machine readable Concepts

Fixed: Why Personal Finance is Broken and How to Make it Work for Everyone

by John Y. Campbell and Tarun Ramadorai · 25 Jul 2025

Other Businesses When consumers are unable to make sense of disclosures, an alternative approach is to require financial businesses to disclose information in a standardized machine-readable format that other businesses—or the government—can process. Richard Thaler and Will Tucker have argued for such “smart disclosure” as a way to promote

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,” Harvard Business Review 91, no. 1 (2013): 44–54. Thaler and Tucker propose that financial institutions should be required to report these experiences in a machine-readable format so that third parties can aggregate the information and make it easier for consumers to compare lenders. Even in the absence of such aggregation

Reversing: Secrets of Reverse Engineering

by Eldad Eilam · 15 Feb 2005 · 619pp · 210,746 words

-independent format called bytecode (see the following section on bytecodes). Compilers of traditional (non-bytecode-based) programming languages such as C and C++ directly generate machine-readable object code from the textual source code. What this means is that the resulting object code, when translated to assembly language by a disassembler, is

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software, and rarely by people. The bottom line is usually that compilers transform programs from their high-level, human-readable form into a lower-level, machine-readable form. During the translation process, compilers usually go through numerous improvement or optimization steps that take advantage of the compiler’s “understanding” of the program

Programming Python

by Mark Lutz · 5 Jan 2011

asked to be saved in the prior session; it’s simply the raw text of saved emails, with separator lines. This is both human and machine-readable—in principle, another script could load saved mail from this file into a Python list by calling the string object’s split method on the

Data Wrangling With Python: Tips and Tools to Make Your Life Easier

by Jacqueline Kazil · 4 Feb 2016

we will cover files made for human consumption. File formats that store data in a way easily understood by machines are commonly referred to as machine readable. Common machinereadable formats include the following: • Comma-Separated Values (CSV) • JavaScript Object Notation (JSON) • Extensible Markup Language (XML) In spoken and written language, these data

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. This way, we don’t have to worry about locating the files and can focus instead on importing data with Python. CSV Data The first machine-readable file type we will learn about is CSV. CSV files, or CSVs for short, are files that separate data columns with commas. The files themselves

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is quite easy. In the next section, we will explore more customized file handling. XML Data XML is often formatted to be both human and machine readable. However, the CSV and JSON examples were a lot easier to preview and understand than the XML file for this dataset. Luckily for us, the

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extract data from the XML tree structure. These are valuable lessons in your quest to become a better data wrangler. Summary Being able to handle machine-readable data formats with Python is one of the musthave skills for a data wrangler. In this chapter, we covered the CSV, JSON, and XML file

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in this and the following chapter will easily import into Python without a little work. This is because some data formats were made to be machine readable, while others, such as the ones we’ll look at next, were meant to be interacted with through desktop tools. In this chapter and the

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in most cases, the person who generated the file with the data inside simply did not identify the importance of also releasing it in a machine-readable format. Installing Python Packages Before we can continue, we need to learn how to install external Python packages (or libraries). Up until this point, we

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a step further and parse the same data from a PDF. Summary The Excel format is an odd in-between category that is kind of machine readable. Excel files were not meant to be read by programs, but they are parsable. To handle this nonstandard format, we had to install external libraries

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you’ve located your data, you need a place to store it! Sometimes, you’ll have received data in a clean, easy-to-access, and machine-readable format. Other times, you might want to find a different way to store it. We’ll review some data storage tools to use when you

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readable format. Especially if you need to create reports with the data or downloadable files, you’ll want to make sure it goes from being machine readable to human readable. And if your data needs to be used alongside APIs, you might need specially formatted data types. Python gives us a ton

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turning it into a Python object, you can harness the power of Python’s date capabilities and easily turn it back into a human- or machine-readable string later. Let’s take a look at our data holding interview start and end times from our zipped_data list. To refresh our memories

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, 49 csv library, 46 cursor (class name), 365 D data CSV, 44-52 Excel, 73-90 formatting, 162-167 importing, 216-222 JSON, 52-55 machine-readable, 43-71 manual cleanup exercise, 121 from PDFs, 91-126 publishing, 264-272 saving, 192-195 XML, 55-70 data acquisition, 127-140 and fact

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2.7 installation, 443 telling system where to find Homebrew, 440-443 virtual environment testing, 447 virtualenv installation, 444 virtualenvwrapper installation, 446 Mac prompt ($), 12 machine-readable data, 43-71 CSV data, 44-52 file formats for, 43 JSON data, 52-55 XML data, 55-70 magic commands, 150 magic functions, 466

