge of data structures. Without a sufficient understanding of data structures, the level at which a programmer can work will be severely limited. Without a sufficient understanding of data structures, the level at which a programmer can work will be severely limited. An important distinguishing characteristics of data structures is the manner in which they are organized. An important distinguishing characteristics of data structures is the manner in which they are organized. data structures can be linear, hierarchical or unordered. data structures can be linear, hierarchical or unordered. data structures are also categorized as linear or dynamic depending on their allocation strategy. data structures are also categorized as linear or dynamic depending on their allocation strategy.

5. Motivation for Storage Structures The main purpose for computer systems is to execute programs. The main purpose for computer systems is to execute programs. These programs together with the data they act upon, must be stored in main memory during execution. These programs together with the data they act upon, must be stored in main memory during execution. Ideally, we would want the programs and data to reside in the main memory permanently. Ideally, we would want the programs and data to reside in the main memory permanently. However, this is not possible because However, this is not possible because the main memory is usually too small to store all needed data permanently.the main memory is usually too small to store all needed data permanently. the main memory is a volatile storage device that looses its content when power is turned off or lost.the main memory is a volatile storage device that looses its content when power is turned off or lost.

6. Motivation for Storage Structures (contd.) Since large data sets cannot fit into main memory, secondary storage structures are necessary to handle such data. Since large data sets cannot fit into main memory, secondary storage structures are necessary to handle such data. Magnetic disk is the most common form of secondary storage in computer systems. Magnetic disk is the most common form of secondary storage in computer systems. With storage structures, a small portion of the data is kept in primary storage, and additional data is read from secondary storage as needed. With storage structures, a small portion of the data is kept in primary storage, and additional data is read from secondary storage as needed. A data storage structures is a means of organizing data as blocks in secondary memory that optimizes I/O read/write operations. A data storage structures is a means of organizing data as blocks in secondary memory that optimizes I/O read/write operations. Data storage structures include: Sequential files, Random access files, Indexed files, B-Trees, B*-Trees. Data storage structures include: Sequential files, Random access files, Indexed files, B-Trees, B*-Trees.

7. Need for Efficient Storage Structures Secondary memory (disk) is divided into equal-sized blocks. Secondary memory (disk) is divided into equal-sized blocks. The basic I/O operation transfers the contents of one disk block to/from main memory. The basic I/O operation transfers the contents of one disk block to/from main memory. A disk access is unbelievably expensive compared to a typical computer instruction. A disk access is unbelievably expensive compared to a typical computer instruction. Without efficient data storage structures, a program will spend most of its time retrieving data from secondary storage. Without efficient data storage structures, a program will spend most of its time retrieving data from secondary storage. A binary search tree can be ideal for internal data retrievals, but it's performance is inadequate with disk I/O operations. A binary search tree can be ideal for internal data retrievals, but it's performance is inadequate with disk I/O operations.

8. How B-Trees Minimize Disk Accesses B-Trees are used for this purpose and they are considered the bread and butter of the database world. B-Trees are used for this purpose and they are considered the bread and butter of the database world. A block of secondary memory is represented as a node in the B-tree. A block of secondary memory is represented as a node in the B-tree. The more records we can fit into a block the fewer disk accesses are required to find a record. The more records we can fit into a block the fewer disk accesses are required to find a record. A B-Tree is the most flexible storage structure; it exploits the fact that many records can be read in at a time. A B-Tree is the most flexible storage structure; it exploits the fact that many records can be read in at a time. In a B-tree, data has high locality of reference, a feature that is extremely important when using secondary storage. In a B-tree, data has high locality of reference, a feature that is extremely important when using secondary storage.

9. Course Overview: Fundamental Building Blocks Overview of OO concepts Overview of OO concepts –We start with a review of fundamental OO concepts in Java. –In particular, we stress on inheritance, abstract classes and interfaces. – Understanding of these will be assumed and used widely in the remainder of the course. Introduction to design patterns Introduction to design patterns –These are a record of good practice in software development. – Software developers are appreciating the need to document good practices in their trade in the forms of design patterns. – The use of design patterns in software development provides a good blend of abstraction, reusability and flexibility.

10. Course Overview: The Need for Algorithm Analysis Real-life application development requires the ability to choose among competing algorithms for any computer process. Real-life application development requires the ability to choose among competing algorithms for any computer process. Important criteria for measuring algorithms are: simplicity, clarity, and space- and time-efficiency. Important criteria for measuring algorithms are: simplicity, clarity, and space- and time-efficiency. To select better algorithms from a pool of alternatives, we should be able to compute and compare their performances. To select better algorithms from a pool of alternatives, we should be able to compute and compare their performances. This process may require making compromises; e.g., space- and time-efficiency can be improved at the cost of increased programming complexity. This process may require making compromises; e.g., space- and time-efficiency can be improved at the cost of increased programming complexity. We'll introduce the Big-O notation used to estimate comparative performance of algorithms. We'll introduce the Big-O notation used to estimate comparative performance of algorithms.

