Rosaria Silipo

KNIME Analytics Platform is an open source software used to create and design data science workflows. This book is a comprehensive guide to the KNIME GUI and KNIME deep learning integration, helping you build neural network models without writing any code. It’ll guide you in building simple and complex neural networks through practical and creative solutions for solving real-world data problems.

Starting with an introduction to KNIME Analytics Platform, you’ll get an overview of… Mehr anzeigen

KNIME Analytics Platform is an open source software used to create and design data science workflows. This book is a comprehensive guide to the KNIME GUI and KNIME deep learning integration, helping you build neural network models without writing any code. It’ll guide you in building simple and complex neural networks through practical and creative solutions for solving real-world data problems.

Starting with an introduction to KNIME Analytics Platform, you’ll get an overview of simple feed-forward networks for solving simple classification problems on relatively small datasets. You’ll then move on to build, train, test, and deploy more complex networks, such as autoencoders, recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs). In each chapter, depending on the network and use case, you’ll learn how to prepare data, encode incoming data, and apply best practices.

By the end of this book, you’ll have learned how to design a variety of different neural architectures and will be able to train, test, and deploy the final network.
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Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results.

Substantially… Mehr anzeigen

Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results.

Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included.

Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website.

This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one's exploration of the subject.

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Data blending is a very big part of the sexiest job of the 21st century, including data source blending, data type blending, database blending, time blending , and tool blending. In order to help with all specific blending requests, in November 2016 we started a blog post series with the title “Will they blend?”. Each post faces a blending challenge and offers a solution.

This is the second edition of the e-book “Will They Blend?” The e-book has been expanded and updated, and now… Mehr anzeigen

Data blending is a very big part of the sexiest job of the 21st century, including data source blending, data type blending, database blending, time blending , and tool blending. In order to help with all specific blending requests, in November 2016 we started a blog post series with the title “Will they blend?”. Each post faces a blending challenge and offers a solution.

This is the second edition of the e-book “Will They Blend?” The e-book has been expanded and updated, and now contains 32 chapters describing data blending techniques for more than 50 data sources and external tools, from SQL and noSQL databases to cloud resources, from Sharepoint and SAP to web services and social media, from R and Python scripts to text and images, from MS Word to web crawling. If you want to know if your data source - or format or tool - is covered in the book, just scroll down to the Topic Index. It is probably there. We will keep adding more posts on new data blending options as soon as they become available.

We hope you will enjoy our blending stories as much as we do! No previous knowledge of KNIME is required. Weniger anzeigen

There are many declinations of data science projects: with or without labeled data; stopping at data wrangling or involving machine learning algorithms; predicting classes or predicting numbers; with unevenly distributed classes, with binary classes, or even with no examples of one of the classes; with structured data and with unstructured data; using past samples or just remaining in the present; with real time or close to real time execution requirements and with acceptably slower… Mehr anzeigen

There are many declinations of data science projects: with or without labeled data; stopping at data wrangling or involving machine learning algorithms; predicting classes or predicting numbers; with unevenly distributed classes, with binary classes, or even with no examples of one of the classes; with structured data and with unstructured data; using past samples or just remaining in the present; with real time or close to real time execution requirements and with acceptably slower performances; showing the results in shiny reports or hiding the nitty and gritty behind a neutral IT architecture; and - last but not least - with large budgets or no budget at all.

In the course of my professional life, I have seen many of the above projects and their data science nuances. So much experience - and the inevitably of related mistakes - should not be lost. Therefore the idea of this book: a collection of data science case studies from past projects.

