Personal Data Collection and Usage for Mobile Marketing. Customer Awareness and Perception

2021 ◽  
Vol 18 ◽  
pp. 42-50
Author(s):  
Darlynton Yartey ◽  
Oladokun Omojola ◽  
Lanre Amodu ◽  
Naomi Ndubueze ◽  
Babatunde Adeyeye ◽  
...  
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Real Time ◽  
Mobile Devices ◽  
Personal Data ◽  
Mobile Marketing ◽  
Online Information ◽  
Customer Base ◽  
Higher Institutions ◽  
Awareness Level

Marketers have often relied on data to better understand the preferences of the customer base. Whilethe traditional methods were engaged in the retrieval of data, the mobile devices connected to the internetintroduced an influx of data on a real time called big data. Based on this advancement, marketers with thetechnical capacity are able to identify customer needs accurately and identify sway in trends. Although thisstrategy seems beneficial to the marketers, the naïve nature of the customers to the collection and usage ofpersonal online information for mobile marketing remains a crucial poser. Hence, this study through surveysought to identify the awareness level and perception of 700 undergraduates in three higher institutions inLagos, Nigeria. Results show that all the respondents had connected mobile devices, received advertisingmessages on their devices and were active shoppers online. Furthermore, the females were more aware of thecollection and usage of personal data, hence, they embraced the collection based on relevance of advertisingmessages and strict use for mobile marketing. This study therefore recommended marketers’ collection andusage of customers’ personal data to be based on strict use for mobile marketing and assurance of relevance ofadvertising messages.

Survey Research

2018 ◽  
pp. 257-276
Author(s):  
Ernest W. Brewer ◽  
Geraldine Torrisi-Steele ◽  
Victor C. X. Wang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Collection ◽  
Survey Research ◽  
Mobile Devices ◽  
Historical Perspective ◽  
Human Condition ◽  
The Core ◽  
Digital World ◽  
Core Issues

Survey research, in various forms, is the mainstay for social researchers and anyone interested in finding out about people's opinions, attitudes, beliefs, and experiences. Survey research evolved from simple data collection to a more sophisticated scientific method and has proved useful in describing various aspects of the human condition as a basis for further action. However, now survey research is being challenged by the digital world as defined by big data, social media, and mobile devices. In the chapter, the authors provide a historical perspective on survey research, along with a brief presentation of foundational elements of survey research. Then, with the intent of evoking reflective discussion, the authors identify some of the core issues and viewpoints surrounding survey research in the present digital world.

scholarly journals Artificial Intelligence Based Body Sensor Network Framework—Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Ashwin A. Phatak ◽  
Franz-Georg Wieland ◽  
Kartik Vempala ◽  
Frederik Volkmar ◽  
Daniel Memmert
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Collection ◽  
Real Time ◽  
Sensor Network ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Body Sensor Network

AbstractWith the rising amount of data in the sports and health sectors, a plethora of applications using big data mining have become possible. Multiple frameworks have been proposed to mine, store, preprocess, and analyze physiological vitals data using artificial intelligence and machine learning algorithms. Comparatively, less research has been done to collect potentially high volume, high-quality ‘big data’ in an organized, time-synchronized, and holistic manner to solve similar problems in multiple fields. Although a large number of data collection devices exist in the form of sensors. They are either highly specialized, univariate and fragmented in nature or exist in a lab setting. The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location system (RTLS) and wearable biosensors to collect multivariate, low noise, and high-fidelity data. This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis. The study gives a brief overview of wearable sensor technology, RTLS, and provides use cases of AI/ML algorithms in the field of sensor fusion. The study also elaborates sample scenarios using a specific sensor network consisting of pressure sensors (insoles), accelerometers, gyroscopes, ECG, EMG, and RTLS position detectors for particular applications in the field of health care and sports. The AIBSNF may provide a solid blueprint for conducting research and development, forming a smooth end-to-end pipeline from data collection using BSN, RTLS and final stage analytics based on AI/ML algorithms.

Data collection model in hybrid network for participatory sensing

2016 ◽  
Vol 07 (04) ◽  
pp. 1643002
Author(s):  
Jeongseok Choi ◽  
Taeyoung Kim ◽  
Jaekwon Kim ◽  
Sunghwan Moon ◽  
Youngshin Han ◽  
...  
Keyword(s):  
Data Collection ◽  
Mobile Devices ◽  
Personal Data ◽  
Research Area ◽  
Hybrid Network ◽  
Participatory Sensing ◽  
Data Filtering ◽  
Surrounding Environment ◽  
Proposed Model ◽  
Transmission Speed

