Relevant bibliographies by topics / Data Analytics in Litigation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data Analytics in Litigation'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Data Analytics in Litigation"

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Kappiarathel, Shahsoor Muhammad. "Legal Tech and the Future of Litigation: Transforming Justice through Innovation." Indiana Journal of Arts & Literature 6, no. 2 (2025): 01–04. https://doi.org/10.5281/zenodo.14970194.

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<strong>Abstract:</strong> The legal profession is experiencing a fundamental transformation with the application of technology in the litigation process. Technology advancements like blockchain, cloud computing, and data analytics are revolutionizing the manner in which legal professionals handle case management, research, and the production of evidence. These innovations have transformed efficiency, accessibility, and transparency to a significant degree, rendering litigation more efficient. This paper addresses the growing role of technology in litigation and the value it adds in document m
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Molitor, Dominik, Wullianallur Raghupathi, Aditya Saharia, and Viju Raghupathi. "Exploring Key Issues in Cybersecurity Data Breaches: Analyzing Data Breach Litigation with ML-Based Text Analytics." Information 14, no. 11 (2023): 600. http://dx.doi.org/10.3390/info14110600.

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While data breaches are a frequent and universal phenomenon, the characteristics and dimensions of data breaches are unexplored. In this novel exploratory research, we apply machine learning (ML) and text analytics to a comprehensive collection of data breach litigation cases to extract insights from the narratives contained within these cases. Our analysis shows stakeholders (e.g., litigants) are concerned about major topics related to identity theft, hacker, negligence, FCRA (Fair Credit Reporting Act), cybersecurity, insurance, phone device, TCPA (Telephone Consumer Protection Act), credit
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Raghupathi, Viju, Jie Ren, and Wullianallur Raghupathi. "Understanding the nature and dimensions of litigation crowdfunding: A visual analytics approach." PLOS ONE 16, no. 4 (2021): e0250522. http://dx.doi.org/10.1371/journal.pone.0250522.

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The escalating cost of civil litigation is leaving many defendants and plaintiffs unable to meet legal expenses such as attorney fees, court charges and others. This significantly impacts their ability to sue or defend themselves effectively. Related to this phenomenon is the ethics discussion around access to justice and crowdfunding. This article explores the dimensions that explain the phenomenon of litigation crowdfunding. Using data from CrowdJustice, a popular Internet fundraising platform used to assist in turning legal cases into publicly funded social cases, we study litigation crowdf
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Nabirye H, Kato, and Asiimwe Kyomugisha T. "Interpreting Contracts: The Importance of Language Precision." RESEARCH INVENTION JOURNAL OF CURRENT ISSUES IN ARTS AND MANAGEMENT 4, no. 1 (2025): 1–4. https://doi.org/10.59298/rijciam/2025/411400.

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Contracts are integral to modern economic and social systems, providing a structured framework for agreements. However, disputes often arise from ambiguous or imprecise language, necessitating robust principles of interpretation in contract law. This paper examines the critical role of language precision in drafting and interpreting contracts, addressing key concepts such as clarity, ambiguity, and contextual meaning. Through analysis of legal theories, case studies, and practical tools, the paper underscores the implications of precise drafting on reducing litigation risks and fostering equit
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Bhanu Pratap Singh. "ML and Legal Analytics: A Computational Approach to Case Outcome Prediction in Legal Management." Communications on Applied Nonlinear Analysis 32, no. 5s (2024): 43–50. https://doi.org/10.52783/cana.v32.2945.

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The legal industry has undergone a transformation through the combination of machine learning and artificial intelligence techniques. This work focuses the application of such approaches in legal management and also explores how these techniques are useful in various aspects of legal service. With this work, there is an analysis done using case studies from leading organisations such as Lex Machina, JP Morgan, Deloitte, IBM Watson, All State insurance and others. We show that the potential of machine learning is useful in improving the efficiency and decision making in these processes applied
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Pujiyono and Sufmi Dasco Ahmad. "Legal Protection Carried Out by the Financial Service Authority in a Dispute between Consumers and Insurance Companies in Indonesia." International Journal of Social and Administrative Sciences 3, no. 1 (2018): 55–61. http://dx.doi.org/10.18488/journal.136.2018.31.55.61.

