Human-data Interaction Research Articles

Understanding human-data interaction: Literature review and recommendations for design

Abstract The trend of collecting information about human activities to inform and influence actions and decisions poses a series of challenges to analyze this data deluge. The lack of ability to understand and interact with this amount of data prevents people and organizations from taking the best of this information. To investigate how people interact with data, a new area of study called “Human-Data Interaction” (HDI) is emerging. In this article, we conduct a thorough literature review to create the big picture about the subject. We carry out a variety of analyses and visual examinations to understand the characteristics of existing publications, detecting the most frequently addressed research topics and consolidating the research challenges. Based on the needs of HDI we found in the analyzed publications, we organize a set of recommendations and evaluate online systems that demand intensive human-data interaction. The obtained results indicate there are still many open questions for this interesting area, which is maturing with an increase number of publications in the last years, and that systems with large amount of data openly available poorly meet the proposed recommendations.

VINCENT: A visual analytics system for investigating the online vaccine debate.

This paper reports and describes VINCENT, a visual analytics system that is designed to help public health stakeholders (i.e., users) make sense of data from websites involved in the online debate about vaccines. VINCENT allows users to explore visualizations of data from a group of 37 vaccine-focused websites. These websites differ in their position on vaccines, topics of focus about vaccines, geographic location, and sentiment towards the efficacy and morality of vaccines, specific and general ones. By integrating webometrics, natural language processing of website text, data visualization, and human-data interaction, VINCENT helps users explore complex data that would be difficult to understand, and, if at all possible, to analyze without the aid of computational tools. The objectives of this paper are to explore A) the feasibility of developing a visual analytics system that integrates webometrics, natural language processing of website text, data visualization, and human-data interaction in a seamless manner; B) how a visual analytics system can help with the investigation of the online vaccine debate; and C) what needs to be taken into consideration when developing such a system. This paper demonstrates that visual analytics systems can integrate different computational techniques; that such systems can help with the exploration of online public health debates that are distributed across a set of websites; and that care should go into the design of the different components of such systems.

Cognitive ergonomics for data analysis. Experimental study of cognitive limitations in a data-based judgement task

ABSTRACTToday’s ever-increasing amount of data places new demands on cognitive ergonomics and requires new design ideas to ensure successful human–data interaction. Our aim was to identify the cognitive factors that must be considered when designing systems to improve decision-making based on large amounts of data. We constructed a task that simulates the typical cognitive demands people encounter in data analysis situations. We demonstrate some essential cognitive limitations using a behavioural experiment with 20 participants. The studied task presented the participants with critical and noncritical attributes that contained information on two groups of people. They had to select the response option (group) with the higher level of critical attributes. The results showed that accuracy of judgement decreased as the amount of information increased, and that judgement was affected by irrelevant information. Our results thus demonstrate critical cognitive limitations when people utilise data and suggest a cognitive bias in data-based decision-making. Therefore, when designing for cognition, we should consider the human cognitive limitations that are manifested in a data analysis context. Furthermore, we need general cognitive ergonomic guidelines for design that support the utilisation of data and improve data-based decision-making.

Big Data and Us: Human–Data Interactions

The growth of continuously generated, large-scale datasets, and new analytics to handle them, has created expectations, in some quarters, that new insights can be generated that will help us address the biggest challenges that face us as a species and therefore can shape future societal outcomes. It is hoped that these new technologies will lead not just to new discoveries but also to new questions and thinking that will deliver significant scientific advances. Perhaps there will be some genuine scientific advances but since many of the challenges that face us reside in the human world and depend upon how humans behave, we need to turn to the humanities and the social sciences as well as the natural sciences and look at the role Big Data could play there in adding to, or shaping, our future. And here, what concerns us is not just the assumptions guiding the new analytic techniques for data mining, data merging, linking and analysis, born out of smarter AI algorithms, it is a more fundamental issue about the constraints and limitations of the kind of data inputs and outputs being appealed to in Big Data systems and whether they are well served to provide an understanding of the human world.

