How To Read Graphs For Beginners

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How to Help Your Students
Learn to Read English

If you want to help your students learn to read English you need to give them some guidelines, and some interesting materials to read. Here are some suggestions to keep your reading lessons fresh and useful.

Tips to Teach Your Students to Learn to Read English

The first thing you need to do is find interesting texts . No students want to read if they have boring texts. If you can find real news stories or magazine articles then that is much better but make sure the vocabulary and grammar isn’t too difficult. You can also use excerpts from books or scripts, or song lyrics, depending on the level of the group.

Finding interesting texts for beginners can be more difficult. You need to reinforce the vocabulary they know rather than give them too many new words, which means it is difficult for them to understand the text. Of course you can use children’s picture books, but if you are teaching teenagers or adults these often won’t be appropriate. And even kids’ books can use a lot of new words. One option if you have a very low level class is to use the English Short Stories for Complete Beginners. This will help them read but won’t give them complicated vocabulary.

Teaching your students to use dictionaries is important at every level. Either a bi-lingual dictionary or a picture dictionary for lower levels, or a single language dictionary for advanced students. Encourage them to look up words they don’t know, and not guess them. If a student looks up a word, they are more likely to remember it, and you can be sure they understand the meaning.

In class, reading can become a little boring. You can make your reading lessons livelier by trying some of the following techniques. Most of them can be adapted to suit any level.

Running dictation

Jigsaw reading

Comprehension questions

Memory quiz

Re-arranging the text

Grammar or vocabulary races

Ask them to find an example of a certain grammar point or a synonym for another word. They raise their hand when they find it.

If you want your students to learn to read English then the most important thing they can do is practice. That isn’t something you can force them to do. Make them aware of where they can get appropriate reading material and you will be giving them the best help you can.

Graph Databases for Beginners: Why Graph Technology Is the Future

Bryce Merkl Sasaki , Editor-in-Chief, Neo4j Jul 12, 2020 6 mins read

The world of graph technology has changed (and is still changing), so we’re rebooting our “Graph Databases for Beginners” series to reflect what’s new in the world of graph tech – while also helping newcomers catch up to speed with the graph paradigm.

So you’ve heard about graph database technology and you want to know what all the buzz is about.

It’s easy to take the perspective of a cynic: They’re just another passing trend – here today, gone tomorrow – right? Isn’t that the way of all tech buzzwords?

Feel free to be suspicious – skeptical even – but leave your cynicism at home. Instead, I’m inviting you on an adventure of a new way of seeing the world.

The graph paradigm goes well beyond databases and application development; it’s a reimagining of what’s possible around the idea of connections. And just like any new problem-solving framework, approaching a challenge from a different dimension often produces an orders-of-magnitude change in possible solutions.

All that to say: Graph technology is a rising tide your development team – and your business – can’t afford to pass up. Graph databases are the future, and even if you’re just a beginner, it’s never too late to get started. Let’s dive in.

In this Graph Databases for Beginners blog series, I’ll take you through the basics of graph technology assuming you have little (or no) background in the space. This week, we’ll walk through basic definitions and why those distinctions matter.

Why You Should Care about Graph Database Technology

When you’re on your own, new tech might be fun to play around with or to use on a personal side project, but when you’re at work, it’s a whole different story.

Professionally, you have to operate in a world of budgets, timelines, corporate standards and competitors. And in that world, the only test for new tech is that it better work damn well (and way better than anything else you already have on hand). Otherwise, the suits will be asking questions.

Graph databases fit that bill, and here’s why:

      Your data volume will definitely increase in the future, but what’s going to increase at an even faster clip is the connections (or relationships) between your data. Big data will definitely get bigger, but connected data will grow exponentially.

    With traditional databases, relationship queries come to a grinding halt as the number and depth of relationships increase. In contrast, graph database performance stays constant even as your data grows year over year.

    With graph databases, your IT and data architecture teams move at the speed of business because the structure and schema of a graph data model flex as your solutions and industry change. Your team doesn’t have to exhaustively model your domain ahead of time (and then exhaustively remodel and migrate the DB after some exec asks for a change); instead, you can add to the existing structure without endangering current functionality.

    With the graph database model, you are the one dictating changes and taking charge; whereas the RDBMS data model dictates it’s requirements to you, forcing you to adapt to its tabular way of seeing the world.

    Developing with graph technology aligns perfectly with today’s agile, test-driven development practices, allowing your graph-database-backed application to evolve with your changing business requirements.

    Your agile team now has a database that keeps up with your daily demands.

What Is a Graph Database? (a Non-Technical Definition)

You don’t need to understand the arcane mathematical wizardry of graph theory in order to understand graph database technology. On the contrary, they’re more intuitive to understand than relational databases (RDBMS).

