Academic literature on the topic 'Data Analytics in Litigation'

Today, most modern online services make use of big data analytics systems to extract useful information from the raw digital data. The data normally arrives as a continuous data stream at a high speed and in huge volumes. The cost of handling this massive data can be significant. Providing interactive latency in processing the data is often impractical due to the fact that the data is growing exponentially and even faster than Moore’s law predictions. To overcome this problem, approximate computing has recently emerged as a promising solution. Approximate computing is based on the observation that many modern applications are amenable to an approximate, rather than the exact output. Unlike traditional computing, approximate computing tolerates lower accuracy to achieve lower latency by computing over a partial subset instead of the entire input data. Unfortunately, the advancements in approximate computing are primarily geared towards batch analytics and cannot provide low-latency guarantees in the context of stream processing, where new data continuously arrives as an unbounded stream. In this thesis, we design and implement approximate computing techniques for processing and interacting with high-speed and large-scale stream data to achieve low latency and efficient utilization of resources. To achieve these goals, we have designed and built the following approximate data analytics systems: • StreamApprox—a data stream analytics system for approximate computing. This system supports approximate computing for low-latency stream analytics in a transparent way and has an ability to adapt to rapid fluctuations of input data streams. In this system, we designed an online adaptive stratified reservoir sampling algorithm to produce approximate output with bounded error. • IncApprox—a data analytics system for incremental approximate computing. This system adopts approximate and incremental computing in stream processing to achieve high-throughput and low-latency with efficient resource utilization. In this system, we designed an online stratified sampling algorithm that uses self-adjusting computation to produce an incrementally updated approximate output with bounded error. • PrivApprox—a data stream analytics system for privacy-preserving and approximate computing. This system supports high utility and low-latency data analytics and preserves user’s privacy at the same time. The system is based on the combination of privacy-preserving data analytics and approximate computing. • ApproxJoin—an approximate distributed joins system. This system improves the performance of joins — critical but expensive operations in big data systems. In this system, we employed a sketching technique (Bloom filter) to avoid shuffling non-joinable data items through the network as well as proposed a novel sampling mechanism that executes during the join to obtain an unbiased representative sample of the join output. Our evaluation based on micro-benchmarks and real world case studies shows that these systems can achieve significant performance speedup compared to state-of-the-art systems by tolerating negligible accuracy loss of the analytics output. In addition, our systems allow users to systematically make a trade-off between accuracy and throughput/latency and require no/minor modifications to the existing applications.

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 77-79).<br>The phenomenal success of big technology companies founded with a strong emphasis on data, has epitomized the rise of the new "digital economy." Large traditional organizations, that were not long ago "on top of the world" are now at a crossroads. Their business models seem threatened by newcomers as they face pressure to "transform" and "modernize." Publicity has reinforced the perception that data can now be exploited and turned into a source of competitive advantage. In this context, data analytics presumably offers a vehicle to hasten this transformation. Who are the individuals leading these transformation efforts? Where do they come from? What are their challenges and perspectives? This thesis attempted to answer these questions and by doing so, uncover the "faces behind the leadership titles." Interviews of 33 individuals leading data analytics in large traditional organizations and under different capacities, (i.e., at the C-Suite, at the senior leadership level and in middle management) had a few elements in common: They articulated the difficulty of change, and the significant challenges in balancing strategic design with political savviness and cultural awareness. At their core, these are true leadership stories. Change management processes and the "Three Perspectives on Organizations" framework offer mechanisms to better understand the root causes for inhibitors of transformation and provide a path to guide data analytics initiatives. Whether data analytics proves to be a "passing fad" or not, by now, it has served as a catalyst for large traditional organizations to embark on transformation initiatives and reexamine ways to remain relevant. Leadership stories will most certainly abound as these organizations attempt to find ways to survive and prosper in what is now the "digital age."<br>by Javier Mauricio Canon Moreno.<br>M.B.A.

Given the ubiquity of time series data, and the exponential growth of databases, there has recently been an explosion of interest in time series data mining. Finding similar trends and patterns among time series data is critical for many applications ranging from financial planning, weather forecasting, stock analysis to policy making. With time series being high-dimensional objects, detection of similar trends especially at the granularity of subsequences or among time series of different lengths and temporal misalignments incurs prohibitively high computation costs. Finding trends using non-metric correlation measures further compounds the complexity, as traditional pruning techniques cannot be directly applied. My dissertation addresses these challenges while meeting the need to achieve near real-time responsiveness. First, for retrieving exact similarity results using Lp-norm distances, we design a two-layered time series index for subsequence matching. Time series relationships are compactly organized in a directed acyclic graph embedded with similarity vectors capturing subsequence similarities. Powerful pruning strategies leveraging the graph structure greatly reduce the number of time series as well as subsequence comparisons, resulting in a several order of magnitude speed-up. Second, to support a rich diversity of correlation analytics operations, we compress time series into Euclidean-based clusters augmented by a compact overlay graph encoding correlation relationships. Such a framework supports a rich variety of operations including retrieving positive or negative correlations, self correlations and finding groups of correlated sequences. Third, to support flexible similarity specification using computationally expensive warped distance like Dynamic Time Warping we design data reduction strategies leveraging the inexpensive Euclidean distance with subsequent time warped matching on the reduced data. This facilitates the comparison of sequences of different lengths and with flexible alignment still within a few seconds of response time. Comprehensive experimental studies using real-world and synthetic datasets demonstrate the efficiency, effectiveness and quality of the results achieved by our proposed techniques as compared to the state-of-the-art methods.