Designing human data interactions in Healthcare

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Discourse with Visual Health Data: Design of Human-Data Interaction

Previous work has suggested that large repositories of data can revolutionize healthcare activities; however, there remains a disconnection between data collection and its effective usage. The way in which users interact with data strongly impacts their ability to not only complete tasks but also capitalize on the purported benefits of such data. Interactive visualizations can provide a means by which many data-driven tasks can be performed. Recent surveys, however, suggest that many visualizations mostly enable users to perform simple manipulations, thus limiting their ability to complete tasks. Researchers have called for tools that allow for richer discourse with data. Nonetheless, systematic design of human-data interaction for visualization tools is a non-trivial task. It requires taking into consideration a myriad of issues. Creation of visualization tools that incorporate rich human-data discourse would benefit from the use of design frameworks. In this paper, we examine and present a design process that is based on a conceptual human-data interaction framework. We discuss and describe the design of interaction for a visualization tool intended for sensemaking of public health data. We demonstrate the utility of systematic interaction design in two ways. First, we use scenarios to highlight how our design approach supports a rich and meaningful discourse with data. Second, we present results from a study that details how users were able to perform various tasks with health data and learn about global health trends.

Beyond simple charts: Design of visualizations for big health data.

Health data is often big data due to its high volume, low veracity, great variety, and high velocity. Big health data has the potential to improve productivity, eliminate waste, and support a broad range of tasks related to disease surveillance, patient care, research, and population health management. Interactive visualizations have the potential to amplify big data’s utilization. Visualizations can be used to support a variety of tasks, such as tracking the geographic distribution of diseases, analyzing the prevalence of disease, triaging medical records, predicting outbreaks, and discovering at-risk populations. Currently, many health visualization tools use simple charts, such as bar charts and scatter plots, that only represent few facets of data. These tools, while beneficial for simple perceptual and cognitive tasks, are ineffective when dealing with more complex sensemaking tasks that involve exploration of various facets and elements of big data simultaneously. There is need for sophisticated and elaborate visualizations that encode many facets of data and support human-data interaction with big data and more complex tasks. When not approached systematically, design of such visualizations is labor-intensive, and the resulting designs may not facilitate big-data-driven tasks. Conceptual frameworks that guide the design of visualizations for big data can make the design process more manageable and result in more effective visualizations. In this paper, we demonstrate how a framework-based approach can help designers create novel, elaborate, non-trivial visualizations for big health data. We present four visualizations that are components of a larger tool for making sense of large-scale public health data.

Personal Data, Privacy and the Internet of Things: The Shifting Locus of Agency and Control

This paper examines the shifting the locus of agency and control in the processing of personal data, occasioned by the emerging digital ecosystem and the Internet of Things in particular. It reviews legal, policy, and industry initiatives that seek to respond to broad societal concerns over privacy, and initiatives within design that can be seen to respond to the core problem of empowering users and giving them active control over the processing of personal data. It is, in many respects, a clarion call for HCI to respond to a grand challenge in contemporary life. It suggests that current efforts to address the problem have been piecemeal and that a coherent interdisciplinary approach is required to affect the shift. It proposes that the nascent field of Human Data Interaction provides the necessary design framework needed to drive the research community forwards and put users in control in practice.

Granular computing as a basis of human–data interaction: a cognitive cities use case

The article proposes granular computing as a theoretical, formal and methodological basis for the newly emerging research field of human–data interaction (HDI). We argue that the ability to represent and reason with information granules is a prerequisite for data legibility. As such, it allows for extending the research agenda of HDI to encompass the topic of collective intelligence amplification, which is seen as an opportunity of today’s increasingly pervasive computing environments. As an example of collective intelligence amplification in HDI, we introduce a collaborative urban planning use case in a cognitive city environment and show how an iterative process of user input and human-oriented automated data processing can support collective decision making. As a basis for automated human-oriented data processing, we use the spatial granular calculus of granular geometry.

