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Boeva, Veselka, ProfessorORCID iD iconorcid.org/0000-0003-3128-191x
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Publications (6 of 6) Show all publications
Angelova, M., Vishnu Manasa, D., Boeva, V., Linde, P. & Lavesson, N. (2019). An Expertise Recommender System based on Data from an Institutional Repository (DiVA). In: Leslie Chan & Pierre Mounier (Ed.), Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers (pp. 135-149). OpenEdition Press
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2019 (English)In: Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers / [ed] Leslie Chan & Pierre Mounier, OpenEdition Press , 2019, p. 135-149Chapter in book (Refereed)
Abstract [en]

Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors in academy.

Place, publisher, year, edition, pages
OpenEdition Press, 2019
Keywords
Text mining, Recommender system, Institutional repository, Ontology
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:hj:diva-45613 (URN)979-1-0365-3801-8 (ISBN)979-1-0365-3802-5 (ISBN)
Available from: 2019-06-18 Created: 2019-08-19 Last updated: 2019-08-19Bibliographically approved
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. (2019). How to Measure Energy Consumption in Machine Learning Algorithms. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham. Paper presented at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018; Dublin; Ireland; 10 September 2018 through 14 September 2018 (pp. 243-255).
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2019 (English)In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham, 2019, p. 243-255Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning algorithms are responsible for a significant amount of computations. These computations are increasing with the advancements in different machine learning fields. For example, fields such as deep learning require algorithms to run during weeks consuming vast amounts of energy. While there is a trend in optimizing machine learning algorithms for performance and energy consumption, still there is little knowledge on how to estimate an algorithm’s energy consumption. Currently, a straightforward cross-platform approach to estimate energy consumption for different types of algorithms does not exist. For that reason, well-known researchers in computer architecture have published extensive works on approaches to estimate the energy consumption. This study presents a survey of methods to estimate energy consumption, and maps them to specific machine learning scenarios. Finally, we illustrate our mapping suggestions with a case study, where we measure energy consumption in a big data stream mining scenario. Our ultimate goal is to bridge the current gap that exists to estimate energy consumption in machine learning scenarios.

Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11329
Keywords
Computer architecture, Energy efficiency, Green computing, Machine learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:hj:diva-45614 (URN)10.1007/978-3-030-13453-2_20 (DOI)9783030134525 (ISBN)
Conference
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018; Dublin; Ireland; 10 September 2018 through 14 September 2018
Funder
Knowledge Foundation, 20140032
Available from: 2019-08-19 Created: 2019-08-19 Last updated: 2019-08-19Bibliographically approved
Angelova, M., Vishnu Manasa, D., Boeva, V., Linde, P. & Lavesson, N. (2018). An Expertise Recommender SystemBased on Data from an Institutional Repository (DiVA). In: Proceedings of the 22nd edition of the International Conference on ELectronic PUBlishing: . Paper presented at 22nd edition of the International Conference on ELectronic PUBlishing - Connecting the Knowledge Commons: From Projects to Sustainable Infrastructure, Toronto.
Open this publication in new window or tab >>An Expertise Recommender SystemBased on Data from an Institutional Repository (DiVA)
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2018 (English)In: Proceedings of the 22nd edition of the International Conference on ELectronic PUBlishing, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors inacademy.

Keywords
Text mining, Recommender system, Institutional repository, Ontology
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:hj:diva-42989 (URN)0.4000/proceedings.elpub.2018.17 (DOI)
Conference
22nd edition of the International Conference on ELectronic PUBlishing - Connecting the Knowledge Commons: From Projects to Sustainable Infrastructure, Toronto
Note

open access

Available from: 2019-02-15 Created: 2019-02-15 Last updated: 2019-08-20Bibliographically approved
Boeva, V., Angelova, M., Lavesson, N., Rosander, O. & Tsiporkova, E. (2018). Evolutionary clustering techniques for expertise mining scenarios. In: van den Herik J.,Rocha A.P. (Ed.), ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence, Volume 2: . Paper presented at 10th International Conference on Agents and Artificial Intelligence, ICAART, Funchal, Madeira (pp. 523-530). SciTePress
Open this publication in new window or tab >>Evolutionary clustering techniques for expertise mining scenarios
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2018 (English)In: ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence, Volume 2 / [ed] van den Herik J.,Rocha A.P., SciTePress, 2018, p. 523-530Conference paper, Published paper (Refereed)
Abstract [en]

