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Industry 4.0: The Industrial Internet of Things by Alasdair Gilchrist
3D printing, additive manufacturing, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, business intelligence, business process, chief data officer, cloud computing, connected car, cyber-physical system, deindustrialization, DevOps, digital twin, fault tolerance, global value chain, Google Glasses, hiring and firing, industrial robot, inflight wifi, Infrastructure as a Service, Internet of things, inventory management, job automation, low cost airline, low skilled workers, microservices, millennium bug, pattern recognition, peer-to-peer, platform as a service, pre–internet, race to the bottom, RFID, Skype, smart cities, smart grid, smart meter, smart transportation, software as a service, stealth mode startup, supply-chain management, trade route, undersea cable, web application, WebRTC, Y2K
Therefore, it is necessary to identify early in the design process whether the product is to be an IT, network, or a physical system–or a system that has all three, physical, network, and digital processing features. If it has, then it is said to be a cyber-physical system. In some definitions, the networking and communications feature is deemed optional, although that raises the question as to how a CPS differs from an embedded system. Information systems, which are embedded into physical devices, are called “embedded systems”. These embedded systems are found in telecommunication, automation, and transport systems, among many others. Lately, a new term has surfaced, the cyber-physical systems (CPS). This distinguishes between microprocessor based embedded systems and more complex information processing systems that actually integrate with their environment. A precise definition of cyber-physical systems (CPS) is that they are integrations of computation, networking, and physical processes.
Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa. Therefore, a cyber-physical system can be just about anything that has integrated computation, networking, and physical processes. A human operator is a cyber-physical system and so is a smart factory. For example, a human operator has physical and cyber components. In this example, the operator has a computational facility—their brain—and they communicate with other humans and the system through HMI (human machine interface) and interact through mechanical interfaces—their hands—to influence their environment. Cyber-physical systems enable the virtual digital world of computers and software to merge through interaction—process management and feedback control—with the physical analogue world, thus leading to an Internet of Things, data, and services.
For example, an Apple iPhone, the Raspberry Pi, and the Arduino with extension shields all provide the tools to create multi-sensor devices that can sense and influence their analogue environment through their interaction with the digital world. The availability of these development kits has accelerated the design process, by allowing the production of proof-of-concept (PoC) models. They have driven innovation in the way we deploy multi-sensor devices into industrial system automation and integrate M2M with cyber-physical systems to create Industrial Internet of Things environments. Cyber Physical Systems (CPS) The Industrial Internet has come about due to the rapid advancements in digital computers in all their formats and vast improvements in digital communications. These disciplines are considered separate domains of knowledge and expertise, with there being a tendency for specialization in one or the other. This results in inter-disciplinary knowledge being required to design and build products that require information processing and networking; for 35 36 Chapter 3 |TheTechnical and Business Innovators of the Industrial Internet example, a device with embedded microprocessor and ZigBee, such as the Raspberry Pi or a smartphone.
Data Mining: Concepts and Techniques: Concepts and Techniques by Jiawei Han, Micheline Kamber, Jian Pei
bioinformatics, business intelligence, business process, Claude Shannon: information theory, cloud computing, computer vision, correlation coefficient, cyber-physical system, database schema, discrete time, distributed generation, finite state, information retrieval, iterative process, knowledge worker, linked data, natural language processing, Netflix Prize, Occam's razor, pattern recognition, performance metric, phenotype, random walk, recommendation engine, RFID, semantic web, sentiment analysis, speech recognition, statistical model, stochastic process, supply-chain management, text mining, thinkpad, Thomas Bayes, web application
Other examples of moving-object data mining include mining periodic patterns for one or a set of moving objects, and mining trajectory patterns, clusters, models, and outliers. Mining Cyber-Physical System Data A cyber-physical system (CPS) typically consists of a large number of interacting physical and information components. CPS systems may be interconnected so as to form large heterogeneous cyber-physical networks. Examples of cyber-physical networks include a patient care system that links a patient monitoring system with a network of patient/medical information and an emergency handling system; a transportation system that links a transportation monitoring network, consisting of many sensors and video cameras, with a traffic information and control system; and a battlefield commander system that links a sensor/reconnaissance network with a battlefield information analysis system. Clearly, cyber-physical systems and networks will be ubiquitous and form a critical component of modern information infrastructure.
