Key Emerging Technology Trends for Media Professionals
by Tom Mulally
by Tom Mulally
The ever-expanding digital infrastructure and the clockspeed of technological change are continually transforming the way media professionals design, develop, produce and distribute content. How does one keep up? What technologies should media professionals be focused on? Which are most likely to gain traction and impact how you work? Are you prepared to deal with the relentless “gales of change” that are upon us? This paper examines six key emerging trends that affect the way that you, your colleagues, and the general public work, play, and in some cases, think.
The emerging trends we will examine are:
· The Semantic Web and related technologies
· Managing Unstructured Content
· Leveraging the Cloud
· Context aware devices and Augmented Reality
· Social Networking/Social Media and related
· Open Ended Learning and Knowledge Transfer
This paper applies to anyone who has an interest in media design, production and delivery. It is especially relevant to professional designers, producers, editors, writers, web developers, programmers, or most anyone who, in the course of doing their work, uses digital images and sound.
A number of applications and processes profiled in this paper are commercially available, or in various stages of research and development. The objective is to provide a look at what’s on the horizon. This includes the author’s proposal later in this paper for a media production workflow that consolidates and leverages these key technologies.
If you had to pick one technological trend that will have the greatest impact on all areas of a media professional’s workflow, it’s the Semantic Web and related technologies. Ignore semantic technologies, and you’re ignoring the leading edge. “The semantic web leads to possibilities straight from science fiction” (Siegel 139).
The term “Semantic Web” was coined by World Wide Web inventor Tim Berners-Lee. In a 2001 Scientific American article (Hendler, Berners-Lee and Lassila) the authors stated “The Semantic Web... will have uses we haven't dreamed of. It will break out of the virtual realm and extend into our physical world.”
In computer jargon, the semantic web is a group of methods and technologies that allow machines to understand the meaning - or "semantics" - of information on the World Wide Web.1 Semantic web technologies often act behind the scenes, utilizing ontologies2, taxonomies3, metadata4 and special controlled vocabularies5 to create a rich, contextual user experience.
“This technology makes all your business data look like a high-powered database — regardless of whether that data is a document on an employee’s hard drive, an existing database, or a repository of many documents in any format — without having to centralize any of that original data into one place” (Pollock 52).
“Most large enterprise software vendors, and many small ones, have already begun to adopt Semantic Web technologies and embed them into their mainstream products. In fact, leading enterprise software vendors such as HP, IBM, Microsoft, Oracle, SAP, and SoftwareAG all currently provide applications and tools that support Semantic Web specifications” (63).
Google CEO Eric Schmidt refers to the semantic web as “autonomous search,” and calls it “the next great stage of search” (Andrews, Voice). Google’s recent acquisitions of MetaWeb and Freebase confirm their commitment to developing semantic search. It will impact every field, from retail distribution to electronic health records management. It is a ubiquitous thread running through all of the emerging trends, and all facets of the digital media workflow.
TripIt (www.trip.com) provides an example of how the Semantic Web is developing. It’s an application that aggregates your airline travel, rentals, hotel information, and travel data from any website you might have booked it from. It then consolidates and organizes it into an itinerary, and syncs it with your calendar, contacts, and other productivity apps. It also has a suite of context aware smartphone tools (Emerging Trend #4) for the user to keep organized and in contact with others throughout the trip.
Figure 1: “TripIt mobile app, and Semantic Web application screen shot. http://www.tripit.com/press/feature-screenshots/
“The semantic web reveals relationships between concepts, people, and events that are embedded in the wealth of content on the web but not always easy to see using other means” (Johnson et al. 7). An example of how these semantic relationships can work is Apture (www.apture.com), a free semantic application enabling readers to get rich content without leaving their current web page. Figure 2 shows how, when you highlight text on an Apture enabled page, linked content pops-up giving you additional layers of contextual data... text, graphics, and videos.
Figure 2: Highlighting text on an Apture enabled web page. www.apture.com
Apture is but one example of how media professionals will be able to leverage the capabilities of semantic search, to add deeper context to content, and provide new ways to present a richer media experience.
ACTIVE Knowledge Powered Enterprise
What do enterprise-level semantic technologies look like? You can get a look and feel at a semantic initiative by the European Union’s “ACTIVE Knowledge-Powered Enterprise” program (http://www.active-project.eu/).
