User Centered Design

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Requirements Engineering A Good Practice Guide

Anonymization is a practical solution for preserving user’s privacy in data publishing. Data owners such as hospitals, banks, social network (SN) service providers, and insurance companies anonymize their user’s data before publishing it to protect the privacy of users whereas anonymous data remains useful for legitimate information consumers. Many anonymization models, algorithms, frameworks, and prototypes have been proposed/developed for privacy preserving data publishing (PPDP). These models/algorithms anonymize users’ data which is mainly in the form of tables or graphs depending upon the data owners. It is of paramount importance to provide good perspectives of the whole information privacy area involving both tabular and SN data, and recent anonymization researches. In this paper, we presents a comprehensive survey about SN (i.e., graphs) and relational (i.e., tabular) data anonymization techniques used in the PPDP. We systematically categorize the existing anonymization techniques into relational and structural anonymization, and present an up to date thorough review on existing anonymization techniques and metrics used for their evaluation. Our aim is to provide deeper insights about the PPDP problem involving both graphs and tabular data, possible attacks that can be launched on the sanitized published data, different actors involved in the anonymization scenario, and major differences in amount of private information contained in graphs and relational data, respectively. We present various representative anonymization methods that have been proposed to solve privacy problems in application-specific scenarios of the SNs. Furthermore, we highlight the user’s re-identification methods used by malevolent adversaries to re-identify people uniquely from the privacy preserved published data. Additionally, we discuss the challenges of anonymizing both graphs and tabular data, and elaborate promising research directions. To the best of our knowledge, this is the first work to systematically cover recent PPDP techniques involving both SN and relational data, and it provides a solid foundation for future studies in the PPDP field.

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Data visualization is the science of analytical reasoning assisted by interactive data analysis, and it has many applications in business fields. According to business strategies, both adoption and self-service data resources issues do not understand the income maximization planning, and they do not look after all the information accessible. The digitization trend in industry produces enormous amounts of data by sensors and data recordings from almost all devices and machines of the production process with the promise of making new utilization opportunities. The data visualization use quantified the research of attainment. The trials include subjects learning the possessions of static balances. The policy of unforeseen tests to use data visualization has optimized the business income interest. This paper proposes an Interactive IoT Data Visualization for Business Intelligence method to analyze and minimize adoption and self-service data resources issues. An IoT based data visualization method has developed to support Interactive decision-making to acquire data resources from vast business intelligence sources. Data visualization's essential benefit is shown in decision-making by conflicting conventional visualization methods with in-depth process analysis methods via experimental studies. Data visualization places data in a suitable visual context that triggers people’s understanding of its importance. This decreases the total effort to manually examine the data. Consequently, recognition and visualization of patterns within the IoT produced data play an important role in the insight-gaining progression and improve the decision-making procedure. Simulation studies have analyzed the empirical evidence of the decisions. It has been demonstrated that the proposed system ruled out the traditional method with an accuracy rate of 93.6%, with a minimum error rate of 0.9%. The reduced error performance increases the performance ratio with minimum time delay.

Interactive IoT Data Visualization for Decision Making in Business Intelligence

Web contents include text, image, and any visual element that represents in web applications. Users conduct web applications throughout visual contents; therefore, the contents should visible clearly and follow a strict contrast ratio to differentiate from the other contents of the application. The color contrast assists to visualize contents combining the contrast ratio between background and foreground. Whether the web contents not visible clearly or overpass to split its color contrast from the background shall be worthless, and in addition, the human brain and psychology have an impact of colors which lead physiologically effects such as feelings and senses. Numerous web applications existing on the web and some applications failed to follow the design principles of Human-Computer Interaction (HCI). In HCI, visualization is the most widespread research area and, in the context of visual interaction, the HCI facilitates and guides application design that to be user-centric. This research reveals the HCI for color effects on the human eye, brain, phycology, and contrast ratio. Also extended the existing standard minimum contrast ratio for the design of web contents in light and dark background and foreground following HCI principles. The extended ratio experimented on a web application contents to differentiate the accuracy between the existing and the extended ratio.

