Goethe University Frankfurt

 

Todor_Niagaragoogle_scholar_logovisual-guidelines

Todor Ivanov, M.Sc.

Research Assistant & PhD Student

My research interests are in Big Data and distributed systems:

  • Big Data benchmarking / Performance optimizations
  • Complex distributed software systems (Hadoop, Spark etc.)
  • Storage and processing of data-intensive applications
  • Virtualization & Cloud Computing

 

 Address:

Robert-Mayer-Str. 10 (5th floor)
D-60325 Frankfurt/M.
Germany

 Contact:

069-798 – 28087

Email:

todor@dbis.cs.uni-frankfurt.de

 

Publications

2017

  • Niño, M., Zicari, R. V., Ivanov, T., Hee, K., Mushtaq, N., Rosselli, M., Sánchez-Ocaña, C., Tolle, K., Blanco, J. M., Illarramendi, A., Besier, J., & Underwood, H.. (2017). Data Projects for Social Good: Challenges and Opportunities. ICCSS 2017: International Conference on Computational Social Science, Amsterdam, The Netherlands, (May 14-15, 2017), 3(5), 3102.
    [Bibtex]
    @article{ICCSS2017,
      author    = {Mikel Niño and  Roberto V. Zicari and  Todor Ivanov and  Kim Hee and  Naveed Mushtaq and  Marten Rosselli and  Concha Sánchez-Ocaña and  Karsten Tolle and  José Miguel Blanco and  Arantza Illarramendi and  Jörg Besier and  Harry Underwood},
      country   = {Spain},
      title     = {Data Projects for Social Good: Challenges and Opportunities},
      abstract  = {One of the application fields for data analysis techniques and technologies gaining momentum is the area of social good or 'common good', covering cases related to humanitarian crises, global health care, or ecology and environmental issues, among others. The promotion of data-driven projects in this field aims at increasing the efficacy and efficiency of social initiatives, improving the way these actions help humanity in general and people in need in particular. This application field, however, poses its own barriers and challenges when developing data-driven projects, lagging behind in comparison with other scenarios. These challenges derive from aspects such as the scope and scale of the social issue to solve, cultural and political barriers, the skills of main stakeholders and the technological resources available, the motivation to be engaged in such projects, or the ethical and legal issues related to sensitive data. This paper analyzes the application of data projects in the field of social good, reviewing its current state and noteworthy initiatives, and presenting a framework covering the key aspects to analyze in such projects. The goal is to provide guidelines to understand the main challenges and opportunities for this type of data projects, as well as identifying the main differential issues compared to “classical” data projects in general. A case study is presented on the initial steps and stakeholder analysis of a data project for the inclusion of refugees in the city of Frankfurt, Germany, in order to empirically confront the framework with a real example.},
      keywords  = {data-driven projects, humanitarian operations, personal and sensitive data, social good, stakeholders analysis},
      volume    = {3},
      number    = {5},
      year      = {2017},
      pages     = {3102},
      ee        = {http://waset.org/abstracts/62394},
      url       = {http://waset.org/abstracts/62394},
      bibsource = {http://waset.org/abstracts},
      journal={ICCSS 2017: International Conference on Computational Social Science, Amsterdam, The Netherlands, (May 14-15,  2017)},
      conference = {ICCSS 2017: International Conference on Computational Social Science, Amsterdam, The Netherlands, (May 14-15,  2017)},
      issn      = {PISSN:2010-376X, EISSN:2010-3778},
      publisher = {World Academy of Science, Engineering and Technology},
      index     = {International Science Index, Humanities and Social Sciences,  4(5) 2016},
    }
  • [DOI] Ghazal, A., Ivanov, T., Kostamaa, P., Crolotte, A., Voong, R., Al-Kateb, M., Ghazal, W., & Zicari, R. V.. (2017). BigBench V2: The New and Improved BigBench. Paper presented at the 33rd IEEE International Conference on Data Engineering, ICDE 2017, San Diego, CA, USA, April 19-22, 2017.
    [Bibtex]
    @inproceedings{GhazalIKCVAGZ17,
      author    = {Ahmad Ghazal and
                   Todor Ivanov and
                   Pekka Kostamaa and
                   Alain Crolotte and
                   Ryan Voong and
                   Mohammed Al{-}Kateb and
                   Waleed Ghazal and
                   Roberto V. Zicari},
      title     = {BigBench {V2:} The New and Improved BigBench},
      booktitle = {33rd {IEEE} International Conference on Data Engineering, {ICDE} 2017,
                   San Diego, CA, USA, April 19-22, 2017},
      pages     = {1225--1236},
      year      = {2017},
      crossref  = {DBLP:conf/icde/2017},
      url       = {https://doi.org/10.1109/ICDE.2017.167},
      doi       = {10.1109/ICDE.2017.167},
      timestamp = {Wed, 24 May 2017 11:31:57 +0200},
      biburl    = {http://dblp.uni-trier.de/rec/bib/conf/icde/GhazalIKCVAGZ17},
      bibsource = {dblp computer science bibliography, http://dblp.org}
    }
  • [DOI] Felicetti, L., Femminella, M., Ivanov, T., LiÒ, P., & Reali, G.. (2017). A big-data layered architecture for analyzing molecular communications systems in blood vessels. Paper presented at the Proceedings of the 4th ACM International Conference on Nanoscale Computing and Communication, NANOCOM 2017, Washington, DC, USA, September 27-29, 2017.
    [Bibtex]
    @inproceedings{FelicettiFILR17,
      author    = {Luca Felicetti and
                   Mauro Femminella and
                   Todor Ivanov and
                   Pietro Li{\`{o}} and
                   Gianluca Reali},
      title     = {A big-data layered architecture for analyzing molecular communications
                   systems in blood vessels},
      booktitle = {Proceedings of the 4th {ACM} International Conference on Nanoscale
                   Computing and Communication, {NANOCOM} 2017, Washington, DC, USA,
                   September 27-29, 2017},
      pages     = {14:1--14:2},
      year      = {2017},
      crossref  = {DBLP:conf/nanocom/2017},
      url       = {http://doi.acm.org/10.1145/3109453.3109468},
      doi       = {10.1145/3109453.3109468},
      timestamp = {Mon, 11 Sep 2017 08:50:31 +0200},
      biburl    = {http://dblp.uni-trier.de/rec/bib/conf/nanocom/FelicettiFILR17},
      bibsource = {dblp computer science bibliography, http://dblp.org}
    }