Web designers are expected to perform the difficult task of adapting a site’s design to fit changing usage trends. Web analytics tools give designers a window into website usage patterns, but they must be analyzed and applied to a website's user interface design manually. A framework for marrying live analytics data with user interface design could allow for interfaces that adapt dynamically to usage patterns, with little or no action from the designers. The goal of this research is to create a framework that utilizes web analytics data to automatically update and enhance web user interfaces. In this research, we present a solution for extracting analytics data via web services from Google Analytics and transforming them into reporting data that will inform user interface improvements. Once data are extracted and summarized, we expose the summarized reports via our own web services in a form that can be used by our client side User Interface (UI) framework. This client side framework will dynamically update the content and navigation on the page to reflect the data mined from the web usage reports. The resulting system will react to changing usage patterns of a website and update the user interface accordingly. We evaluated our framework by assigning navigation tasks to users on the UNF website and measuring the time it took them to complete those tasks, one group with our framework enabled, and one group using the original website. We found that the group that used the modified version of the site with our framework enabled was able to navigate the site more quickly and effectively.

Game Analytics är ett område som vuxit fram under senare år. Spelutvecklare har möjligheten att analysera hur deras kunder använder deras produkter ned till minsta knapptryckning. Detta kan resultera i stora mängder data och utmaning ligger i att lyckas göra något vettigt av sitt data. Utmaningarna med speldata beskrivs ofta med liknande egenskaper som används för att beskriva Big Data: volume, velocity och variability. Detta borde betyda att det finns potential för ett givande samarbete. Studiens syfte är att analysera och utvärdera vilka möjligheter Big Data ger att utveckla området Game Analytics. För att uppfylla syftet genomförs en litteraturstudie och semi-strukturerade intervjuer med individer aktiva inom spelbranschen. Resultatet visar att källorna är överens om att det finns värdefull information bland det data som kan lagras, framförallt i de monetära, generella och centrala (core) till spelet värdena. Med mer avancerad analys kan flera andra intressanta mönster grävas fram men ändå är det övervägande att hålla sig till de enklare variablerna och inte bry sig om att gräva djupare. Det är inte för att datahanteringen skulle bli för omständlig och svår utan för att analysen är en osäker investering. Även om någon tar sig an alla utmaningar speldata ställer fram finns det en osäkerhet på informationens tillit och användbarheten hos svaren. Framtidsvisionerna inom Game Analytics är blygsamma och inom den närmsta framtiden är det nästan bara effektiviseringar och en utbredning som förutspås vilket inte direkt ställer några nya krav på datahanteringen.<br>Game Analytics is a research field that appeared recently. Game developers have the ability to analyze how customers use their products down to every button pressed. This can result in large amounts of data and the challenge is to make sense of it all. The challenges with game data is often described with the same characteristics used to define Big Data: volume, velocity and variability. This should mean that there is potential for a fruitful collaboration. The purpose of this study is to analyze and evaluate what possibilities Big Data has to develop the Game Analytics field. To fulfill this purpose a literature review and semi-structured interviews with people active in the gaming industry were conducted. The results show that the sources agree that valuable information can be found within the data you can store, especially in the monetary, general and core values to the specific game. With more advanced analysis you may find other interesting patterns as well but nonetheless the predominant way seems to be sticking to the simple variables and staying away from digging deeper. It is not because data handling or storing would be tedious or too difficult but simply because the analysis would be too risky of an investment. Even if you have someone ready to take on all the challenges game data sets up, there is not enough trust in the answers or how useful they might be. Visions of the future within the field are very modest and the nearest future seems to hold mostly efficiency improvements and a widening of the field, making it reach more people. This does not really post any new demands or requirements on the data handling.

Approved for public release; distribution is unlimited<br>The proliferation of big data has significantly expanded the quantity and breadth of information throughout the DoD. The task of processing and analyzing this data has become difficult, if not infeasible, using traditional relational databases. The Navy has a growing priority for information processing, exploitation, and dissemination, which makes use of the vast network of sensors that produce a large amount of big data. This capstone report explores the feasibility of a scalable Tactical Cloud architecture that will harness and utilize the underlying open-source tools for big data analytics. A virtualized cloud environment was built and analyzed at the Naval Postgraduate School, which offers a test bed, suitable for studying novel variations of these architectures. Further, the technologies directly used to implement the test bed seek to demonstrate a sustainable methodology for rapidly configuring and deploying virtualized machines and provides an environment for performance benchmark and testing. The capstone findings indicate the strategies and best practices to automate the deployment, provisioning and management of big data clusters. The functionality we seek to support is a far more general goal: finding open-source tools that help to deploy and configure large clusters for on-demand big data analytics.