This book includes project reviews from IoT, financial industry, customer intelligence, social media, cybersecurity, and more. Weniger anzeigen

Displaying words on a scatter plot and analyzing their relations is just one of the many analytics tasks you can cover with text processing and text mining. From text cleaning to stemming, from topic detection to sentiment analysis, we have tried to describe the “how to” in this book.
The e-book “From Words To Wisdom”covers text data access, text preprocessing, stemming and lemmatization, enrichment via tagging, keyword extraction, word vectors representation, and finally topic detection… Mehr anzeigen

Displaying words on a scatter plot and analyzing their relations is just one of the many analytics tasks you can cover with text processing and text mining. From text cleaning to stemming, from topic detection to sentiment analysis, we have tried to describe the “how to” in this book.
The e-book “From Words To Wisdom”covers text data access, text preprocessing, stemming and lemmatization, enrichment via tagging, keyword extraction, word vectors representation, and finally topic detection and sentiment analysis.
For example, did you know that you can access pdf files or even epub Kindle files? Did you know that you can remove stop words from a dictionary list? Or stem Finnish words? Or build a word cloud of your preferred politician’s talk? Or build a graph of forum connections? Or use Latent Dirichlet Allocation for automatic topic detection? Or use the Word2Vec neural architecture to embed words?
You will find all this and more in the book “From Words to Wisdom” available at the KNIME Press.
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This whitepaper explores and compares some commonly used techniques for dimensionality reduction: Missing Values, Low Variance Filter, High Correlation Filter, PCA, Random Forests, Backward Feature Elimination, and Forward Feature Construction

Once established that it would be beneficial to integrate some big data processing into a KNIME workflow , the problem has just started.

In this whitepaper we show step-by-step how to integrate a big data platform into a KNIME workflow.
KNIME provides a number of connector nodes to connect to databases in general and to big data platforms in particular through KNIME Big Data Extension. Some connector nodes have been specifically designed for specific big data platforms. These… Mehr anzeigen

Once established that it would be beneficial to integrate some big data processing into a KNIME workflow , the problem has just started.

In this whitepaper we show step-by-step how to integrate a big data platform into a KNIME workflow.
KNIME provides a number of connector nodes to connect to databases in general and to big data platforms in particular through KNIME Big Data Extension. Some connector nodes have been specifically designed for specific big data platforms. These dedicated connectors provide a very simple configuration window requiring only the basic access parameters, such as credentials, for example.

Writing a complex SQL query is not for everybody. For the less expert SQL users, KNIME provides a number of SQL transparent nodes , which enable users to set a function without ever touching the underlying SQL query. These SQL helper nodes and the existence of dedicated connector nodes make the implementation of ETL procedures on a big data platform extremely easy and fast.

They also make it very easy to switch from one big data platform to another, preserving the agility feature of the KNIME Analytics Platform even after the integration of a big data platform
in to the workflow.
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There is an explosion of sensor data becoming available, leading to the term Internet of Things. But
how difficult is it to pull all that data together to use it to make more intelligent decisions?
In this paper, we pull 8 public sensory data sources, transform and enrich them with responses from
external RESTful services mainly from the web in order to create profiles and segments around customers.

We then apply machine learning, time series analysis, geo-localization, and… Mehr anzeigen

There is an explosion of sensor data becoming available, leading to the term Internet of Things. But
how difficult is it to pull all that data together to use it to make more intelligent decisions?
In this paper, we pull 8 public sensory data sources, transform and enrich them with responses from
external RESTful services mainly from the web in order to create profiles and segments around customers.

We then apply machine learning, time series analysis, geo-localization, and network visualization to
take that data and make it actionable. In particular, we optimize the machine learning model size in
terms of the smallest number of required input features, and the parameter values of the time series
auto-regression model.

A few different techniques have been employed in visualization: geo-localization by means of
the KNIME Open Street Map (OSM) integration; route localization using the ggplot R library; and
network graph visualization with the KNIME Network Analytics extension.
Each of these visualization techniques shows a different aspect of the data and of the KNIME open architecture, which makes integration of data and tools very easy.

Data and workflows are available for downloads at http://www.knime.com/white-papers#IoT, while the KNIME open source platform is downloadable from the KNIME site at http://www.knime.org/knime-analytics-platform-sdk-download. Weniger anzeigen

There is so much information hidden in the web log file of a company web site!
In this particular study, we concentrate on the geolocalization of the IP addresses that downloa
d the KNIME open source data analytics platform. The goal is to get an idea of where most of the KNIME users are located in the world, to set up future community events.