ACTIVE is a consortium of twelve partner organizations from seven different European countries, co-ordinated by British Telecommunications. The intent of the consortium is to increase productivity of knowledge workers using semantic tools to “convert the ‘hidden intelligence’ of enterprises (Emerging Trend #6) —into transferable, interoperable and actionable knowledge to support collaboration and enable problem solving” (ACTIVE).
According to the ACTIVE web site, (http://www.active-project.eu/about-active.html) they have three integrated research themes:
· Easier sharing of information through a combination of formal techniques based on ontologies and informal techniques based on user tags6—so called folksonomies7.
· Sharing and reusing informal knowledge processes—by learning those knowledge processes from the user’s behavior.
· Understanding the user’s context--- so as to tailor the information presented to the user to fit the current task.
The ACTIVE “knowledge workspace” is designed to run in the background behind common applications, stays with you as you switch tasks, and is “working even when you’re not.” As an example, modules were developed for specific tasks, such as context-based knowledge management tools for Accenture’s proposal development processes, and the “ACTIVated Design Framework” for Cadence (Accenture and Cadence are among several corporate sponsors). In the case for Accenture, what these modules do is increase the efficiency of the normally time and labor-intensive process of creating proposals. ACTIVE improves collaboration while allowing proposal managers to allocate, track, and manage the work of development teams (Djordjevic, and others).
Similarly, specific modules and toolsets could be developed for the content production process. The way these might work is described later in the section “Concept for a Next Generation Content Production Process,” and shown in Figure 10.
The ACTIVE application is available for non-commercial research and demonstrations here: http://www.active-project.eu/publications/knowledge-workspace-software.html
Another example of a semantic application is a Semantic Wiki8. Traditional Wikis, such as Wikipedia have proven effective as collaboration tools in workgroup environments. Media production professionals regularly use wikis during projects for basic knowledge management tasks. However a semantic wiki goes further, using knowledge modeling and the ability to capture or identify information and relationships about the data within pages in ways that can then be queried or exported (Kamel Boulos).
Semantic MediaWiki9 (SMW) is an extension of MediaWiki, which is the wiki application that powers Wikipedia. SMW is currently in active use in hundreds of sites, in many languages around the world, including Fortune 500 companies.
Now developers have combined semantic wikis with social media applications. KiWi (Knowledge in a Wiki) another project in the EU 7th Framework Programme, is an open source development platform for building semantic social media applications. They have developed a “web-based environment (the “KiWi system”) that provides support for knowledge sharing, knowledge creation, and coordination in software and project knowledge management” (KiWi). Documentation, video demos, and the KiWi applications are available at the project site: http://kiwi-community.eu
Emerging Trend #2: Managing Unstructured Content
Viewing and documenting hours of video content is often mind-numbing drudgery that can take hours, sometimes days of valuable production and archiving time. One of the goals for applying semantic technologies and analytics to video production is the ability to make sense of this unstructured content... video, audio, images... content that often does not come with readable text or metadata already attached to its essence. Automatic tagging and creation of metadata for such content would be especially helpful to archivists, news editors, as well as consumers who have many hours of video content, but little idea of what’s there. Foreign language audio further complicates the management of unstructured video content. How can we possibly manage and make sense of this growing flood of video content?
IBM has addressed this head-on, and is on the forefront in developing systems that can “automatically index, classify and search large collections of digital images and videos” (IBM). The IBM Multimedia Analysis and Retrieval System (IMARS)10 works by applying computer-based algorithms that analyze visual features of the images and videos, and subsequently allows them to be automatically organized and searched based on their visual content.
In addition to search and browse features, IMARS also:
· Automatically identifies, and optionally removes, exact duplicates from large collections of images and videos
· Automatically identifies near-duplicates
· Automatically clusters images into groups of similar images based on visual content
· Automatically classifies images and videos as belonging or not to a pre-defined set (taxonomy) of semantic categories (such as ‘Landmark’, ‘Infant’, etc.)
· Performs content-based retrieval to search for similar images based on one or more query images
· Tags images to create user defined categories within the collection
· Performs text based and metadata based searches (IBM site).
IBM IMARS is but one of several initiatives in their larger Unstructured Information Management Architecture (UMIA) program.
