Personal informatics for everyday life: How users without prior self-tracking experience engage with personal data

The spreading of devices and applications allowing people to collect personal information opens new opportunities for Personal Informatics. Although many of these tools are already effectively used by motivated people to gain self-knowledge and produce change in their behaviors, there is a great number of users that are potentially interested in Personal Informatics but do not know of its potentialities and criticalities. In order to investigate how users perceive and use self-tracking tools in everyday life, we conducted a diary study, requiring fourteen participants with no previous experience in Personal Informatics to use a variety of trackers. We discovered that they use and perceive these technologies differently from the ones experienced in self-tracking. Participants considered the act of collecting personal information burdensome, with no beneficial reward. We also uncovered a series of problems that they experienced while tracking, managing, visualizing, and using their data. Among them we found that the lack of suggestions on using data and the excess of abstract visualization in the apps prevented users to gain useful insights. As a result, their interest in self-tracking soon faded, despite their initial curiosity in exploring and “playing” with their data. Starting from the findings of this study, we identified seven design strategies to better Personal Informatics tools, supported by literature and examples that draw from different research fields, from tangible interfaces, to virtual environments and video games. These strategies are primarily addressed to satisfy the inexperienced users' needs, but their applicability can be reasonably extended to all the individuals curious and interested in Personal Informatics.     

Designing a personal informatics system for users without experience in self-tracking: a case study

Thanks to the advancements in ubiquitous and wearable technologies, Personal Informatics (PI) systems can now reach a larger audience of users. However, it is not still clear whether this kind of tool can fit the needs of their daily lives. Our research aims at identifying specific barriers that may prevent the widespread adoption of PI and finding solutions to overcome them. We requested users without competence in self-tracking to use different PI instruments during their daily practices, identifying five user requirements by which to design novel PI tools. On such requirements, we developed a new system that can stimulate the use of these technologies, by enhancing the perceived benefits of collecting personal data. Then, we explored how naïve and experienced users differently explore their personal data in our system through a user trial. Results showed that the system was successful at helping individuals manage and interpret their own data, validated the usefulness of the requirements found and inspired three further design opportunities that could orient the design of future PI systems.     

Deliberate news consumption through the quantified self and the self-regulatory process

Many internet news sites have introduced recommendation systems to help users mitigate information overload. However, these systems may exacerbate mindless information consumption by reducing opportunities for people to voluntarily select news. We propose prototypes of personal informatics tools based on a self-regulatory process and quantified self-theories, which can be used to help people fulfill a eudaimonic motivation through self-observation and self-control and thus lead to deliberate news consumption. A longitudinal field experiment demonstrates that self-control and self-observation tools promote deliberate news consumption and reveals synergistic effects between the two tools. Our results indicate that the effect of the self-observation tool persists longer.     

Measuring Lisbon Patterns: Baixa from 1650 to 2010

The present study, based on a comparative analysis of several plans for Lisbon’s Baixa district, with an emphasis on that area’s public space, contributes to an understanding of the urban design process and presents a fresh perspective on dealing with historical data by conducting a posteriori analysis using mathematical tools to uncover relations in the historical data. The nine plans used were quantified and evaluated in a comparative manner. While CAD was used to quantify the urban morphology of the different plans, comparative tables make it possible to register the data, which was further evaluated through two interrelated processes: mathematical analysis and the urban analysis. The results show the existence of power law relations for the areas of each of the city’s different elements (e.g., blocks, churches, largos and adros). We discuss how this contributes to the understanding of the plans’ elements.     

Dynamic constraints for record matching

This paper investigates constraints for matching records from unreliable data sources. (a) We introduce a class of matching dependencies (mds) for specifying the semantics of unreliable data. As opposed to static constraints for schema design, mds are developed for record matching, and are defined in terms of similarity predicates and a dynamic semantics. (b) We identify a special case of mds, referred to as relative candidate keys (rcks), to determine what attributes to compare and how to compare them when matching records across possibly different relations. (c) We propose a mechanism for inferring mds, a departure from traditional implication analysis, such that when we cannot match records by comparing attributes that contain errors, we may still find matches by using other, more reliable attributes. Moreover, we develop a sound and complete system for inferring mds. (d) We provide a quadratic-time algorithm for inferring mds and an effective algorithm for deducing a set of high-quality rcks from mds. (e) We experimentally verify that the algorithms help matching tools efficiently identify keys at compile time for matching, blocking or windowing and in addition, that the md-based techniques effectively improve the quality and efficiency of various record matching methods.     