Report on the First International Workshop on Personal Data Analytics in the Internet of Things (PDA@IOT 2014)

The 1st International Workshop on Personal Data Analytics in the Internet of Things (PDA@IOT), held in conjunction with VLDB 2014, aims at sparking research on data analytics, shifting the focus from business to consumers services. While much of the public and academic discourse about personal data has been dominated by a focus on the privacy concerns and the risks they raise to the individual, especially when they are seen as the new oil of the global economy. PDA@IOT focus on how persons could effectively exploit the data they massively create in CyberPhysicalworlds. We believe that the full potential of the IoT goes far beyond connecting “things” to the Internet: it is about using data to create new value for people. In a People-centric computing paradigm, both small scale personal data and large scale aggregated data should be exploited to identify unmet needs and proactively offer them to users. PDA@IOT seeks to address current technology barriers that impede existing personal data processing and analytics solutions to empower people in personal decision making. The PDA@IOT ambition is to provide a unique forum for researchers and practitioners that approach personal data from different angles, ranging from data management and processing, to data mining and human-data interaction, as well as to nourish the interdisciplinary synergies required to tackle the challenges and problems emerging in People-centric Computing.

Human-Data Interaction: The Human Face of the Data-Driven Society

The increasing generation and collection of personal data has created a complex ecosystem, often collaborative but sometimes combative, around companies and individuals engaging in the use of these data. We propose that the interactions between these agents warrants a new topic of study: Human-Data Interaction (HDI). In this paper we discuss how HDI sits at the intersection of various disciplines, including computer science, statistics, sociology, psychology and behavioural economics. We expose the challenges that HDI raises, organised into three core themes of legibility, agency and negotiability, and we present the HDI agenda to open up a dialogue amongst interested parties in the personal and big data ecosystems.

Semi-automatic mapping of geological Structures using UAV-based photogrammetric data: An image analysis approach

Recent advances in data acquisition technologies, such as Unmanned Aerial Vehicles (UAVs), have led to a growing interest in capturing high-resolution rock surface images. However, due to the large volumes of data that can be captured in a short flight, efficient analysis of this data brings new challenges, especially the time it takes to digitise maps and extract orientation data.We outline a semi-automated method that allows efficient mapping of geological faults using photogrammetric data of rock surfaces, which was generated from aerial photographs collected by a UAV. Our method harnesses advanced automated image analysis techniques and human data interaction to rapidly map structures and then calculate their dip and dip directions. Geological structures (faults, joints and fractures) are first detected from the primary photographic dataset and the equivalent three dimensional (3D) structures are then identified within a 3D surface model generated by structure from motion (SfM). From this information the location, dip and dip direction of the geological structures are calculated.A structure map generated by our semi-automated method obtained a recall rate of 79.8% when compared against a fault map produced using expert manual digitising and interpretation methods. The semi-automated structure map was produced in 10min whereas the manual method took approximately 7h. In addition, the dip and dip direction calculation, using our automated method, shows a mean±standard error of 1.9°±2.2° and 4.4°±2.6° respectively with field measurements. This shows the potential of using our semi-automated method for accurate and efficient mapping of geological structures, particularly from remote, inaccessible or hazardous sites.

A Geological Structure Mapping Tool using Photogrammetric Data

Accurate and efficient identification and mapping of geological structures has broad application across the minerals industry. Recent advances in data acquisition technologies using Unmanned Aerial Vehicles (UAV), have led to a growing interest in capturing high-resolution rock surface images and analysing those datasets remotely. However due to the large volumes of data that can be captured in a short flight, efficient analyses of these data brings new challenges. We propose a semi-automated method that allows efficient mapping of geological structures using photogrammetry of rock surface data collected by UAV. Our method harnesses advanced automated image analysis techniques and human data interactions to identify structures and calculate dip and dip angles of structures. Geological features were detected in two dimensional (2D) images and the corresponding three dimensional (3D) features were automatically identified from 3D surface models. The location, dip and dip angle of geological features were then calculated. A feature map generated by our semi-automated method correlates well with a fault map resulting from visual interpretation by an expert. Some advantages of our semi-automatic method include the following: Firstly; it generates results in few minutes whilst manual interpretation took around an hour, thus contributing significantly in time saving. Secondly; unlike manual interpretation, our software technology provides objective and consistent results that can be reproduced.

