scholarly journals A Distributed Indexing Method for Timeline Similarity Query

Algorithms ◽  
2018 ◽  
Vol 11 (4) ◽  
pp. 41 ◽  
Author(s):  
Zhenwen He ◽  
Xiaogang Ma
Keyword(s):  
Indexing Method ◽  
Similarity Query ◽  
Distributed Indexing

scholarly journals The efficient implementation of distributed indexing with Hadoop for digital investigations on Big Data

2014 ◽  
Vol 11 (3) ◽  
pp. 1037-1054 ◽  
Author(s):  
Taerim Lee ◽  
Hyejoo Lee ◽  
Kyung-Hyune Rhee ◽  
Uk Shin
Keyword(s):  
Big Data ◽  
Text Processing ◽  
Processing System ◽  
Cloud Service ◽  
Data Indexing ◽  
Hadoop Mapreduce ◽  
Relevant Evidence ◽  
Indexing Method ◽  
Distributed Indexing ◽  
Digital Investigation

Big Data brings new challenges to the field of e-Discovery or digital forensics and these challenges are mostly connected to the various methods for data processing. Considering that the most important factors are time and cost in determining success or failure of digital investigation, the development of a valid indexing method for efficient search should come first to more quickly and accurately find relevant evidence from Big Data. This paper, therefore, introduces a Distributed Text Processing System based on Hadoop called DTPS and explains about the distinctions between DTPS and other related researches to emphasize the necessity of it. In addition, this paper describes various experimental results in order to find the best implementation strategy in using Hadoop MapReduce for the distributed indexing and to analyze the worth for practical use of DTPS by comparative evaluation of its performance with similar tools. To be short, the ultimate purpose of this research is the development of useful search engine specially aimed at Big Data indexing as a major part for the future e-Discovery cloud service.

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.

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