Revisiting Bertin Matrices: New Interactions for Crafting Tabular Visualizations

We present Bertifier, a web app for rapidly creating tabular visualizations from spreadsheets. Bertifier draws from Jacques Bertin's matrix analysis method, whose goal was to “simplify without destroying” by encoding cell values visually and grouping similar rows and columns. Although there were several attempts to bring this method to computers, no implementation exists today that is both exhaustive and accessible to a large audience. Bertifier remains faithful to Bertin's method while leveraging the power of today's interactive computers. Tables are formatted and manipulated through crossets, a new interaction technique for rapidly applying operations on rows and columns. We also introduce visual reordering, a semi-interactive reordering approach that lets users apply and tune automatic reordering algorithms in a WYSIWYG manner. Sessions with eight users from different backgrounds suggest that Bertifier has the potential to bring Bertin's method to a wider audience of both technical and non-technical users, and empower them with data analysis and communication tools that were so far only accessible to a handful of specialists

Identification of regular patterns within sparse data structures

2020 Spring.Includes bibliographical references.Sparse matrix-vector multiplication (SpMV) is an essential computation in linear algebra. There is a well-known trade-off between operating on a dense or a sparse structure when performing SpMV. In the dense version of SpMV, useless operations are performed but the computation is amenable SIMD vectorization. In the sparse version, only useful operations are executed. However, an indirection array must be used, thus hindering the compiler's ability to perform optimizations that exploit the vector units available on the majority of modern processors. Our process automatically builds sets of regular sub-computations from the irregular sparse data structure. We mine for regular regions in the irregular data structure, grouping together non-contiguous points from the reorderable set of coordinates representing the sparse structure. The coordinates become partitioned into groupings of coordinates of pre-defined shapes using polyhedra. This partition models the exact same points from the input set of coordinates in a way that is specialized to the input's sparsity pattern. Once we have obtained a partition of the points into sets of polyhedra, we then scan these polyhedra to synthesize code that does not store any coordinates of zero-valued elements and does not require any indirection array to access data, thus making it amenable to SIMD vectorization

Ceramics from the ‘Sutny’ LBK settlement at Těšetice-Kyjovice, Moravia, Czech Republic : processing and statistical analyses

The aim of this article is to demonstrate the use of multivariate analysis of ceramics to verify the chronology of the 'Sutny' Linearbandkeramik culture (LBK) settlement at Těšetice-Kyjovice, in the Znojmo district of Moravia, Czech Republic. In this article we present the results obtained from the ceramics that have been processed to date. Based on this analysis we dated the settlement to approximately the Ib-IIa phase of the Moravian LBK. A new method for processing Moravian LBK ceramics is suggested, along with a proposed descriptive system, as a formalized description is very important to subsequent statistical evaluation. We introduce several statistical analyses suitable for the evaluation of ceramic assemblages and demonstrate the application of other methods of analysis and possible alternatives for archaeological data visualization. For this purpose we used the 'R' open statistical software package. We hope that this article will contribute to the development of a unified method of processing Moravian LBK ceramics, demonstrate new possibilities for evaluating archaeological data, and simplify its interpretation

Everything on the Table: Tabular, Graphic, and Interactive Approaches for Interpreting and Presenting Monte Carlo Simulation Data

Abstract Monte Carlo simulation studies (MCSS) form a cornerstone for quantitative methods research. They are frequently used to evaluate and compare the properties of statistical methods and inform both future research and current best practices. However, the presentation of results from MCSS often leaves much to be desired, with findings typically conveyed via a series of elaborate tables from which readers are expected to derive meaning. The goal of this dissertation is to explore, summarize, and describe a framework for the presentation of MCSS, and show how modern computing and visualization techniques improve their interpretability. Chapter One describes this problem by introducing the logic of MCSS, how they are conducted, what findings typically look like, and current practices for their presentation. Chapter Two demonstrates methods for improving the display of static tabular data, specifically via formatting, effects ordering, and rotation. Chapter Three delves into semi-graphic and graphical approaches for aiding the presentation of tabular data via shaded tables, and extensions to the tableplot and the hypothesis-error plot frameworks. Chapter Four describes the use of interactive computing applets to aid the exploration of complex tabular data, and why this is an ideal approach. Throughout this work, emphasis is placed on how such techniques improve our understanding of a particular dataset or model. Claims are supported with applied demonstrations. Implementation of the ideas from each chapter have been coded within the R language for statistical computing and are available for adoption by other researchers in a dedicated package (SimDisplay). It is hoped that these ideas might enhance our understanding of how to best present MCSS findings and be drawn upon in both applied and academic environments