■ Mining social and information networks: Mining social and information networks and link analysis are critical tasks because such networks are ubiquitous and complex. The development of scalable and effective knowledge discovery methods and applications for large numbers of network data is essential, as outlined in Section 13.1.2. ■ Mining spatiotemporal, moving-objects, and cyber-physical systems: Cyber-physical systems as well as spatiotemporal data are mounting rapidly due to the popular use of cellular phones, GPS, sensors, and other wireless equipment. As outlined in Section 13.1.3, there are many challenging research issues realizing real-time and effective knowledge discovery with such data. ■ Mining multimedia, text, and web data: As outlined in Section 13.1.3, mining such kinds of data is a recent focus in data mining research.
seecustomer relationship management crossover operation 426 cross-validation 370–371, 386 k-fold 370 leave-one-out 371 in number of clusters determination 487 stratified 371 cube gradient analysis 321 cube shells 192, 211 computing 211 cube space discovery-driven exploration 231–234 multidimensional data analysis in 227–234 prediction mining in 227 subspaces 228–229 cuboid trees 205 cuboids 137 apex 111, 138, 158 base 111, 137–138, 158 child 193 individual 190 lattice of 139, 156, 179, 188–189, 234, 290 sparse 190 subset selection 160see alsodata cubes curse of dimensionality 158, 179 customer relationship management (CRM) 619 customer retention analysis 610 CVQE. seeConstrained Vector Quantization Error algorithm cyber-physical systems (CPS) 596, 623–624 D data antimonotonicity 300 archeology 6 biological sequence 586, 590–591 complexity 32 conversion to knowledge 2 cyber-physical system 596 for data mining 8 data warehouse 13–15 database 9–10 discrimination 16 dredging 6 generalizing 150 graph 14 growth 2 linearly inseparable 413–415 linearly separated 409 multimedia 14, 596 multiple sources 15, 32 multivariate 556 networked 14 overfitting 330 relational 10 sample 219 similarity and dissimilarity measures 65–78 skewed 47, 271 spatial 14, 595 spatiotemporal 595–596 specializing 150 statistical descriptions 44–56 streams 598 symbolic sequence 586, 588–589 temporal 14 text 14, 596–597 time-series 586, 587 “tombs” 5 training 18 transactional 13–14 types of 33 web 597–598 data auditing tools 92 data characterization 15, 166 attribute-oriented induction 167–172 data mining query 167–168 example 16 methods 16 output 16 data classification.
Autonomous Driving: How the Driverless Revolution Will Change the World by Andreas Herrmann, Walter Brenner, Rupert Stadler
Airbnb, Airbus A320, augmented reality, autonomous vehicles, blockchain, call centre, carbon footprint, cleantech, computer vision, conceptual framework, connected car, crowdsourcing, cyber-physical system, DARPA: Urban Challenge, data acquisition, demand response, digital map, disruptive innovation, Elon Musk, fault tolerance, fear of failure, global supply chain, industrial cluster, intermodal, Internet of things, Jeff Bezos, Lyft, manufacturing employment, market fundamentalism, Mars Rover, Masdar, megacity, Pearl River Delta, peer-to-peer rental, precision agriculture, QWERTY keyboard, RAND corporation, ride hailing / ride sharing, self-driving car, sensor fusion, sharing economy, Silicon Valley, smart cities, smart grid, smart meter, Steve Jobs, Tesla Model S, Tim Cook: Apple, uber lyft, upwardly mobile, urban planning, Zipcar
Even the traditional automotive suppliers such as Aisin, Delphi, Bosch, Denso, Continental, TRW, Schaefﬂer or Magna are either preparing their own prototypes of self-driving cars or working on key components for autonomous driving. The technology of autonomous driving will have a signiﬁcant role to play in the success of electric mobility. As automation has a positive impact on energy efﬁciency, increasing vehicle automation will also signiﬁcantly extend the range of electric vehicles . The essence of autonomous driving is the development of vehicles into cyber-physical systems that comprise a combination of mechanical and electronic components. A vehicle’s hardware and software exchange certain data about the infrastructure (the Internet of Things), and the vehicle is controlled or monitored by a processing unit. In the future, each vehicle will communicate with the infrastructure: parking garages, parking spaces, trafﬁc lights, trafﬁc signs and a trafﬁc control centre (vehicle-to-infrastructure communication or V-to-I).
