E-mail: ravi93@gmail.edu CS 315 Data Structures B. Ravikumar Office: 116 I Darwin Hall Phone: 664 3335 E-mail: ravi93@gmail.edu Course Web site: http://ravi.cs.sonoma.edu/cs315sp09

Textbook for the course: Data Structures and Algorithm Analysis in C++ by Mark Allen Weiss Class time Lecture: T Th 10:45 to 12 noon, Darwin Hall 102 Lab: F 9 to 11:50 AM, Darwin Hall # 25

Data Structures – informal definition How to store data sets in memory so that the computation(s) can be solved easily, efficiently etc. Some typical issues: preprocessing vs. updating trade-offs between operations interaction between algorithm and data structure

Course Goals Learn to use fundamental data structures: arrays, linked lists, stacks and queues hash table priority queue – binary heap and others binary trees, binary search trees, AVL trees quad-trees, skip-lists Improve your skill in programming in c++ recursion, classes, algorithm implementation design solutions using different data structures

Course Goals Analytical and experimental analysis quantitative reasoning about the performance of algorithms (time, storage, network, bandwidth etc.) comparing different data structures Applications image storage and manipulation image and data compression sorting and searching spelling checking, backtracking packet routing in a network geometric problems 5

Goals for today’s lecture Course outline Course work lab assignments projects tests, final exam quiz and class participation (new) If any time left, start discussing recursion.

Data Structures – key to software design Data structures play a key role in every type of software. Data structure deals with how to organize the data in the memory while solving a problem in order to reduce overall running time of a program response time (for queries) memory requirements Cost model for data access - array access, pointers

Abstract Data Structure (ADT), supported operations Dictionary search insert primary operations Delete deleteMin Range search Successor secondary operations Merge Priority queue Insert Merge, split etc. Secondary operations primary operations

Linear data structures key properties of the (1-d) array: a sequence of items are stored in consecutive memory locations. array provides a constant time access to k-th element for any k. (access the element by: Element[k].)

Linear data structures inserting at the end is easy. if the current size is s, then we can add x at the end using the single instruction: Element[s++] = x; deleting at the end is also easy. inserting or deleting at any other position is expensive. searching for a key is expensive (unless sorted). Expensive means the number of operations ~ size of the array. 10

Images stored in 2-dim arrays We will work on 2-d arrays by manipulating images: Each pixel is represented by a blue value, a red value and a green value (any color is a combination of these colors). (255, 255, 255) represents white, (255, 0, 0) represents red etc. pic(i , j)-> Blue represents the blue component of the i-th row, j-th column pixel of pic and so on. Some basic operations on images: open, read, write rotate, copy a sub-image filter (remove blemishes) extract features (identify where buildings are in an aerial photograph)