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

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

Challenges and Opportunities in using Analytics Combined with Visualisation Techniques for Finding Anomalies in Digital Communications

Digital communication has changed human life since the invention of the internet. The growth of E-mail, social websites and other interpersonal communication systems in turn have brought rapid development in especially the key technological area of data analytics. Using advanced forms of analytics helps the examination of data and better informs investigative sense-making and decision-making of all kinds. The legal process called Electronic discovery (E-discovery) is used for investigating various events in the digital communication world, for the purpose of producing/obtaining evidence (such as evidence in the form of emails used in the Enron fraud case). Investigating digital communications collected over a period of time, manually, is a strenuous process, time consuming, expensive and not very effective. More recently, within E-discovery there has been development of analytics known in the legal community as “Technology assisted review” (TAR). TAR is a technologydriven assistant in E-discovery for identifying relevance in the documents/data which saves time and improves efficiency in investigation. At the same time, the efficacy of visualisation tools currently available in the market is increasing, where such tools depend on a combination of simple keyword searches and more complex representations (e.g. network graphs). Also in E-discovery, early case assessment is a process of estimating risk (cost and time) to prosecute or defend a legal case based on an early review of potentially relevant electronically stored information (ESI). Legal firms largely determine the duration of the E-discovery process and charge companies based on the volume of information collected and reviewed after an automated search, where ESI may then be manually reviewed intensely to determine relevance and privilege. This results in significant costs for the company or in a number of cases settlement because a party cannot afford to continue with the lawsuit due to Ediscovery costs. This paper examines some of the opportunities and challenges in searching digital communication data for E-discovery and investigations, and will explore how analytics coupled with visualisation techniques may lend support and guidance in these efforts. Addressing these combined techniques may yet yield improved data collection, analysis and understanding of how analysts/lawyers can work together using visualisations. In particular, we attempt to address two challenges: (i) improving comparison of subsets of data, and (ii) identifying anomalies (including sensitivities) in email communication

Towards Natural Language Empowered Interactive Data Analysis

The recent advances in natural language based interaction methodologies offer promising avenues to enhance the interactive processes within the human-machine dialogue of visual analytics. We envisage \textit{Multimodal Data Analytics} as a novel approach for conducting data analysis that builds on the strengths of visual analytics and natural language as an expressive interaction channel. We investigate the potential enhancements from such a multimodal approach and discusses the preliminary outline for a structured methodology to study the role of natural language in data analytics. Our approach builds on a simple model of human machine dialogue for interactive data analysis which we then propose to instantiate as visual analytics workflows -- representations to study and operationalise interactive data analysis routines empowered by natural language interaction

Towards Multimodal Data Analytics: Integrating Natural Language into Visual Analytics

The continuous interaction between users and the system in visual analytics can be considered a dialogue. We propose the use of multiple two-way channels facilitated by a multimodal interface as a central aspect of interactive visualization design, in particular, the use of natural language with interactive visualization. We discuss key related concepts, potential benefits, challenges and opportunities that emerge as a research agenda for multimodal data analysis

Visualising E-mail Communication to Improve E-discovery

Electronic Discovery (E-discovery) is an investigation domain where electronic data is searched to find information and use it as an evidence in a legal case. One of the investigation areas in this domain is electronic mail (E-mail) communication. Lawyers and analysts involved in this activity are usually presented with a large E-mail dataset to manually comb through information in order to discover key information they need, expending large amounts of time, energy, effort and money in the process. We design and develop an interactive visualisation that will support our collaborators in an organisation specialising in E-discovery to unravel the multi-faceted information in the given communicated E-mails to find/discover pertinence, key information, points of interest (PoIs) and to develop evidence through which legal cases can be built