Combining design and performance in a data visualization management system

Interactive data visualizations have emerged as a prominent way to bring data exploration and analysis capabilities to both technical and non-technical users. Despite their ubiquity and importance across applications, multiple design- and performance-related challenges lurk beneath the visualization creation process. To meet these challenges, application designers either use visualization systems (e.g., Endeca, Tableau, and Splunk) that are tailored to domain-specific analyses, or manually design, implement, and optimize their own solutions. Unfortunately, both approaches typically slow down the creation process. In this paper, we describe the status of our progress towards an end-to-end relational approach in our data visualization management system (DVMS). We introduce DeVIL, a SQL-like language to express static as well as interactive visualizations as database views that combine user inpu

ProHealth eCoach: user-centered design and development of an eCoach app to promote healthy lifestyle with personalized activity recommendations

Background: Regular physical activity (PA), healthy habits, and an appropriate diet are recommended guidelines to maintain a healthy lifestyle. A healthy lifestyle can help to avoid chronic diseases and long-term illnesses. A monitoring and automatic personalized lifestyle recommendation system (i.e., automatic electronic coach or eCoach) with considering clinical and ethical guidelines, individual health status, condition, and preferences may successfully help participants to follow recommendations to maintain a healthy lifestyle. As a prerequisite for the prototype design of such a helpful eCoach system, it is essential to involve the end-users and subject-matter experts throughout the iterative design process. Methods: We used an iterative user-centered design (UCD) approach to understend context of use and to collect qualitative data to develop a roadmap for self-management with eCoaching. We involved researchers, non-technical and technical, health professionals, subject-matter experts, and potential end-users in design process. We designed and developed the eCoach prototype in two stages, adopting diferent phases of the iterative design process. In design workshop 1, we focused on identifying end-users, understanding the user’s context, specifying user requirements, designing and developing an initial low-fdelity eCoach prototype. In design workshop 2, we focused on maturing the low-fdelity solution design and development for the visualization of continuous and discrete data, artifcial intelligence (AI)-based interval forecasting, personalized recommendations, and activity goals. Results: The iterative design process helped to develop a working prototype of eCoach system that meets end-user’s requirements and expectations towards an efective recommendation visualization, considering diversity in culture, quality of life, and human values. The design provides an early version of the solution, consisting of wearable technology, a mobile app following the “Google Material Design” guidelines, and web content for self-monitoring, goal setting, and lifestyle recommendations in an engaging manner between the eCoach app and end-users. Conclusions: The adopted iterative design process brings in a design focus on the user and their needs at each phase. Throughout the design process, users have been involved at the heart of the design to create a working.publishedVersio

TULIP 4

Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. Based on more than 15 years of research and development, Tulip is built on a suite of tools and techniques , that can be used to address a large variety of domain-specific problems. With Tulip, we aim to provide Python and/or C++ developers a complete library, supporting the design of interactive information visualization applications for relational data, that can be customized to address a wide range of visualization problems. In its current iteration, Tulip enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualizations. This development pipeline makes the framework efficient for creating research prototypes as well as developing end-user applications. The recent addition of a complete Python programming layer wraps up Tulip as an ideal tool for fast prototyping and treatment automation, allowing to focus on problem solving, and as a great system for teaching purposes at all education levels

Comparison of Interactive Visualization Techniques for Origin-Destination Data Exploration

Origin-Destination (OD) data is a crucial part of price estimation in the aviation industry, and an OD flight is any number of flights a passenger takes in a single journey. OD data is a complex set of data that is both flow and multidimensional type of data. In this work, the focus is to design interactive visualization techniques to support user exploration of OD data. The thesis work aims to find which of the two menu designs suit better for OD data visualization: breadth-first or depth-first menu design. The two menus follow Schneiderman’s Task by Data Taxonomy, a broader version of the Information Seeking Mantra. The first menu design is a parallel, breadth-first menu layout. The layout shows the variables in an open layout and is closer to the original data matrix. The second menu design is a hierarchical, depth-first layout. This layout is derived from the semantics of the data and is more compact in terms of screen space. The two menu designs are compared in an online survey study conducted with the potential end users. The results of the online survey study are inconclusive, and therefore are complemented with an expert review. Both the survey study and expert review show that the Sankey graph is a good visualization type for this work, but the interaction of the two menu designs requires further improvements. Both of the menu designs received positive and negative feedback in the expert review. For future work, a solution that combines the positives of the two designs could be considered. ACM Computing Classification System (CCS): Human-Centered Computing → Visualization → Empirical Studies in Visualization Human-centered computing → Interaction design → Interaction design process and methods → Interface design prototypin

Arthroplasty Data Visualization

This master's thesis presents the work done in the field of visualization and interactivity conducted within the Design Science framework. The main goal was to make the data analysis using the arthroplasty register data into a more independent, easy, and user-friendly experience. The visualization artifact was created to support presentation of data material and results from data mining with a purpose of understand patient outcomes, longevity of implants, and present demographic and other data in a more contemporary way. There is a wealth of information and reports at the website of the Norwegian Arthroplasty Register, but very little in terms of interactivity and independent user exploration of data. The work was carried out as a part of a back- and front-end development with data mining methods developed for knee and hip prosthesis data being the back-end, and the front-end consisted of a user interface in addition to visualization. This setup had several advantages, where the selection of data mining methods and implementation of a high-fidelity user interface all contributed to a better user experience of the visualizations. The resulting artifact is comprised of visualizations of demographic data, Kaplan-Meier, and an interactive map of Norway. Interactivity enabled exploring data for selected periods of time, comparison of performance in different prostheses, and exploring patient population behind certain points on a survival graph. The map of Norway offers features such as demographic data and comparison of top 5 prostheses in different counties. The evaluation was carried out with the use of three different evaluation tools and interviews with domain and usability experts. Feedback during interviews was encouraging and indicated the potential usefulness of the visualizations. The system in its current form is more directed towards expert users, but can be easily adjusted to patients and the wider public, which could be a subject of future research. More visualizations and data analytical methods could further enhance the current solutions.Masteroppgåve i informasjonsvitskapINFO390MASV-INF

