deeda - deeda Blog

Empowering Social Influence With Mark Granovetter

noreply@blogger.com (deeda Inc.) — Fri, 21 Aug 2009 04:40:00 +0000

Hearing the phrase "Social Networking" immediately brings to mind applications such as Facebook, MySpace, and LinkedIn. Depending on where in the world you're reading this post you may identify with a slightly different set of social networks - Friendster is still alive and now hugely popular in Asia, as well as Hi5, Bebo, Orkut, and Mixi (Japan's biggest social network). The diversity of social networks has a rich history that encompasses some of the best Use Cases of what to do, and what not to do when building successful online communities.

Each network began their approach to building their online communities from slightly different directions - specific regional or cultural communities (Mixi), common social (MySpace), collegiate social (Facebook), professional social (LinkedIn), sub-networks (Ning) etc. Over the years many of them have expanded to overlap each other to some degree. While the direction of their approaches may have been different, the one thing they all have in common is that their value lies in the application and understanding of social influence.

However before understanding social influence, trust is where most social networks begin their focus. After all, without trust you can't build large and vibrant communities - and without large communities you can't really get a clear picture of how social influence works.

Over the years social networks have learned valuable lessons through mistakes such as Facebook's Beacon advertising program, and MySpace's extra open development platform which initially let Javascript run amok in ways that MySpace never intended. Whether it was to counteract spammers or preserve control over a person's personal information, all sites have made enormous efforts to offer finer and finer granular control along with enough oversight to ensure a pleasurable and "safe" experience. And as these networks have grown to number 100s of millions of users, so too have their valuations.

But VCs and investors are all still asking the same questions. What justifies these valuations? How do you plan on generating revenue let alone a profit? A look at these tables about various social network valuations from TechCrunch will make this point even clearer:

Although there are a few understandable holes and caveats in how TechCrunch has done their analysis, it's still a great start at normalize the valuations in such a way as to calculate what a user is worth based on demographic/network type, and online ad spending towards these users. As Michael Arrington says at the end of the article, "Two very similar networks may monetize vastly differently based on methods of advertising and even the brute effort and passion of the employees. This model obviously doesn’t take that into account." So what are the various methods of advertising available to these networks?

Ultimately, each network monetizes by offering its advertisers 1) reach (hey! we have X-million users), along with 2) an in depth understanding of their user base (stripped of specific names).

You love being able to find friends who live in your area, went to your Alma Mater, High School, etc. - and advertisers love to be able to target their ads to people in specific demographics, interest groups, and locations. It seems to be a win-win! But in the end this is still very 1.0 in terms of advertising. Having greater control of ad targeting is great, but in order for corporations to make a significant impact to their bottom line, they must start coming up with better "social" ad units while evolving their approach to online advertising.

Enter Social Influence:

Facebook's Beacon was just such an attempt at being innovative. The only problem was that people didn't have control over how their personal information was being used. Even worse, unlike other referral-based ad programs (Adsense) there wasn't any referral revenue split with the person who's picture and information was being used to advertise. You would think that if Facebook did a generous Google-esque 70/30 ad revenue split, people might have been a little more accepting of their program.

In fact, the latest iteration of Facebook's Beacon seems to be charging in just this direction. However, Facebook should be very careful with how any referral payment system is developed before a "Beacon Fiasco Part II" occurs. First of all, Facebook users don't want their recommendations to come across as being in bad taste - basically whoring out their once objective recommendations for profit. But who knows, during these tough economic times a little extra scratch through your social network might go a long way in softening impressions of this program. The other aspect for Facebook to consider is the investment of infrastructure to handle referral payments and customer support. In the end, Facebook needs to ensure that users are willing to support this new model, and support it in large enough numbers to have the approach make sense. Spending millions to roll out an idea that people are still averse to, and that backfires again, would be PR mess that Facebook would like to avoid. Yet with the deep coffers of Facebook, I don't think they'll be hurting to try this new approach. Taking another chance to monetize social influence is also something that researchers and investors alike would be interested in.

Facebook's updated approach involves being able to share your purchase information with specific friends who are also interested in the same types of products. While blasting your purchase history across to all of your friends is what advertisers would still prefer - sharing a purchase you've made with "interested" friends makes more sense. Advertisers can always pay more to have all of your friends see the information (in hope of some additional peer influence) , but this is basically where Facebook is going with this. Assuming such highly targeted and co-opted ads makes for a better ad unit, the cost for a click-through or purchase, based on this type of ad unit, should also command above average rates. But will any of this actually work? What is the science behind the approach, and what is the best way to make use of it without being evil or Big Brother-ish?

Social Influence, and Social Networks have been closely studied by Mathematicians and Sociologists since the late 1940s. In fact, while we tend to think of "Social Networks" as a relatively new phenomenon surrounding online communities, the reality is that a variety of disciplines have been interested in understanding social interactions from a variety of perspectives throughout history (Sociologists, Anthropologists, Mathematicians, Archeologists etc.)

