Data management data structures
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Teaching
Details
Faculty Faculty of Science and Medicine Domain Computer Science Code UE-SIN.08619 Languages English Type of lesson Lecture
Level Master Semester SP-2022 Schedules and rooms
Summary schedule Thursday 14:15 - 17:00, Hebdomadaire (Spring semester)
Struct. of the schedule 3h par semaine durant 14 semaines Contact's hours 42 Teaching
Responsibles - Cudré-Mauroux Philippe
Teachers - Lerner Alberto
Description Data structures are often studied along algorithms but they are also essential for organizing data in database management and analytic systems.
In this class we look at fundamental families of data structures that aim at supporting efficient storage and retrieval tasks typical in the above systems.The families of data structures in which we are interested, and the respective representative instances, are the following:
- Modern Hash-base Structure - This is an important class of structure for point access. We will look into open addressing (cuckoo, swiss table) and close addressing (TBD) options.
- Scalable Trees - These structures cover in addition range access and we study examples of compact/efficient trees such as radix tree (ART) and concurrent access structures such as range index (Masstree)
- Persistent Structures - There are structures that lend themselves well to be operated in-memory but that present a persistent component. We will study structures with In-place updates (B-link* tree) and append-only (Log-Structured Merge Trees)
- Probabilistic Structures - We look into the use of approximation techniques into data structures. In particular, we look into compact set membership (Bloom Filter) and Approximate Balancing techniques (Skiplist).
- Compressed/Learned Structures - Similarly to approximation, compression also creates interesting possibilities. We look into lossless approaches in dictionary compression and lossy ones in learned Index (TBD).
Each of the five modules is comprised of two classes. There are two classes reserved for student projects presentations.Training objectives Upon successful completion of this class, a student will be able to:
- Identify and understand the criteria necessary to select the best data structure to organize a given data set
- Describe a wide array of data organization structures and enumerate their advantages and disadvantages
- Explain the different access methods that each data structure facilitates, along with their performance implications
- Write and analyze source code that implements the data structuresComments MSc-CS BENEFRI - (Code Ue: 63112/ Track: T6) The exact date and time of this course as well as the complete course list can be found at http://mcs.unibnf.ch/.
Softskills No Off field No BeNeFri Yes Mobility Yes UniPop No -
Dates and rooms
Date Hour Type of lesson Place 24.02.2022 14:15 - 17:00 Cours PER 21, Room G120 03.03.2022 14:15 - 17:00 Cours PER 21, Room G120 10.03.2022 14:15 - 17:00 Cours PER 21, Room G120 17.03.2022 14:15 - 17:00 Cours PER 21, Room G120 24.03.2022 14:15 - 17:00 Cours PER 21, Room G120 31.03.2022 14:15 - 17:00 Cours PER 21, Room G120 07.04.2022 14:15 - 17:00 Cours PER 21, Room G120 14.04.2022 14:15 - 17:00 Cours PER 21, Room G120 28.04.2022 14:15 - 17:00 Cours PER 21, Room G120 05.05.2022 14:15 - 17:00 Cours PER 21, Room G120 12.05.2022 14:15 - 17:00 Cours PER 21, Room G120 19.05.2022 14:15 - 17:00 Cours PER 21, Room G120 02.06.2022 14:15 - 17:00 Cours PER 21, Room G120 -
Assessments methods
Examen
Assessments methods By rating -
Assignment
Valid for the following curricula: Additional Courses in Sciences
Version: ens_compl_sciences
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