2016

  • [DOI] Ivanov, T., Izberovic, S., & Korfiatis, N.. (2016). The Heterogeneity Paradigm in Big Data Architectures. In Managing and Processing Big Data in Cloud Computing (pp. 218-245). IGI Global.
    [Bibtex]
    @incollection{2016_IGI_Ivanov,
      doi = {10.4018/978-1-4666-9767-6.ch015},
      url = {http://dx.doi.org/10.4018/978-1-4666-9767-6.ch015},
      publisher = {{IGI} Global},
      pages = {218--245},
      year = {2016},
      author = {Todor Ivanov and Sead Izberovic and Nikolaos Korfiatis},
      title = {The Heterogeneity Paradigm in Big Data Architectures},
      booktitle = {Managing and Processing Big Data in Cloud Computing}
    }
  • [DOI] Zicari, R. V., Rosselli, M., Ivanov, T., Korfiatis, N., Tolle, K., Niemann, R., & Reichenbach, C.. (2016). Big Data Optimization: Recent Developments and Challenges. In Emrouznejad, A. (Ed.), (, pp. 17-47). Cham: Springer International Publishing.
    [Bibtex]
    @Inbook{Zicari2016,
    author="Zicari, Roberto V.
    and Rosselli, Marten
    and Ivanov, Todor
    and Korfiatis, Nikolaos
    and Tolle, Karsten
    and Niemann, Raik
    and Reichenbach, Christoph",
    editor="Emrouznejad, Ali",
    chapter="Setting Up a Big Data Project: Challenges, Opportunities, Technologies and Optimization",
    title="Big Data Optimization: Recent Developments and Challenges",
    year="2016",
    publisher="Springer International Publishing",
    address="Cham",
    pages="17--47",
    isbn="978-3-319-30265-2",
    doi="10.1007/978-3-319-30265-2_2",
    url="http://dx.doi.org/10.1007/978-3-319-30265-2_2"
    }