Real World Haskell

by Bryan O'Sullivan, John Goerzen, Donald Stewart and Donald Bruce Stewart · 2 Dec 2008 · 1,065pp · 229,099 words

files: SimpleJSON.hi and SimpleJSON.o. The former is an interface file, in which ghc stores information about the names exported from our module in machine-readable form. The latter is an object file, which contains the generated machine code. Generating a Haskell Program and Importing Modules Now that we’ve successfully

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for storage is called serialization. It turns out that read and show make excellent tools for serialization. show produces output that is both human- and machine-readable. Most show output is also syntactically valid Haskell, though it is up to people that write Show instances to make it so. Parsing large strings

Mastering Structured Data on the Semantic Web: From HTML5 Microdata to Linked Open Data

by Leslie Sikos · 10 Jul 2015

Web includes the “Web of Data” [6], which connects “things”2 (representing real-world humans and objects) rather than documents meaningless to computers. The machine-readable datasets of the Semantic Web are used in a variety of web services [7], such as search engines, data integration, resource discovery and classification, cataloging

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conventional Web [17]. The real benefit of semantic annotations is that humans can browse the conventional web documents, while Semantic Web crawlers can process the machine-readable annotations to classify data entities, discover logical links between entities, build indices, and create navigation and search pages. Semantic Web Components Structured data processing

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a knowledge domain (field of interest, discipline). Knowledge Representation and Reasoning is the field of Artificial Intelligence (AI) used to represent information in a machine-readable form that computer systems can utilize to solve complex tasks. Taxonomies or controlled vocabularies are structured collections of terms that can be used as metadata

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data model for describing a piece of our world, such as an organization, a research project, a historical event, our colleagues, friends, etc., in a machine-readable manner, by formally defining a set of classes (concepts), properties (attributes), relationship types, and entities (individuals, instances). The most advanced ontology languages (such as

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on data access, so “open data” is a fundamental feature of the Semantic Web. There are already hundreds of government organizations, enterprises, and individuals publishing machine-readable, structured data as open data (https://data.cityofchicago.org, http://data.alberta.ca, http://data.gov.uk, etc.), although not all of them provide

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from open APIs, open protocols, open data formats, and open source software tools to reuse, remix, and republish data. On the Semantic Web, the machine-readable data of persons, products, services, and objects of the world are open and accessible, without registering and paying membership or subscription fees, and software agents

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improve the automated processability of web sites, formal knowledge representation standards are required that can be used not only to annotate markup elements for simple machine-readable data but also to express complex statements and relationships in a machine-processable manner. After understanding the structure of these statements and their serialization

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and processed by search engines are RDFa (RDF in attributes), HTML5 Microdata, and JSON-LD, of which HTML5 Microdata is the recommended format. The machine-readable annotations extend the core (X)HTML markup with additional elements and attributes through external vocabularies that contain the terminology and properties of a knowledge representation

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domain, as well as the relationship between the properties in a machine-readable form. Ontologies can be used for searching, querying, indexing, and managing agent or service metadata and improving application and database interoperability. Ontologies are especially

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field of interest, the relationships between them, and related individuals are collected by semantic knowledge bases. These schemas are the de facto standards used by machine-readable annotations serialized in RDFa, HTML5 Microdata, or JSON-LD, as well as in RDF files of Linked Open Data datasets. Vocabularies and Ontologies Controlled

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the most frequently used collections of structured data markup schemas. Schema.org was launched by Google, Yahoo!, and Bing in 2011. Schema.org contains the machine-readable definitions of the most commonly used concepts, making it possible to annotate actions, creative works, events, services, medical concepts, organizations, persons, places, and products.

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commonly used for describing partner syndication, content aggregation, content repurposing, resource discovery, multiple channel distribution, content archiving, capture rights usage information, RSS, XMP, and machine-readable annotations of web sites. The PRISM namespaces are http://prismstandard.org/namespaces/basic/2.1/ for PRISM 2.1 Basic (typical prefix: prism) and http

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://www.lesliesikos.com">lesliesikos.com</a> These syntaxes will be described in the next sections. Microformats The results of the very first approach to add machine-readable annotations to the (X)HTML markup are called microformats (mF). Some microformats apply and reuse features of existing technologies, such as the rel attribute

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transformer Optimus, or the Microformats Bookmarklet for Safari, Firefox, and IE). 24 Chapter 2 ■ Knowledge Representation However, due to limitations and open issues, other machine-readable annotation formats gradually overtook microformats. Applying various microformats as multiple values on the same a element, such as rel="nofollow" and rel="friend", cannot be

