DATA STRUCTURE & ALGORITHMS (BCS 1223)

1. DATA STRUCTURE & ALGORITHMS(BCS 1223) NURUL HASLINDA NGAH SEMESTER 2 2013/2014

2. What is Data Structure? • Physically, RAM is a random accessible array of bits. • The computer will interpret bits as an address (pointer)

3. What is Data Structure? Digital data must be: • Encoded (binary  wave) • Arranged (stored in an orderly way in memory / disk) • Accessed (insert new data, remove old data, find data matching some condition) • Processed (algorithms: shortest path, etc)

4. Data Structuring • How do we organize information so that we can find, update, add and delete portions of it efficiently

5. Data Structure Example Application • How does Google quickly find web pages that contain a search term? • What’s the fastest way to broadcast a message to a network of computers? • How can a subsequence of DNA be quickly found within the genome? • How does your operating system track which memory (disk or RAM) is free?

6. So, what is Data Structure? • Its an agreement about: • How to store a collection of objects in memory • What operations we can perform for those operations • How time and space efficient those algorithms are

7. Data Organization Principles • Ordering • Put keys into some order so that we know something about where each key is relative to the other keys • Phone books are easier to search because they are alphabetized

8. Data Organization Principles • Linking • Add pointers to each record so that we can find related records quickly • E.g. the index in the back of book provides links from words to the pages on which they appear

9. Data Organization Principles • Partitioning • Divide the records into 2 or more groups, each group sharing a particular property • E.g. Multi-volume encyclopedia • E.g. Folders on your hard drive

10. Data Structure in Data Design • Data structure consists of files or tables that are linked in various ways • A file or table contains data about people, places or events or any important data that the business must kept information about • Depending on how the system’s file and tables are organized and linked, an (IS) is called either • File-oriented system: also called a file processing system, stores and manages data in one or more separate files. • Database system: consists of linked data files, also called tables, that form an overall data structure. Compared to file processing, a database environment offers greater flexibility and efficiency.

11. File Processing • Companies mainly use file processing to handle large volumes of structured data on a regular basis • File processing design approach was well suited to mainframe hardware and batch input • File processing design is less common today, file processing can be more efficient and cost less than a DBMS in certain situations

12. File Processing Advantages • Simplicity: the design of file processing is more simple than designing Database • Efficiency: file processing cost less and can be more speed than Database • Customization: you can customize file processing more easily and efficiently than Database because files are related with the application and it have all the data needed for that application

13. File Processing Problems • Data redundancy: occurs when data common to two or more information systems is stored in several places. Data redundancy requires more storage space, and maintaining and updating data in several locations is expensive. • Data integrity: Refers to the validity of data. Data integrity can be compromised in a number of ways: human errors when data is entered, errors that occur when data is transmitted from one computer to another, software bugs or viruses, hardware malfunctions, such as disk crashes and natural disasters, such as fires and floods. • Rigid data structure: A data structure that is hard to work with and inflexible. File-processing is rigid when compared to a typical database management system.

14. Database • A Database is a collection of stored operational data used by the application systems of some particular enterprise (C.J. Date) • Paper “Databases” • Still contain a large portion of the world’s knowledge • File-Based Data Processing Systems • Early batch processing of (primarily) business data • Database Management Systems (DBMS)

15. Database

16. Advantages of Database • Scalability • Better support for client/server system • Economy of scale • Flexible data sharing • Controlled redundancy • Better security • Data independence

17. ADT • An abstract data type (ADT) is a mathematical model for a certain class of data structures that have similar behavior; or for a certain of one or more programming languages that have similar semantics • It is defined indirectly, only by the operations that may be performed on it and by mathematical constraints on the effect of those operations

18. ADT • For example, an abstract stack could be defined by three operations: • Push : insert some data item onto the structure • Pop: extracts an item from stack • Peek/Top: allows data on top of the structure to be examined without removal

19. Advantages of ADT • Encapsulation – it provides a promise that any implementation of ADT has certain properties and abilities. Users does not need any technical knowledge of how the implementation works to use the ADT • Localization of change – code that uses an ADT object will not need to be edited if the implementation of the ADT is changed. • Flexibility – different implementation of ADT, having all the same properties and abilities, are equivalent and may be used somewhat interchangeably in code that uses the ADT.

20. Objects & Classes • One of the benefits of object oriented programming approach is we can define the object and class • A class is used to specify the form of an object and it combines data representation and methods for manipulating that data into one neat package. The data and functions within a class are called members of the class.

21. Example of class & object in C++ class Box { public: double length; // Length of a box double breadth; // Breadth of a box double height; // Height of a box };

22. Algorithms • Algorithm is a well defined computational procedure that takes some value or a set of values, as input & produces some value or a set of values, as output • A sequence of computational steps that transform the input into the output

23. Example of Algorithm • Adding into stack procedure add(item : items); {add item to the global stack stack; top is the current top of stack and n is its maximum size} beginif top = n then stackfull;     top := top+1;     stack(top) := item; end: {of add}

24. Example of Algorithm • Recursive Algorithms int power( int num, int exponent ) { if ( exponent == 1 ) return num; else return num * power( num, exponent - 1 ); }

25. Data Structure Techniques • Linear Data Structure • Array – dynamic, sparse, matrix, etc. • Linked List – unrolled, doubly, Vlist, etc. • Stack & Queue • Associate array – hash, etc. • Non-linear data structure • Graph data structure – scene graph, binary tree, heap, etc. • Tree data structure – search tree, syntax, etc.

26. End of chapter