Database Important Quotes

We’ve got one huge advantage—people believe what they see in databases. They’ve never learned the most important rule of cyberspace—computers don’t lie but liars can compute.

There were no sex classes. No friendship classes. No classes on how to navigate a bureaucracy, build an organization, raise money, create a database, buy a house, love a child, spot a scam, talk someone out of suicide, or figure out what was important to me. Not knowing how to do these things is what messes people up in life, not whether they know algebra or can analyze literature.

This new science of performance argues that you get better at a skill as you develop more myelin around the relevant neurons, allowing the corresponding circuit to fire more effortlessly and effectively. To be great at something is to be well myelinated. This understanding is important because it provides a neurological foundation for why deliberate practice works. By focusing intensely on a specific skill, you’re forcing the specific relevant circuit to fire, again and again, in isolation. This repetitive use of a specific circuit triggers cells called oligodendrocytes to begin wrapping layers of myelin around the neurons in the circuits—effectively cementing the skill. The reason, therefore, why it’s important to focus intensely on the task at hand while avoiding distraction is because this is the only way to isolate the relevant neural circuit enough to trigger useful myelination. By contrast, if you’re trying to learn a complex new skill (say, SQL database management) in a state of low concentration (perhaps you also have your Facebook feed open), you’re firing too many circuits simultaneously and haphazardly to isolate the group of neurons you actually want to strengthen. In

When the National Transportation Safety Board analyzed its database of major flight accidents, it found that 73 percent occurred on a flight crew’s first day working together. Like surgeries and putts, the best flight is one in which everything goes according to routines long understood and optimized by everyone involved, with no surprises. When the path is unclear—a game of Martian tennis—those same routines no longer suffice. “Some tools work fantastically in certain situations, advancing technology in smaller but important ways, and those tools are well known and well practiced,” Andy Ouderkirk told me. “Those same tools will also pull you away from a breakthrough innovation. In fact, they’ll turn a breakthrough innovation into an incremental one.

Ana has been pretending it wasn’t there, but now Pearson has stated it baldly: the fundamental incompatibility between Exponential’s goals and hers. They want something that responds like a person, but isn’t owed the same obligations as a person, and that’s something she can’t give them. No one can give it to them, because it’s an impossibility. The years she spent raising Jax didn’t just make him fun to talk to, didn’t just provide him with hobbies and a sense of humor. They were what gave him all the attributes Exponential is looking for: fluency at navigating the real world, creativity at solving new problems, judgment you could entrust with an important decision. Every quality that made a person more valuable than a database was a product of experience.

A more welcome fellow traveler on the modern human diaspora from Africa may have been the dog, the first known domestic animal. There is evidence that Aurignacian people living in Goyet Cave, Belgium, already had large dogs accompanying them about 35,000 years ago. The dogs were anatomically distinct from wolves in their shorter and broader snout and dental proportions, and isotope data suggest that they, like the humans, were feeding off horses and wild cattle. Moreover, ancient dog DNA was obtained, which showed that the Belgian dogs were already genetically diverse and that their mitochondrial sequences could not be matched among the large databases of contemporary wolf and dog DNA. These findings are important because they suggest that dog domestication had already been under way well before 35,000 years ago.

One of the patterns from domain-driven design is called bounded context. Bounded contexts are used to set the logical boundaries of a domain’s solution space for better managing complexity. It’s important that teams understand which aspects, including data, they can change on their own and which are shared dependencies for which they need to coordinate with other teams to avoid breaking things. Setting boundaries helps teams and developers manage the dependencies more efficiently. The logical boundaries are typically explicit and enforced on areas with clear and higher cohesion. These domain dependencies can sit on different levels, such as specific parts of the application, processes, associated database designs, etc. The bounded context, we can conclude, is polymorphic and can be applied to many different viewpoints. Polymorphic means that the bounded context size and shape can vary based on viewpoint and surroundings. This also means you need to be explicit when using a bounded context; otherwise it remains pretty vague.

With or without 'college' we are able to use our senses by perceiving the world around us, that in turn shapes and creates ones own reality. Perception is reality. My 'reality' is not the same as your 'reality' since we all have a different mental database, life experience, physiology, different characteristics, environments we grew up and people we hang out with, etc. I might fall in love with a certain smell while it triggers bad memories for someone else. Same goes for the other senses while perceiving 'reality'. And how real is this so called 'reality' anyway? Our senses can be quite limited compared to a camera or other living creatures on the planet. There are sounds and colours humans can not detect with their senses. We in fact do not perceive the whole 'picture'. The most important things in life are unseen. My point is that we do not need hierarchic, indoctrinating, and capitalized institution called 'science' to tell us what, when, why, and how to think, experiment, sense, and live our lives. Long before there was any 'science', there was sense first.