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). Listing 2-50. Using a Typed Literal ex:age rdf:type rdf:Property . ex:age rdfs:range xsd:integer . Web Ontology Language (OWL) While simple machine-readable ontologies can be created using RDFS, complex knowledge domains require more capabilities, such as • Relations between classes (union, intersection, disjointness, equivalence) • Property cardinality constraints

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and can leverage powerful description logic reasoning tools to facilitate machine-processability of semantic web sites. Reasoning derives facts that are not expressed explicitly in machine-readable ontologies or knowledge bases. Description logic reasoners implement the analytic tableau method (truth tree) for semantic reasoning, which is the most popular proof procedure

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in which the term Open Data refers to the free license. 61 Chapter 3 ■ Linked Open Data is querying Linked Data that do not use machine-readable definitions from a vocabulary, which is difficult and almost impossible to interpret with software agents. Furthermore, the quality of the definitions retrieved from vocabularies and

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. DBpedia The hundreds of concept definitions on schema.org are suitable to annotate common knowledge domains, such as persons, events, books, and movies, but complex machine-readable statements require far more. DBpedia, hosted at http://dbpedia.org, extracts structured factual data from Wikipedia articles, such as titles, infoboxes, categories, and links.

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FILTER (?birth < "1901-01-01"^^xsd:date) . } ORDER BY ?name Wikidata Wikidata is one of the largest LOD databases that features both human-readable and machine-readable contents, at http://www.wikidata.org. Wikidata contains structured data from Wikimedia projects, such as Wikimedia Commons, Wikipedia, Wikivoyage, and Wikisource, as well as from

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The most generic objects of datasets are collected in rdf:description containers. Those objects that are representations of real-world objects already defined in a machine-readable vocabulary are usually collected under the corresponding object class (persons in schema:person, books in schema:book, and so on). Because a basic requirement

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resource, usually containing additional RDF links that point to other, related URIs, which, in turn, can also be dereferenced, and so on. Consider the machine-readable description of the book Web Standards: Mastering HTML5, CSS3, and XML, at http://www.masteringhtml5css3.com/metadata/webstandardsbook.rdf#book, which declares the title of

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about the industries that link thousands of LOD datasets to one another. You understand now how semantic agents can make new discoveries, based on the machine-readable definition of objects and subjects, and the typed links between them. You learned the structure, licensing, and interlinking of LOD datasets. The next chapter

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are common tasks that can be made easier and more efficient using software tools. Web designers and search engine optimization (SEO) experts often generate machine-readable annotations or convert existing structured data to a different serialization. While web site markup can be edited in any text editor, some advanced features are

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through additional plug-ins, such as the MIME tools for Base64 encoding and decoding. Semantic Annotators and Converters While there are templates available for all machine-readable metadata annotations and one might also write them manually from scratch, you can use software tools that can evaluate your code, provide a preview

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disambiguation algorithms used. Google Structured Data Testing Tool The Google Structured Data Testing Tool at http://www.google.com/webmasters/tools/richsnippets is suitable for machine-readable metadata testing, including Microformats, RDFa, and HTML5 Microdata annotations online or through direct input. The code length of the direct input is limited to

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The Google Structured Data Testing Tool also indicates properties that are not parts of the vocabulary used for the object. ■■Note Google does not use machine-readable metadata annotations on Search Engine Result Pages if certain properties are missing for a particular object type. For example, an hCard description will be used

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least two of the following three properties: organization, location, or role, while code validity can be achieved even if you omit them. The tool provides machine-readable metadata examples for applications, authors, events, music, people, products, product offers, recipes, and reviews; however, you must log in to your Google account to

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■■Note For usability reasons, Sindice Web Data Inspector displays a maximum of 1,000 triples only. The “Sigma” option is a really good demonstration of machine-readable metadata. Software tools can extract structured data from properly written semantic documents and display them arbitrarily. This is the true essence of the Semantic Web

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is a Linked Data server, SPARQL server, and Linked Data development environment [28]. Marmotta provides a Linked Data Platform (LDP) for human-readable and machine-readable read-write data access via HTTP content negotiation. Marmotta features modules and libraries for LD application development. The modular server architecture makes it possible to

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about the LOD cloud. Semantic Web Browsers Semantic Web browsers are browsing tools for exploring and visualizing RDF datasets enhanced with Linked Data such as machine-readable definitions from DBpedia or geospatial information from GeoData. Semantic Web browsers provide exploration, navigation, and interactivity features different from conventional web browsers. They display

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not only human-readable but also machine-readable annotations and extracted RDF triples. While conventional browsers use hyperlinks for navigating between documents, Semantic Web browsers provide mechanisms for forward and backward navigation with

