Visualization techniques for heterogeneous and multidimensional simulated building performance data sets

The architecture, environment and construction industry is facing, on the one hand, ambitious environmental regulations for low carbon and net zero energy buildings, and on the other hand, the emergence of new techniques such as parametric assessment and cloud computing. As a result, there is a dramatic increase of performance analysis and collected data during the building design phase. However, previous research highlighted major weaknesses of current building performance simulation -BPS- software regarding its ability to represent and explore input and output data, to interact with it, and to extract valuable data patterns and analyses. Therefore, this research aims to identify suitable visualization techniques that might increase the usability and the knowledge extracted from building simulation dataset. To that end, an interdisciplinary approach has been set up. First, a literature review allowed to characterize the specificities of BPS dataset, namely their heterogeneous nature -discrete, ordinal, categorical, and continuous-, their different correlation levels and their medium size. Second, key tasks that should be performed by BPS tools to support the design process are identified: exploration, solutions generation and evaluation. Then, two data visualization techniques that accept the BPS dataset specificities and that enable to perform these key tasks were selected within the information visualization research field: Decision Tree and Parallel Coordinates. Third, these techniques were applied to an extensive BPS dataset, generated from a series of parametric building simulations based on a high-performance building to be, called the smart living building. Finally, a qualitative comparison between the selected visualization techniques was conducted so as to reveal their strengths and weaknesses. This comparison highlights Parallel Coordinates as the most promising approach

A Review and Characterization of Progressive Visual Analytics

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions

Discovering spatio-temporal relationships a case study of risk modelling of domestic fires

A systematic risk analysis for mitigation purposes plays a crucial role in the context of emergency management in modern societies. It supports the planning of the general preparedness of the rescue forces and thus enhances public safety. This study applies the principles of knowledge discovery and data mining to support the development of a risk model for fire and rescue services. Domestic fires, which are a serious threat in an urban environment, are selected to demonstrate the methods. The aim of the research is to identify important factors that contribute to the probability of the occurrence of domestic fires. Various physical and socio-economic conditions in the background environment are analysed to provide an insight into the distribution of domestic fires in relation to underlying factors. Following the cross-disciplinary nature of data mining, this study offers a set of distinct methods that share the same goal - to identify patterns and relationships in data. The methods originate in different scientific fields, such as information visualisation, statistics, or artificial intelligence. Each of them reveals different aspects of the existing relations, which supports an understanding of the phenomenon and thus expands the expert knowledge. The application of data mining techniques is not straightforward because of the specific nature of geospatial data. This study documents the analysis process in order to provide guidelines for potential future users. It considers the suitability of the methods to handle spatial and spatio-temporal data with special attention to the GIS-motivated conceptualisation of the problem being analysed. Furthermore, the requirements for the user to be able to apply the methods successfully are discussed, as is the available software support

Designing multi-sensory displays for abstract data

The rapid increase in available information has lead to many attempts to automatically locate patterns in large, abstract, multi-attributed information spaces. These techniques are often called data mining and have met with varying degrees of success. An alternative approach to automatic pattern detection is to keep the user in the exploration loop by developing displays for perceptual data mining. This approach allows a domain expert to search the data for useful relationships and can be effective when automated rules are hard to define. However, designing models of the abstract data and defining appropriate displays are critical tasks in building a useful system. Designing displays of abstract data is especially difficult when multi-sensory interaction is considered. New technology, such as Virtual Environments, enables such multi-sensory interaction. For example, interfaces can be designed that immerse the user in a 3D space and provide visual, auditory and haptic (tactile) feedback. It has been a goal of Virtual Environments to use multi-sensory interaction in an attempt to increase the human-to-computer bandwidth. This approach may assist the user to understand large information spaces and find patterns in them. However, while the motivation is simple enough, actually designing appropriate mappings between the abstract information and the human sensory channels is quite difficult. Designing intuitive multi-sensory displays of abstract data is complex and needs to carefully consider human perceptual capabilities, yet we interact with the real world everyday in a multi-sensory way. Metaphors can describe mappings between the natural world and an abstract information space. This thesis develops a division of the multi-sensory design space called the MS-Taxonomy. The MS-Taxonomy provides a concept map of the design space based on temporal, spatial and direct metaphors. The detailed concepts within the taxonomy allow for discussion of low level design issues. Furthermore the concepts abstract to higher levels, allowing general design issues to be compared and discussed across the different senses. The MS-Taxonomy provides a categorisation of multi-sensory design options. However, to design effective multi-sensory displays requires more than a thorough understanding of design options. It is also useful to have guidelines to follow, and a process to describe the design steps. This thesis uses the structure of the MS-Taxonomy to develop the MS-Guidelines and the MS-Process. The MS-Guidelines capture design recommendations and the problems associated with different design choices. The MS-Process integrates the MS-Guidelines into a methodology for developing and evaluating multi-sensory displays. A detailed case study is used to validate the MS-Taxonomy, the MS-Guidelines and the MS-Process. The case study explores the design of multi-sensory displays within a domain where users wish to explore abstract data for patterns. This area is called Technical Analysis and involves the interpretation of patterns in stock market data. Following the MS-Process and using the MS-Guidelines some new multi-sensory displays are designed for pattern detection in stock market data. The outcome from the case study includes some novel haptic-visual and auditory-visual designs that are prototyped and evaluated