IBM TALES (Translingual Automatic Language Exploitation System) is a UIMA-based system which performs multimedia mining and translation of broadcast news and news Web sites. For broadcast video news, TALES performs video capture, keyframe extraction, automatic speech-to-text conversion, machine translation of the foreign text to English, and information extraction. Figure 3 below shows how English speakers can monitor the translated news in near real time, or place English language queries over the stored foreign language content, and get results, both video segments as well as web pages from any of the supported languages all translated into English into a single search result page. TALES has been deployed for several IBM customers, using it for monitoring Arabic, Chinese, and English broadcast news sources.
Figure 3: TALES performs automatic multimedia mining and translation of broadcast news and news Web sites
Figure 4: Screen shot of “Abstraction Framework for Story-Oriented Video” Korea Advanced Institute of Science and Technology and Korean Broadcasting. http://nclab.kaist.ac.kr/papers/Journal/narrativebasedabstrationframework.pdf
The Salzburg Media Lab has been working for several years to develop media platforms that provide “meaning-based management” (Burger) of digital audio-visual archives, especially unstructured video content. They call this next generation of video search a “semantic turn in rich media analysis,” and have developed systems that are hybrids for doing both semi-automatic and automatic “semantic enrichment” of content
One such system, called the “Smart Content Factory” is designed to use automatic feature extraction, including speech-to-text transcription combined with ontologies for semantic search within their domain. Their second system called LIVE, “combines methods of both automatic and semi-automatic detection, extraction and annotation of content with a knowledge-base under the control of a semantic based media framework.” The framework “propagates knowledge and contextual information to a recommender system which thus to some degree becomes aware of the meaning of the media” (Burger). Salzburg Research incorporates third party systems such as Virage Video Logger and Smart Encoding.
Emerging Trend #3: Leveraging The Cloud
Once you get past the frequent hype and misinformation, you’ll find that cloud computing is in fact a long running trend that “radically simplifies how you deploy, maintain, and access software, platforms, and infrastructure” (LiveOps).
“Basically, cloud computing means obtaining computing resources—processing, storage, messaging, databases and so on—from anyplace outside your own four walls, and paying for only what you use” (Fitzgerald, 2008). Media professionals stand to benefit on all levels, because everything you need is available through the Internet as a service, from concept development through final distribution of content. The cloud, especially when combined with the proliferation of 4G and other high-speed wireless services, is a key enabler for the digital infrastructure.
The “cloud” concept is not new. Datacenters, remote storage and remote computing have existed for years. What is new is “the way high-speed Internet access and almost limitless supplies of storage and processing power can now be pulled together” (Fitzgerald). A Pew Research study predicts that over the next ten years, most people will use cloud apps daily, sharing and accessing information over networks (Pew). Gartner predicts that in 2013 the cloud services market will reach $150 billion (Gartner).
Cloud computing is the convergence of three major trends. Virtualization, where applications are separated from infrastructure. Utility computing, where server capacity is accessed across a grid as a variably priced shared service, and Software as a Service (SaaS) where applications are available on demand on a subscription basis” (rPath).
Figure 6: “Cloud” resources for a typical video post-production workgroup configuration (Mulally)
The cloud enables media professionals to access and share data and applications from virtually anywhere in the world (given the limitations of network speed). As shown in Figure 6 above, a single content creator has connectivity to diverse resources that once required huge investments, or weren’t available at all.
Cloud services are breaking down the last barriers-to-entry for anyone to achieve their potential as a professional content creator or distributor. A small, independent production group can rent cloud storage, applications, and rendering resources on an as-needed, pay-as-you-go basis. This is a sea change from the traditional model of the production business in a bricks-and-mortar facility, heavily leveraged from technology purchases and leases, with full time engineers and I.T. specialists to maintain the operations.
One case of a media-related company that has successfully leveraged the cloud is Animoto (www.animoto.com). As profiled in a May 28, 2008 New York Times article about Cloud Computing by Michael Fitzgerald, Animoto has created what co-founder Brad Jefferson calls “an on-demand video platform.” Since 2006 Animoto has provided a service whereby you can turn your event photos and videos into user-generated, automated-yet-customizable videos. The Animoto interface lets consumers, businesses and educational users upload photos and videos, then select from royalty-free music provided through a partnership with music licensing and marketing firm Rumblefish.
