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Miles and Huberman's seminal text has helped thousands of graduate students and researchers find meaning from their qualitative data. New to this edition is the integration of qualitative analysis software, coverage of new approaches of inquiry, inclusion of mixed methods, and examples from a wider range of social science disciplines.

Social sciences --- Education --- Research --- Research Design --- Biomedical Research --- methods --- Data Collection. --- Social Sciences. --- Research Design. --- Data Interpretation, Statistical. --- Research. --- Biomedical Research - methods --- Data Collection --- Data Interpretation, Statistical

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Health Services Research --- Data Collection --- Qualitative Research --- Quality Assurance, Health Care --- Research Design --- methods

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"Vehicle Scanning Method for Bridges is the first book on vehicle scanning the dynamic properties of bridges, written by the leading author on the subject. This vehicle scanning method (VSM) allows engineers to monitor every bridge of concern on a regular and routine basis, for the purpose of maintenance and damage detection. This method is efficient, economic and mobile in bridge health monitoring, compared with the conventional direct measurement approach. Vehicle Scanning Method for Bridges includes a review of the existing literature on the topic and presents the basic concept of extracting bridge frequencies from a moving test vehicle fitted with vibration sensors. How road surface roughness affects the vehicle scanning method is considered and a finite element simulation is conducted to demonstrate how surface roughness affects the vehicle response. Case studies and experimental results are also included" [Publisher]

Bridges --- Structural health monitoring. --- Automatic data collection systems. --- Dynamic testing. --- Ponts --- Contrôle de santé intégré. --- Acquisition automatique des données. --- Essais dynamiques. --- Testing. --- Live loads. --- Essais. --- Surcharges

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Use this practical guide to the Splunk operational data intelligence platform to search, visualize, and analyze petabyte-scale, unstructured machine data. Get to the heart of the platform and use the Search Processing Language (SPL) tool to query the platform to find the answers you need. With more than 140 commands, SPL gives you the power to ask any question of machine data. However, many users (both newbies and experienced users) find the language difficult to grasp and complex. This book takes you through the basics of SPL using plenty of hands-on examples and emphasizes the most impactful SPL commands (such as eval, stats, and timechart). You will understand the most efficient ways to query Splunk (such as learning the drawbacks of subsearches and join, and why it makes sense to use tstats). You will be introduced to lesser-known commands that can be very useful, such as using the command rex to extract fields and erex to generate regular expressions automatically. In addition, you will learn how to create basic visualizations (such as charts and tables) and use prescriptive guidance on search optimization. For those ready to take it to the next level, the author introduces advanced commands such as predict, kmeans, and cluster. What You Will Learn Use real-world scenarios (such as analyzing a web access log) to search, group, correlate, and create reports using SPL commands Enhance your search results using lookups and create new lookup tables using SPL commands Extract fields from your search results Compare data from multiple time frames in one chart (such as comparing your current day application performance to the average of the past 30 days) Analyze the performance of your search using Job Inspector and identify execution costs of various components of your search This book is for application developers, architects, DevOps engineers, application support engineers, network operations center analysts, security operations center (SOC) analysts, and cyber security professionals who use Splunk to search and analyze their machine data. Karun Subramanian is an IT operations expert and a Splunk certified architect. He is committed to helping IT organizations implement world-class observability by making use of machine-generated data. His IT career has spanned more than two decades, ranging from systems administrator to software engineer to IT director. Possessing deep expertise of the Splunk platform, he has assisted teams to solve complex problems in the area of DevOps, security, and business analytics. He has worked in engineering roles for firms including Wells Fargo Bank, Express Scripts, Federal Reserve Bank, and Optum.

Big data. --- Information technology. --- Business—Data processing. --- Statistics . --- Big Data. --- IT in Business. --- Statistics, general. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- IT (Information technology) --- Technology --- Telematics --- Information superhighway --- Knowledge management --- Data sets, Large --- Large data sets --- Data sets --- Data mining. --- Automatic data collection systems. --- Data collection systems, Automatic --- Factory data acquisition systems, Automatic --- Factory monitoring systems, Automatic --- In-plant data collection systems, Automatic --- Automation --- Communication in management --- Data transmission systems --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching

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The ever-increasing digitalization of businesses has accelerated the need to address the many shortcomings and unresolved issues within the international corporate income tax system. In particular, the customer or “user”—through their online activities—is now considered by many as being a critical driving force behind the value of digital services. Furthermore, the rapid growth of digital service providers over the last decade has made them an increasingly popular target for special taxes—similar to wealth and solidarity taxes—which can also help mobilize much-needed revenues in the wake of a crisis. This paper argues that a plausible conceptual case can be made to tax the value generated by users under the corporate income tax. However, a number of issues need to be tackled for user-based tax measures to become a reality, which include agreement among countries on whether user value justifies a reallocation of taxing rights, establishing the legal right to tax income derived from user value, as well as an appropriate metric for valuing user-generated data if it is ever to be used as a tax base. Furthermore, attempting to tax only certain types of business is ill-advised, especially as user data is now being exploited widely enough for it to be recognized as an input for almost all businesses. Several options present themselves for consideration—from a modified permanent establishment definition combined with taxation by formulary apportionment, to user-based royalty-type taxes—each with their own merits and misdemeanors.

Public Finance --- Taxation --- Corporate Taxation --- Databases --- Organizational Behavior --- Transaction Costs --- Property Rights --- Market Design --- Antitrust Law --- Business Taxes and Subsidies --- Taxation, Subsidies, and Revenue: General --- Personal Income and Other Nonbusiness Taxes and Subsidies --- Data Collection and Data Estimation Methodology --- Computer Programs: General --- Public finance & taxation --- Corporate & business tax --- Data capture & analysis --- Corporate income tax --- Income and capital gains taxes --- Data collection --- Income tax systems --- Revenue administration --- Taxes --- Economic and financial statistics --- Corporations --- Income tax --- Economic statistics --- Revenue --- United States