Towards Understanding and Improving Geoscientific Data Interpretation

Geoscientific data interpretation is a highly subjective and complex task as human intuition and biases play a significant role. Based on these interpretations, however, mining and petroleum industries make decisions with paramount of financial implications. As a first step towards understanding and improving the interpretation process, we carried out two experiments to monitor the human-data interactions during the process of identifying â??targetsâ?? (porphyry-style intrusive systems) within the aeromagnetic imagery. This is achieved by capturing the eye gaze position using an eye tracker system and the brain responses using electroencephalography (EEG). The first experiment was intended to analyse the target spotting performance and the data observation patterns. For this experiment participants performed exercises, where the same magnetic image was presented in different orientations. Some key findings include: inconsistencies in target spotting performance within and between the interpreters; an improvement performance when the data were viewed in multiple orientations; and a strong correlation between the target spotting performance and efficient (systematic) data observation pattern. There was no correlation between success in identifying targets and the participantsâ?? perception of their expertise. The second experiment was designed to identify the characteristics of the targets that are easier to detect using EEG. For this experiment images with targets and without targets were presented in a rapid visual display. The analysis on the image characteristics based on the human visual attention model show a strong correlation between target spotting difficulty and dispersion of the visual attention.

Identifying effective interpretation methods for magnetic data by profiling and analyzing human data interactions

Geoscientific data interpretation is a highly subjective and complex task because human intuition and biases play a significant role. Based on these interpretations, however, the mining and petroleum industries make decisions with paramount financial and environmental implications. To improve the accuracy and efficacy of these interpretations, it is important to better understand the interpretation process and the impact of different interpretation techniques, including data processing and display methods. As a first step toward this goal, we aim to quantitatively analyze the variability in geophysical data interpretation between and within individuals. We carried out an experiment to analyze how individuals interact with magnetic data during the process of identifying prescribed targets. Participants performed two target spotting exercises where the same magnetic image was presented at different orientations. The task was to identify the magnetic response from porphyry-style intrusive systems. The experiment involved analyzing the data observation pattern during the interpretation process using an eye tracker system that captures the interpreter’s eye gaze motion and the target-spotting performance. The time at which targets were identified was also recorded. Fourteen participants with varying degrees of experience and expertise participated in this study. The results show inconsistencies within and between the interpreters in target-spotting performance. The results show a correlation between a systematic data observation pattern and target-spotting performance. Improved target-spotting performance was obtained when the magnetic image was observed from multiple orientations. These findings will help to identify and quantify the effective interpretation practices, which can provide a roadmap for the training of geoscientific data interpreters and contribute toward the understanding of the uncertainties in the data interpretation process.

Visual to Parametric Interaction (V2PI)

Typical data visualizations result from linear pipelines that start by characterizing data using a model or algorithm to reduce the dimension and summarize structure, and end by displaying the data in a reduced dimensional form. Sensemaking may take place at the end of the pipeline when users have an opportunity to observe, digest, and internalize any information displayed. However, some visualizations mask meaningful data structures when model or algorithm constraints (e.g., parameter specifications) contradict information in the data. Yet, due to the linearity of the pipeline, users do not have a natural means to adjust the displays. In this paper, we present a framework for creating dynamic data displays that rely on both mechanistic data summaries and expert judgement. The key is that we develop both the theory and methods of a new human-data interaction to which we refer as “ Visual to Parametric Interaction” (V2PI). With V2PI, the pipeline becomes bi-directional in that users are embedded in the pipeline; users learn from visualizations and the visualizations adjust to expert judgement. We demonstrate the utility of V2PI and a bi-directional pipeline with two examples.

Enquête sur l’habitat traditionnel du Grand Vaux (Haut-Jura méridional)

Enquiry on traditional housing of the Grandvaux valley. The Grandvaux traditional house remains one of the last testimonies of a dying way of life. This building essentially makes up a functionalised working-tool, fruit of the progressive human adaptation to the hardy physical background. Nevertheless, neither economic activities nor geo-climatic environment alone render an account of it. This housing shall be considered as a «total social fact», fruit of three basic human data's interaction : climatic integration, economic needs and socialization. As a result of it, a typical form of the edifice, characteristic building’s teenies and an ingenious use of volumes are born. To understand them today, it is necessary to take into account the social history of the area.