The cornerstones of social network studies have largely been based on, 1) formal theories organized in mathematical terms, and 2) systematic analysis of empirical data. By the late 1970s with the advent of discrete combinatorics (particularly graph theory), the study of social networks really began to take off (insna.org). One of the most influential researchers in this field is famed Stanford sociologist Dr Mark Granovetter, whose 1973 paper "The Strength of Weak Ties" is considered one of the most influential sociology papers ever written. It is also one of the most cited research papers in this field. We are also extraordinarily proud to have Dr Mark Granovetter working with the deeda Team as we continue to build our application.

The Strength of Weak Ties

In marketing, politics, or your personal social network, the weak ties are what enable reaching large populations and audiences that are not typically accessible via strong ties. After all, your closest friends usually travel within the same cliques, so the information that is available is fairly uniform and the ability to widely distribute new information is naturally limited. In his 1973 paper, Granovetter used the example of rumors to show the limit of these strong ties. Because of the close interrelation between strong ties, a rumor would quickly spread through a few friends and then die out. Most conversations would soon start with, "Did you hear about...", and be immediately followed up with, "Yeah, I heard..." - end of story. But weak ties are where the broadest amount of information distribution occurs - and where rumors can take on a life of their own. The Boston Globe recently did a facinating story about the spread of rumors, their importance, and how certain actions either help spread or counter rumors. While rumors don't have a positive connotation, they still act as a simple example for information diffusion.

Getting advice through LinkedIn Answers is great positive example of leveraging the strength of weak ties and information diffusion. While the people within your immediate first degree network are likely to be exposed to the same information and resources that you are, it's the weak 2nd and 3rd degree ties that are likely provide you with some new insights and beneficial information. To be completely accurate, one of Granovetter's original definitions of weak ties involved friends and contacts who were once strong ties, but have since drifted apart. Today, we typically refer to your friends' friends and other broader definitions of what constitutes a weak tie.

In his Harvard dissertation, Granovetter expanded on these ideas to show how people were more likely to find a new job through their weak ties rather than their strong ones. Additionally, Granovetter's research also covered how fads are created. His Threshold Models regarding the "tipping points" of fads were later made famous through Malcolm Gladwell's book, The Tipping Point.

When monetizing any social network or social application, it is therefore important to understand where the value and growth in your network lies. Understanding the strength of weak ties, encouraging weak tie exploration, and creating systems that help diffuse information through weak ties, are all aspects that every successful social application should take advantage of. Combining, linking, and encouraging the growth of sub-networks (smaller groups within the larger mass of a network), are also key components to increasing the overall value and growth of a network.

In the diagram to the left I've tried to quickly outline the mathematical component of our discussion of social influence. These representations show the concepts of node and point centrality as they apply to this subject. The three types of centrality (relationship between nodes) are defined through degree, control, and independence. Visualizing these nodes also helps explain why leveraging weak ties is so valuable.

In this example, You and Tom are connected to the most people and therefore have a high level of degree centrality - or to put it another way, you both have the most amount of friends/connected nodes.
"Me" represent the concept of control centrality, since I provide a bridge between the most nodes and amount of information. My ability to control this information flow is important to the overall network.
Tom also represents independence centrality - since he is the most closely connected to other people through multiple people. Tom is minimally influenced by any single person, and is therefore less exposed to control. He is not dependent on a few specific nodes for information and communication.

A person who controls the greatest flow of information within a network is more important than one who may have more friends. Understanding centrality can also indicate which members are the most useful or well connected and therefore offer the best information resources. (Singh)

Sites such as Spoke leverage these concepts by showing you the shortest path to beneficial connections you'd like to develop within their network. If Tom has the greatest connections to investors, and I'd like to get introduced to them, Spoke will display the best "path" through which to approach Tom.

Monetizing Social Influence - Profits VS Privacy

So far we've covered social networks, the theories and models under which they flourish, and we've also touched on how various networks attempt to generate revenue through applications of social influence. We've also come full circle to the crux of the problem - sky high valuations without sky high revenue. A better understanding of social influence might allow networks to create better ad units for advertisers while also providing a highly profitable revenue stream.

However, a related problem with most monetizing plans around social influence is the amount of transparency, control, and privacy that is taken away from people and given to marketing and advertising companies. While we want our favorite social applications to be successful, that doesn't mean that we want them to pan out our personal behavior and social influence to the highest bidder.

Even though companies typically state they only use "data stripped of personal information", trust is the first thing that begins to fracture around these new business models. As Facebook's Beacon taught us, the backlash from consumers and the PR nightmare just isn't worth breaking the trust that social networks are founded on. What is the optimal business model that protects privacy while producing profits for social applications and advertising agencies?

