By Rosaria Silipo, Michael P. Mazanetz

(Requested via me on WCD and crammed as retail via their standard.)

This e-book is the a lot awaited sequel to the introductory textual content “KNIME Beginner’s Luck”. development upon the reader’s first event with KNIME, this e-book provides a few extra complicated gains, like looping, picking out workflow paths, workflow variables, examining and writing info from and to a database, operating R scripts from inside of a workflow, and more.

All new ideas, nodes, and lines are confirmed via labored examples and the discovered wisdom is bolstered with workouts. All instance workflows, workout suggestions, and information units can be found on line.

The target of this e-book is to raise your facts research from a easy exploratory point to a extra professionally prepared and intricate constitution.

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Additional resources for The KNIME Cookbook: Recipes for the Advanced User

Example text

Exercise 3: Configuration window of the “Database Looping” node 45 This copy of the book “The KNIME Cookbook” is licensed to: Darien Rosado Chapter 3. 1. 1). We have three columns of String type (“product”, “date”, and “country”) and two columns of Integer type (“quantity” and “amount”). 1. The data table produced by the “Database_Operations” workflow implemented in chapter 2 Actually, the data column “date” contains the contract date for each record and should be treated as a date/time variable.

Similarly to the SQL query refining nodes and following a “Database Table Connector” node, there are nodes to implement an SQL query to follow a database connector node (red square port). The “Database SQL Executor”, for example, implements and executes an SQL query on the connected database. Its task is similar to the one of the “Database Query” node, besides the fact that the SQL query is physically executed during the node execution. While the “Database Query” produces an SQL statement that gets appended to previous SQL statements after the nod execution, the “Database SQL Executor” node creates the SQL statement and runs it against the selected database during the node execution.

01”. We set these dates as starting and end points respectively in the configuration window of the “Extract Time Window” node and we obtained a data set with 38 sales data rows at the output port, covering the selected 2-year time span. Let’s suppose now that the year 2010 was a troubled year and that we want to analyze the data of this year in more detail. How can we isolate the data rows with sales during 2010? We could convert the “date” column from the DateTime type to the String type and work on it with the string manipulation nodes offered in the “Data Manipulation”-> “Column” category.

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