This page intentionally left blank INDEX A9 autobahn in Germany, 134, 135, 407 ACCEL, 324 Accelerating, 8, 22, 27, 59, 78, 91, 122, 295, 296 Access Economy, 344 Acoustic signals, 108 Ad-hoc mobility solutions, 354 Ad-hoc networks, 133 Adaptive cruise control, 4, 51, 72 74, 78, 86, 96, 113, 116, 289, 297, 333 Aerospace industry, 153 Agenda for auto industry culture change, 396 increasing speed, 398 service-oriented business model, 397 398 V-to-home and V-to-business applications, 399 Agile operating models, 330 Agriculture, 154 productivity, 155 sector, 154 157 Air pollution, 27 AirBnB, 311 Airplane electronics, 144 Aisin, 9 Albert (head of design at Yahoo), 228 Alexandra (founder and owner of Powerful Minds), 228 Alibaba Alipay payment system, 372 Alternative fuels, autonomous vehicles enabling use of, 305 Altruistic mode (a-drive mode), 252 Amazon, 138, 141, 311 American Trucking Association, 68 Android operating system, 327 Anthropomorphise products, 290 Appel Logistics transports, 167 Apple, 9, 138, 327 CarPlay, 285 Apple Mac OS, 247 Apple-type model, 323 Application layer, 119 software, 118 Artiﬁcial intelligence, 115, 255, 291, 332 333 Artiﬁcial neuronal networks, 114 115 Asia projects, 371 374 Assembly Row, 386 Assessment of Safety Standards for Automotive Electronic Control Systems, 144 Assistance systems, 71 77 Audi, 5, 130, 134, 137, 179, 211, 301, 318, 322, 398 Driverless Race Car, 5 piloted driving, 286 piloted-parking technology, 386 387 Audi A7, 44, 198, 282 427 428 Audi A8 series-car, 79, 180 Audi AI trafﬁc jam pilot, 79 Audi Fit Driver service, 318 319 Audi piloted driving lab, 227, 229 Audi Q7, 74 assistance systems in, 75 Audi RS7, 43, 44, 79 autonomous racing car, 179 driverless, 227 Audi TTS, 43 Audi Urban Future Initiative, 384 386, 406 Augmented reality, 279 vision and example, 279 280 Authorities and cities, 171 173 Auto ISAC, 146 Autolib, 317, 344 Autoliv, 285 Automakers’ bug-bounty programs, 146 Automated car, 233, 246, 264, 289, 384 Automated driving division of labour between driver and driving system, 48 examples, 51 53 image, 177 levels of, 47 51 scenarios for making use of travelling time, 52 strategies, 53 56 technology, 160 Automated vehicles, 9, 174, 246 Automated Vehicles Index, 367 368 Automatic car, 233, 244 Automatic pedestrian highlighting, 78 Automation ironies of, 76 responsibility with increasing, 235 Automobile, 3, 21 locations, 405 manufacturers, 311 Index Automotive design, 265 266 Automotive Ethernet, 126 Automotive incumbents operate, 330 Automotive industry, 332 335, 367, 379, 397 Automotive technology, 327 328 AutoNet2030 project, 369 Autonomous buses, 14, 81, 158, 159, 175, 302 Autonomous cars, 25, 126, 197, 205 206, 233, 244, 270 expected worldwide sales of, 85 savings effects from, 67 68 Autonomous driving, 3, 8, 39, 62, 94, 111, 116, 120, 121 123, 141, 160 162, 171, 173, 207 208, 217, 247, 252, 266, 332 333, 379 applications, 10 12, 160 aspects for, 93 Audi car, 5 autonomous Audi TTS on Way to Pikes Peak, 43 in combination with autonomous loading hubs, 166 driving to hub, 213 ecosystem, 18 20, 131 element, 243 facts about, 306 functions, 74 impression, 40 industry, 16 18 living room in Autonomous Mercedes F015, 44 milestones of automotive development, 4 NuTonomy, 6 projects, 41 45 real-world model of, 92 scenarios, 211 215 science ﬁction, 39 41 technology, 9 