Ultimately, the World Top Incomes Database (WTID), which is based on the joint work of some thirty researchers around the world, is the largest historical database available concerning the evolution of income inequality; it is the primary source of data for this book.24 The book’s second most important source of data, on which I will actually draw first, concerns wealth, including both the distribution of wealth and its relation to income. Wealth also generates income and is therefore important on the income study side of things as well. Indeed, income consists of two components: income from labor (wages, salaries, bonuses, earnings from nonwage labor, and other remuneration statutorily classified as labor related) and income from capital (rent, dividends, interest, profits, capital gains, royalties, and other income derived from the mere fact of owning capital in the form of land, real estate, financial instruments, industrial equipment, etc., again regardless of its precise legal classification). The WTID contains a great deal of information about the evolution of income from capital over the course of the twentieth century. It is nevertheless essential to complete this information by looking at sources directly concerned with wealth. Here I rely on three distinct types of historical data and methodology, each of which is complementary to the others.25 In the first place, just as income tax returns allow us to study changes in income inequality, estate tax returns enable us to study changes in the inequality of wealth.26 This

it would be good to include staleness measurements in the standard set of metrics for databases. Eventual consistency is a deliberately vague guarantee, but for operability it’s important to be able to quantify “eventual.

Include every person you can think of. EVERY person. It doesn’t matter if you think they are a prospect or not. Your database will be one of your most important assets. Everyone goes on the list. If they are negative, put them on your list. If you hate them, put them on your list. If they are your best friend, put them on your list. If they’ve said, “I’ll never be involved in Network Marketing,” put them on your list. If they’re 98 years old, put them on your list. If they’re 18 years old, put them on your list.

Amazon realized the importance of recruiting developers early  —  moving its entire organization to services-based interfaces. At the time, this was revolutionary; while everyone was talking about “Service Oriented Architectures,” almost no one had built one. And certainly no one had built one at Amazon’s scale. While this had benefits for Amazon internally, its practical import was that, if Amazon permitted it, anyone from outside Amazon could interact with its infrastructure as if they were part of the company. Need to provision a server, spin up a database, or accept payments? Outside developers could now do this on Amazon’s infrastructure as easily as employees. Suddenly, external developers could not only extend Amazon’s own business using their services  —  they could build their own businesses on hardware they rented from the one-time bookstore, now a newly minted technology vendor.

As a result, the most important recommendation for organizations of all shapes and sizes moving forward is to anticipate worst case scenarios at a minimum. Even in cases where organizations cannot or will not make some of the operational changes recommended below, the exercise of focusing on nonsoftware areas of a given business can help identify under-realized or -appreciated assets within an organization. Particularly ones for whom the sale of software has been low effort, brainstorming about other potential revenue opportunities is unlikely to be time wasted. One vendor in the business intelligence and analytics space has privately acknowledged doing just this; based on current research and projecting current trends forward, it is in the process of building out a 10-year plan over which it assumes that the upfront licensing model will gradually approach zero revenue. In its place, the vendor plans to build out subscription and data-based revenue streams. Even if the plan ultimately proves to be unnecessary, the exercise has been enormously useful internally for the insight gained into its business.

Symbolist machine learning is an offshoot of the knowledge engineering school of AI. In the 1970s, so-called knowledge-based systems scored some impressive successes, and in the 1980s they spread rapidly, but then they died out. The main reason they did was the infamous knowledge acquisition bottleneck: extracting knowledge from experts and encoding it as rules is just too difficult, labor-intensive, and failure-prone to be viable for most problems. Letting the computer automatically learn to, say, diagnose diseases by looking at databases of past patients’ symptoms and the corresponding outcomes turned out to be much easier than endlessly interviewing doctors. Suddenly, the work of pioneers like Ryszard Michalski, Tom Mitchell, and Ross Quinlan had a new relevance, and the field hasn’t stopped growing since. (Another important problem was that knowledge-based systems had trouble dealing with uncertainty, of which more in Chapter 6.)