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to the RDF implementation, graph databases support automatic inferencing for knowledge discovery. The data stored in these databases can unify vocabularies, dictionaries, and taxonomies through machine-readable ontologies. Graph databases are commonly used in semantic data integration, social network analysis, and Linked Open Data applications. Quadstores It is not always possible

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/overview/index.html. Accessed 10 April 2015. 11. SYSTAP LLC (2015) Blazegraph. www.blazegraph.com/bigdata. Accessed 10 April 2015. Chapter 7 Querying While machine-readable datasets are published primarily for software agents, automatic data extraction is not always an option. Semantic Information Retrieval often involves users searching for the answer

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access data from LOD datasets, you can perform a semantic search, browse dataset catalogs, or run queries directly from a dedicated query interface. For searching machine-readable data, you can use semantic search engines such as Sindice (http://sindice.com) or FactForge (http://factforge.net). Third-party data marketplaces such as

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the Library of Congress are available individually via content negotiation as XHTML+RDFa, RDF/XML, N-Triples, and JSON [21]. To address the limitations of MAchine-Readable Cataloging (MARC), a standard initiated by the Library of Congress, MARC records have been mapped to BIBFRAME vocabulary terms [22] to leverage Linked Data

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JSON-LD), 37 Java Virtual Machine (JVM), 99 K Knowledge representation standards GRDDL, 39 HTML5 microdata attributes, 35 microdata DOM API, 37 JSON-LD, 37 machine-readable annotation formats, 23 microformats drafts and future, 32 hCalendar, 25 hCard, 26 h-event, 26 rel=“license”, 28 rel=“nofollow”, 29 rel=“tag”, 30 URI

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, 86 Object Properties and Data Properties tabs, 88 OntoGraf tab, 88 OWLViz, 88 SPARQL Query tab, 89 URIs, 88 PublishMyData, 195 Q M Quadstores, 149 MAchine-Readable Cataloging (MARC), 213 MicroWSMO, 137 R N Named graph, 149 Natural Language Processing (NLP) methods, 86 Neo4j, 161 Cypher commands, 163 graph style sheet,

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A Project Management Vocabulary�� 17 Licensing Vocabularies�� 17 Media Ontologies�� 18 Vocabularies for Online Communities�� 18 Knowledge Management Standards�� 18 Resource Description Framework (RDF) �� 18 Machine-Readable Annotations�� 23 GRDDL: XML Documents to RDF�� 39 R2RML: Relational Databases to RDF�� 40 RDFS�� 41 Web Ontology Language (OWL)�� 45 Simple Knowledge Organization System

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technical terms and explain complex issues in plain English. Dr. Sikos creates fully standard-compliant, mobile-friendly web sites with responsive web design—complemented by machine-readable annotations—and develops multimedia applications leveraging Semantic Web technologies. He works on the standardization of Linked Data implementations for the precise identification, description, and

Masterminds of Programming: Conversations With the Creators of Major Programming Languages

by Federico Biancuzzi and Shane Warden · 21 Mar 2009 · 496pp · 174,084 words

’t know the name of that does something, I mean you’re sure somewhere in this mess is a function that formats numbers in a machine-readable way and puts commas in or something like that, right? But how do you remember the name? How do you find the names of these

Beautiful Data: The Stories Behind Elegant Data Solutions

by Toby Segaran and Jeff Hammerbacher · 1 Jul 2009

the level of detail he or she requires. The raw data will often be difficult or impossible to present in a form that is naturally machine-readable and processable, so the filtering and refinement process also involves making choices about categorization and simplification to provide clear and clean datafiles that can be

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name and a SMILES code. As with the choice of GoogleDocs as the primary representation of the data, the use of both human-readable and machine-readable representations is crucial to gaining the most benefit from the data set. The only piece of information that does not require two representations is the

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standard in syntax as well as in descriptors. The Resource Description Framework, or RDF, provides a route toward exposing the data set in a recognized, machine-readable format. With this format, any information is transformed into statements made up of a “subject,” a “predicate,” and a “value.” For example, the fragment shown

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for experiments, as well as develop novel applications, by mashing together data and applications. These mashups demonstrate the power of using wellrecognized and easily convertible, machine-readable identifiers. The SMILES code in this case is the key identifier that can be used to obtain further data from other web services, data sources

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-driven sciences such as chemistry, due to both technical and social difficulties in translating from records in the form that experimentalists understand to properly structured machine-readable forms as understood by computers and the people who code on them. Here we have shown the ability to convert data in the form of

