Intuitive User Interface Features

Here at Altova we like to share user interface features across products to give the MissionKit a consistent look and feel, and to help users leverage experience gained using one tool to get up to speed more quickly with the others.

In version 2012 we introduced new visual alignment guides in both UModel and MapForce to make it easy to work very rapidly, yet still produce a neat, organized result that communicates effectively with other team members.

As users drag elements in the diagram window, alignment snap lines appear automatically to allow any component to align with any other component.

Get More Mileage from Your StyleVision Designs with Dynamic Selection of CSS Files

One of the great things about StyleVision is the flexibility that it affords. With StyleVision you can design visually sophisticated stylesheets and reports for output to multiple formats including HTML, PDF, and Authentic electronic forms. Now StyleVision makes it even easier to accommodate different formatting needs in a single design with dynamic selection of CSS files – a new feature introduced in StyleVision 2012.

Although you can still manage virtually all formatting tasks from the StyleVision design itself, dynamic selection of CSS files means that you can create a single template for use in multiple situations. To demonstrate, we’ve created an invoice for the fictional Nanonull Corporation for which the design changes based on the number of days that have elapsed between the invoice issue date and the due date. Although we could have used XPath to change the formatting of each element based on the number of days that have elapsed, in this case it is more efficient to create individual CSS files that apply to different scenarios.

XML Editor Supports HTML5 and CSS3

The Altova MissionKit Web tools received an important update with our recent Version 2012 release: support for HTML5 and CSS3. You’ll find support updated to include the most recent versions of these Web standards in both XMLSpy 2012 for code editing and StyleVision 2012 for graphical stylesheet and report design.

Let’s focus on the HTML5 and CSS3 editors in XMLSpy here – StyleVision functionality will be covered in a future article.

New in StyleVision 2012 – Composite Styles for Enhanced Formatting

StyleVision is an intuitive stylesheet and report designer that transforms XML, XBRL, and database content into HTML, RTF, PDF, Word 2007+, and Authentic electronic forms – all from a single design. With the 2012 release (available for download here), StyleVision now offers even more formatting control so that you can design even more visually sophisticated output.

One of the more versatile features introduced in StyleVision 2012 is support for composite styles for output to RTF, Word, and PDF as well as HTML and Authentic electronic forms. This feature allows you to combine styles defined in an XML instance document with those you set in the report itself. You can also build a composite with XPath to control multiple style features of the same design element (e.g., font, background color, alignment).

Support for composite styles means that you can quickly and easily change the look of a design component by selecting an attribute from the XML instance file or by changing the XPath expression. This feature is used most often in cases where the XML document includes HTML fragments that contain style information – now you can use an XPath selector to extract style information from the HTML fragment and apply it directly to an output document.

Announcing Altova FlowForce® Server Beta 1

Altova FlowForce® Server Beta 1 is an exciting new tool for execution of automated data mappings designed to provide comprehensive management and control over data transformations performed by dedicated high-speed servers, virtual machines, or even regular workstations, depending on the size of the task.

To gather user input and accelerate product development, Altova is offering Altova FlowForce Server Beta 1 as a free public beta test to all licensed users of Altova MapForce® 2012 Enterprise Edition and MapForce® 2012 Professional Edition.

Users employ Altova MapForce data mappings for two different types of data transformations:

• One-time data conversions
• Data transformations that are repeated by date or time, when new source data is available, or based on some other external event

While command-line execution, royalty-free code generation, and the MapForce API can assist with automation of repeated transformations, FlowForce Server Beta 1 provides much greater power and flexibility. FlowForce Server Beta 1 is a server-based tool with a Web interface that makes it much easier to implement, manage, or modify data transformation jobs in a busy data processing environment.

FlowForce Server Beta 1 can administer multiple transformation jobs simultaneously, lets users define and adjust a variety of job triggers and actions on the fly, can perform housekeeping tasks like moving output files or cleaning up intermediate work, records detailed logs of all activity, and much more.

FlowForce Server Beta 1 consists of four components that work together as illustrated in the diagram below.

The FlowForce Server continuously checks for trigger conditions, starts and monitors job execution, and writes detailed logs.

MapForce Server is an implementation of the MapForce Built-in execution engine that executes mapping packages previously deployed via the MapForce graphical environment.

The FlowForce Web Administration Interface is a standalone web application that runs in an internet browser and provides the front-end of FlowForce Server.

MapForce Beta is an enhanced version of the Altova MapForce application with an integrated deployment feature to deploy MapForce data mappings to a FlowForce server package.

Altova FlowForce Server Beta 1 is available immediately as a free public beta test to all licensed users of Altova MapForce® 2012 Enterprise Edition and MapForce® 2012 Professional Edition.

Visit the FlowForce Server Beta 1 page at the Altova Web site for more information!

Processing the Groupon API – Epilogue

Rare edge cases can derail loosely coupled data mapping applications. This is especially true when you are consuming large datasets available over the Internet and have little or no influence over the source data.