Forensic DNA Expert Anil Gupta offer a variety of DNA forensic testing systems including STR, Y-STR, and mitochondrial DNA. The DNA Sample in Forensic Analysis can be collected from blood, saliva, perspiration, hair, teeth, mucus, finger nails, semon and these can be found almost anywhere at crime scence. Anil Gupta is here to help make sense of this complex scientific issue and to testify before the court on these issues when necessary. Initial Consultation is FREE – If you send us the report we will lend you our expertise to help you understand your situation. Written Reports and Affidavits Discovery Documents – free by request, all you need to obtain the entire laboratory case file Mike is a leading forensic DNA expert with considerable experience in forensic biology. He is a clear and balanced expert opinion highly qualified provider to help lawyers, attorneys and lawyers support their clients and the criminal justice system. He is a very experienced scientist, whose career has focused on developing the ability to DNA analysis, defining standards, interpreting results, explaining evidence and providing advice to help both the defense and Processing equipment. Mike has a great depth of technical knowledge. As the chief DNA scientist (head of discipline) with the former Forensic Science Service (FSS), he established technical standards for DNA analytical processes, staff competencies and training. He was head of the Specialist Unit at FSS DNA and led the creation of the first dedicated facility of ultra-clean low template DNA. He has led the validation and implementation of several important new DNA processes. Through audit and process review, it can provide an effective and risk-based quality assurance, as it has for many years to the FSS, to the National DNA Database and to the courts.

It was important to us that for a developer, adding production telemetry didn’t feel as difficult as doing a database schema change.

Of course, the web servers are not always the cause of the problem. I have seen many cases where virtual users timed out waiting for a web server response, only to fnd that the actual problem was a long-running database query that had not yet returned a result to the application or web server tier. This demonstrates the importance of setting up KPI monitoring for all server tiers in the system under test (SUT).

Structured methods for learning Method Uses Useful for Organizational climate and employee satisfaction surveys Learning about culture and morale. Many organizations do such surveys regularly, and a database may already be available. If not, consider setting up a regular survey of employee perceptions. Useful for managers at all levels if the analysis is available specifically for your unit or group. Usefulness depends on the granularity of the collection and analysis. This also assumes the survey instrument is a good one and the data have been collected carefully and analyzed rigorously. Structured sets of interviews with slices of the organization or unit Identifying shared and divergent perceptions of opportunities and problems. You can interview people at the same level in different departments (a horizontal slice) or bore down through multiple levels (a vertical slice). Whichever dimension you choose, ask everybody the same questions, and look for similarities and differences in people’s responses. Most useful for managers leading groups of people from different functional backgrounds. Can be useful at lower levels if the unit is experiencing significant problems. Focus groups Probing issues that preoccupy key groups of employees, such as morale issues among frontline production or service workers. Gathering groups of people who work together also lets you see how they interact and identify who displays leadership. Fostering discussion promotes deeper insight. Most useful for managers of large groups of people who perform a similar function, such as sales managers or plant managers. Can be useful for senior managers as a way of getting quick insights into the perceptions of key employee constituencies. Analysis of critical past decisions Illuminating decision-making patterns and sources of power and influence. Select an important recent decision, and look into how it was made. Who exerted influence at each stage? Talk with the people involved, probe their perceptions, and note what is and is not said. Most useful for higher-level managers of business units or project groups. Process analysis Examining interactions among departments or functions and assessing the efficiency of a process. Select an important process, such as delivery of products to customers or distributors, and assign a cross-functional group to chart the process and identify bottlenecks and problems. Most useful for managers of units or groups in which the work of multiple functional specialties must be integrated. Can be useful for lower-level managers as a way of understanding how their groups fit into larger processes. Plant and market tours Learning firsthand from people close to the product. Plant tours let you meet production personnel informally and listen to their concerns. Meetings with sales and production staff help you assess technical capabilities. Market tours can introduce you to customers, whose comments can reveal problems and opportunities. Most useful for managers of business units. Pilot projects Gaining deep insight into technical capabilities, culture, and politics. Although these insights are not the primary purpose of pilot projects, you can learn a lot from how the organization or group responds to your pilot initiatives. Useful for managers at all levels. The size of the pilot projects and their impact will increase as you rise through the organization.

Our new care pathways were effective because they were led by physicians, enabled by real-time data-based feedback, and primarily focused on improving the quality of patient care,” which “fundamentally motivated our physicians to change their behavior.” Crucial too was the fact that “the men and women who actually work in the service lines themselves chose which care processes to change. Involving them directly in decision making secured their buy-in and made success more likely.” What we can learn from the Geisinger example is the importance of having providers develop and monitor performance measures. The fact that the measures were in keeping with their own professional sense of mission was crucial.

