Need someone to create a source code for a detailed University Database project. I'll handle powerpoint/report.

You should choose some part or all of your project model and implement them with MySQL, php, or other tools. All source code needs to be uploaded to BlackBoard with the instructional documents to verify it.

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Mar 7, 2020 MCIS 5115 – Data Base Management System MCIS 5113 – DBMS Project Assignment spring 2020. Ver. 1 1. Objectives The project aims to improve the understanding of relational databases by analyzing the work being used in real life and designing a relational DBMS model. This is an individual project and you can choose the field of development according to your preferences. For the output of the project, the project report, the project demonstration, and the source code implemented should be submitted. 2. What you will need to complete this assignment: 1. Project Report - 200 2. Project Demonstration -200 3. Project Implementation -200 4. Example Topics a. University Database project b. Payroll Management System Database project c. Health related DBMS Project d. Salary management DBMS e. Medicine Management DBMS f. Any other DBMS Project determined by group members Project Report: 15 -20 Pages The project proposal should include the following information: Project Title & Author Information Abstract 1. Introduction 2. E-R Modeling 3. Logical Database Design and the Relational Model 4. Physical Database Design 5. Implementation methodology 5.1. Create table with MySQL 5.2. Retrieve it with web applications (php, xml, jsp, or others) 6. Experimental Results 7. conclusion References Mar 7, 2020 MCIS 5115 – Data Base Management System Final Project Demonstration: 20 - 25 Slides Each of you must prepare a 5 Minutes presentation 1. Prepare sides to describe your project 2. Present your work using PPT slides with audio recording Project Implementation: You should choose some part or whole of your project model and implement them with MySQL, php, jsp or other tools. All source code need to be uploaded to the Black board with the introductional documents to verify it. Academic Integrity Policy: Academic integrity at SAU is an organizational and individual responsibility to honesty in all learning experiences. Any act of dishonesty in academic work constitutes academic misconduct and is subject to disciplinary action. Acts of dishonesty include, but are not limited to: A.Plagiarism--the act of taking and/or using the ideas, work, and/or writings of another person as one’s own. B. Cheating--an act of dishonesty with the intention of obtaining and/or using information in a fraudulent manner. Fabrication--faking or forging a document, signature or findings of a research project