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we figured out that we didn’t have to extract the data by parsing web pages, but that the data is already available in a machine-readable format. Each human-readable (HTML web page) weekly summary is built from a text file that looks like this: rowid: 1 county: Alameda County city

The Art of UNIX Programming

by Eric S. Raymond · 22 Sep 2003 · 612pp · 187,431 words

to be run by human editors each time the registry is modified. One Unix solution would be a separate auditing program that analyzes either a machine-readable specification of the ruleset format or the source of the server code to determine the set of properties it uses, parses the Freeciv registry to

Mastering Ethereum: Building Smart Contracts and DApps

by Andreas M. Antonopoulos and Gavin Wood Ph. D. · 23 Dec 2018 · 960pp · 125,049 words

return stop sub_0: assembly { /* "Example.sol":26:132 contract example {... */ mstore(0x40, 0x60) 0x0 dup1 revert auxdata: 0xa165627a7a7230582056b99dcb1edd3eece01f27c9649c5abcc14a435efe3b... } The --bin-runtime option produces the machine-readable hexadecimal bytecode: 60606040523415600e57600080fd5b336000806101000a81548173 ffffffffffffffffffffffffffffffffffffffff 021916908373 ffffffffffffffffffffffffffffffffffffffff 160217905550603580605b6000396000f3006060604052600080fd00a165627a7a7230582056b... You can investigate what’s going on here in detail using the opcode list given in “The EVM

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AI in Museums: Reflections, Perspectives and Applications

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The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism

by Nick Couldry and Ulises A. Mejias · 19 Aug 2019 · 458pp · 116,832 words

Zero to Sold: How to Start, Run, and Sell a Bootstrapped Business

by Arvid Kahl · 24 Jun 2020 · 461pp · 106,027 words

Sandworm: A New Era of Cyberwar and the Hunt for the Kremlin's Most Dangerous Hackers

by Andy Greenberg · 5 Nov 2019 · 363pp · 105,039 words

Free culture: how big media uses technology and the law to lock down culture and control creativity

by Lawrence Lessig · 15 Nov 2004 · 297pp · 103,910 words

Computer: A History of the Information Machine

by Martin Campbell-Kelly and Nathan Ensmenger · 29 Jul 2013 · 528pp · 146,459 words

From Airline Reservations to Sonic the Hedgehog: A History of the Software Industry

by Martin Campbell-Kelly · 15 Jan 2003

The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future

by Kevin Kelly · 6 Jun 2016 · 371pp · 108,317 words

Content: Selected Essays on Technology, Creativity, Copyright, and the Future of the Future

by Cory Doctorow · 15 Sep 2008 · 189pp · 57,632 words

The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future

by Orly Lobel · 17 Oct 2022 · 370pp · 112,809 words

The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant From Two Centuries of Controversy

by Sharon Bertsch McGrayne · 16 May 2011 · 561pp · 120,899 words

Seveneves

by Neal Stephenson · 19 May 2015 · 945pp · 292,893 words

Reinventing Discovery: The New Era of Networked Science

by Michael Nielsen · 2 Oct 2011 · 400pp · 94,847 words

San Francisco

by Lonely Planet

Zero: The Biography of a Dangerous Idea

by Charles Seife · 31 Aug 2000 · 233pp · 62,563 words

Coastal California Travel Guide

by Lonely Planet

Speaking JavaScript: An In-Depth Guide for Programmers

by Axel Rauschmayer · 25 Feb 2014 · 692pp · 95,244 words

The Hacker Crackdown

by Bruce Sterling · 15 Mar 1992 · 345pp · 105,722 words

Western USA

by Lonely Planet

Broad Band: The Untold Story of the Women Who Made the Internet

by Claire L. Evans · 6 Mar 2018 · 371pp · 93,570 words

San Francisco

by Lonely Planet

Pocket New York City Travel Guide

by Lonely Planet · 27 Sep 2012

Empire of the Sum: The Rise and Reign of the Pocket Calculator

by Keith Houston · 22 Aug 2023 · 405pp · 105,395 words

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma

by Mustafa Suleyman · 4 Sep 2023 · 444pp · 117,770 words

Cataloging the World: Paul Otlet and the Birth of the Information Age

by Alex Wright · 6 Jun 2014

Turing's Cathedral

by George Dyson · 6 Mar 2012

Utopia Is Creepy: And Other Provocations

by Nicholas Carr · 5 Sep 2016 · 391pp · 105,382 words

USA Travel Guide

by Lonely, Planet

Countdown to Zero Day: Stuxnet and the Launch of the World's First Digital Weapon

