Guided Visual Exploration of Relations in Data Sets

Efficient explorative data analysis systems must take into account both what a user knows and wants to know. This paper proposes a principled framework for interactive visual exploration of relations in data, through views most informative given the user's current knowledge and objectives. The user can input pre-existing knowledge of relations in the data and also formulate specific exploration interests, which are then taken into account in the exploration. The idea is to steer the exploration process towards the interests of the user, instead of showing uninteresting or already known relations. The user's knowledge is modelled by a distribution over data sets parametrised by subsets of rows and columns of data, called tile constraints. We provide a computationally efficient implementation of this concept based on constrained randomisation. Furthermore, we describe a novel dimensionality reduction method for finding the views most informative to the user, which at the limit of no background knowledge and with generic objectives reduces to PCA. We show that the method is suitable for interactive use and is robust to noise, outperforms standard projection pursuit visualisation methods, and gives understandable and useful results in analysis of real-world data. We provide an open-source implementation of the framework.Peer reviewe

Towards Transparent, Reusable, and Customizable Data Science in Computational Notebooks

Data science workflows are human-centered processes involving on-demand programming and analysis. While programmable and interactive interfaces such as widgets embedded within computational notebooks are suitable for these workflows, they lack robust state management capabilities and do not support user-defined customization of the interactive components. The absence of such capabilities hinders workflow reusability and transparency while limiting the scope of exploration of the end-users. In response, we developed MAGNETON, a framework for authoring interactive widgets within computational notebooks that enables transparent, reusable, and customizable data science workflows. The framework enhances existing widgets to support fine-grained interaction history management, reusable states, and user-defined customizations. We conducted three case studies in a real-world knowledge graph construction and serving platform to evaluate the effectiveness of these widgets. Based on the observations, we discuss future implications of employing MAGNETON widgets for general-purpose data science workflows.Comment: To appear at Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing System

Supporting the sensemaking process in visual analytics

Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. It involves interactive exploration of data using visualizations and automated data analysis to gain insight, and to ultimately make better decisions. It aims to support the sensemaking process in which information is collected, organized and analyzed to form new knowledge and inform further action. Interactive visual exploration of the data can lead to many discoveries in terms of relations, patterns, outliers and so on. It is difficult for the human working memory to keep track of all findings during a visual analysis. Also, synthesis of many different findings and relations between those findings increase the information overload and thereby hinders the sensemaking process further. The central theme of this dissertation is How to support users in their sensemaking process during interactive exploration of data? To support the sensemaking process in visual analytics, we mainly focus on how to support users to capture, reuse, review, share, and present the key aspects of interest concerning the analysis process and the findings during interactive exploration of data. For this, we have developed generic models and tools that enable users to capture findings with provenance, and construct arguments; and to review, revise and share their visual analysis. First, we present a sensemaking framework for visual analytics that contains three linked views: a data view, a navigation view and a knowledge view for supporting the sense-making process. The data view offers interactive data visualization tools. The navigation view automatically captures the interaction history using a semantically rich action model and provides an overview of the analysis structure. The knowledge view is a basic graphics editor that helps users to record findings with provenance and to organize findings into claims using diagramming techniques. Users can exploit automatically captured interaction history and manually recorded findings to review and revise their visual analysis. Thus, the analysis process can be archived and shared with others for collaborative visual analysis. Secondly, we enable analysts to capture data selections as semantic zones during an analysis, and to reuse these zones on different subsets of data. We present a Select & Slice table that helps analysts to capture, manipulate, and reuse these zones more explicitly during exploratory data analysis. Users can reuse zones, combine zones, and compare and trace items of interest across different semantic zones and data slices. Finally, exploration overviews and searching techniques based on keywords, content similarity, and context helped analysts to develop awareness over the key aspects of the exploration concerning the analysis process and findings. On one hand, they can proactively search analysis processes and findings for reviewing purposes. On the other hand, they can use the system to discover implicit connections between findings and the current line of inquiry, and recommend these related findings during an interactive data exploration. We implemented the models and tools described in this dissertation in Aruvi and HARVEST. Using Aruvi and HARVEST, we studied the implications of these models on a user’s sensemaking process. We adopted the short-term and long-term case studies approach to study support offered by these tools for the sensemaking process. The observations of the case studies were used to evaluate the models