Advances in mobile technology make most people have their own mobile devices which contain various sensors such as a smartphone. People produce their own personal data or collect surrounding environment data with their mobile devices at every moment. Recently, a broad spectrum of studies on Participatory Sensing, the concept of extracting new knowledge from a mass of data sent by participants, are conducted. Data collection method is one of the base technologies for Participatory Sensing, so networking and data filtering techniques for collecting a large number of data are the most interested research area. In this paper, we propose a data collection model in hybrid network for participatory sensing. The proposed model classifies data into two types and decides networking form and data filtering method based on the data type to decrease loads on data center and improve transmission speed.

scholarly journals Small Data and Its Visualization for Diabetes Self-Management: Qualitative Study

JMIR Diabetes ◽  
10.2196/10324 ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. e10324 ◽  
Author(s):  
Sally Jane Burford ◽  
Sora Park ◽  
Paresh Dawda
Keyword(s):  
Mobile Devices ◽  
Information Seeking ◽  
Self Care ◽  
Personal Data ◽  
The Self ◽  
Self Management ◽  
Small Data ◽  
Data Sets ◽  
Online Information ◽  
The Impact

Background As digital healthcare expands to include the use of mobile devices, there are opportunities to integrate these technologies into the self-management of chronic disease. Purpose built apps for diabetes self-management are plentiful and vary in functionality; they offer capability for individuals to record, manage, display, and interpret their own data. The optimal incorporation of mobile tablets into diabetes self-care is little explored in research, and guidelines for use are scant. Objective The purpose of this study was to examine an individual’s use of mobile devices and apps in the self-management of type 2 diabetes to establish the potential and value of this ubiquitous technology for chronic healthcare. Methods In a 9-month intervention, 28 patients at a large multidisciplinary healthcare center were gifted internet connected Apple iPads with preinstalled apps and given digital support to use them. They were invited to take up predefined activities, which included recording their own biometrics, monitoring their diet, and traditional online information seeking. Four online surveys captured the participants’ perceptions and health outcomes throughout the study. This article reports on the qualitative analysis of the open-ended responses in all four surveys. Results Using apps, participants self-curated small data sets that included their blood glucose level, blood pressure, weight, and dietary intake. The dynamic visualizations of the data in the form of charts and diagrams were created using apps and participants were able to interpret the impact of their choices and behaviors from the diagrammatic form of their small personal data sets. Findings are presented in four themes: (1) recording personal data; (2) modelling and visualizing the data; (3) interpreting the data; and (4) empowering and improving health. Conclusions The modelling capability of apps using small personal data sets, collected and curated by individuals, and the resultant graphical information that can be displayed on tablet screens proves a valuable asset for diabetes self-care. Informed by their own data, individuals are well-positioned to make changes in their daily lives that will improve their health.

Capturing and Analysing Real-Time Data From Tugs

2018 ◽  
Author(s):  
Serena Lim ◽  
Kayvan Pazouki ◽  
Alan J. Murphy ◽  
Ben Zhang
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Real Time ◽  
Sensor Technology ◽  
Maritime Industry ◽  
Time Data ◽  
Raw Data ◽  
Systematic Processing ◽  
Digital Format ◽  
Real Time Data

Holistic energy management in the shipping industry involves reliable data collection, systematic processing and smart analysis. The era of digitisation allows sensor technology to be used on-board vessels, converting different forms of signal into a digital format that can be exported conveniently for further processing. Appropriate sensor selection is important to ensure continuous data collection when vessels sail through harsh conditions. However, without proper processing, this leads to the collection of big data sets but without resulting useful intelligence that benefits the industry. The adoption of digital and computer technology, allows the next phase of fast data processing. This contributes to the growing area of big data analysis, which is now a problem for many technological sectors, including the maritime industry. Enormous databases are often stored without clear goals or suitable uses. Processing of data requires engineering knowledge to ensure suitable filters are applied to raw data. This systematic processing of data leads to transparency in real time data display and contributes to predictive analysis. In addition, the generation of series of raw data when coupled with other external data such as weather information provides a rich database that reflects the true scenario of the vessel. Subsequent processing will then provide improved decision making tools for optimal operations. These advances open the door for different market analyses and the generation of new knowledge. This paper highlights the crucial steps needed and the challenges of sensor installation to obtain accurate data, followed by pre and post processing of data to generate knowledge. With this, big data can now provide information and reveal hidden patterns and trends regarding vessel operations, machinery diagnostics and energy efficient fleet management. A case study was carried out on a tug boat that operates in the North Sea, firstly to demonstrate confidence in the raw data collected and secondly to demonstrate the systematic filtration, aggregation and display of useful information.

scholarly journals Anonymization Based Fisher–Yates Shuffle Method for Streaming of Twitter Data