2015

  • [DOI] Niemann, R., & Ivanov, T.. (2015). Evaluating the Energy Efficiency of Data Management Systems. Paper presented at the 4th IEEE/ACM International Workshop on Green and Sustainable Software, GREENS 2015, Florence, Italy, May 18, 2015.
    [Bibtex]
    @inproceedings{DBLP:conf/greens/NiemannI15,
      author    = {Raik Niemann and
                   Todor Ivanov},
      title     = {Evaluating the Energy Efficiency of Data Management Systems},
      booktitle = {4th {IEEE/ACM} International Workshop on Green and Sustainable Software,
                   {GREENS} 2015, Florence, Italy, May 18, 2015},
      pages     = {22--28},
      year      = {2015},
      crossref  = {DBLP:conf/greens/2015},
      url       = {http://dx.doi.org/10.1109/GREENS.2015.11},
      doi       = {10.1109/GREENS.2015.11},
      timestamp = {Fri, 15 Jan 2016 09:12:13 +0100},
      biburl    = {http://dblp.uni-trier.de/rec/bib/conf/greens/NiemannI15},
      bibsource = {dblp computer science bibliography, http://dblp.org}
    }
  • [DOI] Ivanov, T., Rabl, T., Poess, M., Queralt, A., Poelman, J., Poggi, N., & Buell, J.. (2015). Performance Evaluation and Benchmarking: Traditional to Big Data to Internet of Things: 7th TPC Technology Conference, TPCTC 2015, Kohala Coast, HI, USA, August 31 – September 4, 2015. Revised Selected Papers. In Nambiar, R., & Poess, M. (Eds.), (, pp. 135-155). Cham: Springer International Publishing.
    [Bibtex]
    @Inbook{Ivanov2015,
    author="Ivanov, Todor
    and Rabl, Tilmann
    and Poess, Meikel
    and Queralt, Anna
    and Poelman, John 
    and Poggi, Nicolas
    and Buell, Jeffrey",
    editor="Nambiar, Raghunath
    and Poess, Meikel",
    chapter="Big Data Benchmark Compendium",
    title="Performance Evaluation and Benchmarking: Traditional to Big Data to Internet of Things: 7th TPC Technology Conference, TPCTC 2015, Kohala Coast, HI, USA, August 31 - September 4, 2015. Revised Selected Papers",
    year="2015",
    publisher="Springer International Publishing",
    address="Cham",
    pages="135--155",
    isbn="978-3-319-31409-9",
    doi="10.1007/978-3-319-31409-9_9",
    url="http://dx.doi.org/10.1007/978-3-319-31409-9_9"
    }
  • [DOI] Ivanov, T., Niemann, R., Izberovic, S., Rosselli, M., Tolle, K., & Zicari, R. V.. (2015). Performance Evaluation of Enterprise Big Data Platforms with HiBench. Paper presented at the 2015 IEEE TrustCom/BigDataSE/ISPA, Helsinki, Finland, August 20-22, 2015, Volume 2.
    [Bibtex]
    @inproceedings{DBLP:conf/trustcom/IvanovNIRTZ15,
      author    = {Todor Ivanov and
                   Raik Niemann and
                   Sead Izberovic and
                   Marten Rosselli and
                   Karsten Tolle and
                   Roberto V. Zicari},
      title     = {Performance Evaluation of Enterprise Big Data Platforms with HiBench},
      booktitle = {2015 {IEEE} TrustCom/BigDataSE/ISPA, Helsinki, Finland, August 20-22,
                   2015, Volume 2},
      pages     = {120--127},
      year      = {2015},
      crossref  = {DBLP:conf/trustcom/2015-2},
      url       = {http://dx.doi.org/10.1109/Trustcom.2015.570},
      doi       = {10.1109/Trustcom.2015.570},
      timestamp = {Thu, 10 Dec 2015 20:18:33 +0100},
      biburl    = {http://dblp.uni-trier.de/rec/bib/conf/trustcom/IvanovNIRTZ15},
      bibsource = {dblp computer science bibliography, http://dblp.org}
    }
  • Niemann, R., & Ivanov, T.. (2015). Modelling the Performance, Energy Consumption and Efficiency of Data Management Systems. Paper presented at the 45. Jahrestagung der Gesellschaft für Informatik, Informatik 2015, Informatik, Energie und Umwelt, 28. September – 2. Oktober 2015 in Cottbus, Deutschland.
    [Bibtex]
    @inproceedings{DBLP:conf/gi/NiemannI15,
      author    = {Raik Niemann and
                   Todor Ivanov},
      title     = {Modelling the Performance, Energy Consumption and Efficiency of Data
                   Management Systems},
      booktitle = {45. Jahrestagung der Gesellschaft f{\"{u}}r Informatik, Informatik
                   2015, Informatik, Energie und Umwelt, 28. September - 2. Oktober 2015
                   in Cottbus, Deutschland},
      pages     = {1183--1194},
      year      = {2015},
      crossref  = {DBLP:conf/gi/2015},
      url       = {http://subs.emis.de/LNI/Proceedings/Proceedings246/article161.html},
      timestamp = {Tue, 15 Mar 2016 10:22:59 +0100},
      biburl    = {http://dblp.uni-trier.de/rec/bib/conf/gi/NiemannI15},
      bibsource = {dblp computer science bibliography, http://dblp.org}
    }
  • Ivanov, T., & Beer, M.. (2015). Performance Evaluation of Spark SQL using BigBench. In 6th Workshop on Big Data Benchmarking (6th WBDB), June 16-17, 2015, Toronto, Canada .
    [Bibtex]
    @incollection{2015-WBDB,
      author = {Todor Ivanov and Max-Georg Beer},
