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- Herausgeber: Packt Publishing
- Kategorie: Lebensstil
- Sprache: Englisch
KNIME is an open source data analytics, reporting, and integration platform, which allows you to analyze a small or large amount of data without having to reach out to programming languages like R.
"KNIME Essentials" teaches you all you need to know to start processing your first data sets using KNIME. It covers topics like installation, data processing, and data visualization including the KNIME reporting features. Data processing forms a fundamental part of KNIME, and KNIME Essentials ensures that you are fully comfortable with this aspect of KNIME before showing you how to visualize this data and generate reports.
"KNIME Essentials" guides you through the process of the installation of KNIME through to the generation of reports based on data. The main parts between these two phases are the data processing and the visualization. The KNIME variants of data analysis concepts are introduced, and after the configuration and installation description comes the data processing which has many options to convert or extend it. Visualization makes it easier to get an overview for parts of the data, while reporting offers a way to summarize them in a nice way.
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Seitenzahl: 212
Veröffentlichungsjahr: 2013
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Table of Contents
KNIME Essentials
KNIME Essentials
Copyright © 2013 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: October 2013
Production Reference: 1101013
Published by Packt Publishing Ltd.
Livery Place
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Birmingham B3 2PB, UK.
ISBN 978-1-84969-921-1
www.packtpub.com
Cover Image by Abhishek Pandey (<[email protected]>)
Credits
Author
Gábor Bakos
Reviewers
Thorsten Meinl
Takeshi Nakano
Acquisition Editors
Saleem Ahmed
Edward Gordon
Commissioning Editor
Amit Ghodake
Technical Editors
Iram Malik
Aman Preet Singh
Copy Editors
Gladson Monteiro
Kirti Pai
Mradula Hegde
Sayanee Mukherjee
Project Coordinator
Esha Thakker
Proofreader
Clyde Jenkins
Indexers
Tejal Daruwale
Priya Subramani
Graphics
Ronak Dhruv
Yuvraj Mannari
Production Coordinator
Prachali Bhiwandkar
Cover Work
Prachali Bhiwandkar
About the Author
Gábor Bakos is a programmer and a mathematician, having a few years of experience with KNIME and KNIME node development (HiTS nodes and RapidMiner integration for KNIME).
In Trinity College, Dublin, the author was helping a research group with his data analysis skills (also had the opportunity to improve those), and with the new KNIME node development. When he worked for the evopro Kft. or the Scriptum Informatika Zrt., he was also working on various data analysis software products. He currently works for his own company, Mind Eratosthenes Kft. (www.mind-era.com), where he develops the RapidMiner integration for KNIME (tech.knime.org/community/rapidminer-integration), among other things.
The author would like to thank the reviewers and Packt Publishing for their help in creating this book.
About the Reviewers
Thorsten Meinl is currently a Senior Software Developer at KNIME.com in Zurich. He holds a PhD in Computer Science from the University of Konstanz. He has been working on KNIME for over seven years. His main responsibilities are quality assurance, testing, and the continuous integration infrastructure, as well as managing the KNIME Community Contributions. Besides this, he is also interested in parallel computing and cheminformatics.
Takeshi Nakano is a Senior Research Engineer working for Recruit Technologies Co., Ltd. and leads the Advanced Technology Lab in Japan. He holds a Master's degree from the Nara Institute of Science and Technology (NAIST) in Computer Science. He is the lead author of Hadoop Hacks, a book from O'Reilly Japan, and also the author of Getting Started with Apache Solr, a book from GijutsuHyohron in Japan. He loves to find inspiration for his hobbies (reading, scuba diving, and others).
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Preface
Dear reader, welcome to an intuitive way of data analysis. Using a visual programming language based on dataflows, you can create an easy-to-understand analysis process, while it internally checks signals about some of the common problems. Obviously, any environment that does not help with proper documentation would be destined to fail, but KNIME's success is based not just on its high quality—cross-platform—code, but also on the good description about what it does and how you can use the building blocks.
This book covers the most common tasks that are required during the data preparation and visualization phase of data analysis using KNIME. Because of the size constraints—and to bring the best price/value for those who are already familiar with or not interested in modeling—we have not covered the modeling and machine learning algorithms available for KNIME. If you are already familiar with these algorithms, you will easily get familiar with the options in KNIME, and these are quite obvious to use, so you lose almost nothing. If you have not found time yet to get acquainted with these concepts, we encourage you to first learn for what these procedures are good and when you should use them. There are some good books, courses, and training available—these are the ideal options for learning—but the Wikipedia articles can also give you a basic introduction specific to the algorithm you want to use.
