Machine learning algorithms account for a significant amount of energy con¬sumed in data centers. In Eva Garcia-Martín’s licentiate thesis she focuses on investigating how energy is consumed and optimized in an online learning algorithm.
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. 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.
On the 18th of December Eva Garcia-Martín will defend her licentiate thesis; Extraction and Energy Efficient Processing of Streaming Data. The thesis is comprised 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. This 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.
On the 18th of December Garcia-Martín’s opponent will be Albert Bifet, Professor at LTCI, Telecom ParisTech and Head of the Data, Intelligence and Graphs (DIG) Group at Telecom ParisTech. Professor Bifet is also co-leading the open source project MAO (Massive Online Analysis), the most popular open source framework for data stream mining.
Read Garcia-Martín’s licentiate thesis here;
Welcome to attend Eva Garcia Martin´s licentiate seminar on the 18th of December at 13:00 in J1640, Campus Gräsvik, Karlskrona