” 辅导COMP SCI 4094程序、 辅导Database程序、 写作C++语言程序COMP SCI 4094/4194/7094 – Distributed Databases and Data MiningAssignment 1DUE: 23:59, Sunday, 13 September 2020Important Notes Handins: The deadline for submission of your assignment is 23:59, Sunday, 13 September2020. You must do this assignment individually and make individual submissions. Your program should be coded in C++ and pass test runs on the two test files.The sample input and output files are downloadable in Assignments of MyUni( httpss://myuni.adelaide.edu.au/courses/54718/assignments). You need to use svn to upload and run your source code in the web submission systemfollowing Web-submission instructions stated at the end of this sheet. You shouldattach your name and student number in your submission. Late submissions will attract a penalty: the maximum mark you can obtain will bereduced by 25% per day (or part thereof) past the due date. Marking scheme: 12 marks for testing on 4 randomly generated tests: 3 marks per test, where 1 markis for the affinity matrix AA, and 2 marks for the clustered affinity matrix CA. 3 marks for the structure of your code.If you have any questions, please send them to the student discussion forum. This way youcan all help each other and everyone gets to see the answers.The assignmentIn this assignment you are required to implement the Bond Energy Algorithm of vertical fragmentation.Your code should contains two separate procedures AA Generator and CA Generator,where AA Generator takes the input of all attributes of a relation, a set of queries and theiraccess frequencies at different sites, and produces the output of an affinity matrix AA, and CAGenerator takes input of an affinity matrix AA and produces a clustered affinity matrix CA. Fordescription of the BEA algorithm, definitions of AA and CA, please see lecture slides/textbook.In this assignment, the Attribute Affinity is measured by the extended Otsuka-Ochiai coef-ficient ( httpss://en.wikipedia.org/wiki/Yanosuke Otsuka) instead of the traditional method describedin the textbook. The following Equations show the details of the computation, where qis the number of queries, and m is the number of sites, Aik is the number of times Attribute Aiis accessed by Query qk, considering of all sites. For the result of division, you must round it upto the nearest integer. 辅导COMP SCI 4094作业、 辅导Database作业aff(Ai, Aj ) = dPqk=1 Aik AjkqPqk=1 Aik Pqk=1 Ajke,Aik = use(qk, Ai) Xmj=1acc matrix(qk, Sj ).ExampleFor AA Generator:Input The relation, called PROJ, has the following features Ai:Label NameA1 PNOA2 PNAMEA3 BUDGETA4 LOC Queries (qi):q1: SELECT BUDGET FROM PROJ WHERE PNO=Valueq2: SELECT PNAME, BUDGET FROM PROJq3: SELECT PNAME FROM PROJ WHERE LOC=Valueq4: SELECT SUM(BUDGET) FROM PROJ WHERE LOC=Value Access frequency Matrix ACC, where Si denotes the i-th site:S1 S2 S3q1 15 20 10q2 5 0 0q3 25 25 25q4 5 0 0Output The attribute affinity matrix AA:A1 A2 A3 A4A1 45 0 41 0A2 0 71 1 71A3 41 1 38 1A4 0 71 1 71For CA Generator:Input The attribute affinity matrix AA:A1 A2 A3 A4A1 45 0 41 0A2 0 71 1 71A3 41 1 38 1A4 0 71 1 71Output The attribute affinity matrix CA:A1 A3 A4 A2A1 45 41 0 0A3 41 38 1 1A4 0 1 71 71A2 0 1 71 71Web-submission instructions First, type the following command, all on one line (replacing xxxxxxx with your studentID):svn mkdir – -parents -m DDDM httpss://version-control.adelaide.edu.au/svn/axxxxxxx/2020/s2/dddm/assignment1 Then, check out this directory and add your files:svn co httpss://version-control.adelaide.edu.au/svn/axxxxxxx/2020/s2/dddm/assignment1cd assignment1svn add AAGenerator.cppsvn add CAGenerator.cppsvn commit -m assignment1 solution Next, go to the web submission system at: httpss://cs.adelaide.edu.au/services/websubmission/Navigate to 2020, Semester 2, Distributed Databases and Data Mining, Assignment 1.Then, Click Tab Make Submission for this assignment and indicate that you agree to thedeclaration. The automark script will then check whether your code compiles. You canmake as many resubmissions as you like. If your final solution does not compile you willnot get any marks for this solution. Note:i. The auto-marker script compiles and runs the two cpp files named AAGenerator.cppand CAGenerator.cpp one by one.ii. The auto-marker script Will compile your AAGenerator.cpp and CAGenerator.cpp bythe following command:g++ -std=c++11 AAGenerator.cpp -o runAAg++ -std=c++11 CAGenerator.cpp -o runCAiii. Your AAGenerator.cpp should accept three input text files in the order of Attributes(att), Queries (query) and Access Frequencies (acc), which are randomly generatedby the system, then output and print the required attribute affinity matrix (aa).Your CAGenerator.cpp should accept input affinity matrix (aa) provided by the systemrather than reading your AAGenerators output AA, then output and print theclustered affinity matrix (CA) as the output. In this way of testing AA and CAseparately, your marks Will be maximized you will receive marks for your correctCAGenerator coding even if your AAGenerator produces incorrect AA.iv. The file path and the file name in your local machine will not work with our websubmissionsystem.如有需要，请加QQ：99515681 或邮箱：99515681@qq.com
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