Contextualized mobile recommendation service based on interactive social network discovered from mobile users

Personal context is the most significant information for providing contextualized mobile recommendation services at a certain time and place. However, it is very difficult for service providers to be aware of the personal contexts, because each person’s activities and preferences are very ambiguous and depending on numerous unknown factors. In order to deal with this problem, we have focused on discovering social relationships (e.g., family, friends, colleagues and so on) between people. We have assumed that the personal context of a certain person is interrelated with those of other people, and investigated how to employ his neighbor’s contexts, which possibly have a meaningful influence on his personal context. It indicates that we have to discover implicit social networks which express the contextual dependencies between people. Thereby, in this paper, we propose an interactive approach to build meaningful social networks by interacting with human experts. Given a certain social relation (e.g., isFatherOf), this proposed systems can evaluate a set of conditions (which are represented as propositional axioms) asserted from the human experts, and show them a social network resulted from data mining tools. More importantly, social network ontology has been exploited to consistently guide them by proving whether the conditions are logically verified, and to refine the discovered social networks. We expect these social network is applicable to generate context-based recommendation services. In this research project, we have applied the proposed system to discover the social networks between mobile users by collecting a dataset from about two millions of users.     

Exploring Interpersonal Relationships in Historical Voting Records

Historical records from democratic processes and negotiation of constitutional texts are a complex type of data to navigate due to the many different elements that are constantly interacting with one another: people, timelines, different proposed documents, changes to such documents, and voting to approve or reject those changes. In particular, voting records can offer various insights about relationships between people of note in that historical context, such as alliances that can form and dissolve over time and people with unusual behavior. In this paper, we present a toolset developed to aid users in exploring relationships in voting records from a particular domain of constitutional conventions. The toolset consists of two elements: a dataset visualizer, which shows the entire timeline of a convention and allows users to investigate relationships at different moments in time via dimensionality reduction, and a person visualizer, which shows details of a given person's activity in that convention to aid in understanding the behavior observed in the dataset visualizer. We discuss our design choices and how each tool in those elements works towards our goals, and how they were perceived in an evaluation conducted with domain experts.     

Sensitive-by-distance: quasi-health data in the algorithmic era

This article offers a new perspective on the boundaries between health and non-health data in the age of ‘Quantified-Self’ apps: the ‘data-sensitiveness-by-computational-distance’ approach-or, more simply, the ‘sensitive-by-distance’ approach. This approach takes into account two variables: the intrinsic sensitiveness (a static variable) of personal data and the computational distance (a dynamic variable) between some kinds of personal data and pure health (or sensitive) data, which depends upon computational capacity. From an objective perspective, computational capacity depends on the level of development of data retrieval technologies at a certain moment, the availability of ‘accessory data’, and the applicable legal restraints on processing data. From a subjective perspective, computational capacity depends on the specific data mining efforts (or the ability to invest in them) taken by a given data controller: economic resources, human resources, and the use of accessory data. A direct consequence of the expansion of augmented humanity in collecting and inferring personal data is the increasing loss of health data processing ‘legibility’ for data subjects. In order to address this issue, we propose exploiting the existing legal tools in the General Data Protection Regulation to empower data subjects (the right to data access, the right to know the logic involved in automated decision-making, data portability, etc.).     

Field Design Sessions: Augmenting Whose Reality?

The authors present a design case in which field design sessions are introduced to bridge the designers' imagination and the users' knowledge of the use context. This approach entailed immersing design teams in the environment of the product-to-be during the conceptual design phase. With a background in the Scandinavian tradition of participatory or cooperative design, the design team observed and talked to users, sketched and produced mock-ups, acted out scenarios, and received user feedback during these field trips.

Moving the design into the field provided the team with a number of advantages compared with traditional work in the design studio based on user field observations. Designers achieved direct physical experience of the circumstances and a nonrepresented, nonabstracted introduction to the problems at hand. Through on-the-spot mock-up design and user collaboration, valuable insights into the essence of work and use were gained, and design suggestions embodied by these mock-ups were put on the line. Being aware or unaware of it, these suggestions contained personal assumptions, and preconceived opinions or hypotheses about use and use context, but also contained suggestions of applied technology. The result was new valuable insights toward an understanding of what work really is (or is not) and how it could be supported (or not supported) in the future.

It is argued that, to make the most of augmented reality (AR) in the design of professional tools, knowledge of the state of the art of technology is a prerequisite, but is not in itself sufficient. It needs to be complemented by design approaches that (a) provide insight about the users, their work practice, and use context and (b) support designers in aligning their viewpoints with the viewpoints and experienced reality of the people for whom they are designing. This may form a cornerstone in the successful application of emerging technologies.