A graph is composed of two elements: a node and a relationship.

Each node represents an entity (a person, place, thing, category or other piece of data), and each relationship represents how two nodes are associated. For example, the two nodes cake and dessert would have the relationship is a type of pointing from cake to dessert .

Consider another example: Twitter is a perfect example of a graph database connecting 330 million monthly active users.

In the illustration below, we have a small slice of Twitter users represented in a graph database. Each node (labeled User ) belongs to a single person and is connected with relationships describing how each user is connected. As we see below, Peter and Emil follow each other, as do Emil and Johan, but although Johan follows Peter, Peter hasn’t (yet) reciprocated.

Twitter users represented in a graph database model.

If this example makes sense to you, then you’ve already grasped the basics of what makes up a graph database.

How Graph Databases Work (Explained in a Way You Actually Understand)

Unlike other database management systems (DBMS), relationships take first priority in graph databases. In the graph world, connected data is equally (or more) important than individual data points.

This connections-first approach to data means relationships and connections are persisted (and not just temporarily calculated) through every part of the data lifecycle: from idea, to design in a logical model, to implementation in a physical model, to operation using a query language and to persistence within a scalable, reliable database system.

Unlike other database systems, this approach means your application doesn’t have to infer data connections using things like foreign keys or out-of-band processing, like MapReduce.

The result: Your data models are simpler and yet more expressive than the ones you’d produce with relational databases or NoSQL (Not only SQL) stores.

What Makes Graph Databases Unique

A lot of databases have similar characteristics, but graph databases have a few things that make them unique. Here are the two most important properties of graph database technologies that you need to understand:

    • Graph storage
      Some graph databases use native graph storage that is specifically designed to store and manage graphs – from bare metal on up. Other graph technologies use relational, columnar or object-oriented databases as their storage layer. Non-native storage is often slower than a native approach because all of the graph connections have to be translated into a different data model.
    • Graph processing
      Native graph processing (a.k.a. index-free adjacency) is the most efficient means of processing data in a graph because connected nodes physically point to each other in the database. Non-native graph processing engines use other means to process Create, Read, Update or Delete (CRUD) operations that aren’t optimized for handling connected data.

When it comes to current graph database technologies, Neo4j leads the space as the most native when it comes to both graph storage and processing. If you’re interested in learning more about what makes a native graph database different from non-native graph technology (and why it matters), then read the Native vs. Non-Native Graph Technology later in this Beginners series.

Conclusion: Graphs Are in More Places than You Think (They’re Everywhere)

The real world is richly interconnected, and graph databases aim to mimic those sometimes-consistent, sometimes-erratic relationships in an intuitive way. That’s what makes the graph paradigm different than other database models: It maps more realistically to how the human brain maps and processes the world around it.

And once you start seeing graphs of interconnected data in one place (your recommendation engine, for example), you start seeing them in other places too (like your fraud detection efforts or your master data management). Pretty soon, you’ll have the epiphany: graphs are everywhere.

It comes as no surprise then that graph technology is on the rise (but you don’t have to take my word for it).

There’s a good chance your competitors are at least evaluating or exploring the deployment of a graph database, so this is your opportunity to step up your game and join leading companies like

    • Walmart (recommendation engine)
    • eBay (artificial intelligence)
    • Pitney Bowes (master data)
    • NASA (knowledge graph)
    • Other Fortune 500 financial services customers (fraud detection)

That said, it’s a narrow window.

Learn to leverage graph databases today and your business retains the competitive advantage well past tomorrow.

Ready to dive deeper into the world of graph databases? Learn how to apply graph technologies to real-world problems with O’Reilly’s Graph Databases book. Click below to get your free copy of the definitive book on graph databases and your introduction to Neo4j.

How to Read and Use Histograms

The histogram is a useful but often misunderstood tool that your camera provides to help you get the correct exposure on your images.

In this article we’re going to look at how to read it and use it to your advantage to help you do just that. Getting the best exposure (there is not such thing as the “correct” exposure, as it’s all subjective) in camera should be your goal every time you click the shutter. Using these tips should help you increase your success rate.

What is a histogram?

Dictionary definition: A bar graph of a frequency distribution in which the widths of the bars are proportional to the classes into which the variable has been divided and the heights of the bars are proportional to the class frequencies.

HUH?! Anyone else confused? But what does it do? How do you read it? Let’s have a look!

How to read the Histogram

A histogram is a graphical representation of the pixels exposed in your image. The left side of the graph represents the blacks or shadows, the right side represents the highlights or bright areas and the middle section is mid-tones (middle or 18% grey). How high the peaks reach represent the number of pixels in that particular tone. Each tone from 0-255 (o being black and 255 being white) is one pixel wide on the graph, so imagine the histogram as a bar graph all squished together with no spaces between each bar. Have a look at the diagrams below:

What can we learn from this histogram?