What this book covers
Chapter 1, Installation and Using KNIME, introduces the user interface, the concepts used in the first three chapters, and how you can install and configure KNIME and its extensions.
Chapter 2, Data Preprocessing, covers the most common tasks, so that you can analyze your data, such as loading, transforming, and generating data; it also introduces the powerful regular expressions and some case studies.
Chapter 3, Data Exploration, describes how you can use KNIME to get an overview about your data, how you can visualize them in different forms, or even create publication quality figures.
Chapter 4, Reporting, introduces the KNIME reporting extension with the specific concepts, the user interface, and the basic blocks of reports.
What you need for this book
You only need a KNIME-compatible operating system, which is either a modern Linux, Mac OS X (10.6 or above), or Windows XP or above. The Java runtime is bundled with KNIME, and the first chapter describes how you can download and install KNIME. For this reason, you will need Internet connection too.
Who this book is for
This book is designed to give a good start to the data scientists who are not familiar with KNIME yet. Others, who are not familiar with programming, but need to load and transform their data in an intuitive way might also find this book useful.
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Chapter 1. Installing and Using KNIME
In this chapter, we will go through the installation of KNIME, add some useful extensions, customize the settings, and find out how to use it for basic tasks. You will also be familiarized with the terminology of KNIME, so there's no misunderstanding in the later chapters.
As always, it is a good idea to read the manual of the software you get. You will find a short introduction on KNIME in the file, quickstart.pdf, present in the installation folder. The topics we will cover in the chapter are as follows:
Few words about KNIME
KNIME is an open source (GNU GPL available at http://www.gnu.org/licenses/gpl.html) data analytics platform with a large set of building blocks and third-party tools. You can use it from loading your data to a final report or to predict new values using a previously found model.
KNIME is available in four flavors: Desktop/Professional, Team Space, Server, and Cluster Execution. Only the Desktop version is open source; with a Professional subscription, you will get support for it, and also support the future development of KNIME. We will cover only the open source version. There is also an SDK version for free, but it is intended for use by node developers. Most probably, you will not need it yet.
At the time of writing this book, KNIME Desktop 2.8.0 was the latest version available; all the information presented in this book is based on that version.
Installing KNIME
KNIME is supported by various operating systems on 32-bit and 64-bit x86 Intel-architecture-based platforms. These operating systems are: Windows (from XP to Windows 8 at the time of writing this book) and Linux (most modern Linux operating systems work well with KNIME, Mac OS X (10.6 and above); you can check the list of supported platforms for details at: http://www.eclipse.org/eclipse/development/readme_eclipse_3.7.1.html. It also supports Java 7 on Windows and Linux, so extensions requiring Java 7 can be used too. Unfortunately under Mac OS X, there were some problems with Java 7. So on Mac OS X, the recommended version is Java 6.
There are two ways to install KNIME: an easier way is to unpack the archive you can download from their site, and a bit more complicated way is to install KNIME to an existing Eclipse installation as a plugin. Both have use cases, but the general recommendation is to install it from an archive.
Installation using the archive
We assume you are using the open source version of KNIME, which can be downloaded from the following address (always download the latest version):
http://www.knime.org/knime-desktop-sdk-download
It is not necessary to subscribe to the newsletters, but if you have not done it yet, it might be worth doing it. Some of the newsletters also contain tips for KNIME usage. This is quite infrequent, usually one per month.
The supported operating system versions are 32-bit and 64-bit for Linux and Windows, and 64-bit for Mac OS X.
KNIME for Windows
KNIME is available in an executable file for Windows (in a 7-zip compressed format). You can execute it as a regular user (unless your network administrator blacklists running executable files that are downloaded from the Internet); just double-click on it and in the window that appears, select the destination folder.
Note
On an older version of Windows (7 and older), there is a limitation to the path length; it cannot be longer than 260 characters. KNIME and some extensions can get close to this limit, so it is recommended to install it to a short path. Installing it to Program Files is not recommended.
You do not have to specify the folder name (such as knime), as a folder with the name knime_KNIME version (in our case knime_2.8.0) will be created at the destination address, and it will contain the whole installation. You can have multiple versions installed.
You can start KNIME GUI with the knime.exe executable file from that folder. You can create a shortcut of it on your desktop using the right-click menu by navigating to Send to | Desktop (create shortcut). On its first start, KNIME might ask for permissions to connect to the Internet. This may require administrator rights, but it is usually a good idea to change the firewall settings to let KNIME through.
