Human Centered Data Discovery

This book synthesizes existing research on human-centered data discovery, as well as the recommendations which exist for supporting the design of sustainable, user-centered data search systems. While information-seeking in various settings has been well-researched within computer and information science, not much is known about human-centered data discovery, or how people discover, understand and interact with data that others create. This is particularly relevant given the ever-increasing amounts of data being produced and made available, and the creation of data-specific discovery tools and systems. This book examines how people find the data they need, which search strategies and tools they use, how they understand data, and how search systems can be better designed to meet people’s needs.

This book synthesizes existing research on human-centered data discovery, as well as the recommendations which exist for supporting the design of sustainable, user-centered data search systems. While information-seeking in various settings has been well-researched within computer and information science, not much is known about human-centered data discovery, or how people discover, understand and interact with data that others create. This is particularly relevant given the ever-increasing amounts of data being produced and made available, and the creation of data-specific discovery tools and systems. This book examines how people find the data they need, which search strategies and tools they use, how they understand data, and how search systems can be better designed to meet people’s needs.

Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns.

This lecture was initially intended to cover relevant issues in database user interfaces, mainly query interfaces. However, very soon the authors realized that providing friendly access to information is much more than just designing nice interfaces; rather it has to do with designing interactive systems that suitably fit the users' tasks and this can be achieved by following a user-centered approach. Second, the data users want to access nowadays do not reside only in traditional databases; they are mainly on the Web (either available or hidden--it does not matter). Third, the users do not limit themselves to just extract the data; instead they want to manipulate them, analyze them, to make sense out of them. Thus, user-oriented systems should provide more functionality in addition to querying. Finally, while visual interfaces and information visualization techniques are usually considered the most usable approaches, categories of users and/or contexts exist for which they are not appropriate, so other interactive paradigms need to be explored. The content of this lecture reflects all the above considerations. Chapter 1 discusses the importance of adopting a user-centered approach. The work then, in chapter 2, takes the reader to the early days, where we find the initial use of visual interfaces to support database tasks. Visual representation, interaction, and perception are discussed. Chapter 3 moves on to describe non-traditional interfaces which are relevant to databases. It in particular looks at web data and mobile interfaces. Chapter 4 then sheds more light on two concepts behind database querying. The focus here is on information visualization and visual data mining. Chapter 5 closes by describing interfaces that go beyond the visual dimension. It in particular discusses accessibility and aural interfaces.

Human-Centered e-Business focuses on analysis, design and development of human-centered e-business systems. The authors illustrate the benefits of the human-centered approach in intelligent e-sales recruitment application, integrating data mining technology with decision support model for profiling transaction behavior of internet banking customers, user-centered context dependent data organization using XML, knowledge management, and optimizing the search process through human evaluation in an intelligent interactive multimedia application. The applications described in this work, facilitates both e-business analysis from a business professional's perspective, and human-centered system design from a system development perspective. These applications employ a range of internet and soft computing technologies.

Volume LNCS 13516 is part of the refereed proceedings of the 24th International Conference on Human-Computer Interaction, HCII 2022, which was held virtually during June 26 to July 1, 2022. A total of 5583 individuals from academia, research institutes, industry, and governmental agencies from 88 countries submitted contributions, and 1276 papers and 275 posters were included in the proceedings that were published just before the start of the conference. Additionally, 296 papers and 181 posters are included in the volumes of the proceedings published after the conference, as “Late Breaking Work” (papers and posters). The contributions thoroughly cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas.

The two-volume set LNCS 9734 and 9735 constitutes the refereed proceedings of the Human Interface and the Management of Information thematic track, held as part of the 18th International Conference on Human-Computer Interaction, HCII 2016, held in Toronto, Canada, in July 2016. HCII 2016 received a total of 4354 submissions of which 1287 papers were accepted for publication after a careful reviewing process. These papers address the latest research and development efforts and highlight the human aspects of design and use of computing systems. The papers accepted for presentation thoroughly cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas This volume contains papers addressing the following major topics: information presentation; big data visualization; information analytics; discovery and exploration; interaction design, human-centered design; haptic, tactile and multimodal interaction.

This text surveys research from the fields of data mining and information visualisation and presents a case for techniques by which information visualisation can be used to uncover real knowledge hidden away in large databases.