Part 1 You have been hired to design a web application for an online shop that sells video games. The system requires the following features: • Users can search the different video games available • Video games can be searched/filtered by: • Genre/Category • Platform (console/PC etc.) • Features • Price • Users can sign-up […]
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COMP9318: Data Warehousing and Data Mining — L2: Data Warehousing and OLAP — 1 n Why and What are Data Warehouses? 2 Data Analysis Problems n The same data found in many different systems n Example: customer data across different departments n The same concept is defined differently n Heterogeneous sources n Relational DBMS, OnLine
Data Representation Using binary numbers to represent information Data Representation ■ Goal: Store numbers, characters, sets, database records in the computer. ■ What we got: Circuit that stores 2 voltages, one for logic 0 (0 volts) and one for logic 1 (ex: 3.3 volts). CSE 12 Fall 2020 2 Storing Information Value Representation Value Representation
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Data Mining (EECS 4412) Data Preprocessing Parke Godfrey EECS Lassonde School of Engineering York University Thanks to Professor Aijun An for curation & use of these slides. 2 Process of Data Mining and KDD Pattern Evaluation and Presentation Pattern Extraction Data Preprocessing Data training data target data Goals of Prior Application Knowledge Feedback Data Reduction
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Data Mining Third Edition The Morgan Kaufmann Series in Data Management Systems (Selected Titles) Joe Celko’s Data, Measurements, and Standards in SQL Joe Celko Information Modeling and Relational Databases, 2nd Edition Terry Halpin, Tony Morgan Joe Celko’s Thinking in Sets Joe Celko Business Metadata Bill Inmon, Bonnie O’Neil, Lowell Fryman Unleashing Web 2.0 Gottfried Vossen,
CS 118 Discussion Week 2: The Application Layer Winter 2021 Overview • Brief introduction to the Application Layer • Protocols that live on the Application Layer • Most Important take-aways: • 1: Paradigms • 2: Protocols • 3: Particular uses * What is the Application Layer? • “An application layer is an abstraction layer that
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Detecting Functional Dependencies 21.5.2013 Felix Naumann 2 Overview ■ Functional Dependencies ■ TANE □ Candidate sets □ Pruning Algorithm □ Dependency checking □ Approximate FDs ■ FD_Mine ■ Conditional FDs Felix Naumann | Profiling & Cleansing | Summer 2013 3 Definition – Functional Dependency ■ „X → A“ is a statement about a relation R:
1. Aims Project 1 SQL The deadline for project 1 is: Thu 1 Apr, 5:00 pm (Sydney time) for Q1-Q12 Fri 9 Apr, 5:00 pm (Sydney time) for Q13 This project aims to give you practice in • reading and understanding a moderately large relational schema (MyMyUNSW). • implementing SQL queries and views to satisfy
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COMP9318: Data Warehousing and Data Mining — L7: Classification and Prediction — Data Mining: Concepts and Techniques 1 n Problem definition and preliminaries Data Mining: Concepts and Techniques 2 ML Map Data Mining: Concepts and Techniques 3 Classification vs. Prediction n Classification: n predicts categorical class labels (discrete or nominal) n classifies data (constructs a
Data Mining (EECS 4412) Sequential Pattern Mining Parke Godfrey EECS Lassonde School of Engineering York University Thanks to Professor Aijun An for creation & use of these slides. 2 Outline Basic concepts of sequential pattern mining A Simplified Version of GSP Algorithm PrefixSpan 3 An Example Sequence Database A sequence database consists of a set
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