by Kim Zetter · 11 Nov 2014 · 492pp · 153,565 words

Robot Rules: Regulating Artificial Intelligence

by Jacob Turner · 29 Oct 2018 · 688pp · 147,571 words

Talk to Me: How Voice Computing Will Transform the Way We Live, Work, and Think

by James Vlahos · 1 Mar 2019 · 392pp · 108,745 words

The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory

by Kariappa Bheemaiah · 26 Feb 2017 · 492pp · 118,882 words

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

by Eric Siegel · 19 Feb 2013 · 502pp · 107,657 words

The Formula: How Algorithms Solve All Our Problems-And Create More

by Luke Dormehl · 4 Nov 2014 · 268pp · 75,850 words

Human Compatible: Artificial Intelligence and the Problem of Control

by Stuart Russell · 7 Oct 2019 · 416pp · 112,268 words

Nature's Metropolis: Chicago and the Great West

by William Cronon · 2 Nov 2009 · 918pp · 260,504 words

Rough Guide to San Francisco and the Bay Area

by Nick Edwards and Mark Ellwood · 2 Jan 2009

Artificial Whiteness

by Yarden Katz

Snow Crash

by Neal Stephenson · 15 Jul 2003 · 550pp · 160,356 words

Tracers in the Dark: The Global Hunt for the Crime Lords of Cryptocurrency

by Andy Greenberg · 15 Nov 2022 · 494pp · 121,217 words

Cult of the Dead Cow: How the Original Hacking Supergroup Might Just Save the World

by Joseph Menn · 3 Jun 2019 · 302pp · 85,877 words

The Rough Guide to New York City

by Rough Guides · 21 May 2018

The End of Big: How the Internet Makes David the New Goliath

by Nicco Mele · 14 Apr 2013 · 270pp · 79,992 words

Tools and Weapons: The Promise and the Peril of the Digital Age

by Brad Smith and Carol Ann Browne · 9 Sep 2019 · 482pp · 121,173 words

Frommer's Oregon

by Karl Samson · 26 Apr 2010 · 389pp · 210,632 words

Rebooting India: Realizing a Billion Aspirations

by Nandan Nilekani · 4 Feb 2016 · 332pp · 100,601 words

The Black Box Society: The Secret Algorithms That Control Money and Information

by Frank Pasquale · 17 Nov 2014 · 320pp · 87,853 words

Programming HTML5 Applications

by Zachary Kessin · 9 May 2011 · 210pp · 42,271 words

Arriving Today: From Factory to Front Door -- Why Everything Has Changed About How and What We Buy

by Christopher Mims · 13 Sep 2021 · 385pp · 112,842 words

No Slack: The Financial Lives of Low-Income Americans

by Michael S. Barr · 20 Mar 2012

Frommer's Hawaii 2009

by Jeanette Foster · 2 Jan 2008 · 675pp · 344,555 words

Enshittification: Why Everything Suddenly Got Worse and What to Do About It

by Cory Doctorow · 6 Oct 2025 · 313pp · 94,415 words

Digital Transformation at Scale: Why the Strategy Is Delivery

by Andrew Greenway,Ben Terrett,Mike Bracken,Tom Loosemore · 18 Jun 2018

Cities Under Siege: The New Military Urbanism

by Stephen Graham · 30 Oct 2009 · 717pp · 150,288 words

MacroWikinomics: Rebooting Business and the World

by Don Tapscott and Anthony D. Williams · 28 Sep 2010 · 552pp · 168,518 words

Speculative Everything: Design, Fiction, and Social Dreaming

by Anthony Dunne and Fiona Raby · 22 Nov 2013 · 165pp · 45,397 words

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

by Cathy O'Neil · 5 Sep 2016 · 252pp · 72,473 words

The Road to Ruin: The Global Elites' Secret Plan for the Next Financial Crisis

by James Rickards · 15 Nov 2016 · 354pp · 105,322 words

Finding Alphas: A Quantitative Approach to Building Trading Strategies

by Igor Tulchinsky · 30 Sep 2019 · 321pp

Southwest USA Travel Guide

by Lonely Planet

Peers Inc: How People and Platforms Are Inventing the Collaborative Economy and Reinventing Capitalism

by Robin Chase · 14 May 2015 · 330pp · 91,805 words

Getting Started With Ledger

by Rolf Schröder

Coastal California

by Lonely Planet

Handbook of Modeling High-Frequency Data in Finance

by Frederi G. Viens, Maria C. Mariani and Ionut Florescu · 20 Dec 2011 · 443pp · 51,804 words

The Pay Off: How Changing the Way We Pay Changes Everything

by Gottfried Leibbrandt and Natasha de Teran · 14 Jul 2021 · 326pp · 91,532 words

