Cube Time Series Data Collection & Analysis Cube is a system for collecting timestamped events and deriving metrics. By collecting events rather than metrics, Cube lets you compute aggregate statistics post hoc. It also enables richer analysis, such as quantiles and histograms of arbitrary event sets. Cube is built on MongoDB and available under the Apache License on GitHub. Collecting Data An event in Cube is simply a JSON object with a type, time, and arbitrary data. Cube’s collector receives events and saves them to MongoDB. Querying Events Cube defines a simple language for querying events. You can intersect filters and customize which event fields are returned. request(browser).gt(duration, 250).lt(duration, 500) Cube supports both HTTP GET and WebSockets for retrieving events. Querying Metrics You can also use Cube to group events by time, map to derived values, and reduce to aggregate metrics. The first few results of which appear as: sum(request.eq(path, "/search")) sum(request(duration))
Chartkick Simplify your admin dashboard - create new charts in seconds! Works with Rails, Sinatra and most browsers (including IE 6) A perfect companion to groupdate, hightop, and active_median Get handcrafted updates for new features Usage Line chart You must have groupdate installed to use the group_by_day method Pie chart Grape Column chart Bar chart Area chart Geo chart Timeline Multiple series breakfast or Say Goodbye To Timeouts Make your pages load super fast and stop worrying about timeouts. And in your controller, pass the data as JSON. class ChartsController < ApplicationController def completed_tasks render json: Task.group_by_day(:completed_at).count endend Note: This feature requires jQuery or Zepto at the moment. For multiple series, add chart_json at the end. render json: Task.group(:goal_id).group_by_day(:completed_at).count.chart_json Options Id and height Min and max values min defaults to 0 for charts with non-negative values. Colors Stacked columns or bars Discrete axis Global Options Customize the html
Reverse Snowflake Joins-online demo Visualizing huge SQL SELECT film.film_id AS FID, film.title AS title, film.description AS description, category.name AS category, film.rental_rate AS price, film.length AS length, film.rating AS rating, GROUP_CONCAT(CONCAT(actor.first_name, _utf8' ', actor.last_name) SEPARATOR ', ') AS actors FROM category LEFT JOIN film_category ON category.category_id = film_category.category_id LEFT JOIN film ON film_category.film_id = film.film_id JOIN film_actor ON film.film_id = film_actor.film_id JOIN actor ON film_actor.actor_id = actor.actor_id GROUP BY film.film_id; Algorithm (start with neato) Distance between nodes Please try the new version of SnowflakeJoins Powered by: Python , Graphviz , Pyparsing and CherryPy .Logo by Daniel Saarimäki You can get the BSD-licensed code from
2 Related Work Next: 3 H3: 3D Hyperbolic Up: Interactive Visualization of Large Previous: 1 Motivation There are several relevant threads of related work. We have already discussed some of the core information visualization data- and task-based taxonomies in Chapter 1. We begin with the previous work in the deliberate use of distortion to show as much context as possible around a focus point. The bulk of this chapter is a discussion of the many previous systems for drawing graphs and hierarchies, both topologically and geographically. One of the important challenges in a visualization system is how to present as much important information as possible given a finite display area. The disadvantage of simply providing navigation controls is that users often lose track of the position of their current viewport with respect to the global structure. Early work on automatic graph layout and drawing is scattered through the computer science literature [FPF88,WS79,Moe90]. 2.2.1 Geographical Systems
Clarity or Aesthetics? Part 3 – Tips for Achieving Both Previous Post (Part 2 of 3): A Tale of Four Quadrants We started this series by introducing the notion of a two dimensional plane on which to assess all data graphics, and then followed it up with an example of visualizations in four different quadrants on the plane to illustrate the differences between the two axes, clarity and aesthetics, that define the plane. Now, let’s review some of the basic principles & tips that you will find in the data visualization resources out there. Before showing the three tips, I want to make one thing very clear: as the designer of a data graphic, you are not the audience. Without further ado: Tip #1 – First, avoid confusing your audience with the wrong chart type. Tip #2 – Second, avoid horrifying your audience with poor design elements. Tip #3 – Third, incorporate helpful elements to increase both clarity and aesthetics. I’d love to know what you think about these tips, and what you would add, change, or take away. Thanks for stopping by, as always, Ben
Firefox Web Browser — Getting Started with Mozilla Firefox — mozilla.org Welcome to Firefox! We'll show you all the basics to get you up and running. When you're ready to go beyond the basics, check out the other links for features you can explore later. By default, Firefox gives you access to great content every time you open a new tab. Whether you know the exact web address or you're just searching, Firefox's address bar handles it all. Found a great web page? Browse the Internet without saving any information on your computer about which sites and pages you’ve visited. Click the menu button and then click New Private Window. Set up Firefox Accounts so you can take your browsing information with you wherever you go. , choose Sign in to Sync and follow the instructions to create your account. Choose the page that opens when you start Firefox or click the Home button. Open a tab with the web page you want to use as your home page. We've streamlined the toolbar with the most popular features but Firefox has even more features tucked away.