Course code InfT5052
Credit points 3
Internet Search Techniques
Total Hours in Course81
Number of hours for lectures12
Number of hours for seminars and practical classes12
Independent study hours57
Date of course confirmation12.04.2021
Responsible UnitInstitute of Computer Systems and Data Science
Course developer
Ivars Mozga
Dr. sc. ing.
Course abstract
The aim of the course is to provide basic knowledge and understanding of Internet information retrieval techniques. Students will gain theoretical and practical knowledge of the basics of document processing and text retrieval, principles and algorithms of search tools, as well as methods of web content optimization for search engines.
Learning outcomes and their assessment
After completing the course students:
• will be able to define and explain the principles of Information Retrieval, Internet search tools and web information retrieval.
• will be able to analyse and compare information search models, technologies and principles; algorithms of Internet search; web information retrieval techniques and tools.
• will be able to apply the techniques and tools covered in the course to simulate a simple Internet search system, and develop search/engine friendly websites
• will be able to measure and evaluate the performance of information retrieval techniques and tools.
The assessment is made on the basis of the developed practical work and exam evaluation.
Course Content(Calendar)
Lectures:
1. Introduction to the course. Basic Information Retrieval (document representation, Boolean retrieval model).
2. Basic Information Retrieval (vector-space retrieval model, evaluation of retrieval). (2h)
3. Basic Information Retrieval (query expansion and relevance feedback, document indexing, link analysis). (2h)
4. Search Engines (overview, crawling, query-document matching). (2h)
5. Search Engines (trust, reputation, and quality of websites, Google’s “intelligence”). (2h)
6. Search Engine Optimization.
7. Invisible web, Dark web.
8. Selected topics of Information Retrieval.
Practicals:
1. Quality of Retrieval by Major Search Engines (4h)
2. Impact of Page Rank (4h)
3. Search Engine Optimization. (4h)
Requirements for awarding credit points
The developed and submitted practical work, exam.
Description of the organization and tasks of students’ independent work
Within the course, students must complete three assignments. The assignments will be started in practical work classes, then completed independently and submitted to the e-studies system within a certain deadline.
The tasks of the assignments are related to the practical application of the knowledge acquired during the lectures on the Internet information search techniques.
Criteria for Evaluating Learning Outcomes
The number of points is awarded for the submitted practical work (max. 10 points for each practical work)
The final assessment consists of: evaluation of practical work (40% of the final assessment) and an exam (60% of the final assessment).
Compulsory reading
1. Jones K. B. Search Engine Optimization: Your Visual Blueprint for Effective Internet Marketing. Indianapolis, IN: Visual; 2008. 304 p. Ir LLU bibliotēkas abonētajā e-grāmatu datubāzē „eBook Academic Collection (EBSCOhost)” tiešsaistē LLU tīklā, pieslēdzoties ar LLU IS lietotājvārdu un paroli https://search-ebscohost-com.ezproxy.llu.lv/login.aspx?direct=true&db=e000xww&AN=225662&site=ehost-live&scope=site
2. Ledford J. L. Search Engine Optimization Bible. 2nd ed. Indianapolis, Ind: Wiley. 2009. Ir LLU bibliotēkas abonētajā e-grāmatu datubāzē „eBook Academic Collection (EBSCOhost)” tiešsaistē LLU tīklā, pieslēdzoties ar LLU IS lietotājvārdu un paroli https://search-ebscohost-com.ezproxy.llu.lv/login.aspx?direct=true&db=e000xww&AN=280604&site=ehost-live&scope=site
Further reading
1. Moran M., Hunt B. Search Engine Marketing. Inc. , IBM Press, 2009
2. Mark Levene M. An Introduction to Search Engines and Web Navigation. (Edition: 2:a), Wiley, 2010. 978-0-470-52684-2 NAV Lielākajās b-kās. Nav LLU FB E-grāmatu datubāzēs
3. Wood D., Zaidman M., Ruth L., Hausenblas M. Linked Data: Structured data on the Web. Shelter Island, NY: Manning, 2013.
Wood D. Linked data: structured data on the Web / D. Wood, M. Zaidman and L. Ruth with M.Hausenblas. Shelter Island, NY: Manning, 2014. 276 p.
4. Haynes D. Metadata for Information Management and Retrieval: Understanding Metadata and Its Use. London, United Kingdom: Facet Publishing, 2018.
5. Berkman R.I. Find It Fast: Extracting Expert Information From Social Networks, Big Data, Tweets, and More. Vol 6th edition. Medford, New Jersey: Information Today, 2015.
Ir LLU bibliotēkas abonētajā e-grāmatu datubāzē „eBook Academic Collection (EBSCOhost)” tiešsaistē LLU tīklā, pieslēdzoties ar LLU IS lietotājvārdu un paroli https://search-ebscohost-com.ezproxy.llu.lv/login.aspx?direct=true&db=e000xww&AN=1161564&site=ehost-live&scope=site
Periodicals and other sources
1. Search Engine News. Pieejams: http://www.searchenginenews.com
2. Search Engine Watch. Pieejams: http://searchenginewatch.com
Notes
Compulsory course for Master’s study programme “Information Technologies”.