Social networks are full of exciting data that can help us better understand our real and virtual behavior and social interactions. We can use them to help us find better jobs or fill the knowledge gaps within our current groups. Academic research can make huge strides and test new models with the information that is present in these networks. Advertisers can use you to influence your peers so they buy similar products, or stay loyal to your favorite brands. Yet, along the way each layer is peeling away a bit more of your privacy. And your loss of privacy is being bought and sold to cover the cost of bandwidth bills, servers, and hired employees needed to continually build, fix, and expand services.

Empowerment

Dr Granovetter and I have approached the problem of finding the right balance between profits and privacy from the perspective of Empowerment. The ingredients found in the studies of social influence, strong and weak ties, network structure, centrality, and even social marketing - are not inherently dangerous, meant to strip people of their privacy, or trample on trust. But this is mainly true when approached from a specific direction - and that direction must be the empowerment of the people these theories and models have been based upon.

What everyone has gotten wrong up to this point is in approaching the monetizing question from the direction of the people who want to spend the ad dollars. And just like the same ingredients in your kitchen that can help you, can also harm you - mixing social influence based research from the direction of marketers ends up being poisonous. Advertisers can't assume that they (who are often pretty slow at the technology innovation part) can pull the wool over the eyes of these tech savy online communities.

Are you really not storing my personal information? If I opt out of the program, am I really out of it? For generations advertisers have assumed that what consumers don't know will help them make bigger profits. But today's customers are a different lot. If social networks understand the importance of trust, then advertisers need to also understand how vital it is to their online marketing future. If social networks understand that this trust is the foundation of their network's value, then advertisers must also understand that any attempt to monetize social influence must first be built upon the very same trust.

In the next part I'll explain how Empowerment should look from the perspective of advertisers, social networks and the millions of people who flock to use social applications every day. Empowerment will be the key to ensuring that our Profits and your Privacy can both be produced and protected.

The Brain, Semantic Web, and what it will mean for you!

noreply@blogger.com (deeda Inc.) — Sat, 25 Jul 2009 10:36:00 +0000

I was recently watching a lecture by Jeff Hawkins titled, "Computing beyond Turing" and it inspired me to write something about the state of affairs between Computational Neuroscience, Semantic Web technology, and the future of Web, Desktop and Mobile applications. In the end, I hope this helps explain how we’re working to tie these concepts together with regard to the web, desktop and mobile environments at deeda Inc.

Although the link to this particular inspirational lecture is posted above, here is a quick embedded video primer on the subject. This talk is from TED 2003 and was also given by Jeff Hawkins. If you have some time, definitely try to check it out:

"Coaxing computers to perform basic acts of perception and robotics, let alone high-level thought, has been difficult. No existing computer can recognize pictures, understand language, or navigate through a cluttered room with anywhere near the facility of a child. Hawkins and his colleagues have developed a model of how the neocortex performs these and other tasks. The theory, call Hierarchical Temporal Memory, explains how the hierarchical structure of the neocortex builds a model of its world and uses this model for inference and prediction."

The above image represents the hierarchical data structure that the neocortex uses to store information about the world. The fundamental question we need to answer is how does this memory structure work? It turns out that the lower level of this inverted tree structure is where the widest range of visual information is first processed. You can think of this wide area as the point of first contact with sensory information. There’s a lot of it, and it can be seemingly overwhelming. However, the cells in this area have a very broad range of pattern recognition abilities. This is what allows them to make sense out of the data chaos. They are programmed to recognize spatial patterns that occur at the same time, as well as the sequences of these temporal patterns.

These cells then memorize what patterns are occurring as well as retain the ability to predict what patterns will follow - and then pass these patterns up to their parent. Each parent is looking at its child node, and in turn it is processing patterns of patterns. As you progress from the top down, increasingly broader patterns and sequences are being received. As you move up the hierarchy the temporal component of the patterns and sequences are becoming smaller. Each parent has the ability to pass down predictions on what pattern and sequence each child node should be receiving next. In the example of a musical melody, the prediction sent could be the notes that are expected to be heard next. Together, the 30 billion neurons in the neocortex are constantly managing, retaining, and predicting the inputs from various sensory systems.

There are three fascinating and wonderful components to this HTM model.

This model accounts for learning and prediction of events, patterns and sequences.
This model represents an entirely new way of looking at memory and storage.
These hierarchical systems represent a highly distributed computing model.

While silicon (hardware) representations of this type of memory and processing model are still under development, there are many ways this model has been successfully applied, developed, and tested through software. Jeff Hawkins' Redwood Center for Theoretical Neuroscience (formerly the Redwood Neuroscience Institute) and his group at Numenta have already provided some great software tools for experimenting and developing systems based on the HTM model.

