Data management data structures

  • 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 structures

    Comments

    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
    Paquet indépendant des branches > Specialized courses in Computer Science (Master level)

    Additional programme requirements for PhD studies [PRE-DOC]
    Version: 2020_1/v_01
    Additional programme requirements for PhD studies (Faculty of Science and Medicine) > Specialized courses in Computer Science (Master level)

    Computer Science [3e cycle]
    Version: 2015_1/V_01
    Continuing education > Specialized courses in Computer Science (Master level)

    Computer Science [POST-DOC]
    Version: 2015_1/V_01
    Continuing education > Specialized courses in Computer Science (Master level)

    MSc in Computer science (BeNeFri)
    Version: 2023_1/V_01
    MSc in Computer science (BeNeFri), lectures, seminars and Master thesis > T6: Data Science

    Ma - Business Communication : Business Informatics - 90 ECTS
    Version: 2020/SA_V02
    Courses - 60 ECTS > Option Group > Information Management > Cours > Module Informatik > Data Science

    Ma - Business Informatics - 90 ECTS
    Version: 2020/SA-v01
    Classes - min. 45 ECTS > Module IT and IT Management > Data Science

    MiMa - Business Informatics - 30 ECTS
    Version: 2020/SA_V01
    Cours > Module Informatik > Data Science

    MiMa - Data Analytics - 30 ECTS
    Version: 2020/SA-v01
    À choix 9 crédits ECTS > Data Science