SubjectsSubjects(version: 825)
Course, academic year 2017/2018
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Information Models with Ordering - NDBI037
Czech title: Informační modely s uspořádáním
Guaranteed by: Department of Software Engineering (32-KSI)
Faculty: Faculty of Mathematics and Physics
Actual: from 2017 to 2019
Semester: winter
E-Credits: 4
Hours per week, examination: winter s.:2/1 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech, English
Teaching methods: full-time
Additional information: http://www.ksi.mff.cuni.cz/~vojtas/vyuka/vyuka.html
Guarantor: prof. RNDr. Peter Vojtáš, DrSc.
Class: Informatika Bc.
Classification: Informatics > Informatics, Software Applications, Computer Graphics and Geometry, Database Systems, Didactics of Informatics, Discrete Mathematics, External Subjects, General Subjects, Computer and Formal Linguistics, Optimalization, Programming, Software Engineering, Theoretical Computer Science, Software Engineering
Annotation -
Last update: RNDr. Michal Kopecký, Ph.D. (10.05.2017)

With the current flood of information and services on the Web it is necessary to have models of information processing which provide ordering by relevance tailored to each user/customer separately. The aim of the lecture is to inter-link several information models (mainly declarative: deductive and inductive) and extend them with ordering. Labs are composed of coding algorithms prototypes and getting experienced with inductive methods.
Course completion requirements -
Last update: prof. RNDr. Peter Vojtáš, DrSc. (12.10.2017)

Terms of passing the course consist of coding algorithms prototypes, deductive methods, inductive methods. These are only conditions for getting credits. Exam is oral and requires basic understanding of whole material.

As soon as terminology is introduced, detailed milestones (also form of deliverables) and preferred deadlines will be announced (with possible repeated attempts). There is no evidence on personal presence. Nevertheless, no additional explanation for tasks will be given, except on the respective lab and brief description on the course web. Final deadline is end of semester (without repeated attempts).

Literature -
Last update: T_KSI (26.04.2016)

  • Fagin, Lotem, Naor. Optimal aggregation algorithms for middleware, J. Computer and System Sciences 66 (2003), pp. 614-656, http://researcher.watson.ibm.com/researcher/files/us-fagin/jcss03.pdf
  • Supporting material on the course web

Syllabus -
Last update: RNDr. Michal Kopecký, Ph.D. (10.05.2017)

Information models and ordering

Motivation problems, use-case, data, challenge, goal, who/what is better, ordering as preference

Various representation and presentations of ordering in data, information, knowledge

Linear Monotone Preference Model

Fagin's data model and threshold top-k algorithm - Deduction-Querying, Search, Retrieval, ...

Linear Monotone Preference Model a Fagin’s data model

Threshold algorithm, Correctness

Measures of success of preference learning algorithms - Induction-Learning, Generalization, Estimation, Prediction

Class of models

System of metrics

Experiments, validation, evaluation

Datalog / logic programming - logical/relational domain calculus with ordering

Many valued characteristics of sets as a tool for coding ordering, many valued logic, connectives

Many valued modus ponens - declarative models - model of deduction, induction, querying

Many valued modus ponens, residuated operators and correctness

Many valued logic programming and correctness

 
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