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Scheda Riassuntiva
Anno Accademico 2014/2015
Tipo incarico Dottorato
Insegnamento 093545 - STREAM AND COMPLEX EVENT PROCESSING
Docente Della Valle Emanuele
Cfu 5.00 Tipo insegnamento Monodisciplinare

Corso di Dottorato Da (compreso) A (escluso) Insegnamento
MI (1380) - INGEGNERIA DELL'INFORMAZIONE / INFORMATION TECHNOLOGYAZZZZ093545 - STREAM AND COMPLEX EVENT PROCESSING

Programma dettagliato e risultati di apprendimento attesi

Lecturers

Emanuele Della Valle, Gianpaolo Cugola, and Alessandro Margara

Description

An increasing number of distributed applications requires processing continuously flowing data from geographically distributed sources at unpredictable rate to obtain timely responses to complex queries. Examples of such applications come from the most disparate fields: Smart Cities, Social Media Analytics, Sensor Networks, Security, Intelligent Business management, Agile Enterprises, Robotics.

These requirements led to the development of a number of systems specifically designed to process information as a flow. In particular, two models emerged and are today competing: the data stream processing model [1] and the complex event processing model [2]. More recently, the community working on Semantic Web Technologies has proposed his own contribution to the area in the form of RDF Stream Processing and Stream Reasoning systems [3].

The course aims at presenting the state of the art of the field and the most recent research results in stream and complex event processing. A particular emphasis will be given to the research conducted at DEIB. The students will gain enough background on the topics to be able to use the tools made available by the academic and industrial community to solve prototypical problems. The exams will consist in reporting the experience in using the tools and in discussing the different trade-offs offered by them.

Program

1. History and principles of stream computing and complex event processing
   - Description of the area
   - Typical applications
   - Challenges

2. A modeling framework for DSMS and CEP [4]
   - Functional model
   - Processing model
   - Deployment model
   - Interaction model
   - Data model
   - Time model
   - Rule model
   - Language model

3. The realm of stream reasoning [5]
   - A brief introduction to the semantic Web technologies
   - From relational data stream processing to RDF stream processing
   - Towards stream reasoning

4. The "operator placement" problem
   - Theory
   - Algorithms

5. Programming language integration: reactive programming
  - Principles
  - Implementations: DREAM, Reactive Extensions (Rx)

6. Discovering existing systems
   - Complex event processing systems in practice
   - Data streaming systems in practice
   - RDF Stream Processing and Stream Reasoning systems in practice

7. Putting it all together
   - A practical scenario to test information flow processing systems
   - Experience report

 

More information

An uptodate course Website is available at http://streamreasoning.org/learning/phd-course-2014-15

References

[1] Brian Babcock, Shivnath Babu, Mayur Datar, Rajeev Motwani, and Jennifer Widom. Models and issues in data stream systems. In PODS'02: Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pages 1-16, New York, NY, USA, 2002. ACM.

[2] David C. Luckham. The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2001.

[3] Emanuele Della Valle, Stefano Ceri, Frank van Harmelen, Dieter Fensel: It's a Streaming World! Reasoning upon Rapidly Changing Information. IEEE Intelligent Systems 24(6): 83-89 (2009)

[4] Gianpaolo Cugola and Alessandro Margara. Processing flows of information: From data stream to complex event processing. ACM Computing Surveys, 44(3):15:1–15:62, June 2012.

[5] http://streamreasoning.org/

[6] http://esper.codehaus.org/


Note Sulla Modalità di valutazione

Students are expected to put together what was taught in the course by implementing a partial solution to the practical scenario illustrated during the course (topic 7) using one of the presented tools (e.g., esper [6]), and reporting on their experience in the session(s) dedicated to student reporting.


Intervallo di svolgimento dell'attività didattica
Data inizio
Data termine

Calendario testuale dell'attività didattica
 

Bibliografia
Risorsa bibliografica facoltativaBrian Babcock, Shivnath Babu, Mayur Datar, Rajeev Motwani, and Jennifer Widom., Models and issues in data stream systems, Editore: ACM, New York, NY, USA, Fascicolo: PODS '02, page 1-16
Risorsa bibliografica facoltativaDavid C. Luckham., The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems., Editore: Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, Anno edizione: 2001
Risorsa bibliografica facoltativaEmanuele Della Valle, Stefano Ceri, Frank van Harmelen, Dieter Fensel, It's a Streaming World! Reasoning upon Rapidly Changing Information., Editore: IEEE Intelligent Systems, Anno edizione: 2009, Fascicolo: 24(6): 83-89
Risorsa bibliografica facoltativaGianpaolo Cugola and Alessandro Margara, Processing flows of information: From data stream to complex event processing, Editore: ACM Computing Surveys, Anno edizione: 2012, Fascicolo: Volume 44 Issue 3, June 2012 Article No. 15

Mix Forme Didattiche
Tipo Forma Didattica Ore didattiche
lezione
20.0
esercitazione
0.0
laboratorio informatico
0.0
laboratorio sperimentale
0.0
progetto
60.0
laboratorio di progetto
0.0

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese

Note Docente
The page of the course is now open: http://www.streamreasoning.org/courses/scep2015 It includes the scheduling of the course in June 2015
schedaincarico v. 1.5.0 / 1.5.0
Area Servizi ICT
20/05/2019