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This study aims to find out how the form of legal protection carried out by the Financial Service Authority towards consumers who experience disputes with insurance companies in Indonesia. This research is a normative legal research that is the prescriptive approach. The data are taken from secondary data types that consist of primary and secondary legal materials. Data collection techniques used are library studies, and the analytical techniques used are deductive by syllogism method. The result of the study shows that a form of repressive protections is carried out by the Financial Service A
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Hardian, Randy, Gustati Gustati, and Armel Yentifa. "Pengaruh Asimetri Informasi, Insentif Pajak, Risiko Litigasi, Ukuran Perusahaan Dan Financial Distress Terhadap Prudence Akuntansi (Studi Pada Perusahaan Sektor Property and Real Estate Yang Terdaftar di Bursa Efek Indonesia Periode 2021-2023)." Jurnal Ilmiah Raflesia Akuntansi 11, no. 1 (2025): 242–53. https://doi.org/10.53494/jira.v11i1.849.

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This study aims to examine the influence of information asymmetry, tax incentives, litigation risk, company size, and financial distress on accounting prudence. This research employs a quantitative approach. The sample was obtained using purposive sampling, selecting samples based on predetermined criteria. The purposive sampling resulted in 130 observation data from property and real estate companies listed on the Indonesia Stock Exchange (IDX) during the 2021–2023 period. The analytical method used is multiple linear regression with SPSS version 25. The results of this study indicate that, p
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Zambrano, Guillaume. "Case Law as Data : Prompt Engineering Strategies for Case Outcome Extraction with Large Language Models in a Zero-Shot Setting." Law, Technology and Humans 6, no. 3 (2024): 80–101. http://dx.doi.org/10.5204/lthj.3623.

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This study explores the effectiveness of prompt optimization techniques for legal case outcome extraction using Large Language Models (LLMs). Two state-of-the-art LLMs, LLaMA3 70b and Mixtral 8x7b, are used in a zero-shot data extraction task on a diverse dataset of 400 French appellate court decisions. The results show that LLMs exhibit remarkable efficiency in extraction tasks. Our findings indicate that baseline prompts achieve high performance metrics, with a best F1 score of 0.980 and a worst F1 score of 0.853. Optimized prompts yield varying degrees of improvement, with a best F1 score o
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Syaiful Anam. "PENDEKATAN DALAM PENYELESAIAN SENGKETA PERUSAHAAN ASURANSI." Ar-Ribhu : Jurnal Manajemen dan Keuangan Syariah 2, no. 1 (2021): 47–64. http://dx.doi.org/10.55210/arribhu.v2i1.562.

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Abstrak&#x0D; Introduction: Insurance is an agreement whereby the insurer binds himself to the insured by accepting a premium to compensate him for the loss, damage or loss of expected profit that he may suffer as a result of an event (uncertain event).&#x0D; Methods: The method used in this research is a normative juridical approach, with analytical descriptive specifications, while the data collection techniques use primary data and secondary data.&#x0D; Results: Today, the Indonesian people have realized the important role of the insurance industry in providing security guarantee against th
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Kristina, Dian, and Gede Adi Yuniarta. "Pengaruh Intensitas Modal, Financial Distress, Insentif Pajak dan Risiko Litigasi terhadap Konservatisme Akuntansi Pada Perusahaan Manufaktur Sektor Industri Barang Konsumsi yang Terdaftar di Bursa Efek Indonesia Tahun 2016-2020." Jurnal Akuntansi Profesi 12, no. 2 (2021): 460. http://dx.doi.org/10.23887/jap.v12i2.36433.

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This study was aimed at finding out the effect of (1) capital intensity on accounting conservatism, (2) financial distress on accounting conservatism, (3) tax incentives on accounting conservatism, and (4) litigation risk on accounting conservatism. The type of research used is quantitative research. The population in this study are all manufacturing companies in the consumer goods industry sector listed on the Indonesia Stock Exchange in 2016-2020 which are known to be 54 companies and the sampling technique uses the purposive sampling method. Obtained a sample of 23 companies x 5 years = 115
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Dissertations / Theses on the topic "Data Analytics in Litigation"

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Tata, Maitreyi. "Data analytics on Yelp data set." Kansas State University, 2017. http://hdl.handle.net/2097/38237.

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Master of Science<br>Department of Computing and Information Sciences<br>William H. Hsu<br>In this report, I describe a query-driven system which helps in deciding which restaurant to invest in or which area is good to open a new restaurant in a specific place. Analysis is performed on already existing businesses in every state. This is based on certain factors such as the average star rating, the total number of reviews associated with a specific restaurant, the price range of the restaurant etc. The results will give an idea of successful restaurants in a city, which helps you decide where
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Le, Quoc Do. "Approximate Data Analytics Systems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234219.