Princeton University mathematician York Dobyns found that the seven years of new PEAR RNG results closely replicated the preceding three decades of RNG studies reviewed in the meta-analysis.37 That is, our 1989 prediction had been validated. Because the massive PEAR database provides an exceptionally strong confirmation that mind-matter interactions really do exist, we can confidently use it to study some of the factors influencing these effects. Psychologist Roger Nelson and his colleagues found that the main RNG effect for the full PEAR database of 1,262 independent experiments, generated by 108 people, was associated with odds against chance of four thou sand to one.38 He also found that there were no “star” performers—this means that the overall effect reflected an accumulation of small effects from each person rather than a few outstanding results from “special people.” This finding confirms the expectation that mind-matter interaction effects observed in the hundreds of studies collected in the 1989 RNG meta-analysis were part of a widespread ability distributed throughout the population, and were not due to a few psychic “superstars” or a few odd experiments. Further analysis of the PEAR data showed that the results in individual trials were best interpreted as small changes in the probabilities of individual random events rather than as a few instances of wildly large effects. This means that the results cannot be explained by unexpected glitches in the RNG devices, or by strange circumstances in the lab (like a circuit breakdown). Rather, the effects were small but consistent across individual trials, and across different people.39 If we accept that one person can affect the behavior of an RNG, another question naturally arises: would two people together produce a larger effect? The PEAR database included some experiments where cooperating pairs used the same mental intention on the same RNG. Analysis of these data found that, on average, the effects were indeed larger for pairs than for individuals working alone. However, two people didn’t automatically get results that were twice as large as one person’s results. Instead, the composition of the pairs was important in determining the outcome. Same-sex pairs, whether men or women, tended to achieve null or slightly negative outcomes, whereas opposite-sex pairs produced an effect that was approximately twice that of individuals. Moreover, when the pair was a “bonded” couple, such as spouses or close family members, the effect size was more than four times that of individuals.

Princeton University mathematician York Dobyns found that the seven years of new PEAR RNG results closely replicated the preceding three decades of RNG studies reviewed in the meta-analysis.37 That is, our 1989 prediction had been validated. Because the massive PEAR database provides an exceptionally strong confirmation that mind-matter interactions really do exist, we can confidently use it to study some of the factors influencing these effects. Psychologist Roger Nelson and his colleagues found that the main RNG effect for the full PEAR database of 1,262 independent experiments, generated by 108 people, was associated with odds against chance of four thou sand to one.38 He also found that there were no “star” performers—this means that the overall effect reflected an accumulation of small effects from each person rather than a few outstanding results from “special people.” This finding confirms the expectation that mind-matter interaction effects observed in the hundreds of studies collected in the 1989 RNG meta-analysis were part of a widespread ability distributed throughout the population, and were not due to a few psychic “superstars” or a few odd experiments. Further analysis of the PEAR data showed that the results in individual trials were best interpreted as small changes in the probabilities of individual random events rather than as a few instances of wildly large effects. This means that the results cannot be explained by unexpected glitches in the RNG devices, or by strange circumstances in the lab (like a circuit breakdown). Rather, the effects were small but consistent across individual trials, and across different people.39 If we accept that one person can affect the behavior of an RNG, another question naturally arises: would two people together produce a larger effect? The PEAR database included some experiments where cooperating pairs used the same mental intention on the same RNG. Analysis of these data found that, on average, the effects were indeed larger for pairs than for individuals working alone. However, two people didn’t automatically get results that were twice as large as one person’s results. Instead, the composition of the pairs was important in determining the outcome. Same-sex pairs, whether men or women, tended to achieve null or slightly negative outcomes, whereas opposite-sex pairs produced an effect that was approximately twice that of individuals. Moreover, when the pair was a “bonded” couple, such as spouses or close family members, the effect size was more than four times that of individuals. There were also some gender differences. PEAR lab psychologist Brenda Dunne found that women tended to volunteer more time to the experiments, and thus they accumulated about two-thirds of the full database, compared with one-third for men. On the other hand, their effects were smaller on average than those of men, with odds of the difference being due to chance at eight hundred to one.

One way to make yourself less vulnerable to copycats is to build a moat around your business. How Can I Build a Moat? As you scale your company, you need to think about how to proactively defend against competition. The more success you have, the more your competitors will grab their battering ram and start storming the castle. In medieval times, you’d dig a moat to keep enemy armies from getting anywhere near your castle. In business, you think about your economic moat. The idea of an economic moat was popularized by the business magnate and investor Warren Buffett. It refers to a company’s distinct advantage over its competitors, which allows it to protect its market share and profitability. This is hugely important in a competitive space because it’s easy to become commoditized if you don’t have some type of differentiation. In SaaS, I’ve seen four types of moats. Integrations (Network Effect) Network effect is when the value of a product or service increases because of the number of users in the network. A network of one telephone isn’t useful. Add a second telephone, and you can call each other. But add a hundred telephones, and the network is suddenly quite valuable. Network effects are fantastic moats. Think about eBay or Craigs-list, which have huge amounts of sellers and buyers already on their platforms. It’s difficult to compete with them because everyone’s already there. In SaaS—particularly in bootstrapped SaaS companies—the network effect moat comes not from users, but integrations. Zapier is the prototypical example of this. It’s a juggernaut, and not only because it’s integrated with over 3,000 apps. It has widened its moat with nonpublic API integrations, meaning that if you want to compete with it, you have to go to that other company and get their internal development team to build an API for you. That’s a huge hill to climb if you want to launch a Zapier competitor. Every integration a customer activates in your product, especially if it puts more of their data into your database, is another reason for them not to switch to a competitor. A Strong Brand When we talk about your brand, we’re not talking about your color scheme or logo. Your brand is your reputation—it’s what people say about your company when you’re not around.