In this article we describe a debugging technique that lets developers working on data mapping and transformation projects quickly identify and accommodate unexpected data in a stream from a remote source.

The Problem

Last summer we wrote a series of blog posts describing how to work with the Groupon API to retrieve a subset of offers in all Groupon cities and format the list for a web browser or mobile device.

We concluded with a command line to run a MapForce data mapping that calls the Groupon API over 150 times -- once for each Groupon city, then filters the data to extract deals sold on the Internet instead of a physical location, and formats the results in HTML using StyleVision. Every morning we run the command line in a batch file that saves the HTML output on a local server so our colleagues can check it out with any Web browser to find interesting offers from all over the country.

The mapping ran fine for more than two months until one day it failed with this error message: “Source-value “” of type dateTime could not be converted into target-type dateTime.”

Analyze Football Statistics using the Altova MissionKit

In this article we use stats from NFL.com and ESPN.com to show how easy it can be to process and analyze online data in new ways – even when it uses different metrics and is only available in textual format.

We have seen in previous blog posts how easy it is to gather data from the Internet that is widely available in XML formats. But what about interesting data that is available online but not in an XML format, or data that is buried in legacy data processing systems and only available in textual report format?

One such example involves quarterback ratings. The NFL has used a Passer Rating that rates quarterbacks solely based on a passer’s completions, attempts, touchdowns, and interceptions. ESPN introduced a new rating system this year called the Total QBR (Quarterback Rating). The Total QBR incorporates more data, including an expected points average and a clutch play index, that ESPN claims gives a more accurate measure of a quarterback’s performance.

Let’s compare the rankings that these system produce to see if we can garner some useful information. For this example we’ll be using the data importing and analysis tools of the Altova MissionKit to compare the ratings. If you want to try this out yourself, the MissionKit is available to download for a 30 day free trial from the Altova web site. You can access the files used in this example here.

The first thing we need is the raw data to analyze. Let’s use the entire 2010 season as a data source. We can get the table with Passer Ratings from NFL.com and then copy and paste it as a new text file.

We can access a similar table of Total Quarterback Ratings from the ESPN web site and create a second text file.

We now have two text files with tables of data in different orders. The next step is to combine the tables into one file and generate charts.

Mastering Paid Keywords

Anyone who manages paid keyword search knows it is hard work! You can look at vast reports of raw statistics and quickly get lost in trivia. At Altova we designed a better way to analyze and manage the performance data for our Google Adwords campaigns. We can creatively query the numbers to:

· Quickly aggregate results for subcategories of campaigns, for instance by product, geographical region, or any other grouping

· Easily identify trends over time

The chart below illustrates these advantages by collecting data for a single Altova product – SemanticWorks – from multiple campaigns over six individual months.

Starting Out

Like many keyword advertisers, we were viewing statistics in Adwords, downloading CSV files, then spending hours massaging and manipulating the data in spreadsheets to identify and format the information we required.

We wanted more immediate and in-depth reporting of keyword performance while retaining full control of the process and managing everything internally. SQL queries of a database of keyword statistics offer a powerful and flexible alternative.

In the remainder of this post we explain how the database design, data mapping, and reporting features of the Altova MissionKit can be applied to create an architecture to efficiently track paid keyword performance.

DiffDog Takes to the Cloud

Techy folks generally have a good diff tool they rely on to compare and sync files and directories. But what happens when, as more and more info is bound for the cloud, your data lives on servers accessed via URL?

There are myriad applications today that live on servers accessed via HTPP – but let’s take a look at a common example: SVN. Subversion (SVN) repositories include WebDAV as a commonly used server option. WebDAV is a natural protocol for SVN because its concern is hierarchy, structured metadata, and versions. Since WebDAV is an extension of HTTP it gives easy access to basic information about files and folders to any HTTP-aware client, including DiffDog – Altova’s diff/merge tool for files, directories, and databases. However, DiffDog knows a few tricks that set it apart from the other breeds.

Digging deeper with the Twitter API: iPhone 4S vs. Galaxy Nexus

We found some interesting data when we dug below the surface of the iPhone 4S vs. Galaxy Nexus debate using the Twitter Search API.

In today’s world there is a vast quantity of data available online that can be used for research, market analysis, and competitive intelligence. While “Big Data” can be a problem for those who produce it, store it, and compile it, it is highly beneficial for those of us who are looking for answers.

Some of that data is fortunately available to be queried online, and, in particular, there is a vast quantity of data on social media interactions out there.

In this article we will explore how to use the Twitter Search API from MapForce, Altova’s data mapping/conversion/integration tool, to aggregate data on recent user submissions (“tweets”) on two highly popular topics – the Apple “iPhone 4S” vs. the “Galaxy Nexus” as the latest hot Android phone – and extract some statistical data about the users engaged in those discussions.