11. Course Overview: Linear Data Structures Review and Big-O analyses of linked lists Review and Big-O analyses of linked lists Arrays and linked lists are the “ atoms ” of all implementations encountered in data structures. Arrays and linked lists are the “ atoms ” of all implementations encountered in data structures. They are needed in the implementations of Trees, Graphs, Hashing techniques and Memory management.They are needed in the implementations of Trees, Graphs, Hashing techniques and Memory management. A thorough understanding of them and their Big-O running time is essential in this course. A thorough understanding of them and their Big-O running time is essential in this course. Review & Application of stacks & queues Review & Application of stacks & queues Stacks and queues are revisited using simple applications.Stacks and queues are revisited using simple applications. They are applied further in the implementations of recursive, tree and graph algorithms.They are applied further in the implementations of recursive, tree and graph algorithms.

12. Course Overview: Recursion & Recursive Algorithms Recursion is an important and popular concept in computer science. Recursion is an important and popular concept in computer science. However, recursive algorithms are usually not as efficient as iterative algorithms. However, recursive algorithms are usually not as efficient as iterative algorithms. It is easier to reason about and perform correctness proof of recursive algorithm than to do that for their iterative cousins. It is easier to reason about and perform correctness proof of recursive algorithm than to do that for their iterative cousins. We study different forms of recursion concluding with a study of a backtracking algorithm. We study different forms of recursion concluding with a study of a backtracking algorithm.

13. Course Overview: Trees We dedicate seven lectures to the study of trees and related applications. We dedicate seven lectures to the study of trees and related applications. We discuss the motivation for and how they can be used to provide efficient solutions to algorithms. We discuss the motivation for and how they can be used to provide efficient solutions to algorithms. Present expression trees and outline their common application areas. Present expression trees and outline their common application areas. We study Heap trees and their applications to heap sort and priority queue. We study Heap trees and their applications to heap sort and priority queue. We also cover binary search trees, AVL trees and concluding with B-Trees. We also cover binary search trees, AVL trees and concluding with B-Trees.

14. Course Overview: Graphs We start by introducing graphs, their applications and outline We start by introducing graphs, their applications and outline their possible representations in the computer. their possible representations in the computer. We then attempt to give a full implementation of graphs and We then attempt to give a full implementation of graphs and popular graph traversal techniques. popular graph traversal techniques. The remaining sessions exemplify and provide implementations for the algorithms: The remaining sessions exemplify and provide implementations for the algorithms: The topological sort algorithm.The topological sort algorithm. Algorithms for tests of connectedness and cycles.Algorithms for tests of connectedness and cycles. The shortest path algorithm.The shortest path algorithm.

15. Course Overview: Hashing Techniques Review of searching techniques and introduction to hashing. Review of searching techniques and introduction to hashing. Hash tables and hash functions are then gently introduced using simple examples. Hash tables and hash functions are then gently introduced using simple examples. The problem of collision is demonstrated and classical techniques for collision resolutions are outlined. The problem of collision is demonstrated and classical techniques for collision resolutions are outlined. The possibility of building perfect hash functions is demonstrated. The possibility of building perfect hash functions is demonstrated. Implementation of the different hashing techniques is presented at the end. Implementation of the different hashing techniques is presented at the end.

16. Course Overview: Data Comparison & memory Mgt. Data Compression Data Compression Presents motivations for, classifications of and kinds of data compression schemes.Presents motivations for, classifications of and kinds of data compression schemes. Gives animated examples of the following compression algorithms:Gives animated examples of the following compression algorithms: Huffman coding Huffman coding LZ78 coding LZ78 coding LZW coding LZW coding Memory management & garbage collection Memory management & garbage collection So far, we understood how objects can become garbage and assumed the space they held will be freed.So far, we understood how objects can become garbage and assumed the space they held will be freed. In conclusion, we give an insight into garbage collection schemes and other memory management issues. In conclusion, we give an insight into garbage collection schemes and other memory management issues.

17. Concluding Exercise & Learning Goals Concluding exercise: Concluding exercise: Browse through each of the sections outlined in this presentation. Browse through each of the sections outlined in this presentation. As a guided tour through the course material.As a guided tour through the course material. Upon completing this course, you should be able to: Upon completing this course, you should be able to: Identify,Identify, Evaluate,Evaluate, Select suitable andSelect suitable and Reliable data structuresReliable data structures For a range of applications. For a range of applications.