ENSURING STUDENT-CENTERED, CONSTRUCTIVIST AND PROJECT-BASED EXPERIENTIAL LEARNING APPLYING THE EXPLORATION, RESEARCH, INTERACTION AND CREATION (ERIC) LEARNING MODEL

Experiential learning literally is making meaning from direct experience. It plays vital role in facilitating the process of creating knowledge, sense-making and knowledge transfer in teaching, training and development. This study assessed the effectiveness of Exploration, Research, Interaction and Creation (ERIC) Learning Model which is a framework adopted from various theories and philosophies such those of student-centered, constructivist-based, project-based, experiential, multisensory, reflective, participatory, interactive, cooperative, collaborative and active learning. Mixed method was used employing pre-experimental design and narrative analysis of learning experiences. Pre-test and posttest, survey, interview, observation and focus group discussions were made. There were 32 college students in the Tourism Management Program enrolled in NAS 106 (Environmental Science) and 28 enrolled in BST 323 (Ecotourism) for Academic Year 2018 - 2019 at Columban College, Inc. and were used as subjects. Quantitative data were treated using Mean, Weighted Mean and t-Test for Dependent Samples. Student’s engagement and involvement were maximized by exploration, research, interaction and creation and they adapted the skills and strategies for them to become responsible learners and lifelong learners. There was a significant increase in the performance of students as well as develops more positive attitude towards the topics

Teaching ISO/IEC 12207 software lifecycle processes: a serious game approach

Serious games involve applying game design techniques to tasks of a serious nature. In particular, serious games can be used as informative tools and can be embedded in formal education. Although there are some studies related to the application of serious games for the software development process, there is no serious game that teaches the fundamentals of the ISO/IEC 12207:1995 Systems and software engineering – Software life cycle processes, which is an international standard for software lifecycle processes that aims to be ‘the’ standard that defines all the tasks required for developing and maintaining software. “Floors” is a serious game that proposes an interactive learning experience to introduce ISO/IEC 12207:1995 by creating different floors of a virtual environment where various processes of the standard are discussed and implemented. Inherently, it follows an iterative process based on interactive technical dialogues in a 3D computer simulated office. The tool is designed to assess the novice engineering practitioners knowledge and provide preliminary training for ISO/IEC 12207:1995 processes. By playing such a game, participants are able to learn about the details of this standard. The present study provides a framework for the exploration of research data obtained from computer engineering students. Results suggest that there is a significant difference between the knowledge gained among the students who have played Floors and those who have only participated in paper-based learning sessions. Our findings indicate that participants who played Floors tend to have greater knowledge of the ISO/IEC 12207:1995 standard, and as a result, we recommend the use of serious games that seem to be superior to traditional paper based approach

Supporting the analytical reasoning process in information visualization

ABSTRACT This paper presents a new information visualization framework that supports the analytical reasoning process. It consists of three views -a data view, a knowledge view and a navigation view. The data view offers interactive information visualization tools. The knowledge view enables the analyst to record analysis artifacts such as findings, hypotheses and so on. The navigation view provides an overview of the exploration process by capturing the visualization states automatically. An analysis artifact recorded in the knowledge view can be linked to a visualization state in the navigation view. The analyst can revisit a visualization state from both the navigation and knowledge views to review the analysis and reuse it to look for alternate views. The whole analysis process can be saved along with the synthesized information. We present a user study and discuss the perceived usefulness of a prototype based on this framework that we have developed

The Grammar of Interactive Explanatory Model Analysis

The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best? It turns out that this is an ill-posed question. One cannot sufficiently explain a black-box machine learning model using a single method that gives only one perspective. Isolated explanations are prone to misunderstanding, which inevitably leads to wrong or simplistic reasoning. This problem is known as the Rashomon effect and refers to diverse, even contradictory interpretations of the same phenomenon. Surprisingly, the majority of methods developed for explainable machine learning focus on a single aspect of the model behavior. In contrast, we showcase the problem of explainability as an interactive and sequential analysis of a model. This paper presents how different Explanatory Model Analysis (EMA) methods complement each other and why it is essential to juxtapose them together. The introduced process of Interactive EMA (IEMA) derives from the algorithmic side of explainable machine learning and aims to embrace ideas developed in cognitive sciences. We formalize the grammar of IEMA to describe potential human-model dialogues. IEMA is implemented in the human-centered framework that adopts interactivity, customizability and automation as its main traits. Combined, these methods enhance the responsible approach to predictive modeling.Comment: 17 pages, 10 figures, 3 table