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- García-Martín, Eva, 1989-
(författare)
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Extraction and Energy Efficient Processing of Streaming Data
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 2. |
- García-Martín, Eva, 1989-, et al.
(författare)
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Green Accelerated Hoeffding Tree
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- For the past years, the main concern in machine learning had been to create highly accurate models, without considering the high computational requirements involved. Stream mining algorithms are able to produce highly accurate models in real time, without strong computational demands. This is the case of the Hoeffding tree algorithm. Recent extensions to this algorithm, such as the Extremely Very Fast Decision Tree (EFDT), focus on increasing predictive accuracy, but at the cost of a higher energy consumption. This paper presents the Green Accelerated Hoeffding Tree (GAHT) algorithm, which is able to achieve same levels of accuracy as the latest EFDT, while reducing its energy consumption by 27 percent with minimal effect on accuracy.
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| 3. |
- Tommasini, Riccardo, et al.
(författare)
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Continuous Analytics of Web Streams Half-Day Tutorial at The Web Conference 2019
- 2019
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Ingår i: COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2019 ). - New York, NY, USA : ASSOC COMPUTING MACHINERY. - 9781450366755 ; , s. 1323-1325
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Konferensbidrag (refereegranskat)abstract
- This half-day tutorial provides a comprehensive introduction to web stream processing, including the fundamental stream reasoning concepts, as well as an introduction to practical implementations and how to use them in concrete web applications. To this extent, we intend to (1) survey existing research outcomes from Stream Reasoning / RDF Stream Processing that arise in querying, reasoning on and learning from a variety of highly dynamic data, (2) introduce deductive and inductive stream reasoning techniques as powerful tools to use when addressing a data-centric problem characterized both by variety and velocity, (3) present a relevant use-case, which requires to address data velocity and variety simultaneously on the web, and guide the participants in developing a web stream processing application.
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