=Paper=
{{Paper
|id=Vol-3136/paper5
|storemode=property
|title=Big data for humans or humans for big data?: a human-data interaction perspective
|pdfUrl=https://ceur-ws.org/Vol-3136/paper-5.pdf
|volume=Vol-3136
|authors=Shin'ichi Konomi
|dblpUrl=https://dblp.org/rec/conf/avi/Konomi22
}}
==Big data for humans or humans for big data?: a human-data interaction perspective==
https://ceur-ws.org/Vol-3136/paper-5.pdf
Big data for humans or humans for big data?:
a human-data interaction perspective
Shin’ichi Konomi1
1
HDI Lab, Faculty of Arts and Science, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, JAPAN
Abstract
Designing "big data for humans" would require so-called human-data interaction. In this paper, we
discuss key dimensions of human-data interaction to enable a look at the field from a broader perspective
and facilitate developments of "big data for humans". Our discussion is based on the relevant research
projects in our group at the intersections of human-data interaction and recommendation and search,
pervasive computing, civic computing and learning analytics.
Keywords
Human-data interaction, human-centered big data, calm technology, data science
1. Introduction
Bell and Gray (1997) predicted that all information about physical objects, humans, buildings,
processes, and organizations will be online by 2047 [1]. Twenty five years have passed since
their prediction, and there are only 25 years left before the possible dawn of the fully datafied
world according to their prediction. By 2025, it’s estimated that 463 exabytes of data will be
generated each day globally [2].
The sheer volume, variety and velocity of the ever-increasing data can easily create the
situations of information overload. Quick fixes for the information overload problem often rely
on straightforward automation, which may fail to fit human needs in different contexts. Going
beyond such myopic approaches would require smartness at a different level to embed right
opportunities for people to interact with and intervene big-data systems at the right time and
in the right way. This can be a key step towards the design of calm technology [3].
Having people involved in big-data environments requires human-data interaction. Human-
data interaction (HDI) is an emerging field of interdisciplinary inquiry that is concerned with
understanding and developing technologies for supporting human interactions with digital
data. Such interactions may occur in the contexts of data collection, data wrangling, algorithm
design, analytics, visualization, recommendation, classification, prediction, interpretation, and
so on.
Proceedings of CoPDA2022 - Sixth International Workshop on Cultures of Participation in the Digital Age: AI for
Humans or Humans for AI? June 7, 2022, Frascati (RM), Italy
$ konomi@artsci.kyushu-u.ac.jp (S. Konomi)
http://hdi.ait.kyushu-u.ac.jp/ (S. Konomi)
0000-0001-5831-2152 (S. Konomi)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
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Shin’ichi Konomi CEUR Workshop Proceedings 14–20
Business
Understanding
Data Data Analysis and
Data collection Evaluation Deployment
Understanding preparation modeling
Upstream Downstream
Figure 1: A process for using big data inspired by CRISP-DM [19]
.
Human-data interaction emphasizes the human-centered approach and existing works in this
field focuses on its different facets [4]. Mortier, Haddadi, Henderson, McAuley and Crowcroft
discuss human-data interaction with their proposal to place humans at the center of the flows
of data, and provision of the mechanisms for citizens to interact with these systems and data
explicitly [5]. They also propose and elaborate on the three core themes relevant to human-data
interaction, namely, legibility, agency, and negotiability. Crabtree and Mortier discuss human-
data interaction from social and interactional perspectives, and look at the need to develop
social models and mechanisms of data sharing that enable users to play an active role in the
process [6]. Mashhadi, Kawsar and Acer draw our attention to the importance of human-data
interaction in Internet of Things environments with ubiquitous devices [7]. Cabitza and Locoro
discuss healthcare data through the lens of human-data interaction [8]. Other studies look
into embodied interactions for exploring large data sets [9], and a media service that exploits
personal data to provide content recommendations [10].
In this paper, we discuss key dimensions of human-data interaction to enable a look at the
field from a broader perspective and facilitate developments of "big data for humans". Our
discussion is based on the relevant research projects in our group at the intersections of human-
data interaction and recommendation and search, pervasive computing, civic computing and
learning analytics.
