Seminari sobre "Mining Data Streams"
En Toon Calders de la Université Libre de Bruxelles farà un seminari sobre "Mining Data Streams" durant el mes d'abril a Barcelona. Aquest seminari s'emmarca dins de l'Erasmus Mundus IT4BI però està obert a totes les persones a les que pugui interessar. Per inscriure's cal enviar un correu al professor Óscar Romero (oromero@essi.upc.edu).
Schedule
Two days lecture (4h each day) – room A6106
- Wednesday 22nd of April, 2015: 10h to 14h
- Monday 27th of April, 2015: 10h to 14h
Abstract
Sometimes data is generated unboundedly and at such a fast pace that it is no longer possible to store the complete data in a database. The development of techniques for handling and processing such streams of data is very challenging as the streaming context imposes severe constraints on the computation:
- We are often not able to store the whole data stream and making multiple passes over the data is no longer possible
- As the stream is never finished we need to be able to continuously provide, upon request, up-to-date answers to analysis queries
Even problems that are highly trivial in an off-line context, such as: “How many different items are there in my database?” become very hard in a streaming context.
Nevertheless, in the past decades several clever algorithms were developed to deal with streaming data. In this course we will cover several of these indispensable tools that should be present in every big data scientists’ toolbox.
Schedule
Two days lecture (4h each day) – room A6106
- Wednesday 22nd of April, 2015: 10h to 14h
- Monday 27th of April, 2015: 10h to 14h
Abstract
Sometimes data is generated unboundedly and at such a fast pace that it is no longer possible to store the complete data in a database. The development of techniques for handling and processing such streams of data is very challenging as the streaming context imposes severe constraints on the computation:
- We are often not able to store the whole data stream and making multiple passes over the data is no longer possible
- As the stream is never finished we need to be able to continuously provide, upon request, up-to-date answers to analysis queries
Even problems that are highly trivial in an off-line context, such as: “How many different items are there in my database?” become very hard in a streaming context.
Nevertheless, in the past decades several clever algorithms were developed to deal with streaming data. In this course we will cover several of these indispensable tools that should be present in every big data scientists’ toolbox.
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