ARCHITECTURES DATA LAKES in BIG DATA - Ernesto Damiani and Paolo Ceravolo - Home di ...

DATA LAKES in BIG DATA
   ARCHITECTURES
 Ernesto Damiani and Paolo Ceravolo
            paolo.ceravolo@unimi.it
         Università degli Studi di Milano
          Dipartimento di Informatica

SESAR LAB
➤   SEcure Service-oriented Architectures Research
    Lab (SESAR) http://sesar.di.unimi.it/
➤   Our focus: Big Data Analytics, Artificial
    Intelligence, Secure Systems Design.

          Ernesto             Claudio            Paolo                Valerio
          Damiani             Agostino           Ceravolo             Bellandi
                              Ardagna

                                             Convenzione quadro fra
                                            CRUI e Ministero della
                                                   Giustizia

ABOUT ME

 •   homes.di.unimi.it/ceravolo/
 •   twitter.com/paoloceravolo
 •   www.linkedin.com/in/paolocer
     avolo
 •   scholar.google.com/citation
     s?user=bPLqXrgAAAAJ
 •   www.researchgate.net/profil
     e/Paolo_Ceravolo/

GENERAL INFO
   Data Lakes in Big Data Architectures
   Length: 10 hours - 2 CFU
   22/11 h 10:00 - 12:00 ::: Sala riunioni 3° floor
   26/11 h 10:30 - 13:30 ::: Aula Epsilon
   27/11 h 10:30 - 13:00 — h 14:00 - 16:30 ::: Aula Epsilon
   The assessment consists of paper (at least 4 pages) submitted
   by the student. The paper may address a SotA or explore any of
   the techniques presented during the course

CONNECTED EVENTS
  > SIMPDA 2020
     Tenth International Symposium on Data-Driven Process Discovery and
     Analysis
     Aug 25-28 2020, Lyon, France
  > SNAMS 2020
     Seventh International Conference on Social Networks Analysis,
     Management and Security
     Oct 26-28, 2020, Milan, Italy
  > ICPM 2020
     Second International Conference on Process Mining
     Oct 5-8, 2020, Padua, Italy

ISSUES DISCUSSED

 ➤ Data   Lakes and Big Data Technologies
 ➤ Metadata Management, Data Integration,
   and Data Analytics
 ➤ Graph   Databases
 ➤ Machine   Learning on Graphs

ISSUES DISCUSSED

     To teach details is to bring confusion;
     to establish the relationship between
     things is to bring knowledge

     [Maria Montessori, Childhood to Adolescence]

MATERIALS
  The materials will be provided during classes
  https://homes.di.unimi.it/ceravolo/KG
  Possible reference books are:
     ➤ Big   Data Integration Theory
     Theory and Methods of Database Mappings, Programming Languages, and Semantics
     Authors: Majkić, Zoran
     Springer-Verlag - 2015
     https://opac.unimi.it/SebinaOpac/resource/big-data-using-smart-big-data-analytics-
     and-metrics-to-make-better-decisions-and-improve-performance/USM1957866
     ➤ Big   Data Analytics
     By Venkat Ankam
     Publisher: Packt Publishing - 2016
     http://shop.oreilly.com/product/9781785884696.do

MATERIALS
  The materials will be provided during classes
  https://homes.di.unimi.it/ceravolo/KG
  Possible reference books are:
    ➤   Graph Databases
        Ian Robinson, Jim Webber and Emil Eifrem
        Published by O'Reilly Media
        https://neo4j.com/graph-databases-book/
    ➤   Graph Algorithms: Practical Examples in Apache Spark and Neo4j
        Mark Needham & Amy E. Hodler
        Published by O'Reilly Media
        https://neo4j.com/graph-algorithms-book/

MATERIALS

Material
Interesting readings are:
    • Fakhraei, Shobeir, James Foulds, Madhusudana Shashanka, and
      Lise Getoor. "Collective spammer detection in evolving multi-relational
      social networks." In Proceedings of the 21th acm sigkdd international
      conference on knowledge discovery and data mining, pp. 1769-1778.
      ACM, 2015.
    • Goyal, Palash, and Emilio Ferrara. "Graph embedding techniques,
      applications, and performance: A survey." Knowledge-Based Systems
      151 (2018): 78-94.
    • Narayanan, Annamalai, Mahinthan Chandramohan, Lihui Chen, Yang
      Liu, and Santhoshkumar Saminathan. "subgraph2vec: Learning
      distributed representations of rooted sub-graphs from large graphs."
      arXiv preprint arXiv:1606.08928 (2016).

 • Or JUST ASK the lecturer for further suggestions