Arrays, Arraylists and 2D Arrays Questions

B.9 Programming Environments and Related Costs
Programming environment:
Python 3.9 along with Numpy, Pandas, Seaborn, matplotlib. pyplot, ipywidgets, and
default Python libraries
A file converter to convert any common database file types into CSV files
Sta

Computer Science Application

Arrays, Arraylists and 2D Arrays Solutions

B.9 Programming Environments and Related Costs Programming environment: Python 3.9 along with Numpy, Pandas, Seaborn, matplotlib. pyplot, ipywidgets, and default Python libraries A file converter to convert any common database file types into CSV files Statistics tracking software for tracking usage statistics during the first deployment There are no associated costs with the programming environment as all of these tools are available as no-cost open-source software. Environment Costs: We will need workstations and storage hardware to facilitate the deployment phases. Our developers will need workstations that can connect to a central network to begin programming, testing, and collecting data analytics, We are budgeting $25,000 to the environment costs and these costs will be accounted for in the first installment payment Human Resource Requirements We require a staff of six developers to complete the project. They will each be paid a monthly salary of $5,000 per month The estimate time of completion is two months so a total of $60,000 of the total budget should go towards employee salaries. Phase one (Deployable application): 120 development hours – total cost $150,000 (this includes the equipment cost and the first month salary payments). Phase two (Major Feature Updates and Bug Fixes): 160 development hours + 1 week of allocated backlog - total cost: $350,000. The additional costs for this phase are incurred from the following o More developer hours o Deployment costs associated with getting the product to customers o Hiring outside testers o Increased hardware requirements needed to scale the application o A reserve for unseen costs

6. Discuss the strengths and weaknesses of the self-
adjusting data structures (e.g., the hash table).
The chaining hash table ensures that we never run out of
space and there will be no hash collision. As we scale up
the entries, all that happens is that

Computer Science Application

Arrays, Arraylists and 2D Arrays Solutions

6. Discuss the strengths and weaknesses of the self- adjusting data structures (e.g., the hash table). The chaining hash table ensures that we never run out of space and there will be no hash collision. As we scale up the entries, all that happens is that the linked lists just keep on growing. Insertion and deletion are extremely simple to implement-indeed. As we know chaining hash is simply an array of linked lists. The problem here is that the linked lists grow longer and longer and longer. It will make grow the worst-case space-time complexity at the rate of O(n) for n items in the linked list. This can be eliminated by increasing the size of the hash as the packet numbers grow. D. Identify a self-adjusting data structure, such as a hash table, that can be used with the algorithm identified in part A to store the package data. We used the chaining hash table with linear probing for searching as a self-adjusting data structure for the current solution. The main data structure used is a chaining hash table. It can perform the insertion and deletion as fast as direct hashing. While the searching is comparatively slow with direct hashing, it ensures no hash collision and storage efficiency. It is an array of linked lists; it can expand automatically as needed. The “Get_fastest_route” algorithm uses the unique package id as a key for the hash table. The hash table has four basic functions: insert, update, delete and search. In the first stage of the algorithm, the insert inserts all 40 packets in the hash table by packet id as a key. The packet is used in stages 2 and 3 for searching and sorting those packets further. The only downside of chaining hashing is slow searching, which can be eliminated by increasing the size of the hash as the packet numbers grow.

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