      title = {{Performance Evaluation of Spark SQL using BigBench}},
      booktitle={6th Workshop on Big Data Benchmarking (6th WBDB), June 16-17, 2015, Toronto, Canada},
      month = {June},
      keywords={Big Data, Benchmarking, Hadoop, BigBench},
      url={http://clds.sdsc.edu/wbdb2015.ca/program},
      year = {2015}
    }
  • Ivanov, T., & Izberovic, S.. (2015). Evaluating Hadoop Clusters with TPCx-HS. Frankfurt Big Data Laboratory Technical Paper.
    [Bibtex]
    @UNPUBLISHED{TR-No2015-1,
      author = {Todor Ivanov and Sead Izberovic},
      title = {{Evaluating Hadoop Clusters with TPCx-HS}},
      month = {September},
      publisher={Frankfurt Big Data Laboratory Technical Paper},
      abstract = {The growing complexity and variety of Big Data platforms makes it both difficult and time
    consuming for all system users to properly setup and operate the systems. Another challenge is to
    compare the platforms in order to choose the most appropriate one for a particular application.
    All these factors motivate the need for a standardized Big Data benchmark that can help the users
    in the process of platform evaluation. Just recently TPCx-HS [1][2] has been released as the first
    standardized Big Data benchmark designed to stress test a Hadoop cluster.
    The goal of this study is to evaluate and compare how the network setup influences the
    performance of a Hadoop cluster. In particular, experiments were performed using shared and
    dedicated 1Gbit networks utilized by the same Cloudera Hadoop Distribution (CDH) cluster
    setup. The TPCx-HS benchmark, which is very network intensive, was used to stress test and
    compare both cluster setups. All the presented results are obtained by using the officially
    available version [1] of the benchmark, but they are not comparable with the officially reported
    results and are meant as an experimental evaluation, not audited by any external organization. As
    expected the dedicated 1Gbit network setup performed much faster than the shared 1Gbit setup.
    However, what was surprising is the negligible price difference between both cluster setups,
    which pays off with a multifold performance return.},
      keywords={Benchmarking, Hadoop, TPCx-HS, Big Data},
      url={http://arxiv.org/ftp/arxiv/papers/1509/1509.03486.pdf},
      year = {2015}
    }
  • Ivanov, T., & Beer, M.. (2015). Evaluating Hive and Spark SQL with BigBench. Frankfurt Big Data Laboratory Technical Paper.
    [Bibtex]
    @UNPUBLISHED{TR-No2015-2,
      author = {Todor Ivanov and Max-Georg Beer},
      title = {{Evaluating Hive and Spark SQL with BigBench}},
      month = {December},
      publisher={Frankfurt Big Data Laboratory Technical Paper},
      abstract = {The objective of this work was to utilize BigBench [1] as a Big Data benchmark and evaluate and compare two processing engines: MapReduce [2] and Spark [3]. MapReduce is the established engine for processing data on Hadoop. Spark is a popular alternative engine that promises faster processing times than the established MapReduce engine. BigBench was chosen for this comparison because it is the first end-to-end analytics Big Data benchmark and it is currently under public review as TPCx-BB [4]. One of our goals was to evaluate the benchmark by performing various scalability tests and validate that it is able to stress test the processing engines. First, we analyzed the steps necessary to execute the available MapReduce implementation of BigBench [1] on Spark. Then, all the 30 BigBench queries were executed on MapReduce/Hive with different scale factors in order to see how the performance changes with the increase of the data size. Next, the group of HiveQL queries were executed on Spark SQL and compared with their respective Hive runtimes. This report gives a detailed overview on how to setup an experimental Hadoop cluster and execute BigBench on both Hive and Spark SQL. It provides the absolute times for all experiments preformed for different scale factors as well as query results which can be used to validate correct benchmark execution. Additionally, multiple issues and workarounds were encountered and solved during our work. An evaluation of the resource utilization (CPU, memory, disk and network usage) of a subset of representative BigBench queries is presented to illustrate the behavior of the different query groups on both processing engines. Last but not least it is important to mention that larger parts of this report are taken from the master thesis of Max-Georg Beer, entitled "Evaluation of BigBench on Apache Spark Compared to MapReduce" [5].},
      keywords={Benchmarking, Hadoop, Spark SQL, BigBench, Big Data},
      url={http://arxiv.org/ftp/arxiv/papers/1512/1512.08417.pdf},
      year = {2015}
    }