Frommer's Seattle 2010

by Karl Samson · 10 Mar 2010 · 666pp · 131,148 words

The Rough Guide to New York City

by Martin Dunford · 2 Jan 2009

Hawaii

by Jeff Campbell · 4 Nov 2009

Hawaii Travel Guide

by Lonely Planet

The End of Secrecy: The Rise and Fall of WikiLeaks

by The "Guardian", David Leigh and Luke Harding · 1 Feb 2011 · 322pp · 99,066 words

Data for the Public Good

by Alex Howard · 21 Feb 2012 · 25pp · 5,789 words

HTML5 for Web Designers

by Jeremy Keith · 2 Jan 2010 · 73pp · 17,793 words

Our Kids: The American Dream in Crisis

by Robert D. Putnam · 10 Mar 2015 · 459pp · 123,220 words

The Fifth Risk

by Michael Lewis · 1 Oct 2018 · 157pp · 53,125 words

The Story of the Pony Express

by Glenn D. Bradley · 1 Jan 1913 · 81pp · 28,090 words

Darwin Among the Machines

by George Dyson · 28 Mar 2012 · 463pp · 118,936 words

Surveillance Valley: The Rise of the Military-Digital Complex

by Yasha Levine · 6 Feb 2018 · 474pp · 130,575 words

Rule of the Robots: How Artificial Intelligence Will Transform Everything

by Martin Ford · 13 Sep 2021 · 288pp · 86,995 words

Average Is Over: Powering America Beyond the Age of the Great Stagnation

by Tyler Cowen · 11 Sep 2013 · 291pp · 81,703 words

The rough guide to the Grand Canyon

by Greg Ward and Rough Guides · 27 May 2003

The AI-First Company

by Ash Fontana · 4 May 2021 · 296pp · 66,815 words

Stocks for the Long Run, 4th Edition: The Definitive Guide to Financial Market Returns & Long Term Investment Strategies

by Jeremy J. Siegel · 18 Dec 2007

The Deep Learning Revolution (The MIT Press)

by Terrence J. Sejnowski · 27 Sep 2018

Frommer's California 2009

by Matthew Poole, Harry Basch, Mark Hiss and Erika Lenkert · 2 Jan 2009

Democratizing innovation

by Eric von Hippel · 1 Apr 2005 · 220pp · 73,451 words

Stocks for the Long Run 5/E: the Definitive Guide to Financial Market Returns & Long-Term Investment Strategies

by Jeremy Siegel · 7 Jan 2014 · 517pp · 139,477 words

Cultural Backlash: Trump, Brexit, and Authoritarian Populism

by Pippa Norris and Ronald Inglehart · 31 Dec 2018

The Cultural Logic of Computation

by David Golumbia · 31 Mar 2009 · 268pp · 109,447 words

Humans as a Service: The Promise and Perils of Work in the Gig Economy

by Jeremias Prassl · 7 May 2018 · 491pp · 77,650 words

Frommer's Denver, Boulder & Colorado Springs

by Eric Peterson · 1 Jan 2005

The Art of Scalability: Scalable Web Architecture, Processes, and Organizations for the Modern Enterprise

by Martin L. Abbott and Michael T. Fisher · 1 Dec 2009

Interlibrary Loan Practices Handbook

by Cherie L. Weible and Karen L. Janke · 15 Apr 2011 · 144pp · 55,142 words

The Glass Cage: Automation and Us

by Nicholas Carr · 28 Sep 2014 · 308pp · 84,713 words

Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia

by Anthony M. Townsend · 29 Sep 2013 · 464pp · 127,283 words

The Fifth Domain: Defending Our Country, Our Companies, and Ourselves in the Age of Cyber Threats

by Richard A. Clarke and Robert K. Knake · 15 Jul 2019 · 409pp · 112,055 words

Big Data Glossary

by Pete Warden · 20 Sep 2011 · 58pp · 12,386 words

Your Face Belongs to Us: A Secretive Startup's Quest to End Privacy as We Know It

by Kashmir Hill · 19 Sep 2023 · 487pp · 124,008 words

Riding for Deliveroo: Resistance in the New Economy

by Callum Cant · 11 Nov 2019 · 196pp · 55,862 words

How I Became a Quant: Insights From 25 of Wall Street's Elite

by Richard R. Lindsey and Barry Schachter · 30 Jun 2007

Dark Matters: On the Surveillance of Blackness

by Simone Browne · 1 Oct 2015 · 326pp · 84,180 words

Frommer's New Mexico

by Lesley S. King · 2 Jan 1999 · 420pp · 219,075 words

Triumph of the City: How Our Greatest Invention Makes Us Richer, Smarter, Greener, Healthier, and Happier