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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
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Canon, Moreno Javier Mauricio 1977. "Leading data analytics transformations." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111472.

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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
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Ahsan, Ramoza. "Time Series Data Analytics." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/529.

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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 trend
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Carle, William R. II. "Active Analytics: Adapting Web Pages Automatically Based on Analytics Data." UNF Digital Commons, 2016. http://digitalcommons.unf.edu/etd/629.

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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.
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Erlandsson, Niklas. "Game Analytics och Big Data." Thesis, Mittuniversitetet, Avdelningen för arkiv- och datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-29185.

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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 Ana
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Doucet, Rachel A., Deyan M. Dontchev, Javon S. Burden, and Thomas L. Skoff. "Big data analytics test bed." Thesis, Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/37615.

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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 utiliz
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Naumanen, Hampus, Torsten Malmgård, and Eystein Waade. "Analytics tool for radar data." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353857.

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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 indepen
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Komolafe, Tomilayo A. "Data Analytics for Statistical Learning." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/87468.

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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 oper
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Miloš, Marek. "Nástroje pro Big Data Analytics." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199274.

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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 visual
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Books on the topic "Data Analytics in Litigation"

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Badiru, Adedeji B. Data Analytics. CRC Press, 2020. http://dx.doi.org/10.1201/9781003083146.

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Huang, Shuai, and Houtao Deng. Data Analytics. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003102656.

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Calì, Andrea, Peter Wood, Nigel Martin, and Alexandra Poulovassilis, eds. Data Analytics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60795-5.

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Runkler, Thomas A. Data Analytics. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-14075-5.

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Runkler, Thomas A. Data Analytics. Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29779-4.

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Runkler, Thomas A. Data Analytics. Vieweg+Teubner Verlag, 2012. http://dx.doi.org/10.1007/978-3-8348-2589-6.

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Cuadrado-Gallego, Juan J., and Yuri Demchenko. Data Analytics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-39129-3.

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Runkler, Thomas A. Data Analytics. Springer Fachmedien Wiesbaden, 2025. http://dx.doi.org/10.1007/978-3-658-45951-2.

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Tyagi, Amit Kumar. Data Science and Data Analytics. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003111290.

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Sedkaoui, Soraya. Data Analytics and Big Data. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119528043.

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Book chapters on the topic "Data Analytics in Litigation"

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Steward, Dwight, and Roberto Cavazos. "Data Analytics and Litigation." In Palgrave Advances in the Economics of Innovation and Technology. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31780-5_1.

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Steward, Dwight, and Roberto Cavazos. "Examples of Litigation Involving Big Data Analytics." In Palgrave Advances in the Economics of Innovation and Technology. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31780-5_3.

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Andrews, Joshua Kelly, Karen M. Cheek, Matthew P. Jennings, David W. Rogers, and Vincent M. Walden. "Data Management." In Litigation Services Handbook. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119204794.ch14.

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Cheek, Karen M., Erik W. Gibson, Cathy Hasenzahl, Matthew P. Jennings, Russell L. Miller, and Vincent M. Walden. "Data Management." In Litigation Services Handbook. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119363194.ch15.

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Cuadrado-Gallego, Juan J., and Yuri Demchenko. "Data." In Data Analytics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-39129-3_2.

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Pincetl, Stephanie, Hannah Gustafson, Felicia Federico, Eric Daniel Fournier, Robert Cudd, and Erik Porse. "Data Analytics." In Energy Use in Cities. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55601-3_6.

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Balali, Farhad, Jessie Nouri, Adel Nasiri, and Tian Zhao. "Data Analytics." In Data Intensive Industrial Asset Management. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35930-0_7.

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Fleckenstein, Mike, and Lorraine Fellows. "Data Analytics." In Modern Data Strategy. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-68993-7_13.

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Akhtar, Saaema. "Data Analytics." In Integration of AI-Based Manufacturing and Industrial Engineering Systems with the Internet of Things. CRC Press, 2023. http://dx.doi.org/10.1201/9781003383505-5.

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Treder, Martin. "Data Analytics." In Das Management-Handbuch für Chief Data Officer. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9346-1_20.

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Conference papers on the topic "Data Analytics in Litigation"

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Bala, Jerzy, Michael Kellar, and Fred Ramberg. "Predictive analytics for litigation case management." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258384.

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Vacek, Thomas, Ronald Teo, Dezhao Song, Timothy Nugent, Conner Cowling, and Frank Schilder. "Litigation Analytics: Case Outcomes Extracted from." In Proceedings of the Natural Legal Language Processing Workshop 2019. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-2206.

