FI:PA212 Advanced Search Techniques - Informace o předmětu

PA212 Advanced Search Techniques for Large Scale Data Analytics

Rozsah

2/0/0. 2 kr. (plus ukončení). Ukončení: zk.
Vyučováno prezenčně.

Vyučující

doc. RNDr. Jan Sedmidubský, Ph.D. (přednášející)
prof. Ing. Pavel Zezula, CSc. (přednášející)

Garance

doc. RNDr. Jan Sedmidubský, Ph.D.
Katedra strojového učení a zpracování dat – Fakulta informatiky
Dodavatelské pracoviště: Katedra strojového učení a zpracování dat – Fakulta informatiky

Rozvrh

St 10:00–11:50 B410

Předpoklady

Knowledge of the basic principles of data processing is assumed.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 77 mateřských oborů, zobrazit

Cíle předmětu

The objective of the course is to explain the problems of information retrieval in large collections of unstructured data, such as text documents or multimedia objects. The main emphasis will be on describing the basic principles of distributed algorithms for processing large volumes of data, e.g., Locality Sensitive Hashing, MapReduce, or PageRank. The algorithms for processing stream data will be introduced as well. The students will also acquire practical experience by applying the presented algorithms to specific tasks.

Výstupy z učení

After completing the course, students are able to:

Describe algorithmic-based differences between processing offline data collections and online data streams;

Understand the basic principles of distributed algorithms for processing large volumes of data;

Evaluate the results of algorithms by several metrics;

Apply presented algorithms, such as k-Means, Locality Sensitive Hashing, MapReduce, or PageRank, to specific tasks.

Osnova

• Introduction – what is searching, things useful to know
• Support for distributed processing – distributed processing, MapReduce, performance evaluation
• Retrieval operators and metrics – common similarity search operators, retrieval metrics for evaluating search results
• Clustering – clustering in Euclidean and non-Euclidean spaces; hierarchical, k-means, and BFR clustering algorithms
• Finding frequent item sets – counting frequent items; A-Priori and PCY algorithms
• Finding similar items – near-neighbor search, shingling of documents, min-hashing, Locality Sensitive Hashing
• Processing data streams – sampling data from a stream, queries over sliding windows, filtering a stream
• Link analysis – PageRank, topic sensitive PageRank, link spam
• Search applications – advertising on the web, recommender systems

Literatura

doporučená literatura
• P, Deepak a Prasad M. DESHPANDE. Operators for similarity search : semantics, techniques and usage scenarios. Cham: Springer, 2015, xi, 115. ISBN 9783319212562. info
• LESKOVEC, Jurij, Anand RAJARAMAN a Jeffrey D. ULLMAN. Mining of massive datasets. 2nd ed. Cambridge: Cambridge University Press, 2014, xi, 467. ISBN 9781107077232. info
• BAEZA-YATES, R. a Berthier de Araújo Neto RIBEIRO. Modern information retrieval : the concepts and technology behind search. 2nd ed. Harlow: Pearson, 2011, xxx, 913. ISBN 9780321416919. info

Výukové metody

Lectures with slides in English. The approach combines theory, algorithms, and practical examples.

Metody hodnocení

The final exam consists of only a written part. The student is asked several theoretical and practical questions to verify their knowledge obtained during the course lectures.

Vyučovací jazyk

Angličtina

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• Permalink: https://is.muni.cz/predmet/fi/jaro2024/PA212