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The Impact of Web Contents Color Contrast on Human Psychology in the Lens of HCI

There is a huge amount of data creating during the fourth industry revaluation and the data are generating explosively by various fields of the Internet of Things (IoT). The organizations are producing and storing the huge amount of data into the data servers every moment. This data comes from social media, sensors, tracking, website, and online news articles. The Google, Facebook, Walmart, and Taobao are the most remarkable organizations are generating most of the data in the web servers. Data comes into three forms as structured (text/numeric), semi structured (audio, video, and image) and unstructured (XML and RSS feeds). A business makes revenue from the analysis of 20% of such data, which is a structured form while 80% of data is unstructured. Therefore, unstructured data contains valuable information that can help the organization to improve the business productive, better decision-making, extract the insights, new products and services and understand the market conditions in various fields such as shopping, finance, education, manufacturing, and healthcare. The unstructured data are needed to be analyzed and distribute in a structured manner, that is required information’s are to be gathered through the data mining techniques are used to mining the data. In this paper, expose the importance of data analytics and data management for beneficial usage of business intelligence, big data, data mining and machine and data management. In addition, the different techniques that can be used to discover the knowledge and useful information from such data been analyzed. This can be beneficial for numerous users concern on text mining and convert complex data into meaningful information for researchers, analyst, data scientist, and business decision makers as well.

Business Intelligence Through Big Data Analytics, Data Mining and Machine Learning

Design methodology of the Human-computer interaction (HCI) ensures stability, efficiency, and usability of the future artifacts. HCI concerned for the development of interaction methods as well as user interfaces. Several methods are used since the term HCI was coined. HCI methodologies are formed into various certain features and the features are based on the user acceptance, usability, and technical issues, indeed each method based on a specific affair. A method or process ‘User-Centered design’ (UCD) is exercising in a broad area of HCI. In spite of existing UCD process significance, in a while, several boundaries and limitations arise during the development of the artifacts. UCD process is not static, and the variations in UCD incorporated into various approaches depending on needs, and task. The principle of UCD process is to involve the user participation in the development to ensure users demand and interests. For the variations of UCD process researchers are developing the model by extending diverse technical and conceptual terms depending on respective activities, therefore occasionally the process draws inappropriate images of the future artifacts, although ISO 9241-210 defined an ideal UCD process guidelines to HCI development. This research analyzed the significance, usability, and the absence of existing UCD process and extended a conceptual term as Elicitation. Respective section illustrated the extended term significance, practice and approaches to HCI design. The key concern is to develop the UCD method in this research, alongside discuss the existing methods limitations and absences for future HCI development.

Extended User Centered Design (UCD) Process in the Aspect of Human Computer Interaction

Until the inception of Web 1.0, the Information Retrieval was the center of the stage for library and it was defined as search and passive. Later on, the emergence of Web 2.0 was encouraged into the community, social interaction and user-generated content. Web 3.0 is a modern phenomenon and also known to “3D Web or the Semantic Web”, and it often used for specifically to formats and the technologies. The advanced Web 4.0 is the Ultra-Intelligent Agent Interactions between humans and machines. Semantic web technology finds meanings from various sources to enabling the machines and people to understand and share knowledge. The semantic web technology helps to add, change and implement the new relationships or interconnecting programs in a different way which can be as simple as changing the external model that these programs are shared. To give an information need, the semantic technologies can directly search, capture, aggregate, and make a deduction to satisfy the user needs. The paper presents a framework for knowledge representation assembling semantic technology based on ontology, semantic web, and an intelligent agent algorithm as a connectivity framework to share the appropriate knowledge representation which includes the web ontology language that discovers related information's from various sources to serve the information needs. The research addresses the intelligent agent algorithm is the key contribution that reveals appropriate information and empowers Web 3.0 and embraces Web 4.0 into the coming semantic web technology.

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Empowering Information Retrieval in Semantic Web

Human-computer interaction (HCI) design is the modern and advanced research area that is focused on human or user and system or device interactions. HCI is concerned with new and advanced approaches to a system that must be reliable and efficient to the user. HCI design required a longer and critical development process, methods or framework that passes through various evaluation phases. Due to an inadequacy of user-centered and value sensitive design for a system or device, numerous designs and artifacts do not draw attention to the user. On the other hand, user perception of advanced and new devices are not acceptable and perceived, if not reliable or facile to them. HCI comprises several methods, principles, and criteria which are followed during the development of interaction design. Even then, at times the evolved new and advanced interaction artifacts are unable to reach the user attraction. The HCI methodologies promote the researchers as well as designers to form and develop new and advanced innovations whereas, interaction design term plays a significant role in the design process. In this paper, the key concern is to analyze the conventional design method, Activity Theory (AT) and consider the principles of HCI and bring in a conceptual extended AT model to develop the appearance of Human-computer interaction design.

Ahamed. M Mithun

Papers

Business Intelligence Through Big Data Analytics, Data Mining and Machine Learning

The Impact of Web Contents Color Contrast on Human Psychology in the Lens of HCI

Extended User Centered Design (UCD) Process in the Aspect of Human Computer Interaction

Empowering Information Retrieval in Semantic Web

Revised Theoretical Approach of Activity Theory for Human Computer Interaction Design