This article discusses existing human-computer interaction approaches aimed at engaging the field in design, contrasts them with field design sessions, reflects on the advantages of applying them, and draws attention to a number of method points.     

Mapping the course, marking the trail [personal knowledge management]

Personal knowledge management tools function to help humans consider their information assets and needs before searching, so they can ask insightful questions and form search request better. Extending this approach, Mitretek Systems designed and implemented a prototypes of two post-search tools to use after the initial search. The personal information extraction (PIE) tool helps the user build models to organize and explore the information in a collection of documents. The Knowledge Discovery Trails tool (KD trails) records the routes of information-foraging excursions, so people can share of repeat successful approaches. Tools such as these would let knowledge workers improve the ways they search for and manage information, helping them transform an instinctive behavior into a calculated process.     

The knowledge level reinterpreted: Modeling socio-technical systems

Knowledge acquisition is a process of developing qualitative models of systems in the world—physical, social, technological—often for the first time, not extracting facts and rules that are already written down and filed away in an expert's mind. Models of reasoning describe how people behave—how they interactively gather evidence by looking and asking questions, represent a situation by saying and writing things, and plan to act in some environment. But such models are inherently brittle mechanisms: Human reinterpretation of rules and procedures is metaphorical, based on prelinguistic perceptual categorization and nondeliberated sensory-motor coordination. This view of people, relative to computer models, yields an alternative view of what tools can by and the tool design process. Knowledge engineers are called to participate with social scientists and workers in the codesign of the workplace and tools for enhancing worker creativity and response to unanticipated situations. the emphasis is on augmenting human capabilities as they interact with each other to construct new conceptualizations—facilitating conversations, not just automating routine behavior. Software development in the context of use maintains connection to nontechnical, social factors such as ownership of ideas and authority to participate. the role of knowledge engineering is not merely “capturing knowledge” in a program delivered by technicians to users. Rather, we seek to develop tools that help people in a community in their everyday practice of creating new understandings and capabilities, new forms of knowledge. © 1993 John Wiley & Sons, Inc.     

High Level Data Structures for GPGPU Programming in a Statically Typed Language

To increase software performance, it is now common to use hardware accelerators. Currently, GPUs are the most widespread accelerators that can handle general computations. This requires to use GPGPU frameworks such as Cuda or OpenCL. Both are very low-level and make the benefit of GPGPU programming difficult to achieve. In particular, they require to write programs as a combination of two subprograms, and, to manually manage devices and memory transfers. This increases the complexity of the overall software design. The idea we develop in this paper is to guarantee expressiveness and safety for CPU and GPU computations and memory managements with high-level data-structures and static type-checking. In this paper, we present how statically typed languages, compilers and libraries help harness high level GPGPU programming. In particular, we show how we added high-level user-defined data structures to a GPGPU programming framework based on a statically typed programming language: OCaml. Thus, we describe the introduction of records and tagged unions shared between the host program and GPGPU kernels described via a domain specific language as well as a simple pattern matching control structure to manage them. Examples, practical tests and comparisons with state of the art tools, show that our solutions improve code design, productivity, and safety while providing a high level of performance.     

Roles of Orientation in Tabletop Collaboration: Comprehension, Coordination and Communication

In order to support co-located collaboration, many researchers are now investigating how to effectively augment tabletops with electronic displays. As far back as 1988, orientation was recognized as a significant human factors issue that must be addressed by electronic tabletop designers. As with traditional tables, when people stand or sit at different positions around a horizontal display they will be viewing the contents from different angles. One common solution to this problem is to have the software reorient objects so that a given individual can view them ‘right way up.’ Yet is this the best approach? If not, how do people actually use orientation on tables? To answer these questions, we conducted an observational study of collaborative activity on a traditional table. Our results show that the strategy of reorienting objects to a person’s view is overly simplistic: while important, it is an incomplete view of how people exploit their ability to reorient objects. Orientation proves critical in how individuals comprehend information, how collaborators coordinate their actions, and how they mediate communication. The coordinating role of orientation is evident in how people establish personal and group spaces and how they signal ownership of objects. In terms of communication, orientation is useful in initiating communicative exchanges and in continuing to speak to individuals about particular objects and work patterns as collaboration progresses. The three roles of orientation have significant implications for the design of tabletop software and the assessment of existing tabletop systems.     

