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Scheda Riassuntiva
Anno Accademico 2018/2019
Tipo incarico Dottorato
Insegnamento 053566 - STREAM AND COMPLEX EVENT PROCESSING IN THE BIG DATA ERA
Docente Margara Alessandro
Cfu 5.00 Tipo insegnamento Monodisciplinare

Corso di Dottorato Da (compreso) A (escluso) Insegnamento
MI (1380) - INGEGNERIA DELL'INFORMAZIONE / INFORMATION TECHNOLOGYAZZZZ055135 - STREAM AND COMPLEX EVENT PROCESSING IN THE BIG DATA ERA
053566 - STREAM AND COMPLEX EVENT PROCESSING IN THE BIG DATA ERA

Programma dettagliato e risultati di apprendimento attesi

Lecturers

 

Alessandro Margara, Emanuele Della Valle, Gianpaolo Cugola.


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

 

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

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

 

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B. Discovering abstractions and systems
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3. Complex event processing
- Formalism: automata-based, tree-based, logic-based
- TESLA, T-Rex

4. Stream processing and reactive programming
- The functional approach (the Stream monad)
- Flink, Spark Streaming

5. Continuous query processing
- Formalism: CQL
- Esper, KSQL, Spark Structured Streaming

6. Stream reasoning
- Semantic Web technologies
- From data stream processing to stream reasoning
- C-SPARQL

7. Putting it all together for the final evaluation
- A practical scenario
- Experience report

 

More information

 

An up-to-date course Website is available at http://streamreasoning.org/events/scep2019

 

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, 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

Mon 04/03 10:00--13:00: Topic 1 + 2
Thu 07/03 10:00--13:00: Topic 2
Mon 11/03 10:00--13:00: Topic 3
Wed 13/03 10:00--13:00: Topic 4
Mon 18/03 10:00--13:00: Topic 5
Thu 21/03 10:00--13:00: Topic 6
Mon 25/03 10:00--13:00: Topic 7

Evaluation: within the end of July, 2019


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
25.0
esercitazione
0.0
laboratorio informatico
0.0
laboratorio sperimentale
0.0
progetto
45.0
laboratorio di progetto
0.0

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Possibilità di sostenere l'esame in lingua inglese

Note Docente
schedaincarico v. 1.6.1 / 1.6.1
Area Servizi ICT
08/12/2019