Figure 7: Animoto iPhone application screen shots. http://itunes.apple.com/us/app/animoto-videos/
Animoto also has an iPhone app (Figure 7) for users to produce professional looking videos using photos on their devices. In 2008, “Facebook users went into a small frenzy over the application, and Animoto had nearly 750,000 people sign up in three days. To satisfy that leap in demand with servers, the company would have needed to multiply its server capacity nearly 100-fold. Instead, they added capacity using Amazon’s cloud, at a cost of 10 cents a server per hours, with additional expenses for bandwidth and storage. When demand slowed, Animoto automatically lowered its server use, and its bill” (Fitzgerald).
CitizenGlobal (http://www.citizenglobal.com/) uses the cloud and crowd sourcing11 to provide a global exchange for video and film professionals to directly connect their content with the people and places that require it most. They provide a central hub where global creators can upload full-resolution broadcast-quality footage (not the heavily compressed, consumer grade video on YouTube).
Figure 8: CitizenGlobal website http://www.citizenglobal.com/
Oprah Winfrey Show’s “No Phone Zone” awareness campaign to reduce distracted driving has used Citizen Global. The show leveraged the Citizen Global online studio suite, putting tools directly into the hands of creators to edit “No Phone Zone” Public Service Announcements. Audience members were able to upload their own footage, then create their own custom PSA by mixing it with media assets from The Oprah Winfrey Show's media library using CitizenGlobal's in-browser online editor. By leveraging cloud technologies, CitizenGlobal has created a new means for doing video post-production and content distribution.
The cloud-based business model is still in its early adopter stage, with kinks being worked out. And there are risks that need to be mitigated, especially related to security. But large cloud services innovators such as Salesforce.com and Rackspace have proven that the concept and business model is viable. And many of the large I.T. service providers now offer cloud services... IBM, EMC, SAP and Oracle among them.
Emerging Trend #4: Context Aware Technologies, and Augmented Reality
Many consumers have in their pockets and purses context aware12 smartphones and tablets that, among many things, can transmit where they are and to some degree, what they are doing. The multiple-sensory input capabilities (video, audio, GPS, accelerometer, ambient light sensor, proximity sensor, 3-axis gyro, and Internet connectivity) combine to provide a powerful feature set. The surface has barely been scratched on the possibilities these powerful context-aware smart devices offer, especially when combined with the other trends described here.
“According to a recent Gartner Report, mobiles will be the most common way for people to access the Internet by 2013” (Johnson et al. 9). And when mobile high-speed broadband data rates begin to proliferate in the coming months, smart devices will then have the power to perform magic.
Media professionals need to embrace the potential of high-speed context-aware smart devices and design the future. Google, Facebook, Android/iPhone/iPad app developers, and numerous tech-savvy marketers are frenetically developing applications and channels to exploit them. And context-aware devices also provide niche users such as museums (below), retailers, and various special venues with a new means to engage the public.
Google is among the leading edge developers of context aware technologies, pursuing initiatives outside its core business such as automated self-driving cars, which have been road tested throughout California (Tuttle, 2010). Meanwhile they are aggressively pursuing a convergence of context aware devices and semantic search with their constantly evolving Google search platform integrated with phones and smart devices running the Android operating system. Google CEO Eric Schmidt announced at a Sept. 2010 conference that current technological advancements are nearing the realm of science fiction, that we are “about to see a new age of ‘augmented humanity,’ when computers will make it possible for us to do what we really want to do” (Andrews). This at a conference where Google demonstrated updates to the Android cell phone platform including real-time voice control of devices and real-time language translation.
The Context Aware Museum Guide
One example of these technologies in use is by researchers at Carnegie Mellon University, who developed forward thinking technology that merged semantic search, context awareness, and inferencing engines. Their project “Semantic Web Technologies for Context-Aware Museum Tour Guide Applications”13 (Chou, et.al.) is built around a Semantic Web framework that also utilizes key technologies described in this paper. The application, designed to run on a smartphone, first gathers the visitor’s interests and preferences, including privacy rules, and then utilizes OWL (Web Ontology Language) domain ontologies created specifically for this museum’s experience.
Figure 9: CMU’s “Context Aware Museum Tour Guide Application”
Figure 9 shows how real-time web and intranet semantic searches supplement server-based contextual information as the visitor moves through the exhibit space with the location aware features of the smartphone. What the technology does is give the museum experience designers a new medium in which to engage the visitor.