A Study of Heuristically-based Parametric Performance Optimization Algorithms for BigData Computing Jongyeop Kim ( 99999) Departement of Math and Computer Science, Southern Arkansas University 100 E University, Magnolia, AR 71753, USA. JongyeopKim@saumag.edu Abstract. Performance optimization for mapreduce computing in Hadoop platform is a tedious yet challenging problem due to the complexity of system organization with an extensive list of configuration parameters to be considered. In order to address and resolve this problem, various parameter optimization algorithms are proposed in this research from a naive exhaustive method to a random and a couple of heuristically-based greedy methods to vie with the exponentially nature of the search process for the possible best parameter setting. In the course of exercising those algorithms, there are a few variables to be taken into consideration in order to make each algorithm be a viable option for the given other variables provided such as degree of the arity of each parameter that determines the degree of the base of the search process time, sampling methods in order to relax the complexity of the search process under control or the budget. Keywords: Big Data, Hadoop, Configuration, Performance tuning. 1. Introduction This research concerns about the performance optimization or improvement of computation running on top of the platform, namely Apache Hadoop in particular. However, the methods and algorithms proposed in this research supposed to be applicable to other platforms as well without loss of generality. The diagram below [1] shows the computation flow on Hadoop, which primarily consists of one name node and multiple slave nodes. A MapReduce process [2] is composed of two primary functions as follows: 1) Map function: takes a set of input/key value pairs and produces a set of intermediate ouput key/value pairs; 2) Reduce function: takes intermediate key2 and a set of values for the key as illustrated in the following. Map (Key1, Value1) -> List (Key2, Value 2) Reduce (Key2, List(Value2)) -> List(Value2 2. E-R Modeling A naïve exhaustive method [7] has been proposed as an initial attempt to the problem of this research and its pros and cons are summarized in the following table. The pros of this approach is simple and exhaustive. Cons of this approach is, as more parameters are considered, the benchmark time grows exponentially. And the method is incapable of further optimization due to limited range of parameter values. The cost of this approach is asymptotically a^p, where a is the number of values of parameter under consideration and p is the number of parameters. Thus, the cost of the method is definitely upper bounded by exponential time. 3. Logical Database Design and relation model In this section, it is explained the results of research and at the same time is given the comprehensive discussion. Results can be presented in figures, graphs, tables and others that make the reader understand easily. The discussion can be made in several sub-chapters. A naïve exhaustive method has been proposed and a 2-Phased Exhaustive Sorting-based algorithm has been developed and implemented [7]. The algorithm consists of two phases of sorting: the first phase sorting is performed in order to obtain an extended list (e.g., top 50 parameter settings) of top performing parameter settings for a rough sorting against a small size of data; and then the second phase sorting is performed for detailed sorting against a large size of data instead for a short list (e.g., top 5 out of the above 50) of top performing parameter settings. The flow of the approach [8] is shown in the figure below. 4. Physical Database Design The random method [8] performs r rounds of random sampling and each round samples a number of values from the given range that is supposed to be greater than the a in the naïve exhaustive. The following is a pseudo code for the main routine to implement the proposed random algorithm. Each of the p parameters samples one from a number of values during each of the r rounds of selection. 5. Implementation methodology Flow diagram of the proposed heuristically-based algorithm is shown below. During the parameter generation step (B) p number of parameters are selected. With those parameters, TeraSort benchmark program is executed and during (C) Get results function, the results are collected and saved into the Omega space (i), During (D), the best performing parameter setting is selected and fixed out in a greedy manner. This iterates p number of rounds. 6. Experimental Results Experimental Results Ω (0) · i.s.r.p decreased by -20% and fixed out · CPU time reduced by -17.68% · Cost : a * p ( a=18, p =7 ) Experimental Results Ω (1) · m.j.s.m.p decreased by -40% and fixed out · CPU time reduced by -16.73% · Cost : a * (p -1) ( a=18, p =7 ) 7. Conclusion This research has proposed various parametric performance optimization algorithms to achieve the possible best performance in MapReduce computing on a Hadoop platform. There are three representative algorithms proposed with their pros and cons concluded as follows: - Naive Exhaustive: O (a^p) Infeasibly best performance yet very expensive - Random: O (1*p*r) ≈ O(p^2) Feasibly moderate performance and inexpensive - Heuristically-based: O (p^3) Feasibly great performance and reasonably inexpensive Naive exhaustive parametric optimization took O(a^p) amount of time and the resulting performance of the program is definitely supposed to be the best but benchmarking is infeasible and very expensive. Thus, this is not considered as a viable solution. Random parametric optimization took approximately quadratic time and it is considered as the least inexpensive approach. However, the resulting performance of the benchmark is quite limited to the average. Lastly, the proposed heuristically-based parametric optimization took slightly over the quadratic time or at most the cubic time and the resulting performance is also promising with feasible optimization time. The proposed heuristic employed a greedy approach, and the resulting performance is great instead of optimal since it doesn’t guarantee the optimal performance References [1]."Hadoop Distributed File System Architecture." http://hadoop.apache.org/docs/current/ hadoop-project-dist/hadoop- hdfs/images/hdfsarchitecture.png. Web. 20 Jan. 2015. [2]. Ghemawat, Sanjay, Howard Gobioff, and Shun-Tak Leung. "The Google file system." ACM SIGOPS operating systems review. Vol. 37. No. 5. ACM, 2003. [3]. "The Fresh Open Source Software Archive." Http://fossies.org/linux/misc/hadoop-1.2.1.tar.gz/hadoop-1.2.1/docs/mapred-default.html. Web. 20 Jan. 2015. [4]. Noll, Michael G. "Benchmarking and stress testing a Hadoop cluster with TeraSort, TestDFSIO & co." 2011.4. http://www. michael-noll. com/blog/2011/04/09/benchmarking-and-stress-testing-an-hadoop-cluster-with-terasort-testdfsio-nnbench-mrbench (2011). [5]. Hadoop 1.2.1 Documentation, mapred-dedault configuration parameters. https://hadoop.apache.org/docs/r1.2.1/mapred-default.htm, Web, 20 Jan. 2015. [6]. T. White, Hadoop: The definitive Guide. O’Reilly Media,Inc.,2012, pp 166-168. [7]. Kim, Jongyeop, et al. "Performance evaluation and tuning for MapReduce computing in Hadoop distributed file system." Industrial Informatics (INDIN), 2015 IEEE 13th International Conference on. IEEE, 2015. [8]. Jongyeop Kim and Nohpill Park. "Identifiation of the Optimal Hadoop Configuration Parameter sets for MapReduce Computing", 3rd International Conference on Applied Computing & Information Technology, July 2015. 1 2 3 Ω (1)10%20%30%40%50%60%70%80%90% i.s.m4.49.11-13.25.543.944.71-14.677.678.24 i.s.s.p6.72-16.682.965.259.19-11.588.75-16.473.61 i.s.f-12.766.082.066.644.075.85-12.827.13-10.42 m.j.r.i.b.f5.546.77-13.053.326.545.568.373.55.31 m.j.s.m.p7.34-10.422.786.17.476.8-11.86-13.076.15 m.j.s.i.b.p3.45.828.732.047.425.925.387.11-10.71 Ω(1)%ParIdxP(0)P(1)P(2)P(3)P(4)P(5)P(6)CPUdiff +4041000.040.8100.420.660.7401401290.71 -4051000.040.8100.10.480.732350-6499.3 +4051000.040.8100.10.640.7412202370.71 Ω (1)-90%-80%-70%-60%-50%-40%-30%-20%-10% i.s.m2.866.83.46.446.826.318.57-13.645.54 i.s.s.p-13.876.318.684.252.537.75.696.95-14.28 i.s.f-12.286.938.340.935.154.761.146.625.79 m.j.r.i.b.f3.996.282.62.326.314.662.066.629.4 m.j.s.m.p7.21-14.265.926.87.6-16.734.483.04-9.39 m.j.s.i.b.p5.772.918.09-12.566.64-10.356.98-10.46.8

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