10, 92 Index time management, 215 218 vehicles, 12 16 See also Human driving Autonomous driving failure, 221 consequence, 221 222 decision conﬂict in autonomous car, 223 design options, 222 223 inﬂuencer, 223 224 Autonomous Mercedes F015, living room in, 44 Autonomous mobility, 12, 13, 16 17, 172, 405 establishment as industry of future, 404 405 resistance to, 171 172 Autonomous Robocars, 81 Autonomous sharp, 274 ‘Autonomous soft’ mode, 274 Autonomous trucks, 161 from Daimler, 163 savings effects from, 68 69 Autonomous vehicles, 26, 81, 99, 138, 155, 182, 221, 238, 249, 255, 353 354 enabling use of alternative fuels, 305 integration in cities, 406 promoting tests with, 407 uses, 153 AutoVots ﬂeet, 350 Backup levels, 127 Baidu apps, 338, 372 Base layer, 119 Becker, Jan, 42 43 Behavioural law, 234 Being driven, 61, 63, 78, 342 343 Ben-Noon, Ofer, 142, 143, 145 Benz, Carl, 3, 4 Bertha (autonomous research vehicle), 42 Big data, 313, 332 333 BlaBlaCar, 359 429 Blackfriars bridge, lidar print cloud of, 104 Blind-spot detection, 78 Bloggers, 225 227 Blonde Salad, The, 226 Bluetooth, 130, 142, 154 BMW, 6, 130, 137, 174, 180, 316, 320, 322, 332 333, 372, 398 3-series cars, 338 BMW i3, 27 holoactive touch, 285 Boeing 777 development, 243 Boeing, 787, 261 Bosch, 9, 181 182 Bosch, Robert, 333 Bosch suppliers, 315 BosWash, metropolitan region, 384 Budii car, 272 273 Business models, 311, 353 355 automobile manufacturers, 311 content creators, 319 320 data creators, 320 322 examples, 312 hardware creators, 314 315 options, 312 314 passenger looks for new products, 321 passenger visits website, 321 service creators, 316 319 software creators, 315 316 strategic mix, 322 323 Business vehicle, 15 Business-to-consumer car sharing, 342 343 Cadillac, 180 California PATH Research Reports, 298 299 Cambot, 290 Cameras, 111, 126 CAN bus, 126, 143 Capsule, 33 Car and ride sharing, studies on, 348 430 Car dealers, repair shops and insurance companies, 173 174 Car manufacturers, 328, 396 397 business model, 312 Car-pooling efforts, 364 365 Car-sharing programs, 364 365 service, 383 Car-sharing, 206 Car2Go, 317, 345 Casey Neistat, 226 Castillo, Jose, 364 365 Celebrities and bloggers, 225 227 Central driver assistance control unit, 124 Central processing unit, 96, 124 zFAS, 125 Centre for Economic and Business Research in London, 189 Chevrolet, 40 app from General Motors, 316 Spark EV, 27 Cisco, 41 CityMobil project, 369, 406 CityMobil2, 14, 157 Cognitive distraction, 287 Coherent European framework, 246 Committee on Autonomous Road Transport for Singapore, 347 Communication, 198 200 investing in communication infrastructure, 403 404 technology, 261 Community, 341 detection algorithms, 389 Companion app, 316 Compelling force, 223 Competitiveness Iain Forbes, 368 369 projects in Asia, 371 374 Index projects in Europe and United States, 369 371 projects in Israel, 374 375 Computer operating systems, 247 Computer-driven driving, 108 Computerised information processing, 109 Congestion pricing, 296 Connected car, 129 ad-hoc networks, 133 connected driving, 137 138 connected mobility, 138 development of mobile communication networks, 130 digital ecosystems, 138 eCall, 136 137 online services, 136 137 permanent networks, 130 statement by telecommunications experts, 132 133 V-to-I communication, 134 135 V-to-V communication, 133 134 V-to-X communication, 