This monograph proposes a comprehensive and fully automatic approach to designing text analysis pipelines for arbitrary information needs that are optimal in terms of run-time efficiency and that robustly mine relevant information from text of any kind. Based on state-of-the-art techniques from machine learning and other areas of artificial intelligence, novel pipeline construction and execution algorithms are developed and implemented in prototypical software. Formal analyses of the algorithms and extensive empirical experiments underline that the proposed approach represents an essential step towards the ad-hoc use of text mining in web search and big data analytics. Both web search and big data analytics aim to fulfill peoples’ needs for information in an adhoc manner. The information sought for is often hidden in large amounts of natural language text. Instead of simply returning links to potentially relevant texts, leading search and analytics engines have started to directly mine relevant information from the texts. To this end, they execute text analysis pipelines that may consist of several complex information-extraction and text-classification stages. Due to practical requirements of efficiency and robustness, however, the use of text mining has so far been limited to anticipated information needs that can be fulfilled with rather simple, manually constructed pipelines.

The safety of the U.S. undersea pipeline system is a major national interest and concern, whether the concern focuses on risk to human life or the potential for environmental pollution and damage. Focusing primarily on the Gulf of Mexico system, this book reviews historical examples of pipeline failure, assesses the potential for future pipeline failures and the means of mitigating them, and considers the efficacy of existing safety systems and inspection procedures. It also identifies alternatives for improvements in the regulatory framework and in lawmaking.

With global demand for energy poised to increase by more than half in the next three decades, the supply of safe, reliable, and reasonably priced gas and oil will continue to be of fundamental importance to modern economies. Central to this supply are the pipelines that transport this energy. And while the fundamental economics of the major pipeline networks are the same, the differences in their ownership, commercial development, and operation can provide insight into the workings of market institutions in various nations. Drawing on a century of the world’s experience with gas and oil pipelines, this book illustrates the importance of economics in explaining the evolution of pipeline politics in various countries. It demonstrates that institutional differences influence ownership and regulation, while rents and consumer pricing depend on the size and diversity of existing markets, the depth of regulatory institutions, and the historical structure of the pipeline businesses themselves. The history of pipelines is also rife with social conflict, and Makholm explains how and when institutions in a variety of countries have controlled pipeline behavior—either through economic regulation or government ownership—in the public interest.

This timely collection offers fresh perspectives to the analysis of the 'New Great Game' – the fight for access to the Caspian Sea region's energy resources. To date, the export of the Caspian crude oil and natural gas has only been assessed geopolitically, which oversimplifies the political dynamics of the region and neglects to acknowledge the Caspian countries as actors in their own right.

Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques

Risk Assessment and Security for Pipelines, Tunnels, and Underground Rail and Transit Operations details a quantitative risk assessment methodology for systematically analyzing various alternatives for protecting underground rail, oil and gas pipelines, pipeline freight transportation, and other tunnel systems from terrorism threats and other disas

This report aims to provide policy makers with a comprehensive examination of “project pipelines”, a common concept in infrastructure planning and investment discussions, and one which has become a focal point in countries’ efforts to implement their climate commitments.