2014

  • [DOI] Ivanov, T., Zicari, R. V., & Buchmann, A. P.. (2014). Benchmarking Virtualized Hadoop Clusters. Paper presented at the Big Data Benchmarking – 5th International Workshop, WBDB 2014, Potsdam, Germany, August 5-6, 2014, Revised Selected Papers.
    [Bibtex]
    @inproceedings{DBLP:conf/wbdb/IvanovZB14,
      author    = {Todor Ivanov and
                   Roberto V. Zicari and
                   Alejandro P. Buchmann},
      title     = {Benchmarking Virtualized Hadoop Clusters},
      booktitle = {Big Data Benchmarking - 5th International Workshop, {WBDB} 2014, Potsdam,
                   Germany, August 5-6, 2014, Revised Selected Papers},
      pages     = {87--98},
      year      = {2014},
      url       = {http://dx.doi.org/10.1007/978-3-319-20233-4_9},
      doi       = {10.1007/978-3-319-20233-4_9},
      timestamp = {Mon, 15 Jun 2015 13:54:13 +0200},
      biburl    = {http://dblp.uni-trier.de/rec/bib/conf/wbdb/IvanovZB14},
      bibsource = {dblp computer science bibliography, http://dblp.org}
    }
  • Ivanov, T., Zicari, R. V., Izberovic, S., & Tolle, K.. (2014). Performance Evaluation of Virtualized Hadoop Clusters. Frankfurt Big Data Laboratory Technical Paper.
    [Bibtex]
    @UNPUBLISHED{TR-No2014-1,
      author = {Todor Ivanov and Roberto V. Zicari and Sead Izberovic and Karsten Tolle},
      title = {{Performance Evaluation of Virtualized Hadoop Clusters}},
      month = {November},
      publisher={Frankfurt Big Data Laboratory Technical Paper},
      abstract = {In this report we investigate the performance of Hadoop clusters, deployed with separated storage and compute layers, on top of a hypervisor managing a single physical host. We have analyzed and evaluated the different Hadoop cluster configurations by running CPU bound and I/O­ bound workloads. The report is structured as follows: Section 2 provides a brief description of the technologies involved in our study. An overview of the experimental platform, setup test and configurations are presented in Section 3. Our benchmark methodology is defined in Section 4. The performed experiments together with the evaluation of the results are presented in Section 5. Finally, Section 6 concludes with lessons learned.},
      keywords={Benchmarking, Virtualization, HiBench, Big Data},
      url={http://arxiv.org/ftp/arxiv/papers/1411/1411.3811.pdf},
      year = {2014}
    }
  • Ivanov, T., Niemann, R., Izberovic, S., Rosselli, M., Tolle, K., & Zicari, R. V.. (2014). Benchmarking DataStax Enterprise/Cassandra with HiBench. Frankfurt Big Data Laboratory Technical Paper.
    [Bibtex]
    @UNPUBLISHED{TR-No2014-2,
      author = {Todor Ivanov and Raik Niemann and Sead Izberovic and Marten Rosselli and Karsten Tolle and Roberto V. Zicari},
      title = {{Benchmarking DataStax Enterprise/Cassandra with HiBench}},
      month = {November},
      publisher={Frankfurt Big Data Laboratory Technical Paper},
      abstract = {This report evaluates the new analytical capabilities of DataStax Enterprise (DSE) [1] through the use of standard Hadoop workloads. In particular, we run experiments with CPU and I/O bound micro-benchmarks as well as OLAP-style analytical query workloads. The performed tests should show that DSE is capable of successfully executing Hadoop applications without the need to adapt them for the underlying Cassandra distributed storage system [2]. Due to the Cassandra File System (CFS) [3], which supports the Hadoop Distributed File System API, Hadoop stack applications should seamlessly run in DSE. The report is structured as follows: Section 2 provides a brief description of the technologies involved in our study. An overview of our used hardware and software components of the experimental environment is given in Section 3. Our benchmark methodology is defined in Section 4. The performed experiments together with the evaluation of the results are presented in Section 5. Finally, Section 6 concludes with lessons learned.},
      keywords={Benchmarking, Cassandra, HiBench, Big Data},
      url={http://arxiv.org/ftp/arxiv/papers/1411/1411.4044.pdf},
      year = {2014}
    }

