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Scheda Riassuntiva
Anno Accademico 2019/2020
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 094743 - DATA MANAGEMENT FOR THE WEB
Docente Ceri Stefano
Cfu 5.00 Tipo insegnamento Monodisciplinare

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Des (Mag.)(ord. 270) - BV (1162) DESIGN DELLA COMUNICAZIONE*AZZZZ053669 - DATA MANAGEMENT FOR THE WEB
Des (Mag.)(ord. 270) - BV (1262) DIGITAL AND INTERACTION DESIGN*AZZZZ053669 - DATA MANAGEMENT FOR THE WEB
Ing Ind - Inf (Mag.)(ord. 270) - MI (474) TELECOMMUNICATION ENGINEERING - INGEGNERIA DELLE TELECOMUNICAZIONI*AZZZZ094743 - DATA MANAGEMENT FOR THE WEB
Ing Ind - Inf (Mag.)(ord. 270) - MI (481) COMPUTER SCIENCE AND ENGINEERING - INGEGNERIA INFORMATICA*AZZZZ094743 - DATA MANAGEMENT FOR THE WEB

Obiettivi dell'insegnamento

This course deals with the new technologies and applications that characterize data management on the Web; the course can be seen as one of the many possible continuations of "Database 2". It will provide an introduction to many emerging fields of Internet-based, data-centered technology and systems; at the end of the course, students will have a good knowledge of many data management aspects related to Internet, a core aspect of today's professional preparation which opens up to many job opportunities and responds to emerging needs of the society.

During the course, students will create groups and jointly develop a project, based on the technologies learnt in class: projects will focus on the early phases of project development, from “concept design” to the creation and demonstration of a “proof of concept”; students will deliver progressive public presentations of their project, thereby learning how to make technical presentations.


Risultati di apprendimento attesi

 

Dublin Descriptors

Expected learning outcomes

Knowledge and understanding

Students will learn:

  • Methods of information retrieval including link-based data analysis and semantic search that allows searching for data at the Web scale.
  • Methods of data analytics over socially produced contents and of crowdsourcing information on the Web.
  • How to organize a project, structure it into phases, progressively develop each phase (from concept generation up to a significant demonstration of a technology learnt in the class).
  • How to work in group and how to deliver public presentations which will improve both team-based and individual presentation skills 

Applying knowledge and understanding

Given a problem that the students will autonomously define, students will then also be able to:

  • Detail the corresponding requirements.
  • Analyze and comment on specific design choices
  • Develop and test a “demonstrator prototype” fulfilling the design specification document.

Making judgements

Given a relatively simple but real  problem, students will be able to:

·   Analyze and understand the goals, assumptions and requirements associated with that problem and model them

·    Estimate the resources needed for its development, identify risks and define mitigation actions.

·    Assess the project from an economic perspective (business plan).

Communication

Students will learn to:

·   Interact within groups and develop modern leadership skills

·   Give public presentations in front of the class

·    In the last day of class, provide a short convincing presentation (pitch) addressed to a potential investor.

Lifelong learning skills

  • Students will learn how to develop a realistic project.

 


Argomenti trattati

The core of the course is Web Information Retrieval, the key technology of search engines such as Google - a subject which is not covered in the basic data management courses. Such core part covers classical aspects such as: text processing, index structures, classic data retrieval methods, retrieval evaluation, search engine technology (crawling and indexing), the PageRank and Hits methods, models of advertising. The course will also include monographic lectures concerned with: (a) Semantic Search as the empowering of search methods with semantic sources (such as DBPedia, Freebase and Google Graph) and with semantic technologies, including open and linked data;(b) Human Computations, i.e. the involvement of humans in computational processes through crowdsourcing platforms such as Amazon Turk, as well as local platforms such as Facebook and Twitter; and (c) Social Analytics, i.e. the use of socially provided content for complementing data provided by devices in the analysis of Internet-based applications and social behaviors; during this part of the course, students will also learn how to build applications over social networks, through their published APIs.

The course evasluation is based on experimental activity. Students will be asked to participate to small projects (for 3 credits), that will be performed in teams of 3-4 members. Students will be free to select their own projects, using any of the technologies presented in the course; one possible area for projects is “shared economies”, an emerging phenomenon in which the citizens create and control markets of resources (such as houses, cars, travels). Students will be asked to define their project rather early, and then the requirements, analysis and design phases will be monitored during the course, using the format of several successive project reviews (through public presentations) which has proven to be successful in Alta Scuola Politecnica. During the presentations, the students will be asked (after suitable training) to provide a final short presentation in the form of pitch and proof of concept.  Some projects of past editions of the course evolved into startups or thesis works.

In addition, students will be asked to deepen one of the aspects of the course through the reading of one or more technical papers, and then to present their personal interpretation of the reading; paper presentations will contribute to the evaluation for the residual 2 credits. Topics must be agreed in advance, they could include surveys on ICT trends and its evolution; a reading list will be provided at the beginning of class, but students are encouraged to propose additional readings based on their interest. With a large class, the oral presentation can be substituted by a written text.

Detailed Program:

  • Foundations of Information Retrieval (6 hrs)
  • Web Information Retrieval (6 hrs)
  • Semantic Web, Open/Linked Data (4 hrs)
  • Human Computation, CrowdSourcing/Crowdsearching (4 hrs)
  • Social Analytics (4 hrs)
  • Final presentations (6 hrs)

Project-Based work:

  • Requirement Analysis (5hrs)
  • Concept Design (5hrs)
  • Detailed Design (5hrs)
  • Pitch/Proof of Concept (5hrs)

Prerequisiti

No major prerequisites are required; competence in software programming for web-based or mobile applications are useful in project development.


Modalità di valutazione

The evaluation is based on project assessment (3 credits) and on a final test based on written questions (2 credits). Project evaluation occurs at several presentations and concerns well-defined aspects at each presentation. Alternatively to the final test, students may choose to deepen one of the aspects of the course through the reading of one or more technical papers, and then to present their personal interpretation of the readings, giving an oral presentation. Presentations given in class would then contribute to the evaluation for 2 credits; the final test would be evaluated just on a pass-fail basis. Topics for the paper presentation will be agreed with the professor; a reading list will be provided at the beginning of class, but students are encouraged to propose additional readings based on their interest. The option for paper presentation is limited to a maximum of about 30-35 students. 


Bibliografia
Risorsa bibliografica obbligatoriaCeri, S., Bozzon, A., Brambilla, M., Della Valle, E., Fraternali, P., Quarteroni, S., Web Information Retrieval, Editore: Springer-Verlag, Series: Data-Centric Systems and Applications (Carey & Ceri eds.), Anno edizione: 2013, ISBN: 978-3-642-39314-3

Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
(hh:mm)
Ore di studio autonome
(hh:mm)
Lezione
32:30
48:45
Esercitazione
17:30
26:15
Laboratorio Informatico
0:00
0:00
Laboratorio Sperimentale
0:00
0:00
Laboratorio Di Progetto
0:00
0:00
Totale 50:00 75:00

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.6.1 / 1.6.1
Area Servizi ICT
14/12/2019