“Location-based services offer a number of interesting possibilities to engage in a deeper level of interactivity with visitors” (Johnson et al. 21). Each visit to the museum can now be a unique experience for each visitor. For example as a visitor approaches an exhibit about climate change on Greenland, her museum guide application knows where she is, and synchronizes to content playing on an LCD display in the exhibit (similar to the Nielsen Media Sync below). The smart device acts as an interactive companion throughout the museum experience, pulling content such as real time environmental data from the Internet. Text can be translated in real time. Augmented reality real-time data overlays can be updated with in-field analytical data, such as shown in the Theodolite, Figure 11 below.
Figure 10: Nielsen’s Media Sync Platform http://paidcontent.org/article/419-abc-and-nielsen-partner-on-ipad-app-that-syncs-tv-and-mobile-viewing/
Figure 11: “Theodolite” AR application for iPhone. http://mashable.com/2009/12/05/augmented-reality-iphone/
Augmented Reality (AR), “the concept of blending (augmenting) data—information, rich media, and even live action—with what we see in the real world” (Johnson et al. 16), has been around for years, used in science visualization systems, military “heads up” displays, and other specialized applications. Mobile smart device’s capabilities now offer a new platform for AR applications, with many developers, especially in the U.S. hurriedly competing to create all sorts of novel AR apps primarily for commercial use. Figure 11 above shows an example of a practical application of augmented realty, creating a Theodolite (a device used to measure horizontal and vertical angles) for a mobile phone.
A European project funded under the Sixth Framework Programme for Information Society Technologies has created some of the more interesting AR apps to date. Figures 12 & 13 show their “superimposed environments” and “reality filtering” mobile applications where historical photos and images of classical artwork can be superimposed over the real environment.
Figure 12: iTacitus Project, Winchester Cathedral, http://www.itacitus.org/news/2
Figure 13: iTactitus Project, Palazzo Diana, Turin, Italy. http://www.itacitus.org/news/2
As more practical and task-specific applications are developed, both consumers and professionals will be able to apply AR practicably on their smart phones, tablets, and within the media production process. For consumers and professionals alike, the surface has barely been scratched on how AR can be deployed on smart devices when combined with the location aware capabilities described above.
Emerging Trend #5: Social Networking/Social Media and Related Technologies
Predicting that social networking and social media will have an increasing impact on media professionals is about as obvious as predicting growth in the alternative, clean-energy sector. It’s all around us. What is more challenging is figuring out what “killer apps” will be spawned from and for these new paradigms. One growing area to watch is the opportunity for “learning networks” to proliferate within online communities.
Media professionals have historically been adept at conventional social networking, especially in geographic “spikes”14 like Los Angeles and New York. With much work now being performed by far flung teams who may never meet in person, a new sort of social workplace mindset develops, one that Los Angeles based producer and former Disney executive Larry Gertz calls “intimate autonomy.” The challenge now is for media professionals to embrace the new semantic wikis and other emerging toolkits and invent the future of creative and technical collaboration.
Virtual networks provide for both physical and technological “creation platforms” (Brown, Hagel and Davison 144) to form in all sorts of social networking contexts. Formation and active participation in communities of practice (CoPs), communities of interest, and creation spaces is becoming increasingly important. Larger enterprises have been doing it successfully for years. Practical, efficient tools for anyone to create and maintain professional online communities are now offered by Google and others. Semantic wikis and related technologies as described in Trend #1 will increasingly be used to enhance social networks, especially among professionals.
Emerging Trend #6: “Open Ended Learning” and Knowledge Transfer
How much specialized knowledge have you acquired during the course of your education and career? What if much of this hard-gained knowledge is no longer unique to you, and is now available to anyone, including your co-workers, subordinates, boss, competitors, and customers? How much will that reduce your value as a knowledge worker? In fact, much of our “explicit” knowledge is available to anyone with a web crawler and good search skills. This is compounded by further implementation of semantic search technologies described above.
As John Seely Brown and his co-authors write in their recent book “The Power of Pull,” with so much knowledge just a click away from anyone, professionals can no longer rely solely on the “stores” of knowledge they have built over the years. We all must practice a continuous, virtuous cycle of open-ended learning15, as the sources of economic value move from “stocks” of knowledge to “flows” of new knowledge (Brown, 2010).