First of all, we extract the download data from the Apache web log file of the KNIME web page. W
e restrict these data to the week around… Mehr anzeigen

There is so much information hidden in the web log file of a company web site!
In this particular study, we concentrate on the geolocalization of the IP addresses that downloa
d the KNIME open source data analytics platform. The goal is to get an idea of where most of the KNIME users are located in the world, to set up future community events.

First of all, we extract the download data from the Apache web log file of the KNIME web page. W
e restrict these data to the week around December 6th 2013, when the new version of KNIME
was released. The hypothesis is that, during these days, frequent KNIME users would download
or update to the latest version of the software. The data extracted from the web log file contain the IP addresses that connected to the KNIME web site for download or update.
IP addresses are correlated to geographical locations.

After appending its latitude and longitude coordinates to each IP address, the KNIME Open Street Map integration is used to geolocalize the IP addresses on a world map. The geo-localization of the IP addresses can be performed for all the 7 days or day by day on a map sequence. Such map sequence can then be translated into a movie by means of the KNIME Image Processing extension available from the KNIME Community. Web log reading, geolocalization, and image processing are three very interesting and very common data analytics applications covered in this whitepaper.

All workflows are available on the KNIME EXAMPLES public server in “008_WebAnalytics_and_OpenStreetMap” and the KNIME software can be downloaded from
www.knime.com. Weniger anzeigen

This whitepaper focuses on smart energy data from the Irish Smart Energy Trials. The first goal is to identify a few groups with common electricity behavior to create customized contract offers. The second goal is a reliable prediction of the overall energy consumption using time series prediction techniques.

This whitepaper covers all steps to extract knowledge from a web forum:crawls the forum and downloads the data, calculates some simple statistics, detects the discussed topics, and shows the experts for each topic.

This whitepaper combines the powerfulness of text processing with the social network analytics to better position the users in terms of sentiment and leadership.

This book is the much awaited sequel to the introductory text “KNIME Beginner’s Luck”. Building upon the reader’s first experience with KNIME, this book presents some more advanced features, like looping, selecting workflow paths, workflow variables, reading and writing data from and to a database, running R scripts from inside a workflow, and more.

All new concepts, nodes, and features are demonstrated through worked examples and the learned knowledge is reinforced with exercises. All… Mehr anzeigen

This book is the much awaited sequel to the introductory text “KNIME Beginner’s Luck”. Building upon the reader’s first experience with KNIME, this book presents some more advanced features, like looping, selecting workflow paths, workflow variables, reading and writing data from and to a database, running R scripts from inside a workflow, and more.

All new concepts, nodes, and features are demonstrated through worked examples and the learned knowledge is reinforced with exercises. All example workflows, exercise solutions, and data sets are available on line.
The goal of this book is to elevate your data analysis from a basic exploratory level to a more professionally organized and complex structure. Weniger anzeigen

As a personal experience, I know how difficult it might be to switch from one software tool to another. Even though both tools provide the same functionalities, a change of mind set is needed to discover where and how such functionalities are implemented in the new software tool.

This book is a quick guide to the use of KNIME for users coming from the SAS experience. It is not an introduction to KNIME, since it is assumed that the user is already familiar with the basic concepts of data… Mehr anzeigen

As a personal experience, I know how difficult it might be to switch from one software tool to another. Even though both tools provide the same functionalities, a change of mind set is needed to discover where and how such functionalities are implemented in the new software tool.

This book is a quick guide to the use of KNIME for users coming from the SAS experience. It is not an introduction to KNIME, since it is assumed that the user is already familiar with the basic concepts of data manipulation, analysis, and reporting.
It is more thought as a map of the most commonly used SAS functions into their KNIME equivalents. Weniger anzeigen

This book is born from my lessons on KNIME and KNIME Reporting. It gives a quite detailed overview of the main tools and philosphy of KNIME data analysis platform. The goal is to empower new KNIME users with the necessary knowledge to start analysing, manipulating, and reporting even complex data. No previous knowledge of KNIME is required.

This whitepaper continues the "Usable Customer Intelligence in Social Media" series by clustering the results from the combination of the text mining and the network analytics applied to social media data.