Wellformedness Properties in Euler Diagrams: An Eye Tracking Study for Visualisation Evaluation

In the field of information visualisation, Euler diagrams are an important tool used in various application areas such as engineering, medicine and social analysis. To effectively use Euler diagrams, some of the wellformedness properties needs to be avoided, as they are considered to reduce user comprehension. From the previous empirical studies, we know some properties are swappable but there is no clear justification which property would be the best to use. In this paper, we considered two main wellformedness properties (duplicated curve labels and disconnected zones) to test which among the two affect user comprehension the most, based on the task performance (accuracy and response time), preference and eye movements of the users. Twelve participants performed three different types of tasks with nine diagrams of each property (so, in total eighteen diagrams) and the results showed that duplicated curve labels property slows down and trigger extra eye movements, causing delays for the tasks. Though there is no significant difference in the accuracy but the insights obtained from the response time, preference and eye movements will be useful for software developers on the optimal way to visualise Euler diagrams in real world application areas

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

Improving Visual Investigation Analysis of Digital Communication Data within E-discovery

The main aim of the research is to develop visual designs and frameworks for digital communication data within an investigation domain (i.e E-discovery) to address immediate challenges, support investigative tasks and find information in data to support legal evidence. This will enable analysts to compare time-frames, individuals and groups of data objects from multiple perspectives, identify relevant subsets of data and find anomalous communication behavior. In this research, we will be developing techniques and implementing comparison strategies in software prototypes through a structured process of abstraction, design and testing. Doing so is intended to explore and answer a series of research questions in ways that will improve the role of visualisation in E-discovery

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

Dual MCDRR Scheduler for Hybrid TDM/WDM Optical Networks

In this paper we propose and investigate the performance of a dual multi-channel deficit round-robin (D-MCDRR) scheduler based on the existing single MCDRR scheduler. The existing scheduler is used for multiple channels with tunable transmitters and fixed receivers in hybrid time division multiplexing (TDM)/wavelength division multiplexing (WDM) optical networks. The proposed dual scheduler will also be used in the same optical networks. We extend the existing MCDRR scheduling algorithm for n channels to the case of considering two schedulers for the same n channels. Simulation results show that the proposed dual MCDRR (D-MCDRR) scheduler can provide better throughput when compared to the existing single MCDRR scheduler

Trustee: A Trust Management System for Fog-enabled Cyber Physical Systems

In this paper, we propose a lightweight trust management system (TMS) for fog-enabled cyber physical systems (Fog-CPS). Trust computation is based on multi-factor and multi-dimensional parameters, and formulated as a statistical regression problem which is solved by employing random forest regression model. Additionally, as the Fog-CPS systems could be deployed in open and unprotected environments, the CPS devices and fog nodes are vulnerable to numerous attacks namely, collusion, self-promotion, badmouthing, ballot-stuffing, and opportunistic service. The compromised entities can impact the accuracy of trust computation model by increasing/decreasing the trust of other nodes. These challenges are addressed by designing a generic trust credibility model which can countermeasures the compromise of both CPS devices and fog nodes. The credibility of each newly computed trust value is evaluated and subsequently adjusted by correlating it with a standard deviation threshold. The standard deviation is quantified by computing the trust in two configurations of hostile environments and subsequently comparing it with the trust value in a legitimate/normal environment. Our results demonstrate that credibility model successfully countermeasures the malicious behaviour of all Fog-CPS entities i.e. CPS devices and fog nodes. The multi-factor trust assessment and credibility evaluation enable accurate and precise trust computation and guarantee a dependable Fog-CPS system

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

Multi-Channel Deficit Round-Robin Scheduling for Hybrid TDM/WDM Optical Networks

In this paper we propose and investigate the performance of a multi-channel scheduling algorithm based on the well-known deficit round-robin (DRR), which we call multi-channel DRR (MCDRR). We extend the original DRR to the case of multiple channels with tunable transmitters and fixed receivers to provide efficient fair queueing in hybrid time division multiplexing (TDM)/wavelength division multiplexing (WDM) optical networks. We take into account the availability of channels and tunable transmitters in extending the DRR and allow the overlap of `rounds' in scheduling to efficiently utilize channels and tunable transmitters. Simulation results show that the proposed MCDRR can provide nearly perfect fairness with ill-behaved flows for different sets of conditions for inter-frame times and frame sizes in hybrid TDM/WDM optical networks with tunable transmitters and fixed receivers

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

SYMPTOMATOLOGY OF FEMALE PATIENTS ATTENDING MOBILE MEDICAL CLINICS IN A RURAL BLOCK IN TAMILNADU

Objectives: The objectives of the study were to find out the prevalence of symptoms not elsewhere classified†under the International Classification of Diseases, Tenth Revision, Clinical Modification among the female patients attending mobile medical clinics.Methods: A cross-sectional study was carried out among 7,124 female patients who attended weekly mobile medical clinics in a rural block in Tamil Nadu. Sociodemographic variables, symptomatology, patient history, and clinical examination details were collected using a pre-tested structured questionnaire.Results: The five common symptoms affecting the study population were myalgia (18.3%), nasal congestion (13.6%), headache (13.1%), lumbar pain (12.5%), and knee pain (9.3%). The systems commonly affected among the female patients were in the order of general symptoms and signs (R50-R69), circulatory and respiratory systems (R00-R09), and Nervous and Musculoskeletal Systems (R25-R29). In the age group of 10–19 years and 20–39 years, the most common symptom was headache (25.2% and 18.8%, respectively). In the age group of 40–59 years and 60 years and above, it was myalgia (24.2% and 32.3%, respectively).Conclusion: As pain being most common symptoms, an appropriate strategy and guidelines have to be developed to manage the problem of pain at primary care level