The good news however is that there is knowledge that is not so readily flowing for us to benefit from. As Hagel, Brown and Davison point out in “The Power of Pull,” the most valuable knowledge is in very short supply and extremely hard to access. It is highly distributed and may be embedded in the heads of people who are not well known and who are difficult to identify. As this tacit knowledge becomes more valuable than explicit knowledge, we need to transform our institutions and our work processes with tools that enable us to collaborate and learn more efficiently.
Each technology and process in this paper helps support open-ended learning. Media professionals will need to employ this mindset in all areas of their work in order to maintain a competitive edge. Experts agree that creating more open knowledge-transfer is increasingly critical for all organizations on all levels.
From “Technology- driven” to “Technique-driven”
Most small/medium design and production organizations are contending with the endless cycle of changing technology that often results in continual, painful process disruptions. To succeed, they must move from a technology driven business model, to one that is “technique” driven by each individual; from the knowledge stores and silos that accompany an I.T. driven techno-centric infrastructure, to the knowledge flows and creation spaces that enhance tacit knowledge transfer. These will complement and support the focus on technique over technology. Herein lies the foundation for open-ended learning.
Concept for a Next Generation Content Production Process
If we could apply these trends to a media production process, what would it look like? First, the cloud would give us instant connectivity to whatever we need, when we need it, with transactions handled automatically. Then semantic search and related technologies would be combined to provide decision support, analytics, reasoning... essentially an omniscient intelligent-assistant running in the background. And the multi-sensor capabilities of mobile smart devices (iPhone, Android, iPad, etc...) connected via high speed wireless creates a sort of positive feedback loop, keeping all stakeholders, including the end user/client as active participants throughout the process.
If we build a composite workflow from this “wish list,” it could look like this. Figure 14 is the author’s concept design for an “Intelligent Media Production” framework. A key enabling component of this framework is an “ambient intelligent assistant” system. The underlying goal of this concept is to leverage semantic and related technologies for what they do best, which is inferring meaningful conclusions, and automating certain knowledge-intensive tasks such as classification, monitoring, prediction and planning. This is a conceptual framework, but is a composite of disparate technologies that are either in development or implemented in other applications.
Figure 14: Concept for a Semantic based “Intelligent Media Production” framework (Mulally)
The system, as shown above in Figure 14, consists of semantic search aggregated with powerful decision support, inferencing, reasoning and analytics engines. The “personal assistant” runs in the background while the editors/designers work, providing assistance ranging from pulling relevant assets from “cloud” based resource centers, to consolidating/organizing disparate content to be encoded.
This system leverages the semantic web, with the support analyst providing ongoing asset management, and maintenance of a domain ontology, taxonomies, metadata and tagging. Much of the content in this workflow starts out as unstructured visual media, so the support analyst plays a key role ensuring that content metadata is optimized to support the workflow.
Let’s revisit our questions from the introduction. We asked:
With the ever-expanding digital infrastructure and the clockspeed of technological change ... “how does one keep up?” Answer: Trend #6, Open Ended Learning, as well as leveraging the various enabling technologies as tools for learning. By practicing a continuous, virtuous cycle of ongoing learning, you will be able to keep abreast of the technology, trends, and techniques necessary to keep your stores of knowledge fresh and on the leading edge.
What technologies should media professionals be focused on? A safe bet is to start with Trend #1, use of the Semantic web, and, depending on your area of expertise, work your way down the list.
Which are most likely to gain traction and impact how you work? Certainly all will impact your work to some degree. But Trends #1 the Semantic Web, & #3 the Cloud, though less visible and more “background” technologies, are nevertheless becoming ubiquitous across all disciplines.
Are you prepared to deal with the relentless “gales of change” that are upon us? Yes! Now that you are aware of these trends and technologies, you can forge your own trail in the digital frontier and stay on the leading edge.
1. W3C Semantic Web Frequently Asked Questions. Accessed 11/2/10 at: http://www.w3.org/2001/sw/SW-FAQ#othersw
2. An ontology, as defined by Kimiz Dalkir in Knowledge Management in Theory and Practice, is “an explicit formal specification of how to represent the objects, concepts, and other entities that are assumed to exist in some area of interest and the relationships that hold among them; a formal, explicit specification of a shared conceptualization.” Tim Berners-Lee, James Hendler and Ora Lassila in their article The Semantic Web, (Scientific American, May 2001), define ontologies as “collections of statements written in a language such as RDF that define the relations between concepts and specify logical rules for reasoning about them. Computers will ‘understand’ the meaning of semantic data on a Web page by following links to specified ontologies.”