Relationship types in visual analytics

Visual analytics are practically an application of the task of transforming the data into meaningful and reliable information in order to synthesize the knowledge about the data. Recently, organizations collect large data with aim of extracting useful information for organizational usage for better decision making. When the organization have a large and complex information, it leads to more complex relationship between the variables in the data. As a consequence, the visual analytics representation must be able to show, visualize and handle for more complex relationship of data. However, the research found the lack of relationship research in current visual analytics that lead difficulties to guide and design new relationship representation. Thus, this research aim to recognize, discover and categorize relationships types of visual analytics in representing a set of analytical data. Design Science Research Methodology has been used as research method for this study. It consists of two activities which are i). identify the visual analytic relationship context and its challenge and ii). analysis of relationship in visual analytics representation. At the end, this study is expected to identify and categorize the visual analytics representation according to six relationship types. This identification can help the visual analytics community to understand the primary and basic concept of relationship representation as a guidelines and knowledge for more comprehensive research in the future

Thinking interactively with visualization

Interaction is becoming an integral part of using visualization for analysis. When interaction is tightly and appropriately coupled with visualization, it can transform the visualization from display- ing static imagery to assisting comprehensive analysis of data at all scales. In this relationship, a deeper understanding of the role of interaction, its effects, and how visualization relates to interaction is necessary for designing systems in which the two components complement each other. This thesis approaches interaction in visualization from three different perspectives. First, it considers the cost of maintaining interaction in manipulating visualization of large datasets. Namely, large datasets often require a simplification process for the visualization to maintain interactivity, and this thesis examines how simplification affects the resulting visualization. Secondly, example interactive visual analytical systems are presented to demonstrate how interactivity could be applied in visualization. Specifically, four fully developed systems for four distinct problem domains are discussed to determine the common role of interactivity in these visualizations that make the systems successful. Lastly, this thesis presents evidence that interactions are important for analytical tasks using visualizations. Interaction logs of financial analysts using a visualization were collected, coded, and examined to determine the amount of analysis strategies contained within the interaction logs. The finding supports the benefits of high interactivity in analytical tasks when using a visualization. The example visualizations used to support these three perspectives are diverse in their goals and features. However, they all share similar design guidelines and visualization principles. Based on their characteristics, this thesis groups these visualizations into urban visualization, visual analytical systems, and interaction capturing and discusses them separately in terms of lessons learned and future directions

A two-stage framework for designing visual analytics systems to augment organizational analytical processes

A perennially interesting research topic in the field of visual analytics is how to effectively develop systems that support organizational knowledge worker’s decision-making and reasoning processes. The primary objective of a visual analytic system is to facilitate analytical reasoning and discovery of insights through interactive visual interfaces. It also enables the transfer of capability and expertise from where it resides to where it is needed–across individuals, and organizations as necessary. The problem is, however, most domain analytical practices generally vary from organizations to organizations. This leads to the diversified design of visual analytics systems in incorporating domain analytical processes, making it difficult to generalize the success from one domain to another. Exacerbating this problem is the dearth of general models of analytical workflows available to enable such timely and effective designs. To alleviate these problems, this dissertation presents a two-stage framework for informing the design of a visual analytics system. This two-stage design framework builds upon and extends current practices pertaining to analytical workflow and focuses, in particular, on investigating its effect on the design of visual analytics systems for organizational environments. It aims to empower organizations with more systematic and purposeful information analyses through modeling the domain users’ reasoning processes. The first stage in this framework is an Observation and Designing stage, in which a visual analytic system is designed and implemented to abstract and encapsulate general organizational analytical processes, through extensive collaboration with domain users. The second stage is the User-centric Refinement stage, which aims at interactively enriching and refining the already encapsulated domain analysis process based on understanding user’s intentions through analyzing their task behavior. To implement this framework in the process of designing a visual analytics system, this dissertation proposes four general design recommendations that, when followed, empower such systems to bring the users closer to the center of their analytical processes. This dissertation makes three primary contributions: first, it presents a general characterization of the analytical workflow in organizational environments. This characterization fills in the blank of the current lack of such an analytical model and further represents a set of domain analytical tasks that are commonly applicable to various organizations. Secondly, this dissertation describes a two-stage framework for facilitating the domain users’ workflows through integrating their analytical models into interactive visual analytics systems. Finally, this dissertation presents recommendations and suggestions on enriching and refining domain analysis through capturing and analyzing knowledge workers’ analysis processes. To exemplify the generalizability of these design recommendations, this dissertation presents three visual analytics systems that are developed following the proposed recommendations, including Taste for Xerox Corporation, OpsVis for Microsoft, and IRSV for the U.S. Department of Transportation. All of these systems are deployed to domain knowledge workers and are adopted for their analytical practices. Extensive empirical evaluations are further conducted to demonstrate efficacy of these systems in facilitating domain analytical processes