An information theory based behavioral model for agent-based crowd simulations

Crowds must be simulated believable in terms of their appearance and behavior to improve a virtual environment’s realism. Due to the complex nature of human behavior, realistic behavior of agents in crowd simulations is still a challenging problem. In this paper, we propose a novel behavioral model which builds analytical maps to control agents’ behavior adaptively with agent-crowd interaction formulations. We introduce information theoretical concepts to construct analytical maps automatically. Our model can be integrated into crowd simulators and enhance their behavioral complexity. We made comparative analyses of the presented behavior model with measured crowd data and two agent-based crowd simulators

Design and Implementation of Small Multiples Matrix-based Visualisation to Monitor and Compare Email Socio-organisational Relationships

One of the fundamental organisational questions is how organisations identify anomalies, monitor and compare email communications between staff-staff or staff-clients or staff-customers relationships on a daily basis. The tenacious and substantial relationships are built by the combination of timely replies, frequent engagement and deep interaction between the individuals. To watchdog this periodically, we need an interactive visualisation tool that can help organisational analysts to reconnect some lost relationships and/or strengthen an existing relationship or in some cases identify inside persons (anomalies). From our point of view, Social Intelligence (SI) in an organisation is a combination of self-, social- and organisational-awareness that will help in managing complex socio-organisational changes and can be interpreted in terms of socio-organisational communication efficacy (that is, one's confidence in one's ability to deal with social and organisational information). We considered a case study, an Enron Organisation Email Scandal, to understand the relationships of staff during various parts of the years and we conducted a workshop study with legal experts to gain insights on how they carry out investigation/analysis with respect to email relationships. The outcomes of the workshop helped us develop a novel small multiples matrix-based visualisation in collaboration with our industrial partner, Red Sift UK, to find anomalies, monitor and compare how email relationships change over time and how it defines the meaning of socio-organisational communication efficacy

Visualizing Time Series Predictability

Predicting how temporally varying phenomena will evolve over time, or in other terms forecasting, is one of the fundamental tasks in time series analysis. Prediction has gained particular importance with the advent of real time data collection activities. Although there exist several sophisticated methodologies to predict time series, the success of a predictive analysis process remains mostly dependent on whether a particular phenomena is predictable. This paper introduces a methodology where visualizations coupled with a partition-based sampling strategy informs the analyst on the predictability of time series through the communication of prediction results applied on varying parts of data. We then discuss opportunities and research directions in supporting predictive tasks through visualization and interaction

Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis

In interactive data analysis processes, the dialogue between the human and the computer is the enabling mechanism that can lead to actionable observations about the phenomena being investigated. It is of paramount importance that this dialogue is not interrupted by slow computational mechanisms that do not consider any known temporal human-computer interaction characteristics that prioritize the perceptual and cognitive capabilities of the users. In cases where the analysis involves an integrated computational method, for instance to reduce the dimensionality of the data or to perform clustering, such non-optimal processes are often likely. To remedy this, progressive computations, where results are iteratively improved, are getting increasing interest in visual analytics. In this paper, we present techniques and design considerations to incorporate progressive methods within interactive analysis processes that involve high-dimensional data. We define methodologies to facilitate processes that adhere to the perceptual characteristics of users and describe how online algorithms can be incorporated within these. A set of design recommendations and according methods to support analysts in accomplishing high-dimensional data analysis tasks are then presented. Our arguments and decisions here are informed by observations gathered over a series of analysis sessions with analysts from finance. We document observations and recommendations from this study and present evidence on how our approach contribute to the efficiency and productivity of interactive visual analysis sessions involving high-dimensional data

Is Multi-perspective Visualisation recommended for E-discovery Email Investigations?

Problem Statement: To help improve efficiency and reduce costs involved in an electronic discovery1 (E-discovery) process for email investigations, visualisations can be of great help, and they can change the way analysts/investigators understand contacts, messages in inboxes and their relationship. Though email data is a central resource in E-discovery processes [1,2] but the existing tools such as JigSaw, INSPIRE and DocuBurst are not capable of handling this dynamic, heterogeneous and relational data. As the socio-technical systems have grown in complexity, E-discovery analysts who are not that tech-savvy are looking for a simple and effective visualisation tool to detect, analyse and understand anomaly behaviours in email communication. This project is in close collaboration with the Redsift Limited London who are currently working on E-discovery related projects. Case Study: Enron [3] scam is a well-known case in the data visualisation field. Enron produced fake profit reports and company’s accounts which led to bankruptcy. Most of the top executives were involved in the scam, as they sold their company stock prior to the company’s downfall. The Enron email is available for the public to access. In our work, we will be using the Enron data as a test case for designing and user-testing. Workshop: We conducted couple of workshops to understand analysts requirements. The first workshop was with a legal team of six solicitors in Bangalore, India. They use Excel as a tool for their investigations. They liked the simple visualisations but found the manual search and data arrangements strenuous. The second workshop was with an intelligence analyst who works at the cyber investigation department, Bangalore, India. He uses E-discovery tools such as Jigsaw, Concordance by LexisNexis and/or INSPIRE to analyse unstructured data. He finds the visualisations to be complex and difficult to understand.Workshop Suggestions: The five-point visualisation features summarised for E-discovery email investigation are: 1. Multi-faceted: representation must be supported with a multifaceted search feature to display various granularities. 2. Multi-modality: representation must include temporal behaviours, individuals' action, connections and text/topic responses. 3. Multi-level: representation must have a drill-down approach (multiple levels) to filter and sort the data based on the multimodality and present with some visual cues about what to consider and what not to (investigation cueing). 4. Multi-aggregation: representation must be systematically organised based on the multiple aggregations from the higher level (top-level) to all the consecutive levels which helps in building visual summaries that can be presented legally. 5. Multi-juxtaposition: representations must be effective for displaying multiple relationships and comparison when placed close together or side by side. Proposed Solution: Based on the workshop suggestions and the limitations of the current tools that generate email visualisations, we propose a multi-perspective approach (shown in the Figure 1) that will generate elementary (simple) and intelligible automated visual representations for displaying the most relevant information from the email data and aid in comparing two subsets of information

Words of Estimative Correlation: Studying Verbalizations of Scatterplots

Natural language and visualization are being increasingly deployed together for supporting data analysis in different ways, from multimodal interaction to enriched data summaries and insights. Yet, researchers still lack systematic knowledge on how viewers verbalize their interpretations of visualizations, and how they interpret verbalizations of visualizations in such contexts. We describe two studies aimed at identifying characteristics of data and charts that are relevant in such tasks. The first study asks participants to verbalize what they see in scatterplots that depict various levels of correlations. The second study then asks participants to choose visualizations that match a given verbal description of correlation. We extract key concepts from responses, organize them in a taxonomy and analyze the categorized responses. We observe that participants use a wide range of vocabulary across all scatterplots, but particular concepts are preferred for higher levels of correlation. A comparison between the studies reveals the ambiguity of some of the concepts. We discuss how the results could inform the design of multimodal representations aligned with the data and analytical tasks, and present a research roadmap to deepen the understanding about visualizations and natural language