Face processing: human perception and principal components analysis

Principal component analysis (PCA) of face images is here related to subjects' performance on the same images. In two experiments subjects were shown a set of faces and asked to rate them for distinctiveness. They were subsequently shown a superset of faces and asked to identify those which appeared originally. Replicating previous work, we found that hits and false positives (FPs) did not correlate: those faces easy to identify as being "seen" were unrelated to those faces easy to reject as being "unseen". PCA was performed on three data sets: (i) face images with eye-position standardised; (ii) face images morphed to a standard template to remove shape information; (iii) the shape information from faces only. Analyses based upon PCA of shape-free faces gave high predictions of FPs, while shape information itself contributed only to hits. Furthermore, while FPs were generally predictable from components early in the PCA, hits appear to be accounted for by later components. We conclude that shape and "texture" (the image-based information remaining after morphing) may be used separately by the human face processing system, and that PCA of images offers a useful tool for understanding this system

Visualisation Support for Biological Bayesian Network Inference

Extracting valuable information from the visualisation of biological data and turning it into a network model is the main challenge addressed in this thesis. Biological networks are mathematical models that describe biological entities as nodes and their relationships as edges. Because they describe patterns of relationships, networks can show how a biological system works as a whole. However, network inference is a challenging optimisation problem impossible to resolve computationally in polynomial time. Therefore, the computational biologists (i.e. modellers) combine clustering and heuristic search algorithms with their tacit knowledge to infer networks. Visualisation can play an important role in supporting them in their network inference workflow. The main research question is: “How can visualisation support modellers in their workflow to infer networks from biological data?” To answer this question, it was required to collaborate with computational biologists to understand the challenges in their workflow and form research questions. Following the nested model methodology helped to characterise the domain problem, abstract data and tasks, design effective visualisation components and implement efficient algorithms. Those steps correspond to the four levels of the nested model for collaborating with domain experts to design effective visualisations. We found that visualisation can support modellers in three steps of their workflow. (a) To select variables, (b) to infer a consensus network and (c) to incorporate information about its dynamics.To select variables (a), modellers first apply a hierarchical clustering algorithm which produces a dendrogram (i.e. a tree structure). Then they select a similarity threshold (height) to cut the tree so that branches correspond to clusters. However, applying a single-height similarity threshold is not effective for clustering heterogeneous multidimensional data because clusters may exist at different heights. The research question is: Q1 “How to provide visual support for the effective hierarchical clustering of many multidimensional variables?” To answer this question, MLCut, a novel visualisation tool was developed to enable the application of multiple similarity thresholds. Users can interact with a representation of the dendrogram, which is coordinated with a view of the original multidimensional data, select branches of the tree at different heights and explore different clustering scenarios. Using MLCut in two case studies has shown that this method provides transparency in the clustering process and enables the effective allocation of variables into clusters.Selected variables and clusters constitute nodes in the inferred network. In the second step (b), modellers apply heuristic search algorithms which sample a solution space consisting of all possible networks. The result of each execution of the algorithm is a collection of high-scoring Bayesian networks. The task is to guide the heuristic search and help construct a consensus network. However, this is challenging because many network results contain different scores produced by different executions of the algorithm. The research question is: Q2 “How to support the visual analysis of heuristic search results, to infer representative models for biological systems?” BayesPiles, a novel interactive visual analytics tool, was developed and evaluated in three case studies to support modellers explore, combine and compare results, to understand the structure of the solution space and to construct a consensus network.As part of the third step (c), when the biological data contain measurements over time, heuristics can also infer information about the dynamics of the interactions encoded as different types of edges in the inferred networks. However, representing such multivariate networks is a challenging visualisation problem. The research question is: Q3 “How to effectively represent information related to the dynamics of biological systems, encoded in the edges of inferred networks?” To help modellers explore their results and to answer Q3, a human-centred crowdsourcing experiment took place to evaluate the effectiveness of four visual encodings for multiple edge types in matrices. The design of the tested encodings combines three visual variables: position, orientation, and colour. The study showed that orientation outperforms position and that colour is helpful in most tasks. The results informed an extension to the design of BayePiles, which modellers evaluated exploring dynamic Bayesian networks. The feedback of most participants confirmed the results of the crowdsourcing experiment.This thesis focuses on the investigation, design, and application of visualisation approaches for gaining insights from biological data to infer network models. It shows how visualisation can help modellers in their workflow to select variables, to construct representative network models and to explore their different types of interactions, contributing in gaining a better understanding of how biological processes within living organisms work