There are many things we can learn about an image just by looking at the histogram.

We can tell an image is well exposed if it reaches fully from edge to edge without a space on one side of the graph, and isn’t heavily going up one side or the other. In an ideal world, it should just touch the left and right edges, and not spill up the sides, with a nice arch up in the center. However that doesn’t always apply in every situation, for every scene. Here are a few examples:

This is how an ideal histogram might look, evenly distributed, edge to edge, not up the sides

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This is a histogram for a dark subject, it is not wrong it is just more shifted to the right to represent the tones of the subject. This might be a black cat on the dark pavement.

This is a histogram for a light subject (white cat) with mostly light tones in the scene and few dark areas. See how it is shifted to the right now versus the dark subject. This is correct. If you change your exposure on this to make it in the middle you will have grey cat and not a white one.

When the histogram tells you to adjust your exposure

Gaps on either end indicate you are missing information and your exposure can be shifted safely without losing detail. When your graph is shifted too far in one direction or the other so that it does not even touch the other edge – that means you can safely shift your exposure to cover more of the range of tones. Let’s look!

This graph shows an overexposed image, notice the gap on the left side indicating a lack of any blacks represented. It also means you will lose lots of detail in the white areas that may not be recoverable. In this case shift to give your image less exposure and shoot the scene again.

This one shows the opposite. Now we see a gap on the right side of the graph indicating there are no whites represented so the image will be dark, too dark. You can safely give the image more exposure until you see the graph just touch the right edge of the graph.

What do the spikes up the sides mean?

Spikes up the left or right edge indicate “clipping” of that tone and loss of detail in that area. Clipped areas are often unrecoverable, especially in the highlight area but it is generally advised to expose so you your graph just touches the right edge and keep your highlight details. It is usually easier to recover some shadow detail and retain a decent image, than try and create highlight detail that isn’t there on the file.

In some scenes, however, it may not be possible to keep the graph within an acceptable range. For example, if you are photographing a scene with extreme contrasts such as: a sunset; bright sunlight and deep shadows; or an inside a building where you show outside the windows as well. In all of those cases you will not be able to keep from clipping either your blacks, or whites, or even both.

High contrast graph

This graph shows an image with extreme contrast, lots of blacks, a spike of white and not much in the middle.

Is it wrong? Can you correct for it?

No it’s not wrong. You can’t really “correct” for it but you do have a decision to make when you see something like this. Do you shift the graph left and maintain highlight detail, or shift it right and keep shadow detail?

There is no right or wrong here, it’s how you interpret the scene before you. If in doubt, shoot both and decide later. The graph above comes from the image below, so as you can see it is not the incorrect exposure at all.

There are no mid-tones in this scene.

Here’s another example of a scene that will potentially go off the graph on both ends.

Notice the skylight at the top of the roof is blown out, and the deep shadows have little detail.

Notice in this image the details have been retained in both areas.

Using advanced techniques like image merge/blend, HDR and processing in Lightroom 4 (or PS CS6) you can compress the contrast range of the scene to fit within the histogram and therefore have details in all areas.

In the image above, I’ve used 4 bracketed images (taken 2 stops apart), and the HDR tone mapping process to bring the dynamic range of the scene down within printable range.

One more handy thing on your camera – the “blinkies”

To help you establish how far to go in the image brightening direction, most SLR cameras have a setting called “highlight warning”. It will make any overexposed highlights “flash” or blink when you preview your images on your camera screen. Many people affectionately call this, “the blinkies”.

Notice the flashing areas, that means the highlights are being clipped wherever it is flashing.

To do this on a Nikon, preview an image and press the Up or Down buttons (near the OK button) until you see the highlights flashing or outlined. This is the “highlight mode”. If you choose this setting, the camera will remember your setting for the next image you preview. You may need to activate this feature “highlight warnings” in your settings menu first.

To do this with a Canon press “Display” or “Info” button (depends on your model), until they show up on the screen when previewing images. You also may need to turn on this feature in the menu settings. Check your camera’s manual if you aren’t sure where to find it.


By using the tools your camera provides for you, it is easier to see how to adjust your image exposure. There is a lot more to know about the histogram, and you can use it when you process your images in Photoshop or Lightroom as well. Keep in mind that if you shoot JPG format, nailing the exposure in camera is even more critical. If you shoot RAW format you have some leeway to make adjustments later, but it’s still a better idea to get it right in the first place. If you are still on the fence about shooting RAW perhaps this article will help you decide: “Why shoot in raw format“

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