135 136 See also Digitised car Connected mobility, 129, 138 Connected vehicles, 138 vulnerability of, 142 Connected-car services, 313 Connectivity of vehicles, 147 Consumer-electronics companies, 285 Container Terminal, 159 Content creators, 319 320 Continental (automotive suppliers), 9, 284, 315 Continuous feedback, 281 Convenience, 302 304, 306 Conventional breakthrough approach, 332 Index Conventional broadband applications, 132 Conventional car manufacturing, 10 Cook, Tim, 182 Cooperative intelligent transport system (C-ITS), 369 370 Corporate Average Fuel Economy standard, 297 Cost(s), 187 192, 295 autonomous vehicles enabling use of alternative fuels, 305 fuel economy, 297 299 intelligent infrastructures, 299 301 land use, 304 operating costs, 301 302 relationship between road speed and road throughput, 296 vehicle throughput, 295 297 Croove app, 318 Culture, 330 change, 396 differences, 195 197 and organisational transformation, 395 Curtatone, Joseph, 387 Customers’ expectations attitudes, 204 207 incidents, 203 204 interview with 14 car dealers, 207 persuasion, 207 208 statements by two early adopters, 205 Cyber attacks, 141 Cyber hacking or failures in algorithms, 354 Cyber security, 141 146 Cyber-physical systems, 9 Daimler, 130 Data, 121 categories in vehicle, 147 creators, 320 322 431 from passengers, 94 95 privacy, 147 148 processing, 91 protection principles, 148 recorders, 239 Data-capturing technology, 103 Data-protection issues, 239 Database, 98 Decelerating, 91, 122 Decision-making mechanism, 369 Declaration of Amsterdam, 246 247 Deep learning, 115 Deep neural networks, 115 116 Deere, John, 154, 155 Deere, John, 154, 155, 263 Defense Advanced Research Project Agency (DARPA), 41 Degree of autonomous driving, 53 Degree of autonomy, 262 Degree of market penetration, 84 Degree of not-invented-here arrogance, 332 Degree of vehicle’s automation, 233 234 Delhi municipal government, 21 22 Delphi, 9, 181 Delphi Automotive Systems, 6 Demise of Kodak, 111 Denner, Volkmar, 333 334 Denso, 9 Depreciation, 345 Destination control, 299, 300 Digital company development, 395 396 Digital economy, 225 Digital ecosystems, 138 Digital light-processing technology, 277, 279 Digital maps, 101 Digital products, 267 Digitised car algorithms, 113 117 432 backup levels, 127 car as digitised product, 111 112 data, 121 drive recorder, 125 126 drive-by-wire, 122 over-provisioning, 127 processor, 122 125 software, 117 121 See also Connected car Digitising and design of vehicle, 265 267 Dilemma situations, 61 Direct attacks, 141 Direct connectivity of vehicle, 130 Disruptions in mobility, 31, 34 arguments, 34 35 history, 32 33 OICA, 34 Disruptive technologies, 221, 223, 402 Document operation-relevant data, 263 Doll, Claus, 166 Dongles, 142 Drees, Joachim, 165 ‘Drive boost’ mode, 274 “Drive me” project, 370 Drive recorder, 125 126 ‘Drive relax’ mode, 274 Drive-by-wire, 122 DriveNow, 317, 345 Driver, 235 role, 235 238 Driver distraction, 55 causes and consequences, 278 Driver-assistance systems, 53, 71, 160, 174, 222, 298, 333, 353 Driverless cars, 3, 7, 27 28, 222, 233, 244 taxis, 302 vans, 406 vehicles, 168 Index Driverless Audi RS7, 227 229 Driverless Race Car of Audi, 5 Driving manoeuvres, 91 modes, 107 oneself, 342 343 Drunk driving, 303 Dvorak keyboard, 242 Dynamic patterns of movement in city of London, 390 eCall.
