1. Prof. Amr Goneid, AUC1 CSCE 210 Data Structures and Algorithms Prof. Amr Goneid AUC Part 1. Data Modeling and ADTs

2. Prof. Amr Goneid, AUC2 Data Modeling and ADTs Data Modeling Abstract Data types (ADTs) A Classification of Abstract Structures Another Classification Special Data Structures OOP and Classes Examples on Modeling

3. Prof. Amr Goneid, AUC3 1. Data Modeling Real-world applications need to be reduced to a small number of existing problems (top- down design) Real-world data need to be described in an abstract way in terms of fundamental structures

4. Prof. Amr Goneid, AUC4 Data Modeling The collection of data in some organization is called a “Data Structure” The sequences of operations to be done on the data are called “Algorithms”

5. Prof. Amr Goneid, AUC5 Data Modeling The word Algorithm comes from the name of Abu Ja’afar Mohamed ibn Musa Al Khowarizmi (c. 825 A.D.) An Algorithm is a procedure to do a certain task An Algorithm is supposed to solve a general, well- specified problem

6. Prof. Amr Goneid, AUC6 Data Modeling A real-world application is basically Data Structures + Algorithms

7. Prof. Amr Goneid, AUC7 Data Modeling Data and the Operations on that data are parts of an object that cannot be separated. These two faces of an object are linked. Neither can be carried out independently of the other.

8. Prof. Amr Goneid, AUC8 The Data Cone Real-world Data ADTs Data Structures Fundamental Data Types

9. Prof. Amr Goneid, AUC9 2. Abstract Data Types (ADTs) The most important attribute of data is its type. Type implies certain operation. It also prohibits other operations. For example, + - * / are allowed for types int and double, but the modulus (%) is allowed for int and prohibited for double. When a certain data organization + its operations are not available in the language, we build it as a new data type. To be useful to many applications, we build it as an Abstract Data Type.

10. Prof. Amr Goneid, AUC10 Abstract Data Types (ADTs) An ADT represents the logical or conceptual level of the data. It consists of: 1. A collection of data items in some Data Structure 2. Operations (algorithms) on the data items For example, a Stack supports retrieval in LIFO (Last In First Out) order. Basic operations are push and pop. It can be implemented using arrays (static or dynamic) or linked lists

11. Prof. Amr Goneid, AUC11 Abstract Data Types (ADTs) The Data Structure used in implementing an ADT is usually dependent on the language. In contrast, the definition of the ADT is separated from its implementation (Data Abstraction). (e.g. ADT Stack can be implemented using a static array, a dynamic array or a linked list. An ADT can be used in more than one application.

12. Prof. Amr Goneid, AUC12 Using ADT’s ADT Application Standard Types/LibrariesUser Built ADT’s

13. Prof. Amr Goneid, AUC13 3. A Classification of Abstract Structures According to the relationship between members Abstract Structures SetsLinearTreesGraphs

14. Prof. Amr Goneid, AUC14 Sets Order of elements does not matter. Only that they are members of the same set ({1,3,4} is identical to {1,4,3}). Can be implemented using arrays or linked lists. Used in problems seeking: groups collection selection packaging

15. Prof. Amr Goneid, AUC15 Linear Structures Sequential, one-to-one relationship. Examples: Tables, Stacks, Queues, Strings and Permutations. Can be implemented using arrays and linked lists (structs and pointers). Used in problems dealing with: Searching, Sorting, stacking, waiting lines. Text processing, character sequences, patterns Arrangements, ordering, tours, sequences.

16. Prof. Amr Goneid, AUC16 Trees Non-Linear, hierarchical one-to-many. Examples: Binary Trees, Binary Search Trees (BST) Can be implemented using arrays, structs and pointers Used in problems dealing with: Searching Hierarchy Ancestor/descendant relationship Classification

17. Prof. Amr Goneid, AUC17 Graphs Non-Linear, many-to-many. Can be implemented using arrays or linked lists Used to model a variety of problems dealing with: Networks Circuits Web Relationship Paths

18. Prof. Amr Goneid, AUC18 4. Another Classification of Abstract Structures According to their functionsSpecial Abstract Structures Containers Dictionaries Priority Queues Disjoint Sets Graphs Strings Geometric DS

19. Prof. Amr Goneid, AUC19 Containers Permit storage and retrieval of data items independent of content (access by location only). Support two basic operations: Put (x,C): Insert item x in container C Get (C): Retrieve next item from C.

20. Prof. Amr Goneid, AUC20 Containers Examples: Stacks: Last-In-First-Out (LIFO) structures Queues: First-In-First-Out (FIFO) structures Tables: Retrieval by position.

21. Prof. Amr Goneid, AUC21 Dictionaries A form of container that permits access by content. Support the following main operations: Insert (D,x): Insert item x in dictionary D Delete (D,x): Delete item x from D Search (D,k): search for key k in D

22. Prof. Amr Goneid, AUC22 Dictionaries Examples: Unsorted arrays and Linked Lists: permit linear search Sorted arrays: permit Binary search Ordered Lists: permit linear search Binary Search Trees (BST): fast support of all dictionary operations. Hash Tables: Fast retrieval by hashing key to a position.

23. Prof. Amr Goneid, AUC23 Priority Queues Allow processing items according to a certain order (Priority) Support the following main operations: Insert (Q,x): Insert item x in priority queue Q Remove (Q): Return and remove item with Highest/Lowest Priority

24. Prof. Amr Goneid, AUC24 Priority Queues Examples: Heaps and Partially Ordered Trees (POT) Major DS in HeapSort

25. Prof. Amr Goneid, AUC25 Disjoint Sets  Disjoint sets are collections of elements with no common elements between the sets.  A set can be identified by a parent node and children nodes.