3. A taxonomy is a hierarchical structure for a body of knowledge. This provides a framework for how things are grouped, and how things relate to each other (Dalkir 342).
4. Metadata is often defined simply as “data about data.” To provide more nuance to this definition, Peter Morville and Louis Rosenfeld in Information Architecture for the World Wide Web describe the role metadata plays: “Metadata tags are used to describe documents, pages, images software, video and audio files, and other common objects for the purposes of improved navigation and retrieval.”
5. Controlled vocabularies are predetermined vocabularies of preferred terms that describe a specific domain (e.g. auto racing or orthopedic surgery) (Morville and Rosenfeld 52).
6. A tag is a non-hierarchical keyword or term assigned to a piece of information (such as an Internet bookmark, digital image, or computer file). This kind of metadata helps describe an item and allows it to be found again by browsing or searching. Accessed 11/11/10 at: http://en.wikipedia.org/wiki/Tag_(metadata)
7. Folksonomies are described in Wikipedia as a “system of classification derived from the practice and method of collaboratively creating and managing tags to annotate and catagorize content.” They are also referred to as “mob indexing.” Folksonomies are used in social tagging and social indexing at sites such as Digg, de.licio.us, and Flickr.
8. A Wiki is defined in Wikipedia as “a website that allows the easy creation and editing of any number of interlinked web pages via a web browser using a simplified markup language or a WYSIWYG text editor.” "Wiki" is a Hawaiian word for "fast." Accessed 11/11/10 at: http://en.wikipedia.org/wiki/Wiki#cite_note-0
9. The Semantic MediaWiki site, where the current version of SMW can be downloaded. http://semantic-mediawiki.org/wiki/Semantic_MediaWiki
10. Information about IBM’s TALES, IMARS, UMIA, is at the following IBM Research websites:
TALES (Translingual Automatic Language Exploitation System) at: http://domino.research.ibm.com/comm/research_projects.nsf/pages/tales.index.html?Open&printable
IMARS (IBM Multimedia Analysis and Retrieval System) at: http://www.alphaworks.ibm.com/tech/imars
UIMA (Unstructured Information Management Architecture) at: http://domino.research.ibm.com/comm/research_projects.nsf/pages/tales.index.html
11. Crowdsourcing is the act of outsourcing tasks, traditionally performed by an employee or contractor, to a large group of people or community (a crowd), through an open call. The term has become popular with businesses, authors, and journalists as shorthand for the trend of leveraging the mass collaboration enabled by Web 2.0 technologies to achieve business goals. Wikipedia, accessed at http://en.wikipedia.org/wiki/Crowdsourcing
12. Context aware smart phones and other mobile devices refer to a feature set that enables the device to sense the user’s location, orientation, and other information (sound and imagery of the user’s environment), to thereby infer the “context” of where they are and what they are doing there. iPhones, iPads and Android devices have accelerometers (provides data on how the device is oriented in 3D space, which, combined with its GPS, enables it to also act as a compass) audio capture (to analyze audio and thereby synchronize with external events) and video capture (to overlay information, as in augmented reality applications), (Mulally).
13. In 2005, researchers from the Carnegie Mellon University’s “Context Aware Museum Guide” systems team described in Emerging Trend #4 (Norman M. Sadeh and Fabien L. Gandon) joined with Oh Byung Kwon to propose another Semantic Web/Context Aware framework called “Ambient Intelligence: The MyCampus Experience” http://users.ece.gatech.edu/~dblough/8823/MyCampus.pdf This framework builds upon the “Museum” work, with a better defined architecture that is more relevant to the “Ambient Intelligent Assistant” system described in this paper.
14. Geographic Spikes are described by Brown, Hagel, and Davison in “The Power of Pull” as the urban centers that attract specialized talent, which in turn expand and rapidly attract even more talent. This is especially happening in rapidly developing economies such as China and India, where cities like Bangalore, Shenzhen and Chonqing are attracting more and more talented people.
15. The author’s concept of open-ended learning draws on the description of the “free agent learner” by William Rothwell in The Workplace Learner (p.39), and a conference call with Rothwell 7/28/2009.
16. “The Intelligent Media Production framework is the author’s concept based on personal experience, currently available technologies, and reference material as cited in this paper.
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