I love seeing examples of technology that are based on natural biological processes. It has always been my goal for deeda to help bridge the gap between the two, and to make real world examples that showcase the efficiency and value of such systems. In the mobile environment we're typically working with fairly small screens and buttons. Even in the touch environment not every handset can typically access Flash or other types of Web specific content. It is therefore imperative that our future handsets should have some level of intelligence that reduces the number of commands, websites, or information we have to navigate through to find what we need in a useful manner. Even if predictive analysis reduces the complexity of interactions it will be a great boon for mobile applications. Let's say I'm visiting Boston and I'm hungry. It's 10PM at night, and I'm in Copley Park. Why should I have to navigate to a dozen websites to find recommendations on restaurants in my area, then navigate to a restaurant's website to see if they are open? Sites like WhatsOpen.com will do all of the narrowing down for you, but is this what it has come down to? We have to wait for a website to come around that aggregates some amount of useful information so we don't have to waste time? Why can't we have a system that understands where we are, what we like to eat, what our friends have recommended in the area, as well as what is still open - and then show it to us - all with once click or tap?

Processing data in new and novel ways is only part of the solution. How do we get data to play nicely with each other? How do we provide context and understanding behind the data that is being analyzed? With respect to our work at deeda there is another component to this vision that I'd like to quickly discuss - The Semantic Web.

Tim Berners-Lee, the father of the World Wide Web, described the Semantic Web as follows, "The Web was designed as an information space, with the goal that it should be useful not only for human-human communication, but also that machines would be able to participate and help. One of the major obstacles to this has been the fact that most information on the Web is designed for human consumption, and even if it was derived from a database with well defined meanings (in at least some terms) for its columns, that the structure of the data is not evident to a robot browsing the web. Leaving aside the artificial intelligence problem of training machines to behave like people, the Semantic Web approach instead develops languages for expressing information in a machine processable form."

The fact that Tim wrote this 10 years ago is a testament to just how much work needed to be done before Semantic technology would be ready to be implemented in a meaningful and widespread way. While I wouldn't say that semantic markup or standards are widespread, there are currently a lot of great startups that are beginning to leverage the technology - And the real world examples and value of the semantic web are becoming more and more prevalent every day.

A great book that helps introduce the concepts and technology behind the Semantic Web is "Semantic Web for the Working Ontologist", by Dean Allemang and Jim Hendler. This book was recommended to me by Henry Story of Sun Microsystems (originally of BableFish fame), who has been studying and working on Semantic Web technology since 2004.

Over the past few weeks I’ve had the pleasure of becoming friends with Henry as well as get a chance to take a closer look at some of his work on the Semantic front. The first question most people have when the phrase “Semantic Web” is used is, “What exactly does it mean?” The short answer is that it is a model for making data shareable by machines in a meaningful way. What Hypertext did for linking related web pages of information, the Semantic Web does with Hyperdata – data that can actually be aggregated and linked in a meaningful way.

Another answer that is commonly given is that the Semantic Web is the model for making applications more “intelligent”. Ok, so now what does that mean? After all, the web is currently full of increasingly intelligent applications. Commerce sites can personalize information and deliver suggestions in uncanny ways (think of Amazon’s “other people who bought this, also bought X,Y,Z” feature). Search Engines have also become increasingly better at delivering intuitive and seemingly deep matches that are relevant to simple search queries. It would seem that “intelligence” is just a matter of building smarter algorithms, relational databases, XML stores, or object stores to make the data appear better connected and consistent. So when we say “Semantic Web” are we talking about a new web technology, a different type of Web infrastructure, or both?

The answer lies somewhere in the middle, and if not explained properly can lead to a bit of confusion. What we have accomplished today with intelligent applications is a reflection of the best we can do with the data available to us in the traditional HTML format. To allow smart applications to perform to their full potential we must improve the Web infrastructure to provide results that are not confusing, disconnected, or “dumb”. As Dean Allemang and Jim Hendler state in their book, “The Semantic Web doesn’t make data smart because smart data isn’t what the Semantic Web needs. The Semantic Web just needs to get the right data to the right place so the smart applications can do their work. So the question to ask is not “How can we make the Web infrastructure smarter?” but “What can the Web infrastructure provide to improve the consistency and availability of Web data?”

What's the rationale for such a system? Data that is generally hidden away in HTML files is often useful in some contexts, but not in others. We provide all this great information about ourselves to share, but we only share it in a humanly readable format. Machines have trouble processing the same information because it is seldom (often intentionally) kept out of their grasps. The problem with the majority of data on the Web in its current form is that it is difficult to use on a large scale, because there is no global system for publishing data in such a way as it can be easily processed by anyone. We don’t need a smart Web infrastructure, but we need a Web infrastructure that lets us connect data to smart Web applications so the whole Web experience is enhanced.