26. Prof. Amr Goneid, AUC26 Disjoint Sets Support the following main operations: Find (i): Find Parent (set) containing node (i) Union (i,j): make set (i) the child of set (j) Examples: Representation of disjoint collections of data Representation of Trees, Forests and Graphs

27. Prof. Amr Goneid, AUC27 Graphs Can be used to represent any relationship and a wide variety of structures.

28. Prof. Amr Goneid, AUC28 Graphs Can be used to represent any relationship and a wide variety of structures. Well-known graph algorithms are the basis for many applications. Examples of such algorithms are: Minimum Spanning Trees Graph traversal (Depth-First and Breadth-First) Shortest Path Algorithms

29. Prof. Amr Goneid, AUC29 5. Special Data Structures Strings: Typically represented as arrays of characters. Various operations support pattern matching and string editing

30. Prof. Amr Goneid, AUC30 5. Special Data Structures Geometric Data Structures: Represent collections of data points and regions. Data points can represent segments. Segments can represent polygons that can represent regions.

31. Prof. Amr Goneid, AUC31 6. OOP & Classes The first step in creating an ADT is the process of Data Abstraction Data Abstraction provides a complete description of the following items independent of the way it will be implemented: A definition of the ADT. Elements or members of that ADT. Relationship between the members. The fundamental operations on the members.

32. Prof. Amr Goneid, AUC32 ADT Implementation Usually, an ADT can be implemented in different ways. To the applications, such implementation should be completely hidden. The Implementation part will describe: how the ADT will be implemented using native Data Structures or other pre-defined ADT’s in C++. how the relationships and fundamental operations on the members will be implemented as C++ functions. In Object Oriented Programming, ADTs are created as Classes

33. Prof. Amr Goneid, AUC33 OOP and Classes Object-Oriented Programming (OOP) focuses on creating ADT’s called “Classes” that identify “objects” and how they work together. A class contains “data members” + “function members” in one object. A member function tells an object to “operate on itself” in some way. Objects are “self-contained”, carrying their own operations.

34. Prof. Amr Goneid, AUC34 Data Encapsulation, Classes and Objects A Class of objects is a user-defined Abstract Data Type (ADT) An object is an instance of the class Once a class is defined, an object can be declared to be of that type. For example, we have encountered the string class before. Since it has been defined, we can declare: string message; So, now message is an object of that class Classes can be used by more than one program.

35. Prof. Amr Goneid, AUC35 Sharing Classes Class Program Standard ClassesUser Classes

36. Prof. Amr Goneid, AUC36 Classes & Encapsulation C++ classes are similar to structs, with the main difference being that classes can have member functions, or methods, as well as variables, or data members in their definitions. Combining data and operations (methods) together in an object is called encapsulation. An object of a class can operate on itself by the methods or member functions of that class. e.g., an object of class string can operate by:.find.length.at.erase etc

37. Prof. Amr Goneid, AUC37 7. Examples on Modeling Problem: In Encryption problems, we need to do arithmetic on very large integers (e.g. 300 digits or more) ADTs: List Data Structures: 1-D array or Linked List

38. Prof. Amr Goneid, AUC38 Examples on Modeling Problem: (Knapsack Problem) We have (n) objects each with a weight and a price and a container with maximum capacity (m). Select whole or fractions of the objects such that the total weight does not exceed (m) and the total price is maximum ADTs: List Data Structures: 1-D array or Linked List

39. Prof. Amr Goneid, AUC39 Examples on Modeling Problem: (Chess Games) 8-Queens problem, Knight’s Tour problem, etc ADTs: Board ADT Data Structures: 2-D array

40. Prof. Amr Goneid, AUC40 Examples on Modeling Problem: (Dictionary) We would like to build and use a small dictionary of words that translates from language (A) to language (B) ADTs: Key Table or List Data Structures: 1-D array or linked list

41. Prof. Amr Goneid, AUC41 Examples on Modeling Problem: (Small and fast Directory) We would like to build and use a small and fast directory to check username and pass word logins ADTs: Hash Table Data Structures: 1-D array

42. Prof. Amr Goneid, AUC42 Examples on Modeling Problem: (Large and fast Directory) We would like to build and use a large and fast telephone directory. ADTs: Binary Search Tree Data Structures: Linked Structure (Nodes)

43. Prof. Amr Goneid, AUC43 Examples on Modeling Problems: Evaluation of arithmetic expressions The Hanoi Towers game ADTs: Stack Data Structures: 1-D array or Linked List

44. Prof. Amr Goneid, AUC44 Examples on Modeling Problem: We would like to simulate the waiting process for airplanes to land in an airport. ADTs: Queue Data Structures: 1-D array or Linked List

45. Prof. Amr Goneid, AUC45 Examples on Modeling Problem: Sorting a set of elements Selection of the k th smallest (largest) element ADTs: Priority Queue Data Structures: 1-D array

46. Prof. Amr Goneid, AUC46 Examples on Modeling Problem: Find the shortest path between a source and a destination Find the exit in a Maze ADTs: Graph Data Structures: 2-D array or Linked list

47. Prof. Amr Goneid, AUC47 Examples on Modeling Problem: Find a wiring scheme for electrical power with minimum cost of wiring ADTs: Graph Priority Queue Disjoint Sets Data Structures: 1-D arrays, 2-D array, Linked list