Toward Interactive User Data Analytics

2017 ◽  
pp. 3-6
Author(s):  
Sihem Amer-Yahia
Keyword(s):  
Data Analytics ◽  
User Data ◽  
Interactive User

Survey and Analysis of Current End-user Data Analytics Tool Support

2019 ◽  
pp. 1-1
Author(s):  
Hourieh Khalajzadeh ◽  
Mohamed Abdelrazek ◽  
John Grundy ◽  
John G. Hosking ◽  
Qiang He
Keyword(s):  
Data Analytics ◽  
Tool Support ◽  
End User ◽  
User Data

A Survey of Current End-User Data Analytics Tool Support

2018 ◽  
Author(s):  
Hourieh Khalajzadeh ◽  
Mohamed Abdelrazek ◽  
John Grundy ◽  
John Hosking ◽  
Qiang He
Keyword(s):  
Data Analytics ◽  
Tool Support ◽  
End User ◽  
User Data

scholarly journals VIDEOGAME ANALYTICS, SURVEILLANCE CAPITALISM AND THE RETENTIONAL ECONOMY OF PLAY

2019 ◽  
Vol 2019 ◽  
Author(s):  
Ben Egliston
Keyword(s):  
Preliminary Analysis ◽  
Data Analytics ◽  
User Data ◽  
Using Data ◽  
A Site ◽  
Conceptual Work

Data analytics tools are increasingly prevalent in videogames and are reliant on the surveillant capture and relay of user data. In this paper I present some conceptual work and preliminary analysis of the analytics tool ‘DotaPlus’ used in Dota 2. Through my analysis, I frame DotaPlus as a site of ‘surveillance capitalism’, using data derived from various modes of surveillance to generate potentials for commercially desirable gameplay experience.

scholarly journals Privacy-Aware Data Forensics of VRUs Using Machine Learning and Big Data Analytics

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Muhammad Babar ◽  
Muhammad Usman Tariq ◽  
Ahmed S. Almasoud ◽  
Mohammad Dahman Alshehri
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Traffic Control ◽  
Data Analytics ◽  
Data Privacy ◽  
Big Data Analytics ◽  
Processing Unit ◽  
Privacy And Security ◽  
User Data ◽  
Data Ingestion

The present spreading out of big data found the realization of AI and machine learning. With the rise of big data and machine learning, the idea of improving accuracy and enhancing the efficacy of AI applications is also gaining prominence. Machine learning solutions provide improved guard safety in hazardous traffic circumstances in the context of traffic applications. The existing architectures have various challenges, where data privacy is the foremost challenge for vulnerable road users (VRUs). The key reason for failure in traffic control for pedestrians is flawed in the privacy handling of the users. The user data are at risk and are prone to several privacy and security gaps. If an invader succeeds to infiltrate the setup, exposed data can be malevolently influenced, contrived, and misrepresented for illegitimate drives. In this study, an architecture is proposed based on machine learning to analyze and process big data efficiently in a secure environment. The proposed model considers the privacy of users during big data processing. The proposed architecture is a layered framework with a parallel and distributed module using machine learning on big data to achieve secure big data analytics. The proposed architecture designs a distinct unit for privacy management using a machine learning classifier. A stream processing unit is also integrated with the architecture to process the information. The proposed system is apprehended using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The data ingestion results are also highlighted along with training and validation results.

scholarly journals Data Privacy and Cyber Security in the Age of IoT and Data Analytics: Response of Law

2018 ◽  
Vol 7 (3.12) ◽  
pp. 191
Author(s):  
Rashmi Salpekar
Keyword(s):  
Cyber Security ◽  
Data Analytics ◽  
Data Privacy ◽  
User Preferences ◽  
Maturity Model ◽  
Smart Meters ◽  
Smart Lighting ◽  
Smart Water ◽  
Privacy Laws ◽  
User Data

IoT and Data Analytics are developing and adopted very fast. Utilities are deploying smart meters, smart lighting, etc. Even the water supply distribution agencies are deploying smart water schemes to reduce non-revenue water. Further, data analytics is done by loT of companies to provide targeted advertising and knowing user preferences. All this requires collecting user data to be effective.There is an urgent need to define unambiguous laws, well defined dispute resolution that defines the consumer liability and service provider liability in light of court judgments to that effect. Further, a cyber security framework also needs to be defined and also a cyber security maturity model needs to be in place to rate the cyber security of a given agency and the steps needed to make cyber security better.The paper intends to study national and international laws on cyber security including framework and maturity model and data privacy laws. It will then come up with concrete enforceable suggestions to make cyber security better. The suggestions will include laws, liability, framework and guidelines.  