For a quick example let’s take a look at the current state of affairs on the web. Let’s say the majority of your personal profile information is on Facebook – including your work affiliations, group events, where you live, and maybe even your favorite books, and movies. When you visit Amazon it has no idea about all the latest books you’ve added to your Facebook profile. Maybe you recently moved to a new city, but the information in your Amazon account still says you live at your old address. You might find it helpful to share some of your Facebook information with Amazon so you can get some great suggestions, but the only way to do that is if you put all of your latest information into another closed, centralized system (Amazon). Amazon doesn't even have the same fields or profile setup as Facebook so doing this is hardly an option. Not only is this frustrating, but it's also an enormous waste of time. Wouldn’t it be nice if Facebook, or any major website you were a part of, acted as if you actually owned your personal data? You should be able to take your profile information with you wherever you go. And wouldn't it be great if other websites, like Amazon, allowed you to easily submit your relevant information? The end result would be applications that are intelligent enough to read this shared information in a way that is automatic, and instantly useful.

Since the value of centralized and closed systems like Facebook lies in hording all of your personal data (so they can increase their ad revenue), Amazon was forced to come to Facebook and build two Facebook applications – Amazon Giver and Amazon Grapevine. These applications allow you to solve the above problem while still staying inside the walled garden of Facebook. Amazon Grapevine will go through your favorite books, movies and other profile information, as well as look at people and groups you are a fan of, and then make suggestions for purchases. You can make all of your purchases through Amazon from within Facebook without ever leaving. As in the "I'm hungry" example, is this the best it's going to get? We all have to join Facebook, and then wait for all of our other favorite sites make a compatible Facebook application? Where's the freedom? Where are the choices?

On the surface this seems like an issue of data portability. But that’s mainly the ‘human’ side of the problem. The machine side is where the real challenge resides. If all of your data could merge and play nicely with each other, all web and mobile applications - not just custom Facebook applications - could be more intelligent and useful.

The main idea of the Semantic Web is to support a distributed Web at the level of the data rather than at the level of the presentation. Instead of having one webpage point to another, one data item can point to another, using global references called Uniform Resource Identifiers (URIs). The Web infrastructure provides a data model whereby information about a single entity can be distributed over the Web. This distribution could allow Amazon to access what you want to share with it even though the information is distributed over websites controlled by more than one organization. The single, coherent data model for the application is not held inside one application but rather is part of the Web infrastructure. When you publish information about your profile it shouldn’t just be published a human-readable presentation of this information that is trapped on Facebook, but instead a distributable, machine-readable description of the data. The data model that the Semantic Web infrastructure uses to represent this distributed web of data is called the Resource Description Framework (RDF).

“This single distributed model of information is the contribution that the Semantic Web infrastructure brings to a smarter web. Just as the case with the data-backed Web applications, the Semantic Web infrastructure allows the data to drive the presentation so that various webpages (presentations) can provide views into a consistent body of information. In this way, the Semantic Web helps data not be so dumb.”

Up to this point I’ve tried my best to explain the concepts and ideas surrounding the Semantic Web. But Henry Story has gone a step further by creating a wonderful working example with his Beatnik AddressBook application. You can check it out here.

What Henry’s AddressBook does is aggregate the personal profiles (FOAF files) into a social network that continuously adds deeper and deeper information about your friends and colleagues. Location information is represented in a beautiful NASA mapping component, and makes visualizing where your friends are currently located much easier. Each person in your social network may have unique information about you, and as these files are aggregated a deeper and clearer picture of your relationships and your own profile becomes apparent.

Although this is still a work in progress, I encourage anyone interested in helping with Henry's project to contact him. Often times the best way to get the critical mass to move from the research field to the real world is with the help of an enthusiastic community of supporters. Since deeda has a significant focus on aggregating social graph data across the mobile, desktop and web environments we're also very interested in working with Henry to develop a Semantic Web solution that fits with deeda's vision.

Along these lines, where does all this ‘talk’ leave us? Whether it’s applying Neurosciene to computer science, or exploring the promises of the Semantic Web, the same questions remains: “Where are the applications?” “Where are the working examples?” “If this is so much better, why isn’t anyone else doing it?”.

“There are so many things the Web might usefully do in the future, that it is sometimes hard to see how we can get there from here. W3C's RDF has been around since 1997, yet while it has been adopted in a number of applications (for example by Mozilla, Open Directory, Adobe, RSS 1.0), people often ask why there is as yet no killer app for RDF. While we're not sure that 'killer app' is the right way to think about the problem, it is true that there is relatively little RDF data 'out there in the public Web', in the way that HTML is 'out there'.”

To quote and paraphrase Dan Brickley, 'The (soon to be "Semantic") Web, if it is to reach its full potential, needs to become a lot more automatic. We hope that it will be able to do things (offer us services) based on combining data and services scattered around the Web. It might, for example, be able to find the phone numbers or AOL screen names of all your friends and professional collaborators. Or show you the photos, names and recent publications and shared bookmarks for everyone attending the next party in your deeda calendar. Best of all it should be able to deliver these services instantly across mobile handsets, and allow you to automatically reference your current location as a node in the Semantic data hierarchy.'