The problem addressed in this article concerns the development of evolutionary clustering techniques that can be applied to adapt the existing clustering solution to a clustering of newly collected data elements. We are interested in clustering approaches that are specially suited for adapting clustering solutions in the expertise retrieval domain. This interest is inspired by practical applications such as expertise retrieval systems where the information available in the system database is periodically updated by extracting new data. The experts available in the system database are usually partitioned into a number of disjoint subject categories. It is becoming impractical to re-cluster this large volume of available information. Therefore, the objective is to update the existing expert partitioning by the clustering produced on the newly extracted experts. Three different evolutionary clustering techniques are considered to be suitable for this scenario. The proposed techniques are initially evaluated by applying the algorithms on data extracted from the PubMed repository. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

Place, publisher, year, edition, pages
SciTePress, 2018
Keywords
Data Mining, Expert Finding, Health Science, Knowledge Management, Natural Language Processing, Artificial intelligence, Cluster analysis, Natural language processing systems, Search engines, Clustering approach, Clustering solutions, Data elements, Evolutionary clustering, Retrieval systems, System database
National Category
Computer Sciences
Identifiers
urn:nbn:se:hj:diva-42994 (URN)2-s2.0-85046663632 (Scopus ID)9789897582752 (ISBN)
Conference
10th International Conference on Agents and Artificial Intelligence, ICAART, Funchal, Madeira
Available from: 2018-05-24 Created: 2019-02-15 Last updated: 2019-08-20Bibliographically approved
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. (2018). Hoeffding Trees with nmin adaptation. In: The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018): . Paper presented at IEEE 5th International Conference on Data Science and Advanced Analytics, 1–4 October 2018, Turin (pp. 70-79). IEEE
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2018 (English)In: The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018), IEEE, 2018, p. 70-79Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have fixed parameters from the beginning of the execution. We have observed that having fixed parameters lead to unnecessary computations, thus making the algorithm energy inefficient.In this paper we present the nmin adaptation method for Hoeffding trees. This method adapts the value of the nmin pa- rameter, which significantly affects the energy consumption of the algorithm. The method reduces unnecessary computations and memory accesses, thus reducing the energy, while the accuracy is only marginally affected. We experimentally compared VFDT (Very Fast Decision Tree, the first Hoeffding tree algorithm) and CVFDT (Concept-adapting VFDT) with the VFDT-nmin (VFDT with nmin adaptation). The results show that VFDT-nmin consumes up to 27% less energy than the standard VFDT, and up to 92% less energy than CVFDT, trading off a few percent of accuracy in a few datasets.

Place, publisher, year, edition, pages
IEEE, 2018
Series
Proceedings of the International Conference on Data Science and Advanced Analytics, ISSN 2472-1573
National Category
Computer Sciences
Identifiers
urn:nbn:se:hj:diva-42995 (URN)10.1109/DSAA.2018.00017 (DOI)000459238600008 ()2-s2.0-85062817868 (Scopus ID)978-1-5386-5090-5 (ISBN)
Conference
IEEE 5th International Conference on Data Science and Advanced Analytics, 1–4 October 2018, Turin
Funder
Knowledge Foundation, 20140032
Available from: 2019-02-15 Created: 2019-02-15 Last updated: 2019-08-20Bibliographically approved
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. (2018). How to Measure Energy Consumption in Machine Learning Algorithms. In: Green Data Mining, International Workshop on Energy Efficient Data Mining and Knowledge Discovery: ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham. Paper presented at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Dublin.
Open this publication in new window or tab >>How to Measure Energy Consumption in Machine Learning Algorithms
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2018 (English)In: Green Data Mining, International Workshop on Energy Efficient Data Mining and Knowledge Discovery: ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning algorithms are responsible for a significant amount of computations. These computations are increasing with the advancements in different machine learning fields. For example, fields such as deep learning require algorithms to run during weeks consuming vast amounts of energy. While there is a trend in optimizing machine learning algorithms for performance and energy consumption, still there is little knowledge on how to estimate an algorithm’s energy consumption. Currently, a straightforward cross-platform approach to estimate energy consumption for different types of algorithms does not exist. For that reason, well-known researchers in computer architecture have published extensive works on approaches to estimate the energy consumption. This study presents a survey of methods to estimate energy consumption, and maps them to specific machine learning scenarios. Finally, we illustrate our mapping suggestions with a case study, where we measure energy consumption in a big data stream mining scenario. Our ultimate goal is to bridge the current gap that exists to estimate energy consumption in machine learning scenarios.

National Category
Computer Sciences
Identifiers
urn:nbn:se:hj:diva-42996 (URN)
Conference
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Dublin
Funder
Knowledge Foundation, 20140032
Available from: 2019-02-15 Created: 2019-02-15 Last updated: 2019-08-20
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3128-191x

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