Information Processing and Retrieval

Code:

M.EIC003

Acronym:

PRI

Keywords
Classification	Keyword
OFICIAL	Information Systems

Instance: 2021/2022 - 1S

Active?	Yes
Web Page:	https://web.fe.up.pt/~ssn/wiki/teach/pri
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Informatics and Computing Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M.EIC	138	Syllabus	1	-	6	52	162

Teaching language

English

Objectives

The curricular unit PRI aims to prepare students to know, understand, design and develop solutions for information processing and retrieval.

The specific objectives are:

1. Make students aware of the challenges associated with building information search systems;
2. Familiarize students with the main concepts and techniques associated with information processing and retrieval;
3. Enable students to design, implement and evaluate information search systems on document collections.

Learning outcomes and competences

Upon completing this course, the student should be able to design and implement a system for processing and retrieving information.

In particular, the student must be able to:

• Identify and describe the main tasks associated with information processing and retrieval;
• Describe the architecture and functioning of an information search system;
• Describe the tasks associated with the processing phases of a collection (offline) and interrogation processing (online);
• Distinguish the different information retrieval models, identifying their principles, models for document representation, and similarity measures;
• Describe and implement different techniques for indexing information;
• Describe and implement different techniques for retrieving and ordering results;

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Programming: knowledge and practice with programming languages ​​for application development.

Databases: knowledge and practice of data modeling in UML.

Program

The area of ​​information processing and retrieval

• Information retrieval versus data retrieval;
• The development of the information retrieval area;
• Information retrieval tasks;
• The information retrieval process.

Architecture of information retrieval systems

• Components of a research system;
• Information collection: selection, acquisition and storage;
• Word processing: lexical analysis, root extraction, compression;
• Indexing: inverted indexes, construction and access;
• Processing of queries, interaction, ordering and evaluation of results.

Recovery models

• Boolean model, vector model and probabilistic model;
• Representation of documents;
• Similarity measures.

Web information retrieval

• Link analysis;
• HITS and PageRank algorithms.

Assessment of information retrieval systems

• Test collections, topics and relevance assessments;
• Measures for the evaluation of research systems.

Mandatory literature

Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze; Introduction to Information Retrieval, Cambridge University Press, 2008. ISBN: 0521865719
W. Bruce Croft; Search engines. ISBN: 978-0-13-136489-9

Complementary Bibliography

Ricardo Baeza-Yates; Modern information retrieval. ISBN: 978-0321416919
Marti Hearst; Search User Interfaces, Cambridge University Press, 2009
Martin Kleppmann; Designing Data-Intensive Applications, O'Reilly, 2017. ISBN: 9781449373320

Teaching methods and learning activities

The program topics are exposed in a series of tutorial sessions (theoretical presentation and laboratory work). Each group of students defines and carries out a project throughout the semester, with part of the development, monitoring, and evaluation carried out in class.

Project: design and implementation of an information processing and research system developed in groups of students. The project is organized in deliveries and partial presentations, which correspond to the project development phases.

Exam: multiple choice test, including open-ended questions.

Software

OpenRefine
Apache Lucene
Apache Solr
Docker

keywords

Physical sciences > Computer science > Informatics

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Trabalho escrito	60,00
Exame	40,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	70,00
Estudo autónomo	40,00
Frequência das aulas	52,00
Total:	162,00

Eligibility for exams

The development of the project will be monitored during the theoretical-practical classes, and evaluated based on the monitoring, a technical report, and the final presentation.

Theoretical concepts are assessed through a final exam, with multiple choice questions, including open-ended questions.

Approval in the curricular unit is subject to obtaining a minimum individual assessment of 40% the exam and 50% in the project final evaluation.

Calculation formula of final grade

The final grade will be calculated using the formula

NF = 60% Project + 40% Exam

The final grade of the project can vary from element to element of the same group, by plus or minus 3 values, based on the opinion of the teachers and in the self-assessment and hetero-assessment to be carried out internally in each group.

Students who fail to pass the unit must repeat the two assessment components (Project and Exam) in a new application.

Examinations or Special Assignments

There are no exams or special assignments.

Special assessment (TE, DA, ...)

The distributed assessment, carried out during the semester in which the course unit operates, is required for all students, regardless of the enrollment regime.

Student workers and their equivalents dismissed from classes must, at intervals to be agreed with the teachers, present the progress of their work, as well as present these, simultaneously with ordinary students, and carry out the theoretical tests for individual assessment provided for.

Classification improvement

The classification can be improved in the next occurrence of the curricular unit.