2. Three key dimensions of human-data interaction
In this section, we introduce the three dimensions for classifying human-data interaction
environments. We identified these dimensions based on a survey of related works [4, 5, 6, 7, 8,
9, 10], our own experiences with relevant projects [11, 12, 13, 14, 15, 16, 17, 18] as well as an
existing process model for data science [19]. Table 1 shows these three dimensions in a tabular
format.
The first dimension concerns with the process for using big data (see Figure 1). The process
starts with data collection, followed by data understanding, data preparation through data
wrangling, analysis and modeling via visualization and/or machine learning algorithms, eval-
uation, and deployment of the resulting model or actions based on the gained insights. For
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Shin’ichi Konomi CEUR Workshop Proceedings 14–20
Table 1
The three key dimensions of HDI.
Personal data Public data
Upstream Downstream Upstream Downstream
Synchronous Real-time in- Real-time in- Real-time in- Real-time in-
teraction with teraction with teraction with teraction with
personal data at personal data public data at public data at
upstream steps at downstream upstream steps downstream
(e.g., Collect- steps (e.g., Inter- (e.g., Collecting steps (e.g, Inter-
ing personal active analysis urban public active analysis
health data of data in data interac- of urban pub-
interactively) personal infor- tively) lic data sets,
matics) possibly using
an embodied
interaction
interface)
Asynchronous Long-term in- Long-term in- Long-term in- Long-term in-
teraction with teraction with teraction with teraction with
personal data at personal data public data at public data at
upstream steps at downstream upstream steps downstream
(e.g., Collect- steps (e.g., Per- (e.g., Collecting steps (e.g., Non-
ing personal sonalized news urban public personalized
health data recommenda- data automat- recommenda-
automatically tion based on ically and use tion of popular
and use it at an incremen- it at a later news based on
a later point tally improved point in time. an incremen-
in time. Im- machine- Improving data tally improved
proving data learning model collection to machine-
collection to address ethical learning
address privacy issues.) model)
issues.)
example, human-data interaction can take place downstream in this process during the analysis
and modeling phase by using interactive visualization tools. In other cases, it can take place
upstream during data collection phase by turning on and off GPS tracking on one’s smart phone.
This upstream-downstream dimension captures the point of human-data interaction in this
process, and allows us to consider the differences of human-data interaction accordingly.
The second is the personal-public dimension that concerns with the characteristics of data
with which people interact. For example, embodied interaction with public data sets in a VR
environment is public in this dimension, whereas personal news recommendation systems may
use personal data about people. This dimension allows us to consider different concerns around
the interaction with public and personal data.
The third is the synchronous-asynchronous dimension that concerns with the time aspects of
human-data interaction. This dimension distinguishes the different modes of human-data inter-
action in a similar way as the synchronous-asynchronous classification of computer-supported
cooperative work environments. For example, interactive analysis of data sets using an em-
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Table 2
Classification of existing systems for recommendation and search, pervasive computing, civic computing,
and learning analytics.
Personal data Public data
Upstream Downstream Upstream Downstream
- Askus[13]
- Vacant - CourseQ[11]
- Deai - Deai
Synchronous House[17] - Deai
Explorer[12] Explorer[12]
- Community Explorer[12]
Reminder[14]
- Vacant
- Learning - e-Book
House[17] - Co-location
Asynchronous Analytics Reading
- Community networks [18]
for All[16] Analytics[15]
Reminder[14]
bodied interaction interface falls into the synchronous category. When people improve the
behaviors of a recommendation system by changing some preference settings or by replacing
its algorithm with a more privacy-preserving and less biased one, such interactions can be
considered as asynchronous.
3. Case studies to explore the dimensions
We next look further into the proposed dimensions of human-data interaction based on several
existing systems, which have been developed by our group. The purposes of the systems include
recommendation and search, pervasive computing, civic computing, and learning analytics. Their
HDI features can be classified into different categories as shown in Table 2.
3.1. Recommendation and search
CourseQ [11] is a course recommendation system for university students based on a syllabus
data set and a topic modeling-based algorithm. Although many existing course recommendation
systems focus on the accuracy of recommendation, they may fail to recommend the courses
that the students feel truly relevant. We introduced various interactive features in CourseQ
so as to improve user-centric metrics such as user acceptance as well as understandability of
recommendation results.
