Learn XPath 3.1 with Altova Online Training


We’ve recently updated our free, online XPath Training course to include information on what’s new in XPath 3.1, which is the latest version of the XML Path Language.

XPath 3.1 adds vital new functionality, including support for arrays and maps, functions for processing JSON data, and a collection of new operators and functions.

XPath Training

The XPath Training course covers these in detail, explaining each new function and operator and illustrating its use with helpful examples.  The course also provides sample XML files that you can download for hands-on practice as you progress through the chapters.

Access the free XPath 3.0 and 3.1 Training now!

Or check out our other free online course offerings, including XMLSpy Training, XML Schema 1.1 Training, and more.

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New Support for XPath/XQuery 3.1, WS-Security, and More Debut in R3


Release 3 of the Version 2015 Altova MissionKit desktop developer tools and Altova server software products is now available with updated standards support, customer requested features, and innovative new functionality that will save you time on everything from XPath and XQuery development, to advanced data mapping, to XBRL report rendering and beyond. You can skip right to the good stuff and upgrade now, or click Read More below for all the details.
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Now Available: MobileTogether 1.4 with Support for Geolocation, Android 5, iOS 8, and Much More


We are pleased announce version 1.4 of MobileTogether, Altova’s multi-platform mobile development framework that empowers organizations of any size to quickly design and deploy custom enterprise mobile solutions that are accessible on any mobile device.

New features in Altova MobileTogether 1.4

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Altova Software Version 2015 Now Available


We are pleased to announce availability of Version 2015 of Altova’s MissionKit desktop developer tools and server software products! In this latest version, you’ll find updated standards support across the product line, powerful new support for XQuery Update Facility and previewing the layout of XBRL Tables in real-time in XMLSpy, data mapping support for JSON and XBRL Table Linkbases in MapForce, support for HTML fragments in StyleVision, and much more.

Read on for more details below, and visit the What’s New page for more information.

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New XBRL Tools for Compliance and Beyond


It can be hard to keep up with all the exciting developments in the XBRL space. Standards are constantly evolving to make compliance easier and deepen the benefits provided by XBRL for internal analysis and reporting. As a leader in XBRL development and validation, Altova is committed to supporting the latest standards, coupling strict conformance with high-performance, easy to use tools. With the latest product launch, Version 2014 Release 2, we’ve added some important enhancements, including significant increases in validation speeds, XBRL Table Linkbase support, and extended XBRL Formula tools. Below is a portion of an XBRL Table generated in XMLSpy, using XBRL Table Linkbase to render revenue calculations in new and productive ways:

XBRL Table

Let’s take a look at the benefits of these new features for your XBRL implementation.

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Editing, Converting, and Generating JSON


As the use of JSON as a data transport protocol increases, I thought it would be useful to take a look at JSON support in XMLSpy. There’s been much debate about advantages of JSON vs. XML, but when you boil it down, there are simply some cases for which JSON is the best choice, and others where XML makes more sense. This article on the XML Aficionado blog unpacks this topic quite well.
While you might need to choose between JSON and XML depending on the development task at hand, you don’t have to choose between code editors – XMLSpy supports both technologies and will even convert between the two. Let’s take a look at how that works.
Editing JSON
To make JSON editing as easy as possible, Altova extended its intelligent XML editing features to the JSON editor. If you choose to edit JSON in text view, XMLSpy helps you along with syntax coloring, bracket matching, source folding, entry helper windows and menus, and so on.

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Here’s that same JSON file in grid view, which offers a graphical representation of the JSON structure with drag and drop editing. Both views provide JSON syntax checking and advanced error checking features.

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Another common requirement is converting XML to/from JSON, which is a one-click option on the XMLSpy convert menu. The JSON data above has been converted to valid XML:

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Having JSON editing and conversion functionality directly inside the XML editor is quite useful, especially if you’re using the free Eclipse or Visual Studio integration package.
Check it out and let us know what you think.

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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.TweetsQueryingSearchAPIIn 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. One of the benefits of this abundance of data available to us today is that we can query it in interesting ways and extract new meaning from it. While there are undoubtedly many existing services that already provide trends over Twitter topics (e.g., Trendistic), those services only offer very simple trends and do not allow us to query any deeper.But all of the underlying data is available for grabs if you are just willing to learn a tiny bit about web service APIs and how to use them to extract XML data for further processing. As a starting point, let’s use the Twitter Search API to query the stream of recent tweets for the last 100 postings that are about the “Galaxy Nexus”. The Usage Guidelines for Twitter Search tell us that using both words in a query will result in the use of the default operator, which is AND, so we are going to search for posts that contain “Galaxy AND Nexus”. So let’s try that and request the most recent 100 items:

http://search.twitter.com/search.atom?q=galaxy+nexus&rpp=100

If you follow this link, you will get a second window with a lot of raw XML data that is formatted according to the Atom Syndication Format specifications. Alternatively, you could request the data in JSON format, if you wanted to directly process it via JavaScript code by hand, but we will use the XML-based Atom format so that we can easily analyze the data and extract the information we want.Viewing the above search result in a browser is not very user-friendly, so we can take a quick peek at the XML data in our favorite XML Editor using the Open from URL function:TweetsAtomGridAs you can see, the data for each entry includes a language code, so for this example we will extract data from this Twitter feed as well as from a second search result on the “iPhone 4S” and combine them into one intermediate XML file for further analysis.Extracting XML data is really easy in MapForce: using the “Insert XML File” option to drop in an XML source, we can again specify the same URL as before. If needed, MapForce will automatically create an XML Schema for the supplied data so we can visualize it and extract information from it:TweetAtomMappingIn our mapping we have dropped in two sources on the left side – one using a query string to search for “Galaxy Nexus” and the other to search for “iPhone 4S” – and on the right side we have dropped in a simple XML Schema that will allow us to aggregate our data and analyze it more conveniently going forward. In this case the mapping between the two sides is straight-forward as we are only extracting basic information about the user, the date, and the language of the tweet, but in other applications the mapping could be more complicated and include functions as well as queries to other data sources, databases, or web services…Previewing the resulting XML data can be done directly inside MapForce using the output tab, and this is what we see as a result of our data transformation:TweetsRawDataNow we can easily use the reporting capabilities of StyleVision to group this data by language within each topic and count the number of posts in each language. We can then report this data in the form of pie charts, which produces the following interesting results:TweetsByLanguageObviously, this data is highly dependent on the date of execution and time of day, as well as the particular announcements happening about these products, so the numbers will fluctuate quite a bit, but it can be used as a nice monitoring for seeing different language-specific trends. And once this has been set up, the report can be refreshed easily with the click of a button to get a snapshot at that point in time. For more long-term analysis it would of course be necessary to modify the mapping a bit to query more than 100 recent tweets.In this article we have used Twitter’s Search API as one example data source and only looked at language as one unique data point, but there are many more interesting sources of data available online today, and this approach can be used on all of them in a similar fashion.If you want to experiment with other data sources and other kinds of information that you want to extract, we invite you to try for yourself. A free 30-day evaluation version of MapForce is available, and there are no limits on how you can use the other features of Altova’s data mapping and conversion tool for data processing tasks that go beyond analyzing social media trends…

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