Answered: By using the list-processing algorithm,…

By using the list-processing algorithm, how long will it take if the tasks are scheduled using 2 processors and using the priority list: Т5, T9, T1, т3, т2, Т6, т8, т4, т7

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Suppose Dr. WhyLie comes up to you and claims that he has invented a super-fastcomparison based priority queue. The speed of the priority queue operations are as follows(n is the number of items currently in the priority queue):a. insert a new item in O(sqrt(log n)) timeb. extract (remove and return) the smallest item from the priority queue in O(sqrt(logn)) time.Explain why Dr. WhyLie must be lying.
1. Which of the following sorting algorithms has the fastest average time complexity? Group of answer choices Selection Sort Insertion Sort Merge Sort 2. We use dynamic programming approach when the solution has optimal substructure. Group of answer choices True False 3. Which one is the data structure used in standard implementation of Breadth First Search? Group of answer choices Stack Queue Linked List 4. What is the Time Complexity of Depth First Search? (V – number of vertices, E – number of edges) Group of answer choices O(V+E) O(V*E) O(V) O(E) 5. What is the output of the below code? np.arange(2,8) Group of answer choices array([2, 3, 4, 5, 6, 7]) array([2, 3, 4, 5, 6, 7, 8]) array([3, 4, 5, 6, 7, 8])
Given a sequence of integers, e.g., \[ 9,-1,45,6,8,21,34,5,55,65,543,18,90,122,132,0,66,100,-12,17 \] design an algorithm/method to re-arrange the order of the data items (i.e., form a new sequence of the integers) so that when the data items are inserted sequentially into an initially empty BST, the newly created BST will be a balanced BST. a) Design the algorithm (in pseudo code) and analyse your algorithm. b) Write a Python function/method to implement your algorithm. c) Write a program that calls the above function, thereby demonstrating your algorithm by (i). Print the re-arranged data items (i.e., a new sequence) generated by the algorithm, and (ii). Build a BST using the newly generated data sequence as input (that is, insert the data items, oneby-one, into an initially empty BST). Print the tree shape of the BST. (Note: The program should use at least two datasets to demonstrate the algorithm, one of which is the dataset given above).
What is the time and space complexity of this function? It is a dfs function which goes through every possible path from node to another without cycles. Time complexity could be either O(n!) or O(2^n), or is there another answer? What is the space complexity? def dfs(currency_pairs, source, target): graph = defaultdict(dict) for s1, s2, rate_val1, rate_val2 in currency_pairs: graph[s1][s2] = rate_val1 graph[s2][s1] = rate_val2 def backtrack(current, seen): if current == target: return 1 product = 0 if currentingraph: neighbors = graph[current] for neighbor in neighbors: if neighbor not in seen: seen.add(neighbor) product = max(product, graph[current][neighbor] * backtrack(neighbor, seen)) seen.remove(neighbor) return product return backtrack(source,…
Instead of a single link list, if a circular and double link list is used, namely, the list is a circle and can move both ways, what is the optimal number of skip nodes (express stations) for N original nodes (local stations)? Justify your conclusion with mathematical details
When choosing the last item of a sequence as the pivot, the QuickSort algorithm will yield its worst case performance of O(n2) when it is passed a sorted list as input. Using the media-of-three approach for pivot select, the QuickSort algorithm will execute on an already-sorted list in O(nlog n). Explain why Quicksort runs in O(n2) on an already-sorted list when choosing the last item of the sequence as the pivot. You must provide a recurrence relation or recursion tree as part of your answer. Provide an input sequence of 10 integers that, when passed to the QuickSort algorithm, would lead to the worst case running time of O(n2) whether pivot selection is performed using the median-of-three approach, or the pivot is selected as the last item in the sequence.
Consider a scheduling problem, where there are five activities to be scheduled in four time slots. Suppose we represent the activities by the variables A, B, C, D, and E, where the domain of each variable is {1, 2, 3, 4} and the constraints are A > D, D > E, C ̸= A, C > E, C ̸= D, B ≥ A, B ̸= C, and C ̸= D + 1. Show how backtracking solves this problem. To do this, you should draw the searchtree generated to find all answers. Indicate clearly the valid schedule(s) aka solution(s).Make sure you choose a reasonable variable (and value) ordering if needed, and mention them explicitly.
Given a jungle matrix NxM: jungle = [ [1, 0, 0, 0], [1, 1, 0, 1], [0, 1, 0, 0], [1, 1, 1, 1,] ] Where 0 means the block is dead end and 1 means the block can be used in the path from source to destination. Task: Starting at position (0, 0), the goal is to reach position (N-1, M-1). Your program needs to build and output the solution matrix – a 4x4 matrix with 1’s in positions used to get from the starting position (0,0) to the ending position (N-1,M-1) with the following constraints: You can only move one space at a time You can only in two directions: forward and down. You can only pass thru spaces on the jungle matrix mark ' 1 ' If you cannot reach the ending position - print a message that you're trapped in the jungle Algorithm: If destination is reached print the solution matrix Else Mark current cell in the solution matrix Move forward horizontally and recursively check if this leads to a solution If there is no solution, move down and recursively check if this leads to…
Given the following sequence of arrivals and called to the counter, what will be the content of the following queue: 1. Alia 2. Shaari 3. Alwee 4. Marion 5. Call from counter 6. Aliffa 7. Call from counter 8. Call from counter 9. Shukri 10. Leon Select one: a.Marion, Aliffa, Shukri, Leon b.Alia, Shaari, Alwee, Marion c.Alia, Shaari, Shukri, Leon d.Alia, Shaari, Alwee, Marion, Aliffa, Shukri, Leon
Which of these two algorithms has a better big-O run-time? For each, list the big-O run-time notation for each and how you determined it. def gnomesort(seq): i=0 while i<len(seq): if i==0 or seq[i-1]<=seq[i]: i+=1 else: seq[i], seq[i-1] = seq[i-1], seq[i] i-=1 and def mergesort(seq): mid=len(seq)//2 l,r=seq[:mid],seq[mid:] if len(l)>1: l=mergesort(l) if len(r)>1: r=mergesort(r) res=[] while l and r: if l[-1] >= r[-1]: res.append(l.pop()) else: res.append(r.pop()) res.reverse() return (l or r) + res
Recall that histograms are used for constructing load-balanced range partitions. Suppose you have a histogram where values are between 1 and 100, and are partitioned into 10 ranges, 1–10, 11–20,...,91–100, with frequencies 15, 5, 20, 10, 10, 5, 5, 20, 5, and 5, respectively. Give a load-balanced range partitioning function to divide the values into 5 partitions. Write an algorithm for computing a balanced range partition with p partitions, given a histogram of frequency distributions containing n ranges.
Consider a system with four page frames and a program that uses eight pages. Consider the reference string 0 1 7 2 0 3 1 7 0 1 7 and assume that all four page frames are initially empty. Consider the three following algorithms: a. Optimal page replacement b. FIFO page replacement c. LRU page replacement In each cases show a diagram (ASCII art recommended) that shows which pages are in which frames throughout time, and page faults at the bottom. For instance: would mean that at step 1 page 0 is referenced and loaded into frame 0, which is a page fault; then page 1 is referenced, and so on. The goal is to fill this table to see what in memory when and thus infer the number of page faults. The first 4 steps are ALWAYS the same, as above (i.e., just fill the four frames, with one page fault each time)