by Edward L. Glaeser · 1 Jan 2011 · 598pp · 140,612 words

Big Data Analytics: Turning Big Data Into Big Money

by Frank J. Ohlhorst · 28 Nov 2012 · 133pp · 42,254 words

Ghost in the Wires: My Adventures as the World's Most Wanted Hacker

by Kevin Mitnick · 14 Aug 2011

Data Source Handbook

by Pete Warden · 15 Feb 2011 · 39pp · 4,665 words

Exceptional People: How Migration Shaped Our World and Will Define Our Future

by Ian Goldin, Geoffrey Cameron and Meera Balarajan · 20 Dec 2010 · 482pp · 117,962 words

How We Got to Now: Six Innovations That Made the Modern World

by Steven Johnson · 28 Sep 2014 · 243pp · 65,374 words

Messy: The Power of Disorder to Transform Our Lives

by Tim Harford · 3 Oct 2016 · 349pp · 95,972 words

Fault Lines: How Hidden Fractures Still Threaten the World Economy

by Raghuram Rajan · 24 May 2010 · 358pp · 106,729 words

We Were Soldiers Once...and Young: Ia Drang - the Battle That Changed the War in Vietnam

by Harold G. Moore and Joseph L. Galloway · 19 Oct 1991 · 496pp · 162,951 words

Trading at the Speed of Light: How Ultrafast Algorithms Are Transforming Financial Markets

by Donald MacKenzie · 24 May 2021 · 400pp · 121,988 words

New Laws of Robotics: Defending Human Expertise in the Age of AI

by Frank Pasquale · 14 May 2020 · 1,172pp · 114,305 words

The Idea Factory: Bell Labs and the Great Age of American Innovation

by Jon Gertner · 15 Mar 2012 · 550pp · 154,725 words

Rigged: How the Media, Big Tech, and the Democrats Seized Our Elections

by Mollie Hemingway · 11 Oct 2021 · 595pp · 143,394 words

Cogs and Monsters: What Economics Is, and What It Should Be

by Diane Coyle · 11 Oct 2021 · 305pp · 75,697 words

Frommer's San Diego 2011

by Mark Hiss · 2 Jan 2007

Crapshoot Investing: How Tech-Savvy Traders and Clueless Regulators Turned the Stock Market Into a Casino

by Jim McTague · 1 Mar 2011 · 280pp · 73,420 words

Nudge: Improving Decisions About Health, Wealth, and Happiness

by Richard H. Thaler and Cass R. Sunstein · 7 Apr 2008 · 304pp · 22,886 words

Kiln People

by David Brin · 15 Jan 2002 · 625pp · 167,097 words

The Idealist: Aaron Swartz and the Rise of Free Culture on the Internet

by Justin Peters · 11 Feb 2013 · 397pp · 102,910 words

The Paths Between Worlds: This Alien Earth Book One

by Paul Antony Jones · 19 Mar 2019

We Are Data: Algorithms and the Making of Our Digital Selves

by John Cheney-Lippold · 1 May 2017 · 420pp · 100,811 words

Eastern USA

by Lonely Planet

The Future of Money: How the Digital Revolution Is Transforming Currencies and Finance

by Eswar S. Prasad · 27 Sep 2021 · 661pp · 185,701 words

Frommer's Kauai

by Jeanette Foster · 27 Feb 2004 · 260pp · 130,109 words

Coding Freedom: The Ethics and Aesthetics of Hacking

by E. Gabriella Coleman · 25 Nov 2012 · 398pp · 107,788 words

No Ordinary Disruption: The Four Global Forces Breaking All the Trends

by Richard Dobbs and James Manyika · 12 May 2015 · 389pp · 87,758 words

Zucked: Waking Up to the Facebook Catastrophe

by Roger McNamee · 1 Jan 2019 · 382pp · 105,819 words

Free Speech: Ten Principles for a Connected World

by Timothy Garton Ash · 23 May 2016 · 743pp · 201,651 words

Hacking Politics: How Geeks, Progressives, the Tea Party, Gamers, Anarchists and Suits Teamed Up to Defeat SOPA and Save the Internet

by David Moon, Patrick Ruffini, David Segal, Aaron Swartz, Lawrence Lessig, Cory Doctorow, Zoe Lofgren, Jamie Laurie, Ron Paul, Mike Masnick, Kim Dotcom, Tiffiniy Cheng, Alexis Ohanian, Nicole Powers and Josh Levy · 30 Apr 2013 · 452pp · 134,502 words