IRIS: Efficient Visualization, Data Analysis and Experiment Management for Wireless Sensor Networks

The design of ubiquitous computing environments is challenging, mainly due to the unforeseeable impact of real-world environments on the system performance. A crucial step to validate the behavior of these systems is to perform in-field experiments under various conditions. We introduce IRIS, an experiment management and data processing tool allowing the definition of arbitrary complex data analysis applications. While focusing on Wireless Sensor Networks, IRIS supports the seamless integration of heterogeneous data gathering technologies. The resulting flexibility and extensibility enable the definition of various services, from experiment management and performance evaluation to user-specific applications and visualization. IRIS demonstrated its effectiveness in three real-life use cases, offering a valuable support for in-field experimentation and development of customized applications for interfacing the end user with the system

Interactive Visual Self-service Data Classification Approach to Democratize Machine Learning

Machine learning algorithms often produce models considered as complex black-box models by both end users and developers. Such algorithms fail to explain the model in terms of the domain they are designed for. The proposed Iterative Visual Logical Classifier (IVLC) is an interpretable machine learning algorithm that allows end users to design a model and classify data with more confidence and without having to compromise on the accuracy. Such technique is especially helpful when dealing with sensitive and crucial data like cancer data in the medical domain with high cost of errors. With the help of the proposed interactive and lossless multidimensional visualization, end users can identify the pattern in the data based on which they can make explainable decisions. Such options would not be possible in black box machine learning methodologies. The interpretable IVLC algorithm is supported by the Interactive Shifted Paired Coordinates Software System (SPCVis). It is a lossless multidimensional data visualization system with interactive features. The interactive approach provides flexibility to the end user to perform data classification as self-service without having to rely on a machine learning expert. iv Interactive pattern discovery becomes challenging while dealing with large datasets with hundreds of dimensions/features. To overcome this problem, an automated classification approach combined with new Coordinate Order Optimizer (COO) algorithm and a Genetic algorithm (GA) is proposed. The COO algorithm automatically generates the coordinate pair sequences that best represent the data separation and GA helps optimizing the proposed IVLC algorithm by automatically generating the areas for data classification. The feasibility of the approach is shown by experiments on benchmark datasets covering both interactive and automated processes used for data classification

Design of a graphical user interface for home energy monitoring system

Excellent graphics are the instruments used for representing quantitative information. Graphics help people to understand complex things easily. Hence the user interfaces developed should be clear, illustrative and designed from the user point of view with respect to their applications. The thesis work deals with the design of a graphical user interface (GUI) developed for a home energy monitoring system. Design methodologies like user centric design and empathic design are followed while creating the user interface and also the effect of various colors on human perception is studied. Hence the final design of user interface provides the end-users a visualization of the energy produced and consumed in a monitored environment. The monitoring devices are connected to ThereGate system (data logger) via the M-Bus communication protocol. The ThereGate platform uses an Open Source Linux system as the operating language. The other communication platform used over the ThereGate platform is an oBIX bridge, a web based service interface rich in XML support for transferring the data. The interface has been programmed by using Visual basics 2008 and VB.NET, developed by Microsoft. The work progresses with an initial explanation on the availability of various home energy monitoring systems on the market and their comparison. The other units discuss the architecture of the ThereGate system, give a brief overview of the M-bus system, and discuss the development of graphical user interface (GUI) from a user centric design perspective using Microsoft's Visual Basics and VB.NET and configuration of M-Bus. The last unit contains a discussion of the goals achieved at the end of the design and the future developments that can be made to have more user interactions with the user interface

TULIP 5

International audienceTulip is an information visualization framework dedicated to the analysis and visualization of relational data. Based on more than 16 years of research and development, Tulip is built on a suite of tools and techniques, that can be used to address a large variety of domain-specific problems. With \tulip, we aim to provide Python and/or C++ developers a complete library, supporting the design of interactive information visualization applications for relational data, that can be customized to address a wide range of visualization problems. In its current iteration, \tulip enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualizations. This development pipeline makes the framework efficient for creating research prototypes as well as developing end-user applications. The recent addition of a complete Python programming layer wraps up Tulip as an ideal tool for fast prototyping and treatment automation, allowing to focus on problem solving, and as a great system for teaching purposes at all education levels

Smart Cities: A Case Study in Waste Monitoring and Management

This paper explores the potential of employing sensor enabled solutions to improve on waste monitoring and collection in public trash bins. Through a user-centered design approach, an inexpensive monitoring system developed and tested in pilot study. The system consists of wireless nodes that use ultrasonic sensors to measure the empty space in the bins, a sensor gateway that is based on Long Rage Wide Area Network (LoRaWAN) protocol and cloud-based back/front end for data collection, analysis and visualization. The system was evaluated through a pilot test, where six outdoor trash bins were remotely monitored at a university campus and a number of stakeholders were observed and interviewed. The results show that the existing technologies are mature enough to be able to develop and implement inexpensive add-on sensors to exiting trash bins, and employing such a system can provide the necessary insights to optimize waste collection processes, to avoid overfilled bins, and to improve the experience of the citizens