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Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0003-4973-9255
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0001-9947-1088
2017 (engelsk)Inngår i: Trends in Social Network Analysis: Information Propagation, User Behavior Modeling, Forecasting, and Vulnerability Assessment / [ed] Rokia Missaoui, Talel Abdessalem, Matthieu Latapy, Cham, Switzerland: Springer, 2017, s. 229-252Kapittel i bok, del av antologi (Fagfellevurdert)
Abstract [en]

Data mining algorithms are usually designed to optimize a trade-off between predictive accuracy and computational efficiency. This paper introduces energy consumption and energy efficiency as important factors to consider during data mining algorithm analysis and evaluation. We conducted an experiment to illustrate how energy consumption and accuracy are affected when varying the parameters of the Very Fast Decision Tree (VFDT) algorithm. These results are compared with a theoretical analysis on the algorithm, indicating that energy consumption is affected by the parameters design and that it can be reduced significantly while maintaining accuracy.

sted, utgiver, år, opplag, sider
Cham, Switzerland: Springer, 2017. s. 229-252
Serie
Lectures Notes in Social Networks, ISSN 2190-5428
Emneord [en]
Energy efficiency, Green computing, Very Fast Decision Tree, Big Data
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-15489DOI: 10.1007/978-3-319-53420-6_10ISBN: 978-3-319-53419-0 (tryckt)ISBN: 978-3-319-53420-6 (digital)OAI: oai:DiVA.org:bth-15489DiVA, id: diva2:1156925
Forskningsfinansiär
Knowledge Foundation, 20140032Tilgjengelig fra: 2017-11-14 Laget: 2017-11-14 Sist oppdatert: 2019-12-03bibliografisk kontrollert
Inngår i avhandling
1. Extraction and Energy Efficient Processing of Streaming Data
Åpne denne publikasjonen i ny fane eller vindu >>Extraction and Energy Efficient Processing of Streaming Data
2017 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

The interest in machine learning algorithms is increasing, in parallel with the advancements in hardware and software required to mine large-scale datasets. Machine learning algorithms account for a significant amount of energy consumed in data centers, which impacts the global energy consumption. However, machine learning algorithms are optimized towards predictive performance and scalability. Algorithms with low energy consumption are necessary for embedded systems and other resource constrained devices; and desirable for platforms that require many computations, such as data centers. Data stream mining investigates how to process potentially infinite streams of data without the need to store all the data. This ability is particularly useful for companies that are generating data at a high rate, such as social networks.

This thesis investigates algorithms in the data stream mining domain from an energy efficiency perspective. The thesis comprises of two parts. The first part explores how to extract and analyze data from Twitter, with a pilot study that investigates a correlation between hashtags and followers. The second and main part investigates how energy is consumed and optimized in an online learning algorithm, suitable for data stream mining tasks.

The second part of the thesis focuses on analyzing, understanding, and reformulating the Very Fast Decision Tree (VFDT) algorithm, the original Hoeffding tree algorithm, into an energy efficient version. It presents three key contributions. First, it shows how energy varies in the VFDT from a high-level view by tuning different parameters. Second, it presents a methodology to identify energy bottlenecks in machine learning algorithms, by portraying the functions of the VFDT that consume the largest amount of energy. Third, it introduces dynamic parameter adaptation for Hoeffding trees, a method to dynamically adapt the parameters of Hoeffding trees to reduce their energy consumption. The results show an average energy reduction of 23% on the VFDT algorithm.

sted, utgiver, år, opplag, sider
Karlskrona: Blekinge Tekniska Högskola, 2017
Serie
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 3
Emneord
machine learning, green computing, data mining, data stream mining, green machine learning
HSV kategori
Identifikatorer
urn:nbn:se:bth-15532 (URN)
Presentation
2017-12-18, J1640, Blekinge Tekniska Högskola, 371 79, Karlskrona, 13:00 (engelsk)
Opponent
Veileder
Prosjekter
Scalable resource-efficient systems for big data analytics
Forskningsfinansiär
Knowledge Foundation, 20140032
Tilgjengelig fra: 2017-11-22 Laget: 2017-11-22 Sist oppdatert: 2018-01-13bibliografisk kontrollert
2. Energy Efficiency in Machine Learning: Approaches to Sustainable Data Stream Mining
Åpne denne publikasjonen i ny fane eller vindu >>Energy Efficiency in Machine Learning: Approaches to Sustainable Data Stream Mining
2020 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Energy efficiency in machine learning explores how to build machine learning algorithms and models with low computational and power requirements. Although energy consumption is starting to gain interest in the field of machine learning, still the majority of solutions focus on obtaining the highest predictive accuracy, without a clear focus on sustainability.

This thesis explores green machine learning, which builds on green computing and computer architecture to design sustainable and energy efficient machine learning algorithms. In particular, we investigate how to design machine learning algorithms that automatically learn from streaming data in an energy efficient manner.

We first illustrate how energy can be measured in the context of machine learning, in the form of a literature review and a procedure to create theoretical energy models. We use this knowledge to analyze the energy footprint of Hoeffding trees, presenting an energy model that maps the number of computations and memory accesses to the main functionalities of the algorithm. We also analyze the hardware events correlated to the execution of the algorithm, their functions and their hyper parameters.

The final contribution of the thesis is showcased by two novel extensions of Hoeffding tree algorithms, the Hoeffding tree with nmin adaptation and the Green Accelerated Hoeffding Tree. These solutions are able to reduce their energy consumption by twenty and thirty percent, with minimal effect on accuracy. This is achieved by setting an individual splitting criteria for each branch of the decision tree, spending more energy on the fast growing branches and saving energy on the rest.

This thesis shows the importance of evaluating energy consumption when designing machine learning algorithms, proving that we can design more energy efficient algorithms and still achieve competitive accuracy results.

sted, utgiver, år, opplag, sider
Karlskrona: Blekinge Tekniska Högskola, 2020. s. 267
Serie
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2
Emneord
machine learning, energy efficiency, data stream mining, green machine learning, edge computing
HSV kategori
Identifikatorer
urn:nbn:se:bth-18986 (URN)978-91-7295-396-3 (ISBN)
Disputas
2020-01-31, Blekinge Institute of Technology, Karlskrona, 09:59 (engelsk)
Opponent
Veileder
Forskningsfinansiär
Knowledge Foundation, 20140032
Tilgjengelig fra: 2019-12-03 Laget: 2019-12-03 Sist oppdatert: 2019-12-04bibliografisk kontrollert

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