scholarly journals Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation

2021 ◽  
Vol 15 ◽  
Author(s):  
Florian Porrmann ◽  
Sarah Pilz ◽  
Alessandra Stella ◽  
Alexander Kleinjohann ◽  
Michael Denker ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Spike Activity ◽  
Pattern Mining ◽  
Spike Trains ◽  
Embedded Devices ◽  
Data Set ◽  
Benchmark Data ◽  
Valuation Method ◽  
Distributed Execution ◽  
Spatio Temporal

The SPADE (spatio-temporal Spike PAttern Detection and Evaluation) method was developed to find reoccurring spatio-temporal patterns in neuronal spike activity (parallel spike trains). However, depending on the number of spike trains and the length of recording, this method can exhibit long runtimes. Based on a realistic benchmark data set, we identified that the combination of pattern mining (using the FP-Growth algorithm) and the result filtering account for 85–90% of the method's total runtime. Therefore, in this paper, we propose a customized FP-Growth implementation tailored to the requirements of SPADE, which significantly accelerates pattern mining and result filtering. Our version allows for parallel and distributed execution, and due to the improvements made, an execution on heterogeneous and low-power embedded devices is now also possible. The implementation has been evaluated using a traditional workstation based on an Intel Broadwell Xeon E5-1650 v4 as a baseline. Furthermore, the heterogeneous microserver platform RECS|Box has been used for evaluating the implementation on two HiSilicon Hi1616 (Kunpeng 916), an Intel Coffee Lake-ER Xeon E-2276ME, an Intel Broadwell Xeon D-D1577, and three NVIDIA Tegra devices (Jetson AGX Xavier, Jetson Xavier NX, and Jetson TX2). Depending on the platform, our implementation is between 27 and 200 times faster than the original implementation. At the same time, the energy consumption was reduced by up to two orders of magnitude.

scholarly journals PSOTSC: A Global-Oriented Trajectory Segmentation and Compression Algorithm Based on Swarm Intelligence

2021 ◽  
Vol 10 (12) ◽  
pp. 817
Author(s):  
Zhihong Ouyang ◽  
Lei Xue ◽  
Feng Ding ◽  
Da Li
Keyword(s):  
Data Storage ◽  
Pattern Mining ◽  
Real Data ◽  
Compression Algorithm ◽  
Compression Rate ◽  
Data Set ◽  
Segmentation Accuracy ◽  
Spatio Temporal ◽  
Update Strategy ◽  
Trajectory Segmentation

Linear approximate segmentation and data compression of moving target spatio-temporal trajectory can reduce data storage pressure and improve the efficiency of target motion pattern mining. High quality segmentation and compression need to accurately select and store as few points as possible that can reflect the characteristics of the original trajectory, while the existing methods still have room for improvement in segmentation accuracy, reduction of compression rate and simplification of algorithm parameter setting. A trajectory segmentation and compression algorithm based on particle swarm optimization is proposed. First, the trajectory segmentation problem is transformed into a global intelligent optimization problem of segmented feature points, which makes the selection of segmented points more accurate; then, a particle update strategy combining neighborhood adjustment and random jump is established to improve the efficiency of segmentation and compression. Through experiments on a real data set and a maneuvering target simulation trajectory set, the results show that compared with the existing typical methods, this method has advantages in segmentation accuracy and compression rate.

Spatial-temporal data collection process models using mobile sinks

2019 ◽  
Vol 942 (12) ◽  
pp. 22-28
Author(s):  
A.V. Materuhin ◽  
V.V. Shakhov ◽  
O.D. Sokolova
Keyword(s):  
Energy Consumption ◽  
Operation Time ◽  
Point Of View ◽  
Process Models ◽  
Temporal Data ◽  
Network Nodes ◽  
Mobile Sinks ◽  
Autonomous Operation ◽  
Data Collection Process ◽  
Spatio Temporal

