csc 177 data warehouse and mining project pooja vora vishma shah guided by – prof. meiliu lu
TRANSCRIPT
Agenda
Data Warehouse Project Introduction Background Scope of study Implementation Data Cleaning and Preprocessing Data Mart
Data Mining Project Introduction Background Scope of study Implementation Data mining
Learning experience Future Scope References
Data Warehouse Introduction
• The objective of our project is to create a data mart with star schema
• Data mart will be used to find answers related to various company key factors and statistics.
Background
• Source website : Navathe company schema • Dataset :
• Company dataset
• Company dataset : Fact table - 7 attribtues,1000 entries
Scope Of Study
• Data Preprocessing • Microsoft Office Excel • Microsoft SQL Server
• Data Mart • Microsoft SQL server , Visio, convertCSVtoSQL
• Olap Operations• SQL server queries
Data Cleaning & Preprocessing
The company schema had different tables as per navathe , we also added few dimension for analytical processing and created a fact table with star schema.
Data Mart
• We have 5 dimension tables in our data mart and one fact table which forms star schema.
• The Fact table tables consists of around 1000 rows having various details about ssn, project, work_id etc
Data Mart Question-Answers
• How many products were produced over the months?
• Rollup
• How to find employee current working project?
• Slicing on employee dimension
• How to find the statistics of days where more than 5 products were produced
• Dicing on product and work dimension
• How to find which days and how many products of particular product were produced?
• Scoping
Olap Operations Example
• Roll Upselect t.date_year, t.date_month, sum(w.NumberOfProduct) as 'No. Of Products' from EmpFactTable f, DimTime t, DimEmp_work_record wwhere f.date_key= t.date_key and f.work_id = w.work_idgroup by date_year, date_month with rollup
date_year date_month No. Of Products2014 1 9802014 2 7612014 3 1274 2014 4 2402014 NULL 3255NULL NULL 3255
winning month
Introduction
• Perform Data mining on data set to discover knowledge
• Apply data mining algorithms using tools
• compare the performance of algorithms using these tools.
• Compare the tools performance
Background
• Source Website – www.data.gov
• Dataset :
• Consumer complaints
• Data:
- 14 attribtues, 55000 entries (Data from 2012 to 2014)
Scope Of Study
• Data Preprocessing• Microsoft Office Excel
• Tools (Weka, Rapidminer)
• Data Mining• Tools : Weka, Rapidminer
• Algorithms : K-Means, Naïve Bayes
Data Cleaning & Preprocessing
• Data Cleaning - Replaced missing values with “unknown”
• Data selection – Selected Consumer complaints data of two months (Sept , Oct) for mining
• Sample Data selected as 3000 rows
Data Mining
We have used One Classification & One Clustering Algorithm
Classification – Naïve Bayes
Clustering – K-means
Learning Experience
• Learned the analytical processing through data mart project.• Helped to improve knowledge for Database statistics• Learned to gain information out of the querying results. • Learned different data mining tools like weka and rapid
miner • Improved understanding of various algorithms and their
practical implementation through tools• Learned to make sense out of the results obtained from the
tools
Future Scope
• Data Warehouse
• Create a snowflake schema by introducing dimension like employee types contractors/Fulltime and then take it further for analytical processing for different statistics
• Data Mining
• Can implement other algorithms and tools like orange etc
References
• Elmasri and Navathe, Fundamentals of Database System, 6th Edition, Addison-Wesley Publishing
• OLAP Courseware http://athena.ecs.csus.edu/~olap/olap/introduction.php
• DM dataset http://www.data.gov/consumer/
• Data Mining Courseware http://athena.ecs.csus.edu/~datamini
• https://rapidminer.com/wpcontent/uploads/2013/10/RapidMiner_RapidMinerInAcademicUse_en.pdf