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Gadalla, Mohamed, and Ahmed Azab. "Decision Support for Locating Manufacturing Plants in Emerging Economies Using a Reliability Approach." In ASME 2022 17th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/msec2022-83098.

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Abstract In today’s distributed manufacturing reality, investors worldwide are faced with the dilemma of deciding on the optimal geographic spot for their manufacturing plants. On the one hand, emerging economies could be appealing because of their cheap labor as well as possibly their lack of or reduced regulations, litigation, and paperwork in some cases. On the other hand, these very same emerging economies can be quite risky because of the lack of stability of their political systems and hence, the associated economic volatility. Such economies can collapse in a relatively short period of
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Vacek, Thomas, Dezhao Song, Hugo Molina-Salgado, Ronald Teo, Conner Cowling, and Frank Schilder. "Litigation Analytics: Extracting and querying motions and orders from." In Proceedings of the 2019 Conference of the North. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-4020.

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Gallese, Chiara, Elena Falletti, Marco S. Nobile, Lucrezia Ferrario, Fabrizio Schettini, and Emanuela Foglia. "Preventing litigation with a predictive model of COVID-19 ICUs occupancy." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378295.

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Franceschina, Luciano. "Data Analytics." In CCS'16: 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2016. http://dx.doi.org/10.1145/2996429.2996441.

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Triantafillou, Peter. "Data-Less Big Data Analytics (Towards Intelligent Data Analytics Systems)." In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00205.

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Baron, Jason R., and Paul Thompson. "The search problem posed by large heterogeneous data sets in litigation." In the 11th international conference. ACM Press, 2007. http://dx.doi.org/10.1145/1276318.1276344.

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Zhang, Chi, and Hossain Shahriar. "Health Data Analytics." In SIGITE '18: The 19th Annual Conference on Information Technology Education. ACM, 2018. http://dx.doi.org/10.1145/3241815.3241887.

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Madden, Samuel. "Interactive data analytics." In SoCC '15: ACM Symposium on Cloud Computing. ACM, 2015. http://dx.doi.org/10.1145/2806777.2809956.

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Reports on the topic "Data Analytics in Litigation"

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Murphy, David Patrick, and Matthew Thomas Calef. Data Analytics for SAR. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1396159.

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Zhang, Jovan Yang, Hari Viswanathan, Jeffery Hyman, and Richard Middleton. Data Analytics of Hydraulic Fracturing Data. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1304742.

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Hollis, Andrew Nathan. Data Visualization for Threat Analytics. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1212630.

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Krishnamurthy, Narayanan, Siddharth Maddali, Amit Verma, et al. Data Analytics for Alloy Qualification. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1456238.

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Ruch, Marc Lavi. Data Analytics for Nonproliferation Applications. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1529508.

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Doucet, Rachel A., Deyan M. Dontchev, Javon S. Burden, and Thomas L. Skoff. Big Data Analytics Test Bed. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada589903.

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Calderon, Marta, and Luis Jugo. Leveraging Data Analytics Beyond Assurance. Inter-American Development Bank, 2014. http://dx.doi.org/10.18235/0006992.

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This presentation gives an overview of the importance of creating a data analytics strategy in an internal audit department. It describes reasons for having a data analytics strategy, where to start (steps) when formulating a strategy, typical challenges to be addressed, what should be the focus of the strategy, and how to analyze the data analytics capability and usage maturity. Finally, it presents the IDB experience implementing a data analytics strategy for its internal audit activities.
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Gungor, Osman, Imad Al-Qadi, and Navneet Garg. Pavement Data Analytics for Collected Sensor Data. Illinois Center for Transportation, 2021. http://dx.doi.org/10.36501/0197-9191/21-034.

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The Federal Aviation Administration instrumented four concrete slabs of a taxiway at the John F. Kennedy International Airport to collect pavement responses under aircraft and environmental loading. The study started with developing preprocessing scripts to organize, structure, and clean the collected data. As a result of the preprocessing step, the data became easier and more intuitive for pavement engineers and researchers to transform and process. After the data were cleaned and organized, they were used to develop two prediction models. The first prediction model employs a Bayesian calibra
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Phillips, Thurman B., and Raymond J. Lanclos III. Data Analytics in Procurement Fraud Prevention. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada626749.

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10

Allen, Courtney, Samuel Faluade, Cameron Payseno, Zachary Thomander, Cody Kasten, and Alex Darwin. Data Analytics: Project-level Cost Estimates. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1762650.

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