Optimization of energy consumption in geosensor networks is a very important factor in ensuring stability, since geosensors used for environmental monitoring have limited possibilities for recharging batteries. The article is a concise presentation of the research results in the area of increasing the energy consumption efficiency for the process of collecting spatio-temporal data with wireless geosensor networks. It is shown that in the currently used configurations of geosensor networks there is a predominant direction of the transmitted traffic, which leads to the fact that through the routing nodes that are close to the sinks, a much more traffic passes than through other network nodes. Thus, an imbalance of energy consumption arises in the network, which leads to a decrease in the autonomous operation time of the entire wireless geosensor networks. It is proposed to use the possible mobility of sinks as an optimization resource. A mathematical model for the analysis of the lifetime of a wireless geosensor network using mobile sinks is proposed. The model is analyzed from the point of view of optimization energy consumption by sensors. The proposed approach allows increasing the lifetime of wireless geosensor networks by optimizing the relocation of mobile sinks.

scholarly journals A data acquisition setup for data driven acoustic design

2021 ◽  
pp. 1351010X2098690
Author(s):  
Romana Rust ◽  
Achilleas Xydis ◽  
Kurt Heutschi ◽  
Nathanael Perraudin ◽  
Gonzalo Casas ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Interdisciplinary Approach ◽  
Computational Design ◽  
Surface Structures ◽  
Future Research ◽  
Data Set ◽  
Process Measures ◽  
First Results ◽  
Spatio Temporal ◽  
Automated Data Acquisition

In this paper, we present a novel interdisciplinary approach to study the relationship between diffusive surface structures and their acoustic performance. Using computational design, surface structures are iteratively generated and 3D printed at 1:10 model scale. They originate from different fabrication typologies and are designed to have acoustic diffusion and absorption effects. An automated robotic process measures the impulse responses of these surfaces by positioning a microphone and a speaker at multiple locations. The collected data serves two purposes: first, as an exploratory catalogue of different spatio-temporal-acoustic scenarios and second, as data set for predicting the acoustic response of digitally designed surface geometries using machine learning. In this paper, we present the automated data acquisition setup, the data processing and the computational generation of diffusive surface structures. We describe first results of comparative studies of measured surface panels and conclude with steps of future research.

Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D + T: The Cook Agronomy Farm data set

2015 ◽  
Vol 14 ◽  
pp. 70-90 ◽  
Author(s):  
Caley K. Gasch ◽  
Tomislav Hengl ◽  
Benedikt Gräler ◽  
Hanna Meyer ◽  
Troy S. Magney ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Water Temperature ◽  
Soil Water ◽  
Data Set ◽  
Temporal Interpolation ◽  
Spatio Temporal

Nuclide Composition Benchmark Data Set for Verifying Burnup Codes on Spent Light Water Reactor Fuels

2002 ◽  
Vol 137 (2) ◽  
pp. 111-126 ◽  
Author(s):  
Yoshinori Nakahara ◽  
Kenya Suyama ◽  
Jun Inagawa ◽  
Ryuji Nagaishi ◽  
Setsumi Kurosawa ◽  
...  
Keyword(s):  
Light Water Reactor ◽  
Data Set ◽  
Light Water ◽  
Water Reactor ◽  
Benchmark Data ◽  
Nuclide Composition

Understanding the Importance of Capturing Climate and Occupancy Trends During Concept-Stage Sustainable Building Design

2018 ◽  
Author(s):  
Sean Lin ◽  
Bahaa Albarhami ◽  
Salvador Mayoral ◽  
Joseph Piacenza
Keyword(s):  
Energy Consumption ◽  
Simulation Models ◽  
Building Design ◽  
Building Energy ◽  
Simulation Software ◽  
Phase Iii ◽  
Data Set ◽  
Climate Zones ◽  
Leed Certification ◽  
Actual Energy Consumption

This paper presents a comparison of concept stage computational model predictions to capture how building energy consumption is affected by different climate zones. The California State University, Fullerton (CSUF) Student Housing Phase III, which received a Platinum Leadership in Energy and Environmental Design (LEED) certification for the Building Design and Construction category, and its performance in a LEED California Nonresidential Title 24 (NRT24) and ASHRAE 90.1 climate zones is used as a case study to illustrate the method. Through LEED approved simulation software, the standard compliant energy simulation models are compared to the occupancy scheduled models along with the actual energy consumption in different climate zones. The results provide insight to how variables within student dormitory life affect total building energy usage. Total amount of energy consumed per area is one new factor providing understanding into occupancy trends. This new data set reveals more understanding regarding how and where the energy is consumed to maintain a comfortable learning environment.

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