The interactive features of CourseQ include keyword-based search and filtering, interactive
visualization of recommended courses, dynamic presentation of relevant auxiliary information
and explanation with the recommended results, and a ’like’ button. These features mainly
support synchronous interactions with the publicly available data set, however, the interactivity
does not allow users to change the data sets and other elements in the upstream process.
CourseQ thus provides synchronous downstream human-data interaction based on public data.
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3.2. Pervasive computing
DeaiExplorer [12] is a social-network display that responds to RFID badges carried by conference
participants and displays social connections between colocated conference participants. The
system exploits a public data set from a publication database as well as data collected from
RFID readers based on participants’ agreement. The system visualizes social network structures
based on these two types of data in order to facilitate social interactions among conference
participants. The interactive feature of DeaiExplorer allows conference participants to access
their social network visualizations just by showing their RFID badges to the RFID reader. This
feature allows users to interact with public and private data in a synchronous manner. As this
interactivity allows users to control the capture of their RFID data by (not) showing badges to
the RFID reader, the system allows synchronous human-data interaction in both upstream and
downstream processes.
Askus [13] is a type of so-called participatory sensing systems, which allows users to collect
data manually by using mobile phones. It thus concerns with the upstream process, and mainly
supports synchronous human-data interaction with public environmental data, etc.
Co-location networks [18] analyze urban mobility data sets based on network analysis
techniques. This analysis was performed multiple times based on an iterative improvements of
network analysis techniques. It thus concerns with asynchronous interaction with public data
in the downstream process.
3.3. Civic computing
Our WiFi-based sensing tool to predict vacant houses [17] is a type of so-called opportunistic
sensing systems, which allows local community members to collect data automatically by just
walking around in their community with their mobile phones in their backpacks. Users’ choices
of walking routes can control data collection in a synchronous manner. Data collection can be
controlled asynchronously by changing the setting of WiFi sensing software. It thus concerns
with the upstream process, and mainly supports synchronous and asynchronous human-data
interaction with WiFi signals in public spaces.
Community Reminder [14] is also a type of participatory sensing systems, which allows local
community members to collect information about the safety in their communities using mobile
phones. Local community members can also participate in the design of the data collection
mechanisms of this system using an intuitive tangible user interface. This system concerns
with both synchronous and asynchronous aspects of public data collection.
3.4. Learning analytics
Our research projects in this area include analysis of e-book reading patterns [15] as well as an
effort to provide learning analytics for all age groups and in developing communities without
reliable internet access [16]. The former analysis can be performed in an iterative manner based
on different research questions and techniques. It thus concerns with asynchronous interaction
with personal data in the downstream process. The interactive features of the latter includes
delayed data transmission of learning log data using mobile phones[16]. It thus concerns with
asynchronous interaction with personal data in the upstream process.
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4. Discussion and conclusion
We introduced the three key dimensions for classifying human-data interaction environments,
i.e., the upstream-downstream, personal-public, and synchronous-asynchronous dimensions. Ex-
isting research projects tend to focus on one or more areas with respect to these dimensions.
The discussions of the several existing systems for recommendation and search, pervasive
computing, civic computing, and learning analytics provided an opportunity for a further look
into the proposed HDI dimensions, and demonstrated how they can highlight commonalities
and differences of various human-data interaction systems.
Interaction is everywhere in the space defined by the proposed dimensions. Thinking about
big data systems from the perspectives enabled by these dimensions would help us analyze
and/or design a broader range of big data and AI systems with humans at the center and
their data interactions in mind. It reminds the designers and the users of such systems that
they are embedded in larger contexts, and the importance of providing the right opportunities
for humans to play active roles in the context. One of the major advantages of emphasizing
interactions in big data and AI systems can be, as our experiences with CourseQ [11] suggests,
people’s increased trust with data-centric smart mechanisms such as recommender systems,
which could in turn lead to people’s improved satisfaction with such systems.
Acknowledgments
This work was supported by JSPS KAKENHI Grant Number JP20H00622.
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