CENG0023 Algorithms for Web Data Analysis

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code CENG0023
Name Algorithms for Web Data Analysis
Term 2022-2023 Academic Year
Term Spring
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language İngilizce
Level Doktora Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator
Course Instructor
1


Course Goal / Objective

The aim of this course is to design and analyze algorithms for processing data on the Web.

Course Content

The course covers algorithms including information retrieval and web search, pagerank, hits, mapreduce, crawling algorithms, structured data extraction, information integration, and web usage mining.

Course Precondition

As prerequisite of this course the instructor expects that the students have strong algorithm design, analysis, and implementation background.

Resources

Bing Liu, “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data”, Second Edition, July 2011.

Notes

Recent papers about the course content


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Identifies algorithms that can be used for web data analysis.
LO02 Explains recent developments about Web data analysis.
LO03 To be able to analyze algorithms for processing data on the Web.
LO04 To be able to apply algorithms for processing data on the Web.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. 3
PLO02 Bilgi - Kuramsal, Olgusal By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. 3
PLO03 Yetkinlikler - Öğrenme Yetkinliği Being aware of the new and developing practices of his / her profession and examining and learning when necessary. 4
PLO04 Yetkinlikler - Öğrenme Yetkinliği Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions.
PLO05 Yetkinlikler - Öğrenme Yetkinliği Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. 2
PLO06 Yetkinlikler - Öğrenme Yetkinliği Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design.
PLO07 Beceriler - Bilişsel, Uygulamalı Has the skills of learning.
PLO08 Beceriler - Bilişsel, Uygulamalı Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. 3
PLO09 Beceriler - Bilişsel, Uygulamalı Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering. 1
PLO10 Beceriler - Bilişsel, Uygulamalı Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. 2
PLO11 Beceriler - Bilişsel, Uygulamalı Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. 3
PLO12 Bilgi - Kuramsal, Olgusal Observes social, scientific and ethical values in all professional activities.


Week Plan

Week Topic Preparation Methods
1 Introduction to information retrieval systems Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Tartışma
2 Structure of Web search engines Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Tartışma
3 Pagerank and hits algorithms Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Tartışma
4 Mapreduce algorithm and its applications Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Tartışma
5 Web crawling algorithms Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Tartışma
6 Structured data extraction algorithms Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Tartışma
7 Data integration algorithms Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Tartışma
8 Mid-Term Exam Reading the lecture notes Ölçme Yöntemleri:
Ödev
9 Web usage mining Reading the lecture notes Öğretim Yöntemleri:
Anlatım, Tartışma
10 Opinion mining Literature survey, preparing presentation Öğretim Yöntemleri:
Örnek Olay, Soru-Cevap
11 Sentiment analysis Literature survey, preparing presentation Öğretim Yöntemleri:
Örnek Olay, Soru-Cevap
12 Social network analysis Literature survey, preparing presentation Öğretim Yöntemleri:
Örnek Olay, Soru-Cevap
13 Focused crawling Literature survey, preparing presentation Öğretim Yöntemleri:
Örnek Olay, Soru-Cevap
14 Crawler ethics and conflict resolution Literature survey, preparing presentation Öğretim Yöntemleri:
Örnek Olay, Soru-Cevap
15 Project presentation Coding of the selected algorithm, experimental analysis Öğretim Yöntemleri:
Proje Temelli Öğrenme
16 Writing the project report Preparing the project report Ölçme Yöntemleri:
Proje / Tasarım, Sözlü Sınav
17 Term Exams Preparing the project report Ölçme Yöntemleri:
Proje / Tasarım, Sözlü Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 5 70
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 15 15
Final Exam 1 30 30
Total Workload (Hour) 157
Total Workload / 25 (h) 6,28
ECTS 6 ECTS

Update Time: 16.11.2022 07:43