The most important difference is that a relational database models data by relationships whereas Cassandra models data by query.

The ability to quickly analyze data is vital for a system of countering the laundering of the proceeds of crime, and computerized databases and analytical tools are an important element in achieving this goal. Nevertheless, it is important to keep in mind that electronic databases and software can only facilitate the work of analysts, not replace it.

had learned four important lessons: The Google Books database is an enormously powerful and valuable tool for researchers. Dates (and other items of metadata) provided by Google Books are sometimes inaccurate. When a book is reprinted it may be revised, and a revision may shift the date of publication. Precise details about editions must be collected. A book in the Google Books database that is only visible in snippets must be examined directly in hard copy to verify the quotation and to allow the construction of a complete and accurate citation. Idealistically,

in Canada, Hawaii, Chicago, or Washington, D.C., police are unable to point to a single instance of gun registration aiding the investigation of a violent crime. In a 2013 deposition, D.C. Police Chief Cathy Lanier said that the department could not “recall any specific instance where registration records were used to determine who committed a crime.”1 The idea behind a registry is that guns left at a crime scene can be used to trace back to the criminals. Unfortunately, guns are very rarely left at the scene of the crime. Those that are left behind are virtually never registered—criminals are not stupid enough to leave behind guns registered to them. In the few cases where registered guns were left at the scene, the criminal had usually been killed or seriously injured. Canada keeps some of the most thorough data on gun registration. From 2003 to 2009, a weapon was identified in fewer than a third of the country’s 1,314 firearm homicides. Of these identified weapons, only about a quarter were registered. Roughly half of these registered guns were registered to someone other than the person accused of the homicide. In just sixty-two cases—4.7 percent of all firearm homicides—was the gun identified as being registered to the accused. Since most Canadian homicides are not committed with a gun, these sixty-two cases correspond to only about 1 percent of all homicides. From 2003 to 2009, there were only sixty-two cases—just nine a year—where registration made any conceivable difference. But apparently, the registry was not important even in those cases. Despite a handgun registry in effect since 1934, the Royal Canadian Mounted Police and the Chiefs of Police have not yet provided a single example in which tracing was of more than peripheral importance in solving a case. No more successful was the long-gun registry that started in 1997 and cost Canadians $2.7 billion before being scrapped. In February 2000, I testified before the Hawaii State Senate joint hearing between the Judiciary and Transportation committees on changes that were being proposed to the state gun registration laws.2 I suggested two questions to the state senators: (1) how many crimes had been solved by their current registration and licensing system, and (2) how much time did it currently take police to register guns? The Honolulu police chief was notified in advance about those questions to give him time to research them. He told the committee that he could not point to any crimes that had been solved by registration, and he estimated that his officers spent over 50,000 hours each year on registering guns. But those aren’t the only failings of gun registration. Ballistic fingerprinting was all the rage fifteen years ago. This process requires keeping a database of the markings that a particular gun makes on a bullet—its unique fingerprint, so to speak. Maryland led the way in ballistic investigation, and New York soon followed. The days of criminal gun use were supposedly numbered. It didn’t work.3 Registering guns’ ballistic fingerprints never solved a single crime. New York scrapped its program in 2012.4 In November 2015, Maryland announced it would be doing the same.5 But the programs were costly. Between 2000 and 2004, Maryland spent at least $2.5 million setting up and operating its computer database.6 In New York, the total cost of the program was about $40 million.7 Whether one is talking about D.C., Canada, or these other jurisdictions, think of all the other police activities that this money could have funded. How many more police officers could have been hired? How many more crimes could have been solved? A 2005 Maryland State Police report labeled the operation “ineffective and expensive.”8 These programs didn’t work.

Jenny and I cannot imagine a relationship based on anything but our own choices. It’s safe to say that both of us would have preferred to live our lives searching for love than to have our parents find it for us in a database. But a proponent of arranged marriage would wonder why we’d leave such an important decision to the whims of mere emotion, without a dispassionate examination of the potential spouse’s career trajectory, religious compatibility and family background. The odds of finding true love are actually seen to be better in an arranged system because they seek the ideal spouse scientifically; we just stumble through life, hoping that our perfect match just happens to be sitting on that bar stool over there. What could be a more irrational way to make the most important decision of our lives? No wonder that in America, forty-five percent of marriages end in divorce; in India, that number is around one percent.

