PA212 Advanced Search Techniques for Large Scale Data Analytics

Fakulta informatiky
jaro 2024
Rozsah
2/0/0. 2 kr. (plus ukončení). Ukončení: zk.
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
    Další komentáře
    Studijní materiály
    Předmět je vyučován každoročně.
    Předmět je zařazen také v obdobích jaro 2017, jaro 2018, jaro 2019, jaro 2020, jaro 2021, jaro 2022, jaro 2023, jaro 2025.