Sergi Nadal

Sergi Nadal

Research topicSelf-Optimizing Data Stream Processing

 
Advisor (Home University): Alberto Abelló (UPC), Oscar Romero (UPC)
Advisor (Host University):  Stijn Vansummeren (ULB)
 
Research Interests: Big Data management, business intelligence, data stream management, data-intensive flows, metadata management
 
EDUCATION
 
Sept 2015 to the present:
PhD (IT4BI-DC). Universitat Politècnica de Catalunya, Université Libre de Bruxelles
Sept 2013 - Sept 2015:
MSc (IT4BI), Erasmus Mundus Master in Information Technologies for Business Intelligence. Université Libre de Bruxelles, Université François Rabelais, Universitat Politècnica de Catalunya
Thesis title: “Multi-Objective Materialized View Selection in Data-Intensive Flows”
Sept 2007 - Jun 2013:
BSc, Informatics Engineering. Universitat Politècnica de Catalunya
Thesis title: “Relational To Non-Relational Schema Translation Techniques”
 
WORK EXPERIENCE
 
2014:
Universitat Politècnica de Catalunya as Assistant - Barcelona, Spain
2014:
Incubio as Research Engineer - Barcelona, Spain
2011 - 2013:
Computer Sciences Brand as BI Consultant - Barcelona, Spain
2010 - 2011:
Praktics as Developer - Barcelona, Spain
 
RESEARCH
 
Currently, organizations strive for faster, usually near-real time, decision making processes including complex event processing and data stream analysis techniques. However, the inclusion of continuous data streams in such process entails new challenges not present in traditional data management systems. First, the data stream management, relying on a sliding window buffering model to smooth arrival irregularities. Second, the data stream processing, relying on linear or sublinear algorithms to provide near real-time analysis. Recently, many efforts have been put to provide adaptation mechanisms to the multiple, continuous, rapid, time-varying nature of data streams into Data Stream Management Systems (DSMS). Nonetheless, these works do not have a holistic view of the architecture they are part of, missing potential optimizations. Even though some works consider Linked Data streams, overcoming Variety issues, they are not exploiting such semantics for optimization. To this end, in this doctoral project we propose to extend the λ-architecture enabling it with semantic aware self-tuning features for optimal real-time processing. Several data stream characteristics must be considered, e.g. arrival rate, data distribution or schema evolution. Combining those with system requirements, e.g. window buffer size and user requirements, e.g. time constraints; will provide a complete knowledge base to enable run-time tuning.

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