A second level of analysis for conceptualizing spirituality is in terms of personalized goals or strivings, or what Emmons and colleagues have called "ultimate concerns" (Emmons, 1999; Emmons, Cheung, & Tehrani, 1998). A rapidly expanding database now exists that demonstrates that personal goals are a valid representation of how people structure and experience their lives: They are critical constructs for understanding the ups and downs of everyday life, and they are key elements for understanding both the positive life as well as psychological dysfunction (Karoly, 1999). People's priorities, goals, and concerns are key determinants of their overall quality of life. The possession of and progression toward important life goals are essential for long-term well-being. Several investigators have found that individuals who are involved in the pursuit of personally meaningful goals possess greater emotional well-being and better physical health than do persons who lack goal direction (see Emmons, 19 9 9, for a review).

retrieve?� When it comes to databases, chances are you’ll need to retrieve your data as often than you’ll need to insert it. That’s where this chapter comes in: you’ll meet the powerful SELECT statement and learn how to gain access to that important information you’ve been putting in your tables. You’ll even learn how to use WHERE, AND, and OR to selectively get to your data and even avoid displaying the data that you don’t need. I’m a star! Date or no date? 54 A better SELECT 57 What the * is that? 58 How to query your data types 64 More punctuation problems 65 Unmatched single quotes 66 Single quotes are special characters 67 INSERT data with single quotes in it 68 SELECT specific columns to limit results 73 SELECT specific columns for faster results 73 Combining your queries 80 Finding numeric values 83 Smooth Comparison Operators

Another recent study, this one on academic research, provides real-world evidence of the way the tools we use to sift information online influence our mental habits and frame our thinking. James Evans, a sociologist at the University of Chicago, assembled an enormous database on 34 million scholarly articles published in academic journals from 1945 through 2005. He analyzed the citations included in the articles to see if patterns of citation, and hence of research, have changed as journals have shifted from being printed on paper to being published online. Considering how much easier it is to search digital text than printed text, the common assumption has been that making journals available on the Net would significantly broaden the scope of scholarly research, leading to a much more diverse set of citations. But that’s not at all what Evans discovered. As more journals moved online, scholars actually cited fewer articles than they had before. And as old issues of printed journals were digitized and uploaded to the Web, scholars cited more recent articles with increasing frequency. A broadening of available information led, as Evans described it, to a “narrowing of science and scholarship.”31 In explaining the counterintuitive findings in a 2008 Science article, Evans noted that automated information-filtering tools, such as search engines, tend to serve as amplifiers of popularity, quickly establishing and then continually reinforcing a consensus about what information is important and what isn’t. The ease of following hyperlinks, moreover, leads online researchers to “bypass many of the marginally related articles that print researchers” would routinely skim as they flipped through the pages of a journal or a book. The quicker that scholars are able to “find prevailing opinion,” wrote Evans, the more likely they are “to follow it, leading to more citations referencing fewer articles.” Though much less efficient than searching the Web, old-fashioned library research probably served to widen scholars’ horizons: “By drawing researchers through unrelated articles, print browsing and perusal may have facilitated broader comparisons and led researchers into the past.”32 The easy way may not always be the best way, but the easy way is the way our computers and search engines encourage us to take.

DECISIONS Useful: Graphical Presentation Monitor Key Indicators Effective Measurements Wisdom Knowledge The Goal: Strategic Thinking Predictive Value Experience and Judgment Automated Exception Notification Information Structured: Voluminous Grouped and Summarized Relationships Not Always Evident Raw Data: Massive Fragmented Meaningless Data EVENTS Figure 1-01. The Pyramid of KnowledgeToyota, this begins with genchi genbutsu, or gemba, which means literally “go see it for yourself. ” Taiichi Ohno, a founding father of Lean, once said, “Data is of course important in manufacturing, but I place the greatest emphasis on facts. ” 2 A direct and intuitive understanding of a situation is far more useful than mountains of data. The raw data stored in a database adds value for decision-making only if the right information is presented in the right format, to the right people, at the right time. A tall stack of printout may contain the right data, but it’s certainly not in an accessible format. Massive weekly batch printouts do not enable timely and proactive decisions. Raw data must be summarized, structured, and presented as digestible information. Once information is combined with direct experience, then the incredible human mind can extract and develop useful knowledge. Over time, as knowledge is accumulated and combined with direct experience and judgment, wisdom develops. This evolution is described by the classic pyramid of knowledge shown in Figure 1-01. BACK TO CHICAGO So what happened in Chicago? We can speculate upon several possible perspectives for why the team and its change leader were far from a true Lean system, yet they refused any help from IT providers: 1. They feared wasteful IT systems and procedures would be foisted on them.