Where HTMs and Semantic Web technologies overlap, we see a very valuable model begin to develop:

A Model that helps people communicate
A Model that explains and make predictions
A Model that mediates among multiple viewpoints
A Model that is spread across a distributed system
A Model that represents a new structure for memory

But for people to adopt these features deeda must make its system as easy to understand and operate as possible. In fact, it should "just work" out of the box. From the development standpoint we must provide:

An ontology modeling module
Intuitive web interfaces for everyday people for managing ontologies in a collaborative mode
Ontology import and export modules
Truth and Reasoning Maintenance, Reporting and Editing modules

From the user's standpoint everything should be as familiar as it currently is in a traditional centralized system (Facebook, Twitter, Amazon etc). Rather than force average people to learn RDF, FOAF, OWL or other Semantic formats, we must do the conversion of their data for them - quickly, efficiently, and in the background. Furthermore, skilled users should have access to their raw RDF/FOAF files for editing, storing, or transporting elsewhere.

Handset companies have traditionally been 'hardware only' companies. With Apple on the scene they are reluctantly trying to catchup and understand the importance on the software side. Personally, I believe the real wake up call for handset manufacturers was when RIM appeared on the scene with their Blackberry handsets. Blackberries are one of the best examples of how custom, purpose-built Network Operations Centers (Infrastructure), Middleware (RIM Enterprise Software), and Handsets (with RIM software) are what lead to an exemplary user experience. Most companies however are too afraid to risk investing on all three fronts, and typically see their value in only one of these three areas. This is what leads to handsets and applications that never quite live up to their full potential.

deeda understands the value in optimizing data across the web, desktop and mobile environments. We hope you continue to support us as we develop our solutions and understand just how much is at stake. Unlike most applications, there is quite a bit of thought and hard work that is going into our system. We hope to have something fun for you to test and play with by the end of the year.

A fun little talk at Google about deeda

noreply@blogger.com (deeda Inc.) — Tue, 09 Jun 2009 06:31:00 +0000

Atif Khan, Founder and CEO of deeda.com presents the Anatomy of a Startup. This four part presentation was presented to the WebMapSocial Meetup.com group at Google Headquarters in Mountain View, CA. Atif covers our company's beginnings in Boston through its move to Silicon Valley.

Part I - The "Direction of Convergence" - Devices

Part II - Building Software

Part III - The Value Silicon Valley - Leveraging Weak Ties

Part IV - The deeda Demo.

Meet the deeda Team

noreply@blogger.com (deeda Inc.) — Wed, 06 May 2009 05:58:00 +0000

The deeda Team

Atif K Khan

Atif is the CEO and Founder of deeda Inc. His vision of a unified ecosystem for web, desktop and devices began with his graduate work in Neuroscience at Harvard University. Atif's research focused on "Proustian Memories" - the kind of memory, where an unexpected re-encounter with a scent from the distant past brings back a rush of memories. How does the brain create and maintain such complex links between such seemingly separated senses? More important, why don't we use this type of contextual interlinking between data and content in the real world? We keep our pictures in our picture folder, documents in a document folder - on the web we similarly isolate visual, auditory and text based content in separate sites such as YouTube, Flickr, Blogger, etc. There must be a better way to organize, link and recall such diversely related content.

The word deeda originates from “Dynamic End-to-End Data Aggregation”, the contextual mapping and data linking technology developed by Atif to bring Proustian Memories to the web, desktop and wireless device environment. Today, deeda Inc. is the corporate face for our set of Open Platform products: deeda.com, deeda Desktop, and deeda Mobile.

As the 2005 developer of his own set of capacitive-touch devices with wireless device-to-device capabilities, Atif has consistently remained ahead of this rapidly developing area in technology. Many aspects of his independent design and development work can be seen in technology that was eventually validated through Microsoft’s Zune, and Apple’s iPod Touch and iPhone. His work in developing wireless capacitive-touch devices and systems began in 2004 and has been acknowledged by the Boston Globe as well as through patents filed in this field. These include application 60/668,650, filed: 04/05/2005, as well as applications 60/881,815, and 60/881,808, Filed: 01/19/2007. These patents also cover Atif's contextual mapping and data visualization models for dynamically sharing and retrieving content across a web, desktop and device ecosystem.

Atif’s early vision for building wireless devices and web systems on an open-platform Linux based OS, along with an easily accessible open-development platform, have been further endorsed through strategies recently adopted by such industry stalwarts as Google and Facebook. Atif plays a principal role in all aspects of deeda’s web, desktop and device designs, as well as actively designs and develops all of the applications that run across the deeda ecosystem. As the principal architect of the deeda system he is also responsible for maintaining deeda’s intellectual property. Accordingly, his roles and functions at deeda Inc. are extremely diverse. When not managing and developing deeda's suite of products, Atif loves working on his second passion - Classic Muscle Cars.

