|
Lecturer(s)
|
-
Konečný Jan, doc. RNDr. Ph.D.
-
Masopust Tomáš, prof. RNDr. Ph.D., DSc.
-
Laštovičková Adéla, Mgr.
|
|
Course content
|
The course is devoted to advanced analysis of search algorithms and data structures. Hashing - collision resolution, consistent hashing Bloom and quotient filters Count-min sketch HyperLogLog Sampling streaming data B-trees and their variants, LSM-trees Algorithms in external memory
|
|
Learning activities and teaching methods
|
|
Lecture, Demonstration
|
|
Learning outcomes
|
The students become familiar with selected advanced concepts of algorithms and complexity.
2. Comprehension. Understand basic concepts of algorithms and complexity.
|
|
Prerequisites
|
unspecified
|
|
Assessment methods and criteria
|
Oral exam, Written exam
Active participation in class. Completion of assigned homeworks. Passing the oral or written exam.
|
|
Recommended literature
|
-
Andrii Gakhov. Probabilistic Data Structures and Algorithms for Big Data Applications. 2019.
-
Cormen T. H. (2013). Algorithms Unlocked.
-
Dzejla Medjedovic, Emin Tahirovic, Ines Dedovic. (2022). Algorithms and Data Structures for Massive Datasets.
-
Elden, L. (2007). Matrix Methods in Data Mining and Pattern Recognition. SIAM.
-
Jure Leskovec, Anand Rajaraman, Jeff Ullman. (2020). Mining of Massive Datasets.
-
Knuth D. E. (1973). The Art of Computer Programming, Volumes I & III. Addison-Wesley.
-
Manolopoulos Y., et al. (2005). R-Trees: Theories and Applications..
-
Skiena S. S. (1998). The Algorithms Design Manual. New York.
-
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein. (2022). Introduction to Algorithms, 4th edition.
|