...SQL is very far from being the “perfect” relational language—it suffers from numerous sins of both omission and commission. ...the overriding issue is simply that SQL fails in all too many ways to support the relational model properly. As a consequence, it is not at all clear that today's SQL products really deserve to be called “relational” at all! Indeed, as far as this writer is aware, there is no product on the market today that supports the relational model in its entirety. This is not to say that some parts of the model are unimportant; on the contrary, every detail of the model is important, and important, moreover, for genuinely practical reasons. Indeed, the point cannot be stressed too strongly that the purpose of relational theory is not just “theory for its own sake”; rather, the purpose is to provide a base on which to build systems that are 100 percent practical. But the sad fact is that the vendors have not yet really stepped up to the challenge of implementing the theory in its entirety. As a consequence, the “relational” products of today regrettably all fail, in one way or another, to deliver on the full promise of relational technology.

As psychologists, Ericsson and the other researchers in his field are not interested in why deliberate practice works; they’re just identifying it as an effective behavior. In the intervening decades since Ericsson’s first major papers on the topic, however, neuroscientists have been exploring the physical mechanisms that drive people’s improvements on hard tasks. As the journalist Daniel Coyle surveys in his 2009 book, The Talent Code, these scientists increasingly believe the answer includes myelin—a layer of fatty tissue that grows around neurons, acting like an insulator that allows the cells to fire faster and cleaner. To understand the role of myelin in improvement, keep in mind that skills, be they intellectual or physical, eventually reduce down to brain circuits. This new science of performance argues that you get better at a skill as you develop more myelin around the relevant neurons, allowing the corresponding circuit to fire more effortlessly and effectively. To be great at something is to be well myelinated. This understanding is important because it provides a neurological foundation for why deliberate practice works. By focusing intensely on a specific skill, you’re forcing the specific relevant circuit to fire, again and again, in isolation. This repetitive use of a specific circuit triggers cells called oligodendrocytes to begin wrapping layers of myelin around the neurons in the circuits—effectively cementing the skill. The reason, therefore, why it’s important to focus intensely on the task at hand while avoiding distraction is because this is the only way to isolate the relevant neural circuit enough to trigger useful myelination. By contrast, if you’re trying to learn a complex new skill (say, SQL database management) in a state of low concentration (perhaps you also have your Facebook feed open), you’re firing too many circuits simultaneously and haphazardly to isolate the group of neurons you actually want to strengthen.

Johnny developed a list of what he calls “Google Dorks,” or a string that can be used to search in Google to find out information about a company. For example if you were to type in: site:microsoft.com filetype:pdf you be given a list of every file with the extension of PDF that is on the microsoft.com domain. Being familiar with search terms that can help you locate files on your target is a very important part of information gathering. I make a habit of searching for filetype:pdf, filetype:doc, filetype:xls, and filetype:txt. It is also a good idea to see if employees actually leave files like DAT, CFG, or other database or configuration files open on their servers to be harvested.

CRM (Customer Relationship Management) is a marketing strategy that focuses on managing interactions and relationships with customers. CRM enables businesses to improve customer satisfaction, loyalty, and retention by providing personalized experiences that meet their needs. CRM is an essential aspect of modern marketing as it enables businesses to understand their customers' behavior, preferences, and needs and develop targeted marketing campaigns that resonate with them. In Go High Level, CRM (Customer Relationship Management) is a core component of the platform. The CRM functionality in Go High Level enables businesses to manage their customer interactions and relationships more effectively, improving customer satisfaction, loyalty, and retention. The CRM functionality in Go High Level includes a range of features and tools designed to help businesses automate and streamline their customer-facing processes, as well as provide them with insights into their customers' behavior, preferences, and needs. In essence, CRM is a set of practices, technologies, and strategies that businesses use to manage their customer interactions and relationships. The goal of CRM is to build stronger, more meaningful relationships with customers by providing them with personalized experiences and tailored solutions. CRM in marketing can be divided into three main categories: operational CRM, analytical CRM, and collaborative CRM. Operational CRM focuses on automating and streamlining customer-facing processes, such as sales, marketing, and customer service. This type of CRM is designed to improve efficiency and productivity by automating repetitive tasks and providing a centralized database of customer information. Operational CRM includes features such as sales pipeline management, lead nurturing, and customer service management. Analytical CRM focuses on analyzing customer data to gain insights into their behavior, preferences, and needs. This type of CRM enables businesses to make data-driven decisions by providing them with a better understanding of their customers' needs and preferences. Analytical CRM includes features such as customer segmentation, data mining, and predictive analytics. Collaborative CRM focuses on enabling businesses to collaborate and share customer information across different departments and functions. This type of CRM helps to break down silos within organizations and improve communication and collaboration between different teams. Collaborative CRM includes features such as customer feedback management, social media monitoring, and knowledge management. CRM is important for marketing because it enables businesses to build stronger, more meaningful relationships with customers. By understanding their customers' behavior, preferences, and needs, businesses can develop targeted marketing campaigns that resonate with them. This results in higher customer satisfaction, loyalty, and retention. CRM can also help businesses to improve their sales and marketing processes by providing them with better visibility into their sales pipeline and enabling them to track and analyze their marketing campaigns' effectiveness. This enables businesses to make data-driven decisions to improve their sales and marketing strategies, resulting in increased revenue and growth. Another benefit of CRM in marketing is that it enables businesses to personalize their marketing campaigns. Personalization is essential in modern marketing as it enables businesses to tailor their marketing messages and solutions to meet their customers' specific needs and preferences. This results in higher engagement and conversion rates, as customers are more likely to respond to marketing messages that resonate with them. Lead Generation: Go High Level provides businesses with a range of tools to generate leads, including customizable landing pages, web forms, and social media integrations.