Original SolidWorks renderings - 2005:

Dylan Schiemann

Co-Founder Dojo/ CEO Sitepen – As CEO of SitePen and co-founder of the Dojo Toolkit, Dylan is best known for building Web applications that make use of JavaScript/AJAX, Dojo, Comet and other Web development technologies. He has helped develop Web apps for companies including Renkoo, Informatica, Security FrameWorks, and Vizional Technologies. Dylan has been our friend and advisor from a very early stage. From advice on web hosting solutions to ‘big picture’ development strategies as well as inviting us to various Valley networking events, Dylan has been an extremely valuable asset to deeda Inc. Today Dylan and his team at Sitepen are working to help complete our products and realize our vision. deeda Inc. is also a client of SitePen.

John M. Hann

John is deeda's Director of Engineering and is a Web 2.0 developer and evangelist, pioneering DHTML and Ajax techniques while leveraging Open Source tools and languages to deliver world-class products that are impressive yet usable, rich yet responsive. During his career as a programmer, team leader, and CTO, Mr. Hann has successfully delivered more than 65 enterprise-class, web-based Internet or intranet applications. Notable projects include a Web 2.0-class product created in 2000 (US Patent 7,016,751) and several Rich Internet Applications (RIAs) as far back as 1996 (using browser plug-ins before 1999 and hidden IFRAMEs until 2004). Mr. Hann graduated cum laude with a Bachelor's of Science in Electrical Engineering from the University of Massachusetts.

Chris Ash

Restless and always with an eye towards a challenge, Chris is usually found doing what he always has ... going in several directions at once. Having been involved with developing Rich Internet Applications since 1996, he has spent time as a consultant, developer and inventor. A long time proponent of what later be known as Web 2.0 and "Cloud Computing", he has helped develop web applications and solutions for many of the Fortune 500 financial and telecommunication companies. Chris Ash is deeda's Lead Developer responsible for understanding and developing solutions for the data flow between deeda and various third-party APIs. As with everything Chris has done in the past, he is also capable of working with every aspect of our development needs.

Andy Liu

Director of Android Development.

Andy comes to us after a stellar career at Motorola where he first started working with the Android platform. Today he's successfully completed our first Android Contact Sync application which merges your friend's Facebook pictures into your Android Contact List. Always focused on emerging Android development Andy is the cornerstone of the Mobile component of our three part ecosystem.

Brian Barrick

Senior Web Applications Developer

Brian comes to us after putting in his dues at FileMaker. While there, Brian was the main architect and project manager for Bento. If you check it out you'll see why his past work and our future vision make for the perfect marriage. Brian is currently busy developing our web application that will neatly tie our desktop and mobile ecosystem together.

Representation:

Advisory Roles

Dr Mark Granovetter (Stanford University)

Mark Granovetter is an American sociologist who has created some of the most influential theories in modern sociology since the 1970s. He is best known for his work in social network theory and in economic sociology, particularly his theory on the spread of information in social networks known as "The Strength of Weak Ties" (1973). Dr Granovetter is working with deeda to help us develop models for analyzing our aggregated social web data. Our goal is to help empower our users by offering a deeper understanding of their social graph information, strong and weak-tie social influences, and something we've coined, "Personal Analytics".

The strength of weak ties

Dr Granovetter's most famous work, "The Strength of Weak Ties", is considered to be one of the most influential sociology papers ever written.

In marketing or politics, the weak ties enable reaching populations and audiences that are not accessible via strong ties. The concepts of this work were later published in the related monograph "Getting A Job", an adaptation of Granovetter's doctoral dissertation at Harvard University's Department of Social Relations, with the title: "Changing Jobs: Channels of Mobility Information in a Suburban Population".

Economic sociology: embeddedness

In the field of economic sociology, Mark Granovetter has been a leader ever since the publication in 1985 of an article that launched "new economic sociology", "Economic Action and Social Structure: The Problem of Embeddedness". This article caused Granovetter to be identified with the concept of "embeddedness", the idea that economic relations between individuals or firms are embedded in actual social networks and do not exist in an abstract idealized market (a concept originally described in Karl Polanyi's book The Great Transformation). He is currently working on a book provisionally called Society and Economy.

"Tipping points" / threshold models

Dr Granovetter has also done research on a model of how fads are created. Consider a hypothetical mob assuming that each person's decision whether to riot or not is dependent on what everyone else is doing. Instigators will begin rioting even if no one else is, while others need to see a critical number of trouble makers before they riot, too. This threshold is assumed to be distributed to some probability distribution. The outcomes may diverge largely although the initial condition of threshold may only differ very slightly. This threshold model of social behavior was proposed previously by Thomas Schelling and later popularized by Malcolm Gladwell's book The Tipping Point.

Security influence

Dr Granovetter's work has influenced some researchers working in the field of capability-based security. Interactions in these systems can be described using "Granovetter diagrams", which illustrate changes in the ties between objects.

