scholarly journals TK-Indexing : An Indexing Method for SNS Data Based on NoSQL

2012 ◽  
Vol 19D (4) ◽  
pp. 271-280
Author(s):  
Hyung-Nam Shim ◽  
Jeong-Dong Kim ◽  
Kwang-Soo Seol ◽  
Doo-Kwon Baik
Keyword(s):  
Indexing Method

U-Tree Based Indexing Method for Uncertain Moving Objects

2008 ◽  
Vol 19 (10) ◽  
pp. 2696-2705 ◽  
Author(s):  
Xiao-Feng DING ◽  
Yan-Sheng LU ◽  
Peng PAN ◽  
Liang HONG ◽  
Qiong WEI
Keyword(s):  
Moving Objects ◽  
Indexing Method

scholarly journals Understanding Smartwatch Battery Utilization in the Wild

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3784 ◽  
Author(s):  
Morteza Homayounfar ◽  
Amirhossein Malekijoo ◽  
Aku Visuri ◽  
Chelsea Dobbins ◽  
Ella Peltonen ◽  
...  
Keyword(s):  
Real World ◽  
Binary Classification ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Indexing Method ◽  
In The Wild ◽  
Art Research ◽  
Battery Discharge ◽  
Changes Over Time ◽  
Application Developers

Smartwatch battery limitations are one of the biggest hurdles to their acceptability in the consumer market. To our knowledge, despite promising studies analyzing smartwatch battery data, there has been little research that has analyzed the battery usage of a diverse set of smartwatches in a real-world setting. To address this challenge, this paper utilizes a smartwatch dataset collected from 832 real-world users, including different smartwatch brands and geographic locations. First, we employ clustering to identify common patterns of smartwatch battery utilization; second, we introduce a transparent low-parameter convolutional neural network model, which allows us to identify the latent patterns of smartwatch battery utilization. Our model converts the battery consumption rate into a binary classification problem; i.e., low and high consumption. Our model has 85.3% accuracy in predicting high battery discharge events, outperforming other machine learning algorithms that have been used in state-of-the-art research. Besides this, it can be used to extract information from filters of our deep learning model, based on learned filters of the feature extractor, which is impossible for other models. Third, we introduce an indexing method that includes a longitudinal study to quantify smartwatch battery quality changes over time. Our novel findings can assist device manufacturers, vendors and application developers, as well as end-users, to improve smartwatch battery utilization.

A Similarity Indexing Method for the Data Warehousing - Bit-Wise Indexing Method

2001 ◽  
pp. 525-537
Author(s):  
Wei-Chou Chen ◽  
Shian-Shyong Tseng ◽  
Lu-Ping Chang ◽  
Mon-Fong Jiang
Keyword(s):  
Data Warehousing ◽  
Indexing Method
Sign in / Sign up

Export Citation Format

Share Document