Future War: Preparing for the New Global Battlefield by Robert H. Latiff
Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, Berlin Wall, cyber-physical system, Danny Hillis, defense in depth, drone strike, Elon Musk, failed state, friendly fire, Howard Zinn, Internet of things, low earth orbit, Nicholas Carr, orbital mechanics / astrodynamics, self-driving car, South China Sea, Stephen Hawking, Stewart Brand, Stuxnet, Wall-E
Sometimes we think we need a new system because the enemy might have one. At other times, however, we see a new capability and just have to have it. I saw this in the development of new satellites. What starts out as a reasonable design grows and grows, because of so many desired new features, until the project becomes unworkable, unnecessarily expensive, or even useless upon arrival. The way we’re headed with advanced artificial intelligence, autonomy, and cyber-physical systems seems like more technology seduction. Technology always promises something better, often with an illusion of objectivity. Solving problems seems to require little subjective thought. If we want more performance, we just need more technology. But technology tends to limit our need to think about alternatives. Unlike problems that must be solved by making a change to an institution or a process, which have multiple competing solutions and may take a long time to show results, technology solutions most often promise immediate impact.
The Truth Machine: The Blockchain and the Future of Everything by Paul Vigna, Michael J. Casey
3D printing, additive manufacturing, Airbnb, altcoin, Amazon Web Services, barriers to entry, basic income, Berlin Wall, Bernie Madoff, bitcoin, blockchain, blood diamonds, Blythe Masters, business process, buy and hold, carbon footprint, cashless society, cloud computing, computer age, computerized trading, conceptual framework, Credit Default Swap, crowdsourcing, cryptocurrency, cyber-physical system, dematerialisation, disintermediation, distributed ledger, Donald Trump, double entry bookkeeping, Edward Snowden, Elon Musk, Ethereum, ethereum blockchain, failed state, fault tolerance, fiat currency, financial innovation, financial intermediation, global supply chain, Hernando de Soto, hive mind, informal economy, intangible asset, Internet of things, Joi Ito, Kickstarter, linked data, litecoin, longitudinal study, Lyft, M-Pesa, Marc Andreessen, market clearing, mobile money, money: store of value / unit of account / medium of exchange, Network effects, off grid, pets.com, prediction markets, pre–internet, price mechanism, profit maximization, profit motive, ransomware, rent-seeking, RFID, ride hailing / ride sharing, Ross Ulbricht, Satoshi Nakamoto, self-driving car, sharing economy, Silicon Valley, smart contracts, smart meter, Snapchat, social web, software is eating the world, supply-chain management, Ted Nelson, the market place, too big to fail, trade route, transaction costs, Travis Kalanick, Turing complete, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, universal basic income, web of trust, zero-sum game
Security expert Bruce Schneier laid it all bare in a 2016 article in Motherboard: “It’s one thing if your smart lock can be eavesdropped upon to know who is home. It’s another thing entirely if it can be hacked to allow a burglar to open the door—or prevent you from opening your door. A hacker who can deny you control of your car, or take over control, is much more dangerous than one who can eavesdrop on your conversation or track your car’s location.” With the Internet of Things and other such “cyber-physical systems,” Schneier said, “we’ve given the Internet hands and feet: the ability to directly affect the physical world. What used to be attacks against data and information have become attacks against flesh, steel, and concrete.” Making matters worse is the challenge people face in upgrading software to their devices; we have a hard enough time keeping up with Microsoft’s and our app providers’ security patch updates on laptops and smartphones, let alone having to update the software on our Internet-connected fridge.
