Curated for content, computing, and digital experience professionals
The term metadata refers to “data about data”, but both uses of “data” in practice are loose, in that they can refer to structured, unstructured, or semi-structured data, and can be descriptive or prescriptive. Metadata can also refer to physical objects.
Metadata is especially useful for creating, managing, publishing, categorizing, searching, and enhancing digital information. See the Wikipedia page on the Dublin Core for a good description.
https://en.wikipedia.org/wiki/Dublin_Core
This term came into widespread use with the emergence of electronic documents, especially after the Web was created. Since then “multi-channel” has grown to mean any number of channels and even an unknown, n + 1, number of channels given the proliferation of devices and applications.
“Omnichannel” or omni-channel publishing became a popular concept among marketers to refer to “all the channels”, including physical and digital, to a customer, and the desire to synchronize all channels and touch points for a good customer experience. Marketers are of course hopeful by nature.
Also see single source publishing, multipurpose publishing, SGML, and XML.
Now more commonly known as machine translation (MT), refers to the the use of software to translate text or speech from one language to another. In the 80s and 90s MT software was rule-based, but in the 2000s statistical analysis and the re-emergence of neural networking and more advanced machine learning techniques have proved to be far more successful.
Digital rights management (DRM) is a class of controversial access control technologies that are used by hardware manufacturers, publishers, copyright holders, and individuals with the intent to limit the use of digital content and devices after sale. DRM is any technology that inhibits uses of digital content that are not desired or intended by the content provider. DRM also includes specific instances of digital works or devices. Companies such as Amazon, AT&T, AOL, Apple Inc.
For an early view of digital rights management and XrML see:
XrML and Emerging Models of Content Development and Distribution
An XML editor is a markup language editor with added functionality to facilitate the editing of XML. This can be done using a plain text editor, with all the code visible, but XML editors have added facilities like tag completion and menus and buttons for tasks that are common in XML editing, based on data supplied with document type definition (DTD) or the XML tree.
Some mixed news about Google for publishers and advertisers in the past few weeks. We’ll start with the not-so-good news about clicks, especially as it turns out, for mobile, detailed by Rand Fishkin…
We’ve passed a milestone in Google’s evolution from search engine to walled-garden. In June of 2019, for the first time, a majority of all browser-based searches on Google resulted in zero-clicks. Read More
Nieman Labs’ Laura Hazard Owen provides some context on the most welcome change Google’s Richard Gingras announced last week. Of course there are questions around what ‘original reporting’ means, for Google and all of us, and we’ll have to see how well Google navigates this fuzziness. Read More
The efficiency and effectiveness of multi-purpose content strategies are well known, as are many techniques for successful implementation. What is not so easy is justifying, assembling, and educating a multi-discipline content team. Content strategist Michael Andrews provides a clear explanation and example of the benefits of multi-purpose content designed by a cross-functional team that is accessible for non-specialists. Read More
Benedict Evans…
We worry about face recognition just as we worried about databases – we worry what happens if they contain bad data and we worry what bad people might do with them … we worry what happens if it [facial recognition] doesn’t work and we worry what happens if it does work.
This comparison turns out to be a familiar and fertile foundation for exploring what can go wrong and what we should do about it.
The article also serves as a subtle and still necessary reminder that face recognition and other machine learning applications are vastly more limited than what ‘AI’ conjures up for many. Read More
A few more links in this issue as we catch up from our August vacation.
The Gilbane Advisor curates content for content management, computing, and digital experience professionals. We focus on strategic technologies. We publish more or less twice a month except for August and December.
Digital asset management (DAM) systems emerged in the 1990s to help publishers and media manage the digitization and retrieval, workflows, and distribution of digital assets such as photographs, videos, animations, and music. Most systems were complex and custom built. Canto and XiNet were early commercial vendors. By the late 1990s most large organizations needed much of the same capability for managing digital brand assets, training materials, and websites, and document management systems and early content management systems didn’t provide much, if any, of the required functionality. The broadening of the market to corporate applications led to additional digital asset management system vendors such as Artesia, North Plains, and MediaBeacon. While larger content management, or digital experience platform, vendors have acquired DAM vendors, the vendor market landscape has continued to grow.
Media asset management (MAM) systems are a type DAM system, but typically focused more on audio visual production.
Also see: https://gilbane.com/category/content-management-strategy/
An XML database a data persistence software system that allows data to be stored in XML format. These data can then be queried, exported and serialized into the desired format. XML databases are usually associated with document-oriented databases, and are a type of NoSQL database.
Relational databases and full-text search mechanisms that have been the backbone of many applications are not designed to manage XML content effectively. A new class of databases has emerged that is designed specifically to manage XML content. Typically called “XML Native Databases” or just “XML databases,” they incorporate functionality that greatly improves the management, searching, and manipulation of XML to produce the most effective XML data management solution.
The World Wide Web Consortium (W3C), the standards organization that developed XML, has also developed many standards that can be used to access, search, process, and store XML data. XML databases take advantage of these standards to provide efficient and precise access, query, storage, and processing capabilities not found in traditional database technology. The result is that applications using XML databases are more efficient and better suited for managing XML data.
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