An Edge Device for Personal Big Data Tracking

2021 ◽  
Author(s):  
Haichao Huang ◽  
Yuqing Xie ◽  
Zhangchi Ying ◽  
Chang Yao ◽  
Peng Lu ◽  
...  
Keyword(s):  
Big Data ◽  
Data Tracking

scholarly journals PhilDB - The time series database with built-in change logging

2015 ◽  
Author(s):  
Andrew MacDonald
Keyword(s):  
Time Series ◽  
Big Data ◽  
Open Source ◽  
High Performance ◽  
Time Series Data ◽  
Handling Time ◽  
Series Data ◽  
Meta Data ◽  
Static Data ◽  
Data Tracking

PhilDB is an open-source time series database. It supports storage of time series datasets that are dynamic, that is recording updates to existing values in a log as they occur. Recent open-source systems, such as InfluxDB and OpenTSDB, have been developed to indefinitely store long-period, high-resolution time series data. Unfortunately they require a large initial installation investment before use because they are designed to operate over a cluster of servers to achieve high-performance writing of static data in real time. In essence, they have a ‘big data’ approach to storage and access. Other open-source projects for handling time series data that don’t take the ‘big data’ approach are also relatively new and are complex or incomplete. None of these systems gracefully handle revision of existing data while tracking values that changed. Unlike ‘big data’ solutions, PhilDB has been designed for single machine deployment on commodity hardware, reducing the barrier to deployment. PhilDB eases loading of data for the user by utilising an intelligent data write method. It preserves existing values during updates and abstracts the update complexity required to achieve logging of data value changes. PhilDB improves accessing datasets by two methods. Firstly, it uses fast reads which make it practical to select data for analysis. Secondly, it uses simple read methods to minimise effort required to extract data. PhilDB takes a unique approach to meta-data tracking; optional attribute attachment. This facilitates scaling the complexities of storing a wide variety of data. That is, it allows time series data to be loaded as time series instances with minimal initial meta-data, yet additional attributes can be created and attached to differentiate the time series instances as a wider variety of data is needed. PhilDB was written in Python, leveraging existing libraries. This paper describes the general approach, architecture, and philosophy of the PhilDB software.

scholarly journals PhilDB: the time series database with built-in change logging

2016 ◽  
Vol 2 ◽  
pp. e52 ◽  
Author(s):  
Andrew MacDonald
Keyword(s):  
Time Series ◽  
Big Data ◽  
Open Source ◽  
High Performance ◽  
Time Series Data ◽  
Handling Time ◽  
Series Data ◽  
Meta Data ◽  
Static Data ◽  
Data Tracking

PhilDB is an open-source time series database that supports storage of time series datasets that are dynamic; that is, it records updates to existing values in a log as they occur. PhilDB eases loading of data for the user by utilising an intelligent data write method. It preserves existing values during updates and abstracts the update complexity required to achieve logging of data value changes. It implements fast reads to make it practical to select data for analysis. Recent open-source systems have been developed to indefinitely store long-period high-resolution time series data without change logging. Unfortunately, such systems generally require a large initial installation investment before use because they are designed to operate over a cluster of servers to achieve high-performance writing of static data in real time. In essence, they have a ‘big data’ approach to storage and access. Other open-source projects for handling time series data that avoid the ‘big data’ approach are also relatively new and are complex or incomplete. None of these systems gracefully handle revision of existing data while tracking values that change. Unlike ‘big data’ solutions, PhilDB has been designed for single machine deployment on commodity hardware, reducing the barrier to deployment. PhilDB takes a unique approach to meta-data tracking; optional attribute attachment. This facilitates scaling the complexities of storing a wide variety of data. That is, it allows time series data to be loaded as time series instances with minimal initial meta-data, yet additional attributes can be created and attached to differentiate the time series instances when a wider variety of data is needed. PhilDB was written in Python, leveraging existing libraries. While some existing systems come close to meeting the needs PhilDB addresses, none cover all the needs at once. PhilDB was written to fill this gap in existing solutions. This paper explores existing time series database solutions, discusses the motivation for PhilDB, describes the architecture and philosophy of the PhilDB software, and performs an evaluation between InfluxDB, PhilDB, and SciDB.

scholarly journals PhilDB: The time series database with built-in change logging

2016 ◽  
Author(s):  
Andrew MacDonald
Keyword(s):  
Time Series ◽  
Big Data ◽  
Open Source ◽  
High Performance ◽  
Time Series Data ◽  
Handling Time ◽  
Series Data ◽  
Meta Data ◽  
Data Tracking ◽  
Simple Evaluation

