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

Time management : how to better manage your workload and time

Meeting proceedings of a seminar by the same name, held September 17, 2020

Enhancing Mobile Device System using Information from Users and Upper Layers

Despite the rapid hardware upgrades, a common complaint among smartphone owners is the poor battery life. to many users, being required to charge the smartphone after a single day of moderate usage is unacceptable. Moreover, current smartphones suffer various unpredictable delays during operation, e.g., when launching an app, leading to poor user experience. In this dissertation, we provide solutions that enhance systems on portable devices using information obtained from their users and upper layers on the I/O path. First, we provide an experimental study on how storage I/O path upper layers affect power levels in smartphones, and introduce energy-efficient approaches to reduce energy consumption facilitating various usage patterns. at each layer, we investigate the amount of energy that can be saved, and use that to design and implement a prototype with optimal energy savings named SmartStorage. We evaluate our prototype by using the 20 most popular android applications, and our energy-efficient approaches achieve from 23% to 52% of energy savings compared to using the current techniques. Next, we conduct the first large-scale user study on the I/O delay of android using the data collected from our android app running on 2611 devices within nine months. Among other factors, we observe that reads experience up to 626% slowdown when blocked by concurrent writes for certain workloads. We use this obtained knowledge to design a system called SmartIO that reduces application delays by prioritizing reads over writes. SmartIO is evaluated extensively on several groups of popular applications. The results show that our system reduces launch delays by up to 37.8%, and run-time delays by up to 29.6%. Finally, we study the impact of memory on smartphone user-perceived performance. Our heap usage investigation of 20 popular applications indicates that rich multimedia applications have high heap usage and go above allowed boundaries, up to 5.63 times more heap than guaranteed by the system, and may cause crashes and erroneous behaviors. Moreover, limited heap may not only cause an app to crash, but may even prevent an app from launching. Therefore, we present iRAM, a system that maintains optimal heap size limits to avoid crashes, efficiently maximizes free memory levels, and cleans low-priority processes to reduce application delays. The evaluation indicates that iRAM reduces application crashes by up to 14 percent

Discovering Relations by Entity Search in Lightweight Semantic Text Graphs

Entity search is becoming a popular alternative for full text search. Recently Google released its entity search based on confirmed, human-generated data such as Wikipedia. In spite of these developments, the task of entity discovery, search, or relation search in unstructured text remains a major challenge in the fields of information retrieval and information extraction. This paper tries to address that challenge, focusing specifically on entity relation discovery. This is achieved by processing unstructured text using simple information extraction methods, building lightweight semantic graphs and reusing them for entity relation discovery by applying algorithms from graph theory. An important part is also user interaction with semantic graphs, which can significantly improve information extraction results and entity relation search. Entity relations can be discovered by various text mining methods, but the advantage of the presented method lies in the similarity between the lightweight semantics extracted from a text and the information networks available as structured data. Both graph structures have similar properties and similar relation discovery algorithms can be applied. In addition, we can benefit from the integration of such graph data. We provide both a relevance and performance evaluations of the approach and showcase it in several use case applications

A Customer-Support Knowledge Network Integrating Different Communication Elements for an E-Commerce Portal Using Self Organizing Maps

Successful e-commerce portal organizations focus intensely on customers. They try to consider every bit of information that flows from the customer to their system as an input for analyzing and identifying their needs precisely and catering to them. Being mostly ‘click and mortar’ or completely e-enabled, they have a lot of operational flexibility to address customer requirements in a more personalized and customized way than their brick-andmortar counterparts with more operational rigidity and resource constraints. Managing diverse range of channels is a challenge because of exponential and sometimes disruptive growth of diverse technologies that are used for supporting highvolume e-commerce operations. Customers are bouncing between phone, email and the web with greater fluidity than ever and therefore, fragmented, ‘stove-pipe’ communications, in such situations, can create problems as they loose out the holistic view on the basic nature of the problems and customer priorities. Therefore, the use of a common knowledge base across all channels is a dire necessity for an e-commerce portal, especially the ones which do not have a ‘brick-and-mortar’ back-end. The customer-support knowledge network as proposed in this paper addresses these issues. Using Self Organizing Maps(SOM), the network becomes incrementally self learning representing various groups of communication instances at any point of time. The advantages include the integration of all communication elements and an assimilation of all the customer communication issues into a reusable form of self-learning network. It adds an immense value for a customer-focused ecommerce company for identification of generic issues, better understanding of customer concerns and priorities and designing products/ services/ promotions accordingly, to ensure an overall better success of business

Law in the Age of Exabytes: Some further Thoughts on ‘Information Inflation’ and Current Issues in E-Discovery Search

In 2007, in the pages of this Journal, George L. Paul and I posed a question to the legal profession at large, to wit: can the legal system adapt to the new reality of an era of rapid inflation in the amount of electronically stored information (ESI) at issue in civil litigation? After surveying the history of technological innovation that led to an explosion of new data, we proceeded to discuss various legal strategies for success in our current inflationary epoch. These strategies included: consideration of new and emerging ways in which to think about search and information retrieval in light of the limitations of traditional keyword searching the legal profession had come to rely upon; greater use of sampling and iteration so as to ensure greater quality; the use of multiple meet-and- confers to produce a “virtuous feedback cycle” of cooperation amongst counsel; predicting congressional enactment of Federal Rule of Evidence 502, enabling parties to leverage resources by providing large amounts of data in open discovery; and finally, making tentative predictions about the future of artificial intelligence as applied to information law problems