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WFRF:(Garcia Eva)
Sökning: WFRF:(Garcia Eva) > Licentiatavhandling > Extraction and Ener...
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- García-Martín, Eva,1989-Blekinge Tekniska Högskola,Institutionen för datalogi och datorsystemteknik,Department of Computer Science and Engineering (författare)
Extraction and Energy Efficient Processing of Streaming Data
- BokEngelska2017
Förlag, utgivningsår, omfång ...
- Karlskrona :Blekinge Tekniska Högskola,2017
- electronicrdacarrier
Nummerbeteckningar
- LIBRIS-ID:oai:DiVA.org:bth-15532
- https://urn.kb.se/resolve?urn=urn:nbn:se:bth-15532URI
Kompletterande språkuppgifter
- Språk:engelska
- Sammanfattning på:engelska
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- SwePubSwePub
Klassifikation
Anmärkningar
- 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.
Ämnesord och genrebeteckningar
- NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
- machine learning
- green computing
- data mining
- data stream mining
- green machine learning
Biuppslag (personer, institutioner, konferenser, titlar ...)
- Lavesson, Niklas,ProfessorBlekinge Tekniska Högskola,Institutionen för datalogi och datorsystemteknik (preses)
- Grahn, Håkan,ProfessorBlekinge Tekniska Högskola,Institutionen för datalogi och datorsystemteknik (preses)
- Bifet, Albert (opponent)
- Blekinge Tekniska HögskolaInstitutionen för datalogi och datorsystemteknik (creator_code:org_t)
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- Extraction and Energy Efficient Processing of Streaming Data (Sök publikationen i LIBRIS)
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