KNIME for Linux
This file is just a simple tar.gz archive. You can unzip it using a command similar to the one shown as follows:
Alternatively, you can use your favorite archive-handling tool to achieve similar results. The executable you need is named knime. Your window manager's manual might help you create application launchers for this executable if you prefer to have one.
KNIME for Mac OS X
You should drag the dmg file to the Applications place, and if you have Java installed, it should just work. The executable to start is called knime.app from the command line, knime.app/Contents/MacOS/knime.
Troubleshooting
If you have problems installing KNIME, maybe others also had similar problems; please check the FAQ page of KNIME at http://tech.knime.org/faq first. If it does not solve your problem, you should search the forum at http://tech.knime.org/forum; if even that fails to help, ask the experts there.
KNIME terminologies
It is important to share your thoughts and problems using the same terms. This makes it easier to reach your goal, and others will appreciate if it is easy to understand. This section will introduce the main concepts of KNIME.
Organizing your work
In KNIME, you store your files in a workspace. When KNIME starts, you can specify which workspace you want to use. The workspaces are not just for files; they also contain settings and logs. It might be a good idea to set up an empty workspace, and instead of customizing a new one each time, you start a new project; you just copy (extract) it to the place you want to use, and open it with KNIME (or switch to it).
The workspace can contain workflow groups (sometimes referred to as workflow set) or workflows. The groups are like folders in a filesystem that can help organize your workflows. Workflows might be your programs and processes that describe the steps which should be applied to load, analyze, visualize, or transform the data you have, something like an execution plan. Workflows contain the executable parts, which can be edited using the workflow editor, which in turn is similar to a canvas. Both the groups and the workflows might have metadata associated with them, such as the creation date, author, or comments (even the workspace can contain such information).
Workflows might contain nodes, meta nodes, connections, workflow variables (or just flow variables), workflow credentials, and annotations besides the previously introduced metadata.
Workflow credentials is the place where you can store your login name and password for different connections. These are kept safe, but you can access them easily.
Tip
It is safe to share a workflow if you use only the workflow credentials for sensitive information (although the user name will be saved).
Nodes
Each node has a type, which identifies the algorithm associated with the node. You can think of the type as a template; it specifies how to execute for different inputs and parameters, and what should be the result. The nodes are similar to functions (or operators) in programs.
The node types are organized according to the following general types, which specify the color and the shape of the node for easier understanding of workflows. The general types are shown in the following image:
Example representation of different general types of nodes
The nodes are organized in categories; this way, it is easier to find them.
Each node has a node documentation that describes what can be achieved using that type of node, possibly use cases or tips. It also contains information about parameters and possible input ports and output ports. (Sometimes the last two are called inports and outports, or even in-ports and out-ports.)
Parameters are usually single values (for example, filename, column name, text, number, date, and so on) associated with an identifier; although, having an array of texts is also possible. These are the settings that influence the execution of a node. There are other things that can modify the results, such as workflow variables or any other state observable from KNIME.
Node lifecycle
Nodes can have any of the following states:
There are possible warnings in most of the states, which might be important; you can read them by moving the mouse pointer over the triangle sign.
Meta nodes
Meta nodes look like normal nodes at first sight, although they contain other nodes (or meta nodes) inside them. The associated context of the node might give options for special execution. Usually they help to keep your workflow organized and less scary at first sight.
A user-defined meta node
Ports
The ports are where data in some form flows through from one node to another. The most common port type is the data table. These are represented by white triangles. The input ports (where data is expected to get into) are on the left-hand side of the nodes, but the output ports (where the created data comes out) are on the right-hand side of the nodes. You cannot mix and match the different kinds of ports. It is also not allowed to connect a node's output to its input or create circles in the graph of nodes; you have to create a loop if you want to achieve something similar to that.
Note
Currently, all ports in the standard KNIME distribution are presenting the results only when they are ready; although the infrastructure already allows other strategies, such as streaming, where you can view partial results too.
The ports might contain information about the data even if their nodes are not yet executed.
Data tables
These are the most common form of port types. It is similar to an Excel sheet or a data table in the database. Sometimes these are named example set or data frame.
Each data table has a name, a structure (or schema, a table specification), and possibly properties. The structure describes the data present in the table by storing some properties about the columns. In other contexts, columns may be called attributes, variables, or features.
A column can only contain data of a single type (but the types form a hierarchy from the top and can be of any type). Each column has a type, a name, and a position within the table. Besides these, they might also contain further information, for example, statistics about the contained values or color/shape information for visual representation. There is always something in the data tables that looks like a column, even if it is not really a column. This is where the identifiers for the rows are held, that is, the row keys.
There can be