Analytics tool for radar data was a project that started when radar specialists at Saab needed to modernize their tools that analyzes binary encoded radar data. Today, the analysis is accomplished using inadequate and ineffective applications not designed for that purpose, and consequently this makes the analysis tedious and more difficult compared to using an appropriate interface. The applications had limitations regarding different radar systems too, which restricted their usage significantly. The solution was to design a new software that imports, translates and visualizes the data independent of the radar system. The software was developed with several parts that communicates with each other to translate a binary file. A binary file consists of a series of bytes containing the information of the targets and markers separating the revolutions of the radar. The byte stream is split according to the ASTERIX protocol that defines the length of each Data Item and the extracted positional values are stored in arrays. The code is then designed to convert the positional values to cartesian coordinates and plot them on the screen. The software has implemented features such as play, pause, reverse and a plotting history that allows the user to analyze the data in a simple and user-friendly manner. There are also numerous ways the software could be extended. The code is constructed in such a way that new features can be implemented for additional analytical abilities without affecting the components already designed.

The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. Big data is a widely-used term without a clear definition. The difference between big data and traditional data can be characterized by four Vs: velocity (speed at which data is generated), volume (amount of data generated), variety (the data can take on different forms), and veracity (the data may be of poor/unknown quality). As many industries begin to recognize the value of big data, organizations try to capture it through means such as: side-channel data in a manufacturing operation, unstructured text-data reported by healthcare personnel, various demographic information of households from census surveys, and the range of communication data that define communities and social networks. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called statistical learning of the data, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies in the process. However, several open challenges still exist in this framework for big data analytics. Recently, data types such as free-text data are also being captured. Although many established processing techniques exist for other data types, free-text data comes from a wide range of individuals and is subject to syntax, grammar, language, and colloquialisms that require substantially different processing approaches. Once the data is processed, open challenges still exist in the statistical learning step of understanding the data. Statistical learning aims to satisfy two objectives, (1) develop a model that highlights general patterns in the data (2) create a signaling mechanism to identify if outliers are present in the data. Statistical modeling is widely utilized as researchers have created a variety of statistical models to explain everyday phenomena such as predicting energy usage behavior, traffic patterns, and stock market behaviors, among others. However, new applications of big data with increasingly varied designs present interesting challenges. Consider the example of free-text analysis posed above. There's a renewed interest in modeling free-text narratives from sources such as online reviews, customer complaints, or patient safety event reports, into intuitive themes or topics. As previously mentioned, documents describing the same phenomena can vary widely in their word usage and structure. Another recent interest area of statistical learning is using the environmental conditions that people live, work, and grow in, to infer their quality of life. It is well established that social factors play a role in overall health outcomes, however, clinical applications of these social determinants of health is a recent and an open problem. These examples are just a few of many examples wherein new applications of big data pose complex challenges requiring thoughtful and inventive approaches to processing, analyzing, and modeling data. Although a large body of research exists in the area of anomaly detection increasingly complicated data sources (such as side-channel related data or network-based data) present equally convoluted challenges. For effective anomaly-detection, analysts define parameters and rules, so that when large collections of raw data are aggregated, pieces of data that do not conform are easily noticed and flagged In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This paper focuses on the healthcare, manufacturing and social-networking industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: • In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. • In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection o I address the research area of statistical modeling in two ways: -There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups -In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: -A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network based anomaly detection technique and introduce methodological improvements -Manufacturing enterprises which are now more connected than ever are vulnerably to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process<br>PHD<br>The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. The fields of manufacturing and healthcare are two examples of industries that are currently undergoing significant transformations due to the rise of big data. The addition of large sensory systems is changing how parts are being manufactured and inspected and the prevalence of Health Information Technology (HIT) systems in healthcare systems is also changing the way healthcare services are delivered. These industries are turning to big data analytics in the hopes of acquiring many of the benefits other sectors are experiencing, including reducing cost, improving safety, and boosting productivity. However, there are many challenges that exist along with the framework of big data analytics, from pre-processing raw data, to statistical modeling of the data, and identifying anomalies present in the data or process. This work offers significant contributions in each of the aforementioned areas and includes practical real-world applications. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called ‘statistical learning of the data’, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies or outliers in the process. In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This work focuses on the healthcare and manufacturing industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection I address the research area of statistical modeling in two ways: There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network-based anomaly detection technique and introduce methodological improvements Manufacturing enterprises which are now more connected than ever are vulnerable to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process.

The thesis covers the term for specific data analysis called Big Data. The thesis firstly defines the term Big Data and the need for its creation because of the rising need for deeper data processing and analysis tools and methods. The thesis also covers some of the technical aspects of Big Data tools, focusing on Apache Hadoop in detail. The later chapters contain Big Data market analysis and describe the biggest Big Data competitors and tools. The practical part of the thesis presents a way of using Apache Hadoop to perform data analysis with data from Twitter and the results are then visualized in Tableau.