PhilDB is an open-source time series database that supports storage of time series datasets that are dynamic, that is it records updates to existing values in a log as they occur. PhilDB eases loading of data for the user by utilising an intelligent data write method. It preserves existing values during updates and abstracts the update complexity required to achieve logging of data value changes. It implements fast reads to make it practical to select data for analysis. Recent open-source systems have been developed to indefinitely store long-period high-resolution time series data without change logging. Unfortunately such systems generally require a large initial installation investment before use because they are designed to operate over a cluster of servers to achieve high-performance writing of static data in real time. In essence, they have a 'big data' approach to storage and access. Other open-source projects for handling time series data that avoid the 'big data' approach are also relatively new and are complex or incomplete. None of these systems gracefully handle revision of existing data while tracking values that changed. Unlike 'big data' solutions, PhilDB has been designed for single machine deployment on commodity hardware, reducing the barrier to deployment. PhilDB takes a unique approach to meta-data tracking; optional attribute attachment. This facilitates scaling the complexities of storing a wide variety of data. That is, it allows time series data to be loaded as time series instances with minimal initial meta-data, yet additional attributes can be created and attached to differentiate the time series instances when a wider variety of data is needed. PhilDB was written in Python, leveraging existing libraries. While some existing systems come close to meeting the needs PhilDB addresses, none cover all the needs at once. PhilDB was written to fill this gap in existing solutions. This paper explores existing time series database solutions, discusses the motivation for PhilDB, describes the architecture and philosophy of the PhilDB software, and performs a simple evaluation between InfluxDB, PhilDB, and SciDB.

scholarly journals PhilDB: The time series database with built-in change logging

2016 ◽  
Author(s):  
Andrew MacDonald
Keyword(s):  
Time Series ◽  
Big Data ◽  
Open Source ◽  
High Performance ◽  
Time Series Data ◽  
Handling Time ◽  
Series Data ◽  
Meta Data ◽  
Data Tracking ◽  
Simple Evaluation

PhilDB is an open-source time series database that supports storage of time series datasets that are dynamic, that is it records updates to existing values in a log as they occur. PhilDB eases loading of data for the user by utilising an intelligent data write method. It preserves existing values during updates and abstracts the update complexity required to achieve logging of data value changes. It implements fast reads to make it practical to select data for analysis. Recent open-source systems have been developed to indefinitely store long-period high-resolution time series data without change logging. Unfortunately such systems generally require a large initial installation investment before use because they are designed to operate over a cluster of servers to achieve high-performance writing of static data in real time. In essence, they have a 'big data' approach to storage and access. Other open-source projects for handling time series data that avoid the 'big data' approach are also relatively new and are complex or incomplete. None of these systems gracefully handle revision of existing data while tracking values that changed. Unlike 'big data' solutions, PhilDB has been designed for single machine deployment on commodity hardware, reducing the barrier to deployment. PhilDB takes a unique approach to meta-data tracking; optional attribute attachment. This facilitates scaling the complexities of storing a wide variety of data. That is, it allows time series data to be loaded as time series instances with minimal initial meta-data, yet additional attributes can be created and attached to differentiate the time series instances when a wider variety of data is needed. PhilDB was written in Python, leveraging existing libraries. While some existing systems come close to meeting the needs PhilDB addresses, none cover all the needs at once. PhilDB was written to fill this gap in existing solutions. This paper explores existing time series database solutions, discusses the motivation for PhilDB, describes the architecture and philosophy of the PhilDB software, and performs a simple evaluation between InfluxDB, PhilDB, and SciDB.

scholarly journals Big Data Tracking and Automatic Measurement Technology for Unmanned Aerial Vehicle Trajectory Based on MEMS Sensor

2021 ◽  
Author(s):  
Jun Xing ◽  
Xinzhe Wang ◽  
Jie Dong
Keyword(s):  
Differential Equation ◽  
Big Data ◽  
Automatic Measurement ◽  
Magnetic Data ◽  
Discrete Equation ◽  
Measurement Technology ◽  
Measurement Information ◽  
Gradient Descent Method ◽  
Mems Sensor ◽  
Data Tracking

Abstract Due to the high cost and large error of traditional UAV big data tracking and automatic measurement technology, a method of big data tracking and automatic measurement for UAV trajectory based on MEMS sensor was put forward. The iterative learning control algorithm was used to estimate the repetitive disturbance and modeling error of system based on the simplified dynamics model of four-rotor helicopter and the optimal estimation characteristics of Kalman filter. The discrete equation of quadratic performance function in time domain was selected to compensate the estimated model error disturbance, and then the big data tracking was completed. Based on the data of gyroscope, the quaternion differential equation was established. The differential equation was solved by first-order Picard method, and a set of quaternion data was obtained. The gradient descent method was used to process the acceleration data and magnetic data, and thus to get the optimal quaternion. Finally, the measurement results were obtained by fusing the two quaternions with MEMS sensors. Simulation results prove that the proposed method can obtain the trajectory tracking data and measurement information of UAV accurately.

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

“Big Data in Psychology: Methods and Applications”

2017 ◽  
Vol 225 (3) ◽  
pp. 287-288
Keyword(s):  
Big Data

An associated conference will take place at ZPID – Leibniz Institute for Psychology Information in Trier, Germany, on June 7–9, 2018. For further details, see: http://bigdata2018.leibniz-psychology.org

When the Sample Is the Population: Big Data

PsycCRITIQUES ◽  
2014 ◽  
Vol 59 (2) ◽  
Author(s):  
David J. Pittenger
Keyword(s):  
Big Data
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