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

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

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

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

Design of Small Multiples Matrix-based Visualisation to Understand E-mail Socio-organisational Relationships

One of the fundamental organisational questions is how organisations identify anomalies, monitor and compare E-mail 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. 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 E-mail 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 E-mail relationships. The outcomes of the workshop helped us develop a novel small multiples matrix-based visualisation in collaboration with the company, Red Sift UK to find anomalies, monitor and compare how email relationships changes over time and how it defines the meaning of socio-organisational communication efficacy

Visual Analytics of Event Data using Multiple Mining Methods

Most researchers use a single method of mining to analyze event data. This paper uses case studies from two very differentdomains (electronic health records and cybersecurity) to investigate how researchers can gain breakthrough insights by com-bining multiple event mining methods in a visual analytics workflow. The aim of the health case study was to identify patternsof missing values, which was daunting because the 615 million missing values occurred in 43,219 combinations of fields. How-ever, a workflow that involved exclusive set intersections (ESI), frequent itemset mining (FIM) and then two more ESI stepsallowed us to identify that 82% of the missing values were from just 244 combinations. The cybersecurity case study’s aim wasto understand users’ behavior from logs that contained 300 types of action, gathered from 15,000 sessions and 1,400 users.Sequential frequent pattern mining (SFPM) and ESI highlighted some patterns in common, and others that were not. For thelatter, SFPM stood out for its ability to action sequences that were buried within otherwise different sessions, and ESI detectedsubtle signals that were missed by SFPM. In summary, this paper demonstrates the importance of using multiple perspectives,complementary set mining methods and a diverse workflow when using visual analytics to analyze complex event data

A perceptual-statistics shading model

The process of surface perception is complex and based on several influencing factors, e.g., shading, silhouettes, occluding contours, and top down cognition. The accuracy of surface perception can be measured and the influencing factors can be modified in order to decrease the error in perception. This paper presents a novel concept of how a perceptual evaluation of a visualization technique can contribute to its redesign with the aim of improving the match between the distal and the proximal stimulus. During analysis of data from previous perceptual studies, we observed that the slant of 3D surfaces visualized on 2D screens is systematically underestimated. The visible trends in the error allowed us to create a statistical model of the perceived surface slant. Based on this statistical model we obtained from user experiments, we derived a new shading model that uses adjusted surface normals and aims to reduce the error in slant perception. The result is a shape-enhancement of visualization which is driven by an experimentally-founded statistical model. To assess the efficiency of the statistical shading model, we repeated the evaluation experiment and confirmed that the error in perception was decreased. Results of both user experiments are publicly-available datasets

Visual analytics of contact tracing policy simulations during an emergency response

Epidemiologists use individual-based models to (a) simulate disease spread over dynamic contact networks and (b) to investigate strategies to control the outbreak. These model simulations generate complex ‘infection maps’ of time-varying transmission trees and patterns of spread. Conventional statistical analysis of outputs offers only limited interpretation. This paper presents a novel visual analytics approach for the inspection of infection maps along with their associated metadata, developed collaboratively over 16 months in an evolving emergency response situation. We introduce the concept of representative trees that summarize the many components of a time-varying infection map while preserving the epidemiological characteristics of each individual transmission tree. We also present interactive visualization techniques for the quick assessment of different control policies. Through a series of case studies and a qualitative evaluation by epidemiologists, we demonstrate how our visualizations can help improve the development of epidemiological models and help interpret complex transmission patterns

Characterizing Cancer Subtypes Using Dual Analysis in Caleydo StratomeX

Dual analysis uses statistics to describe both the dimensions and rows of a high-dimensional dataset. Researchers have integrated it into StratomeX, a Caleydo view for cancer subtype analysis. In addition, significant-difference plots show the elements of a candidate subtype that differ significantly from other subtypes, thus letting analysts characterize subtypes. Analysts can also investigate how data samples relate to their assigned subtype and other groups. This approach lets them create well-defined subtypes based on statistical properties. Three case studies demonstrate the approach's utility, showing how it reproduced findings from a published subtype characterization

Determining and Visualising E-mail Subsets to Support E-discovery

Electronic discovery (E-discovery) is a legal process for investigating various events in the corporate world, for the purpose of producing/obtaining evidence, one such example is an email communication (eg. Enron case). Investigating emails collected over a period of time, manually, is a strenuous process and the tools currently available on the market are based on simple keyword search and legal firms charge companies based on the volume of information produced by the search, which is then manually reviewed intensely. This results in significant costs for the company or in a number of cases settlement because they can’t afford the costs of E-discovery. So, there is a great need to determine, visualise and understand whether email subsets are normal or abnormal, pertinent or privileged, relevant (interesting) or immaterial in a quick time. In order to determine relevant subsets for a legal case and to gain invaluable insight in a quick time from the email communications, we propose a multi-modal and multi-level approach which will generate automated visual representations using a manual keyword search facility that will extract the most relevant information from the email data and aids in comparing two subsets of information. In this paper, we discuss the literature review carried out, initial design process, prototypes developed and the workshops conducted. As a future work, we aim to develop a full-fledged E-discovery tool that could be implemented by the organisations to investigate email communications