2013

  • [DOI] Graefe, G., Petrov, I., Ivanov, T., & Marinov, V.. (2013). A hybrid page layout integrating PAX and NSM. Paper presented at the 17th International Database Engineering & Applications Symposium, IDEAS ’13, Barcelona, Spain – October 09 – 11, 2013.
    [Bibtex]
    @inproceedings{DBLP:conf/ideas/GraefePIM13,
      author    = {Goetz Graefe and
                   Ilia Petrov and
                   Todor Ivanov and
                   Veselin Marinov},
      title     = {A hybrid page layout integrating {PAX} and {NSM}},
      booktitle = {17th International Database Engineering {\&} Applications Symposium,
                   {IDEAS} '13, Barcelona, Spain - October 09 - 11, 2013},
      pages     = {86--95},
      year      = {2013},
      crossref  = {DBLP:conf/ideas/2013},
      url       = {http://doi.acm.org/10.1145/2513591.2513643},
      doi       = {10.1145/2513591.2513643},
      timestamp = {Tue, 05 Nov 2013 09:56:48 +0100},
      biburl    = {http://dblp.uni-trier.de/rec/bib/conf/ideas/GraefePIM13},
      bibsource = {dblp computer science bibliography, http://dblp.org}
    }
  • Ivanov, T., Korfiatis, N., & Zicari, R. V.. (2013). On the inequality of the 3V’s of Big Data Architectural Paradigms: A case for Heterogeneity. Frankfurt Big Data Laboratory Working Paper.
    [Bibtex]
    @UNPUBLISHED{2013-WP-BIGDATAHET,
      author = {Ivanov, T. and Korfiatis, N. and Zicari, Roberto V.},
      title = {{On the inequality of the 3V’s of Big Data Architectural Paradigms:  A case for Heterogeneity}},
      month = {November},
      publisher={Frankfurt Big Data Laboratory Working Paper},
      abstract = {The well-known 3V architectural paradigm for Big Data introduced by Laney (2011) provides a simplified framework for defining the architecture of a big data platform to be deployed in various scenarios tackling processing of massive datasets. While additional components such as Variability and Veracity have been discussed as an extension to the 3V model, the basic components (Volume, Variety, and Velocity) provide a quantitative framework while varia-bility and veracity target a more qualitative approach. In this paper we argue why the basic 3V’s are not equal due to the different requirements that need to be covered in case higher demands for a particular “V”. Similar to other conjectures such as the CAP theorem, 3V based architectures differ on their implementation. We call this paradigm heterogeneity and we provide a taxonomy of the existing tools (as of 2013) covering the Hadoop ecosystem from the perspective of heterogeneity. This paper contributes on the understanding of the Hadoop ecosystem from the perspective of different workloads and aims to help researchers and practitioners on the design of scalable platforms targeting different operational needs.},
      keywords={3V’s, Heterogeneity, Big data platforms, Big data systems architecture},
      url={http://arxiv.org/ftp/arxiv/papers/1311/1311.0805.pdf},
      year = {2013}
    }

Presentations

Supervised Theses

 

Teaching

Background

Research Projects & Cooperations

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(C) Big Data Laboratory. Design By Tea Sets