2019 ◽  
Vol 8 (2S8) ◽  
pp. 408-411
Keyword(s):  
Big Data ◽  
Real Time ◽  
Privacy Preservation ◽  
Personal Data ◽  
Large Data ◽  
Information Loss ◽  
Streaming Data ◽  
Privacy Concern ◽  
Data Utility ◽  
Data Anonymization

In this era of Big Data, many organizations are functioning with personal data, that has to be preserved for privacy reason. There are hazards to identify the individual details by using Quasi Identifier (QI). So to preserve the privacy, anonymization points us to convert the personal data into unidentified personal data. There are many organizations that produce the large data in real time. With the help of Hadoop components like HDFS and MapReduce and with its ecosystems, large volume of data can be processed in real time. There are many basic data anonymization techniques like cryptographic, substitution, character masking, shuffling, nulling out, date variance and number variance. Here privacy preservation is achieved for streaming data by using one of the anonymization techniques called ‘shuffling’ with Big data concept. K-anonymity, t-closeness, l-diversity are usually used technique for privacy concern in a data. But in all these techniques information loss and data utility are not preserved very well. Dynamically Anonymizing Data Shuffling (DADS) technique is used to overcome this information loss and also to improve data utility in streaming data.

scholarly journals Construction of an Intelligent Analysis Model for Website Information Based on Big Data and Cloud Computing Technology

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Ronghua Chen ◽  
Bei Yang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Collection ◽  
Real Time ◽  
Delivery System ◽  
Pearson Correlation ◽  
System Optimization ◽  
Practical Significance ◽  
Computing Technology ◽  
Intelligent Analysis

Based on big data and cloud computing technology, the development process of this system includes hardware cluster deployment of the delivery system, optimization of website delivery strategy, and development of the delivery management background system. The main functions in the following aspects are realized: first, we built the website back-end delivery subsystem and data collection and analysis subsystem to realize the control of website delivery and data collection; second, we designed and developed a process management subsystem for booking, management, and delivery of website resources and developed the contract management and order management subsystems to realize the accurate placement of user portraits on the website; then, the placement data monitoring and effect feedback subsystems, and the data inventory subsystem of the website system were designed and developed. Finally, based on the research of Android and based on the Eclipse platform, this article has completed the construction of the Android 4.0.3 version environment, successfully used the Java development language to develop a website information intelligent analysis and navigation system, and analyzed the various functional modules of the entire system. Experimental results show that the system not only realizes ordinary route and site query functions but also combines map API, integrates big data and cloud computing technology, and realizes congestion avoidance query, time optimal query, population heat map, and real-time viewing. The nearby use of this software’s personnel density distribution and other functions provides great convenience for personal travel, which can facilitate the real-time planning of travel plans, and has great practical and practical significance. Through the different levels of testing of various subsystems, the website delivery system meets the functional and nonfunctional requirements proposed by the network and on this basis realizes the use of group wisdom based on Pearson correlation coefficient, Cosine similarity, and Tanimoto coefficient for collaborative filtering website recommendation algorithm.

Enhancing Clinical Supervision Using High-Definition Real-Time Digital Recording, Annotation, and Bluetooth Technology

2019 ◽  
Vol 4 (2) ◽  
pp. 356-362
Author(s):  
Jennifer W. Means ◽  
Casey McCaffrey
Keyword(s):  
Graduate Students ◽  
Data Collection ◽  
Real Time ◽  
Clinical Supervision ◽  
Video Recording ◽  
Advanced Technology ◽  
High Definition ◽  
Recording Technology ◽  
Self Confidence ◽  
Additional Data Collection

Purpose The use of real-time recording technology for clinical instruction allows student clinicians to more easily collect data, self-reflect, and move toward independence as supervisors continue to provide continuation of supportive methods. This article discusses how the use of high-definition real-time recording, Bluetooth technology, and embedded annotation may enhance the supervisory process. It also reports results of graduate students' perception of the benefits and satisfaction with the types of technology used. Method Survey data were collected from graduate students about their use and perceived benefits of advanced technology to support supervision during their 1st clinical experience. Results Survey results indicate that students found the use of their video recordings useful for self-evaluation, data collection, and therapy preparation. The students also perceived an increase in self-confidence through the use of the Bluetooth headsets as their supervisors could provide guidance and encouragement without interrupting the flow of their therapy sessions by entering the room to redirect them. Conclusions The use of video recording technology can provide opportunities for students to review: videos of prospective clients they will be treating, their treatment videos for self-assessment purposes, and for additional data collection. Bluetooth technology provides immediate communication between the clinical educator and the student. Students reported that the result of that communication can improve their self-confidence, perceived performance, and subsequent shift toward independence.

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