Associate Professor in Radiology, Harvard Medical School
Assistant in Radiology, Mass General Hospital
Affiliated Faculty, Harvard-MIT-HST
Professeur Boursier, EPFL

During her post-doctoral work in Sweden, Nouchine Hadjikhani used positron emission tomography to scan the visual cortex of the brain. It was the first time she got to peek into someone’s head. “I was trying to understand how vision and touch are connected,” she says. “When you put your hand in your pocket and you feel your key, you know which one it is even though you don’t see it. The question was, how can you visualize what you feel with your hand?”

In 1995, she had her first encounter with MRI. “That was quite something,” she says. “I was in the magnet myself and they showed me an image of my own brain. It was a very impressive moment. Suddenly you can have somebody alive and look at different things in the brain. There’s no danger and you can repeatedly get data about how the brain is organized.”

Today, Hadjikhani also studies the brain’s perception of facial and body expressions. What part of the brain recognizes fear, aggravation or anger in someone’s stance? Hadjikhani and Dr. Beatrice de Gelder, a colleague at Tilburg University in the Netherlands, are trying to find out.

“There are more people looking at faces now and the amount of literature is amazing,” says Hadjikhani. “But you don’t normally see a face alone, you see whole bodies.”

Today, Nouchine and Atif are hard at work developing the contextual mapping, linking and retrieval abilities of deeda to mimic what our minds do on a daily basis. Nouchine is working to ensure that deeda will benefit both medical research as well as human interactions across the web, desktop and devices.

Joshua Schachter - Joshua released the first version of Delicious (then called del.icio.us) in September 2003. The service coined the term social bookmarking and featured tagging, a system he'd invented for organizing links suggested to Memepool and publishing some of them on his personal linkblog, Muxway. On March 29, 2005, Schachter announced he would work full-time on Delicious. On December 9, 2005, Yahoo! acquired Delicious for an undisclosed sum. Prior to working full-time on Delicious, Schachter was an analyst in Morgan Stanley's Equity Trading Lab. He created geoURL in 2002 and ran it until 2004. Shortly after leaving Yahoo! this year Joshua met with Atif to provide some advice and guidance to deeda Inc.

Casey Hill - Casey joined the deeda Team in May 2007. After receiving his engineering degree from the Georgia Institute of Technology, Casey joined Motorola in 1985 as a sales engineer in Atlanta, Georgia. He has held technical and management positions with Motorola in engineering, research, marketing, standards and product development. Casey has contributed to a broad range of Motorola business units in Georgia, Florida, Texas and Illinois, and he has led several start up ventures within Motorola, including a location based services business and a cellular handset applications business. He is currently Senior Director of IPR Strategy and Corporate Licensing in Motorola’s law department in Schaumburg, IL. Casey is a published author in technical journals and publications, and is the charter chair of the IEEE Atlanta Vehicular Technology society. He, as a representative of Motorola, earned the Licensing Executive Society Deal of the Year Award in 2005. He was a member of the Motorola Science Advisory Board, is an elected Dan Noble Fellow, Motorola’s highest technical honor, and currently holds 37 US patents.

Casey moved on to Research in Motion earlier this year and is now their new Vice President of Intellectual Property Strategy.

Casey’s roles at deeda Inc. include guiding corporate strategy. He champions a strong vision of future location based services through the deeda ecosystem and also provides us with significant experience from his time at Motorola, and now at RIM, in building and deploying such strategies.

Jim O'Connor – MVC Capital – Mr. O'Connor is a senior investment professional with over a decade of private equity and venture capital experience. Prior to joining TTG Advisers, Mr. O'Connor held senior management positions within Motorola, Inc. (NYSE:MOT). Jim was Managing Director and Co-Founder of Motorola Ventures the venture capital investment arm for Motorola, where he led numerous global transactions. In his most recent role, Jim led Motorola's Technology Acceleration Program where he worked closely with a global team of technologists, to prioritize technology programs, create value from intellectual property, and guide creative research from innovation through early-stage commercialization. In 2006, Jim was named to the American Ventures Magazine (AVM) "40 UNDER 40" list. Before Motorola, he worked for A.T. Kearney as a management consultant and the U.S. Treasury Department in the areas of Domestic and International Finance as a White House Fellow. Additionally, he held roles at Ariel Capital Management and Sidley & Austin. He is Co-Chair of the Chicago Entrepreneurial Center (CEC) and a Board member of the Chicagoland Chamber of Commerce, the Chicago Urban League, the Big Shoulders Fund for the Archdiocese of Chicago's inner-city school fund and serves as a Trustee on the Board of the Field Museum of National History. He holds a BA (Government) and JD from Georgetown University and an MBA from the Northwestern University J.L. Kellogg Graduate School of Management. Jim is actively involved in helping deeda advance its funding and management goals.