Agent mediated retailing in the connected local community

This paper reports on and discusses recent research into software agent mediated retailing and considers how it may act as a catalyst to the formation of local electronic retail communities. Each member of the e-retail community has their own software agent that acts on their behalf. Their software agent acts as an electronic personal sales assistant (ePSA), interacting with the person and assisting with the selection and purchase of products. We consider issues that arise when each person can express their likes and dislikes for various aspects of the product range to their software agent, both in terms of the software agent's ability to serve them better, and how such preferences may be shared with others in the larger community to help them—and others in the community—make better and more informed choices. The investigations reported use the multimedia access through personal persistent agents (MAPPA) system, an experimental kiosk based e-retail system. MAPPA integrates a software agent based architecture, a novel form of product display and the use of an animated character agent to enhance the user's sense of personalisation.We report on user evaluation studies of the MAPPA system to investigate the effectiveness of the character agent as an interface to the e-retail system using both conventional usability evaluation techniques and eye-tracking technology. We also describe a simulation program that allows us to investigate the dynamics of e-retail community cohesion when a number of different parameters are considered, including the characteristics of the individual community members and the algorithms employed by the software agents to share information. Lastly, we develop and discuss notions of loyalty, trust, reputation and preference, four issues critical to the development and maintenance of the relationships between individual people, their personal software agents, businesses and the larger e-retail connected community. We propose these issues as ‘supra-functional’ criteria for human–computer interaction design in this area.     

“Old” theories, “New” technologies: Understanding knowledge sharing and learning in Brazilian software development companies

ContextNew technologies such as social networks, wikis, blogs and other social software enable collaborative work and are important facilitators of the learning process. They provide a simple mechanism for people to communicate and collaborate and thus support the creation of knowledge. In software-development companies they are used to creating an environment in which communication and collaboration between workers take place more effectively.ObjectiveThis paper identifies the main tools and technologies used by software-development companies in Brazil to manage knowledge and attempts to determine how these tools and technologies relate to important knowledge-sharing and learning theories and how they support the concepts described by these theories.MethodA survey was conducted in a group of Brazilian software development companies with high levels of process software maturity to see how they implement the Brazilian Software Processes Improvement model (MPS.Br) and use new tools and technologies. The survey used a qualitative analysis to identify which tools are used most and how frequently employees use them. The results of the analysis were compared with data from the literature on three knowledge-sharing and learning theories to understand how the use of these tools relates to the concepts proposed in these theories.ResultsThe results show that some of the tools used by the companies do not apply the concepts described in the theories as they do not help promote organizational learning. Furthermore, although the companies have adopted the tools, these are not often used, mainly because they are felt not to organize information efficiently.ConclusionThe use of certain tools can help promote several concepts described in the theories considered. Moreover, the use of these tools can help reduce the impact of, some common organizational problems. However, companies need to improve existing organizational policies that encourage employees to use these tools more regularly.     

Long Term Distributed File Reference Tracing: Implementation and Experience

DFSTrace is a system to collect and analyze long-term file reference data in a distributed UNIX workstation environment. The design of DFSTrace is unique in that it pays particular attention to the efficiency, extensibility and the logistics of long-term trace data collection in a distributed environment. The components of DFSTrace are a set of kernel hooks, a kernel buffer mechanism, a data extraction agent, a set of collection servers and post-processing tools. Our experience with DFSTrace has been highly positive. Tracing has been virtually unnoticeable, degrading performance 3–7per cent, depending on the level of detail of tracing. We have collected file reference traces from approximately 30 workstations continuously for over two years. We have implemented a post-processing library to provide a convenient programmer interface to the traces and have created an on-line database of results from a suite of analysis programs to aid trace selection. Our data has been used for a wide variety of purposes, including file system studies, performance measurement and tuning and debugging. Extensions of DFSTrace have enabled its use in applications such as field reliability testing and determining disk geometry. This paper presents the design, implementation and evaluation of DFSTrace and associated tools and describes how they have been used.     

Schema-based Web wrapping

An effective solution to automate information extraction from Web pages is represented by wrappers. A wrapper associates a Web page with an XML document that represents part of the information in that page in a machine-readable format. Most existing wrapping approaches have traditionally focused on how to generate extraction rules, while they have ignored potential benefits deriving from the use of the schema of the information being extracted in the wrapper evaluation. In this paper, we investigate how the schema of extracted information can be effectively used in both the design and evaluation of a Web wrapper. We define a clean declarative semantics for schema-based wrappers by introducing the notion of (preferred) extraction model, which is essential to compute a valid XML document containing the information extracted from a Web page. We developed the SCRAP (SChema-based wRAPper for web data) system for the proposed schema-based wrapping approach, which also provides visual support tools to the wrapper designer. Moreover, we present a wrapper generalization framework to profitably speed up the design of schema-based wrappers. Experimental evaluation has shown that SCRAP wrappers are not only able to successfully extract the required data, but also they are robust to changes that may occur in the source Web pages.