PA212 Advanced Search Techniques for Large Scale Data Analytics

Faculty of Informatics
Spring 2023
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Taught in person.
doc. RNDr. Jan Sedmidubský, Ph.D. (lecturer)
prof. Ing. Pavel Zezula, CSc. (lecturer)
Guaranteed by
doc. RNDr. Jan Sedmidubský, Ph.D.
Department of Machine Learning and Data Processing - Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing - Faculty of Informatics
Thu 16. 2. to Thu 11. 5. Thu 12:00–13:50 B410
Knowledge of the basic principles of data processing is assumed.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 77 fields of study the course is directly associated with, display
Course objectives
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 given on describing 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 the specific tasks.
Learning outcomes
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 the specific tasks.
  • Introduction – What is searching, Things useful to know
  • Support for Distributed Processing – Distributed file system, MapReduce, Algorithms using MapReduce, Cost model and performance evaluation
  • Retrieval Operators and Result Evaluations – Common similarity search operators, Retrieval metrics
  • Clustering – K-means algorithms, Clustering in non-Euclidean spaces, Clustering for streams and parallelism
  • Finding Frequent Item Sets – Handling large datasets in main memory, Counting frequent items in a stream
  • Finding Similar Items – Applications of near-neighbor search, Shingling of documents, Similarity-preserving summaries of sets, Locality sensitive hashing
  • Searching in Data Streams – The stream data model, Filtering streams
  • Link Analysis – Page Rank, Topic sensitive, Link spam
  • Search Applications – Advertising on the web, Recommendation systems (collaborative filtering), Mining social-network graphs
    recommended literature
  • P, Deepak and Prasad M. DESHPANDE. Operators for similarity search : semantics, techniques and usage scenarios. Cham: Springer, 2015. xi, 115. ISBN 9783319212562. info
  • LESKOVEC, Jurij, Anand RAJARAMAN and Jeffrey D. ULLMAN. Mining of massive datasets. 2nd ed. Cambridge: Cambridge University Press, 2014. xi, 467. ISBN 9781107077232. info
  • BAEZA-YATES, R. and Berthier de Araújo Neto RIBEIRO. Modern information retrieval : the concepts and technology behind search. 2nd ed. Harlow: Pearson, 2011. xxx, 913. ISBN 9780321416919. info
Teaching methods
Lectures with slides in English. The approach combines theory, algorithms and practical examples.
Assessment methods
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.
Language of instruction
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022.
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