Inventors at Work: The Minds and Motivation Behind Modern Inventions by Brett Stern
Apple II, augmented reality, autonomous vehicles, bioinformatics, Build a better mousetrap, business process, cloud computing, computer vision, cyber-physical system, distributed generation, game design, Grace Hopper, Richard Feynman, Silicon Valley, skunkworks, Skype, smart transportation, speech recognition, statistical model, stealth mode startup, Steve Jobs, Steve Wozniak, the market place, Yogi Berra
But it soon became mine, because I was the only one who had patience to learn how to use it. I was making just the kind of things eleven-year-olds would make: video games, space invaders, keyboard races, etc. Stern: Jumping forward, could you define the technology or your inventions in technical terms, and then define them in layperson’s terms? Greiner: I like to build integrated robot systems, or “cyber-physical systems,” that are able to negotiate unstructured environments using dynamic sensing and onboard intelligence. Robotics encompasses many of the other disciplines, like artificial intelligence, dynamic sensing, and electrical engineering. You’d be hard-pressed to come up with an area that robotics doesn’t use. Stern: Could you define it in one sentence in very simple terms? Greiner: I guess I’d have to say that I build practical robot systems.
Building Secure and Reliable Systems: Best Practices for Designing, Implementing, and Maintaining Systems by Heather Adkins, Betsy Beyer, Paul Blankinship, Ana Oprea, Piotr Lewandowski, Adam Stubblefield
anti-pattern, barriers to entry, bash_history, business continuity plan, business process, Cass Sunstein, cloud computing, continuous integration, correlation does not imply causation, create, read, update, delete, cryptocurrency, cyber-physical system, database schema, Debian, defense in depth, DevOps, Edward Snowden, fault tolerance, fear of failure, general-purpose programming language, Google Chrome, Internet of things, Kubernetes, load shedding, margin call, microservices, MITM: man-in-the-middle, performance metric, pull request, ransomware, revision control, Richard Thaler, risk tolerance, self-driving car, Skype, slashdot, software as a service, source of truth, Stuxnet, Turing test, undersea cable, uranium enrichment, Valgrind, web application, Y2K, zero day
Proceedings of the 40th International Conference on Software Engineering: 163–171. doi:10.1145/3183519.3183521. 3 For more discussion of common unit testing pitfalls encountered at Google, see Wright, Hyrum, and Titus Winters. 2015. “All Your Tests Are Terrible: Tales from the Trenches.” CppCon 2015. https://oreil.ly/idleN. 4 See Chapter 2 of the SRE workbook. 5 The fuzz target compares the results of two modular exponentiation implementations inside OpenSSL, and will fail if the results ever differ. 6 For an example, see Bozzano, Marco et al. 2017. “Formal Methods for Aerospace Systems.” In Cyber-Physical System Design from an Architecture Analysis Viewpoint, edited by Shin Nakajima, Jean-Pierre Talpin, Masumi Toyoshima, and Huafeng Yu. Singapore: Springer. 7 You can install Clang-Tidy using standard package managers. It is generally called clang-tidy. 8 See Sadowski, Caitlin et al. 2018. “Lessons from Building Static Analysis Tools at Google.” Communications of the ACM 61(4): 58–66. doi:10.1145/3188720. 9 See Cousot, Patrick, and Radhia Cousot. 1976.