A Data Analytics Approach to Persona Development for The Future Mobile Office

2020 ◽  
Vol 64 (1) ◽  
pp. 1000-1004
Author(s):  
Amudha V. Kamaraj ◽  
Atefeh Katrahmani ◽  
Mengyao Li ◽  
John D. Lee
Keyword(s):  
Data Analytics ◽  
Automated Vehicles ◽  
User Interactions ◽  
Work Related ◽  
Target User ◽  
Occupational Information Network ◽  
User Data ◽  
User Groups ◽  
Persona Development ◽  
Using Data

The concept of using automated vehicles as mobile workspaces is now emerging. Consequently, the in- vehicle environment of automated vehicles must be redesigned to support user interactions in performing work-related tasks. During the design phase, interaction designers often use personas to understand target user groups. Personas are representations of prototypical users and are constructed from user surveys and interview data. Although data-driven, large samples of user data are typically assessed qualitatively and may result in personas that are not representative of target user groups. To create representative personas, this paper demonstrates a data analytics approach to persona development for future mobile workspaces using data from the occupational information network (O*NET). O*NET consists of data on 968 occupations, each defined by 277 features. The data were reduced using dimensionality reduction and 7 personas were identified using cluster analysis. Finally, the important features of each persona were identified using logistic regression.

A Privacy-Preserving Feature Extraction Method for Big Data Analytics Based on Data-Independent Reusable Projection

2019 ◽  
pp. 151-169 ◽  
Author(s):  
Siddharth Ravindran ◽  
Aghila G.
Keyword(s):  
Big Data ◽  
Mobile Networks ◽  
Data Analytics ◽  
Input Data ◽  
Big Data Analytics ◽  
High Dimensional ◽  
Feature Extraction Method ◽  
Research Areas ◽  
User Data ◽  
Low Dimensional

Big data analytics is one of the key research areas ever since the advancement of internet technologies, social media, mobile networks, and internet of things (IoT). The volume of big data creates a major challenge to the data scientist while interpreting the information from raw data. The privacy of user data is an important issue faced by the users who utilize the computing resources from third party (i.e., cloud environment). This chapter proposed a data independent reusable projection (DIRP) technique for reducing the dimension of the original high dimensional data and also preserves the privacy of the data in analysis phase. The proposed method projects the high dimensional input data into the random low dimensional space. The data independent and distance preserving property helps the proposed method to reduce the computational complexity of the machine learning algorithm. The randomness of data masks the original input data which helps to solve the privacy issue during data analysis. The proposed algorithm has been tested with the MNIST hand written digit recognition dataset.

BiDaML: A Suite of Visual Languages for Supporting End-User Data Analytics

2019 ◽  
Author(s):  
Hourieh Khalajzadeh ◽  
Mohamed Abdelrazek ◽  
John Grundy ◽  
John Hosking ◽  
Qiang He
Keyword(s):  
Data Analytics ◽  
End User ◽  
Visual Languages ◽  
User Data

scholarly journals Designing of a Biometric Fingerprint Scanner-Based, Secure, and Low-Cost Electronic Voting Machine for India

2021 ◽  
Author(s):  
Shubhranil Chakraborty ◽  
Debabrata Bej ◽  
Dootam Roy ◽  
Sekh Arif Mahammad
Keyword(s):  
Human Resources ◽  
Low Cost ◽  
Electronic Voting ◽  
Election Process ◽  
User Data ◽  
Systematic Development ◽  
The Cost ◽  
Interactive User ◽  
Interactive User Interface ◽  
Voting Machine

A reliable Electronic Voting Machine (EVM) is proposed and implemented in this study, which is integrated with a biometric fingerprint scanner to ensure a secure election process. This biometric EVM includes features such as an interactive user interface, hack-free design and master lock. The EVM system has the capability of registering user data and storing them in a database through proper authentication. Moreover, the system proposed lowers the requirement for human resources. This paper provides a detailed description of the systematic development of the hardware and software used. The software part includes algorithm development and implementation. A thorough and in-depth understanding of the data and the communication protocols along with the pathways used for storage of data in the devices is provided. Additionally, the cost of the system is 62.82% less than the officially existing EVM machines of India. Furthermore, this study seeks to demonstrate the benefits of such an approach from a technological and a social standpoint.

A Privacy-Preserving Feature Extraction Method for Big Data Analytics Based on Data-Independent Reusable Projection

2021 ◽  
pp. 386-405
Author(s):  
Siddharth Ravindran ◽  
Aghila G.
Keyword(s):  
Big Data ◽  
Mobile Networks ◽  
Data Analytics ◽  
Input Data ◽  
Big Data Analytics ◽  
High Dimensional ◽  
Feature Extraction Method ◽  
Research Areas ◽  
User Data ◽  
Low Dimensional

Big data analytics is one of the key research areas ever since the advancement of internet technologies, social media, mobile networks, and internet of things (IoT). The volume of big data creates a major challenge to the data scientist while interpreting the information from raw data. The privacy of user data is an important issue faced by the users who utilize the computing resources from third party (i.e., cloud environment). This chapter proposed a data independent reusable projection (DIRP) technique for reducing the dimension of the original high dimensional data and also preserves the privacy of the data in analysis phase. The proposed method projects the high dimensional input data into the random low dimensional space. The data independent and distance preserving property helps the proposed method to reduce the computational complexity of the machine learning algorithm. The randomness of data masks the original input data which helps to solve the privacy issue during data analysis. The proposed algorithm has been tested with the MNIST hand written digit recognition dataset.

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