The years she spent raising Jax didn’t just make him fun to talk to, didn’t just provide him with hobbies and a sense of humor. They were what gave him all the attributes Exponential is looking for: fluency at navigating the real world, creativity at solving new problems, judgment you could entrust with an important decision. Every quality that made a person more valuable than a database was a product of experience

WHY STUDY DISCRETE MATHEMATICS? There are several important reasons for studying discrete mathematics. First, through this course you can develop your mathematical maturity: that is, your ability to understand and create mathematical arguments. You will not get very far in your studies in the mathematical sciences without these skills. Second, discrete mathematics is the gateway to more advanced courses in all parts of the mathematical sciences. Discrete mathematics provides the mathematical foundations for many computer science courses, including data structures, algorithms, database theory, automata theory, formal languages, compiler theory, computer security, and operating systems. Students find these courses much more difficult when they have not had the appropriate mathematical foundations from discrete mathematics.

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...since there is so much confusion surrounding it in the industry. You will often hear claims to the effect that relational attributes can only be of very simple types (numbers, strings, and so forth). The truth is, however, that there is absolutely nothing in the relational model to support such claims. ...in fact, types can be as simple or as complex as we like, and so we can have attributes whose values are numbers, or strings, or dates, or times, or audio recordings, or maps, or video recordings, or geometric points (etc.). The foregoing message is so important‒and so widely misunderstood‒that we state it again in different terms: The question of what data types are supported is orthogonal to the question of support for the relational model.

■ Types are (sets of) things we can talk about. ■ Relations are (sets of) things we say about the things we can talk about. (There is a nice analogy here that might help you appreciate and remember these important points: Types are to relations as nouns are to sentences.) Thus, in the example, the things we can talk about are employee numbers, names, department numbers, and money values, and the things we say are true utterances of the form “The employee with the specified employee number has the specified name, works in the specified department, and earns the specified salary.” It follows from all of the foregoing that: 1. Types and relations are both necessary (without types, we have nothing to talk about; without relations, we cannot say anything). 2. Types and relations are sufficient, as well as necessary—i.e., we do not need anything else, logically speaking. 3. Types and relations are not the same thing. It is an unfortunate fact that certain commercial products—not relational ones, by definition!—are confused over this very point.

the fundamental incompatibility between Exponential’s goals and hers. They want something that responds like a person, but isn’t owed the same obligations as a person, and that’s something she can’t give them. No one can give it to them, because it’s an impossibility. The years she spent raising Jax didn’t just make him fun to talk to, didn’t just provide him with hobbies and a sense of humor. They were what gave him all the attributes Exponential is looking for: fluency at navigating the real world, creativity at solving new problems, judgment you could entrust with an important decision. Every quality that made a person more valuable than a database was a product of experience. She wants to tell them that Blue Gamma was more right than it knew: experience isn’t merely the best teacher; it’s the only teacher.

AWS and the Seven-Year Lead When creating Amazon Web Services (cloud computing), Amazon was essentially creating their own internal Internet Operating System (IOS) and then leveraging their technology infrastructure into a profit center. He said, “IT departments are recognizing that when they adopt AWS, they get more done. They spend less time on low value-add activities like managing datacenters, networking, operating system patches, capacity planning, database scaling, and so on and so on. Just as important, they get access to powerful APIs [Application Programing Interfaces] and tools that dramatically simplify building scalable, secure, robust, high-performance systems. And those APIs and tools are continuously and seamlessly upgraded behind the scenes, without customer effort.” —Bezos (2014 Letter) In other words, Amazon took the proprietary infrastructure they built for themselves and turned it into a service that any developer could use for their own purposes.