Data management data structures

  • Unterricht

    Details

    Fakultät Math.-Nat. und Med. Fakultät
    Bereich Informatik
    Code UE-SIN.08619
    Sprachen Englisch
    Art der Unterrichtseinheit Vorlesung
    Kursus Master
    Semester SP-2023

    Zeitplan und Räume

    Vorlesungszeiten Donnerstag 14:15 - 17:00, Wöchentlich (Frühlingssemester)
    Strukturpläne 3h par semaine durant 14 semaines
    Kontaktstunden 42

    Unterricht

    Verantwortliche
    • Cudré-Mauroux Philippe
    Dozenten-innen
    • Lerner Alberto
    Beschreibung

    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.

    Lernziele

    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

    Bemerkungen

    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/.

    Course and exam registration on ACADEMIA (not myunifr.ch). Please follow the instructions on https://mcs.unibnf.ch/organization/

     

    Soft Skills Nein
    ausserhalb des Bereichs Nein
    BeNeFri Ja
    Mobilität Ja
    UniPop Nein
  • Einzeltermine und Räume
    Datum Zeit Art der Unterrichtseinheit Ort
    23.02.2023 14:15 - 17:00 Kurs PER 21, Raum E230
    02.03.2023 14:15 - 17:00 Kurs PER 21, Raum E230
    09.03.2023 14:15 - 17:00 Kurs PER 21, Raum E230
    16.03.2023 14:15 - 17:00 Kurs PER 21, Raum E230
    23.03.2023 14:15 - 17:00 Kurs PER 21, Raum E230
    30.03.2023 14:15 - 17:00 Kurs PER 21, Raum E230
    06.04.2023 14:15 - 17:00 Kurs PER 21, Raum E230
    20.04.2023 14:15 - 17:00 Kurs PER 21, Raum E230
    27.04.2023 14:15 - 17:00 Kurs PER 21, Raum E230
    04.05.2023 14:15 - 17:00 Kurs PER 21, Raum E230
    11.05.2023 14:15 - 17:00 Kurs PER 21, Raum E230
    25.05.2023 14:15 - 17:00 Kurs PER 21, Raum E230
    01.06.2023 14:15 - 17:00 Kurs PER 21, Raum E230
  • Leistungskontrolle

    Prüfung

    Bewertungsmodus Nach Note
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