Sency has recently upgraded its site with the following new features:

Sharing – Sency now makes it extremely simple to share both real time results and real time links that are found on its site – you will see the share links under each result and link on Sency.com.  You can share this content on your Facebook and Twitter page – and you can also easily email the information as well.

Link Mentions – When you visit Today’s Most Popular links for a particular search term – you can see how many times this link was mentioned.  You can now click on the blue mentions box to see the past 20 users whom have mentioned this link on the real time web.  This can give you an idea as to whom is sharing the links which are popular on the real time web right now.

All links posted by a respective user – When you get to a user page – which lists information and a picture about a respective user – you will see a link to see all links posted by this user.  This will show you the past 20 links posted by this user on the real time web.

The Top 100 –  Sency has published a The Top 100 page which shows you 100 things going on over the past few days.  While the Sency homepage will show you 10 topics going on lately, this new page goes much deeper to give you a wide range of topics which have been talked about recently on the real time web.

Ex: RT @zephomega The dot over the i is called a tittle. #uselessFacts

Sency will continue to add more features, as it is becoming a value able resource for internet users looking to find out what’s going on right now.

Evan Britton
Founder, Sency
Sency – What’s Going On?

Please see this related video from Sency – editor.

I’ve known Wolf and Gosia Garbe from FAROO for a long time, and so I asked Wold if he would write a guest post explaining the differences between single index Google type search architecture and the decentralized / distributed peer-to-peer model that FAROO is based on.

What follows is a pretty long essay, but it is meant to be. If you want to be up on what challenges Google is going to face in the coming decade, you really must allocate some time to read this article.

A guest post by Wolf Garbe, FAROO

Crisis reveals character, and this is especially true for distributed systems. Everything beyond the standard case may led to a crisis if not considered beforehand.

The network wide scale adds a new dimension to everything, completely changing the perspective and puting many centralized approaches into question.

Joining peers, updates and recovery look different from a bird’s eye view than from the ground:

*When the network size grows the bootstrapping algorithm needs to scale.

*Even if the whole system fails, and all peers want to reconnect at the same time the system should be able to recover gracefully.

*Every system needs to evolve over time, hence software distribution is required to work on large scale, perhaps frequently or immediately.

That’s why it is important to look at the scaling of all operational aspects, not only at the main search functionality. The weakest element defines the overall scalability and reliability of a system.

The benefits of a distributed architecture (as low cost, high availability and autonomy) can be fully used only, if the operational side is also fully distributed.

There should nowhere be a centralized element, which can fail, be attacked or blocked as a single point of failure or which does simply not scale. Not for crawling, not for indexing & search, not for ranking and discovery, not for bootstrap, and not for update.

Let’s have a closer look at those six degrees of distribution:

Distributed Crawling

Sometimes only the crawler is distributed, while the index and the search engine are still centralized. An example is the Grub distributed crawler, once used by Wikia Search of Wikipedia Founder Jimmy Wales.

A distributed crawler itself provides only limited benefit. Transferring the crawled pages back to a central server doesn’t save much of the bandwidth, compared to what the server would need to download the pages itself. Additionally there is overhead for reliably distributing the workload across the unreliable crawler elements.

The benefits of such hybrid approach are rather for applications beyond a search engine: if only selected information are transferred back (like scraping email-addresses), and the spider is harder to detect and block for the webmaster, as the load comes from different ip’s.

Distributed crawling will live up to its promises only as part of fully distributed search engine architecture. Where the crawlers are not controlled by a single instance, but crawling autonomous led solely by wisdom of crowd of its users. Huge network wide effects can be achieved by utilizing the geographic or contextual proximity between distributed index and crawler parts.

With FAROO’s user powered crawling pages which are changing often (e.g. news) are also re-indexed more often. So the FAROO users implicitly control the distributed crawler in a way that frequently changing pages are kept fresh in the distributed index, while preventing unnecessary traffic on rather static pages.

Distributed Discovery

Even for big incumbents in the search engine markets, it is impossible to crawl the whole web (100 billion pages?) within minutes, to discover new content timely (billion pages per day). Only if the crawler is selectively directed to the new created pages, the web scale real time search becomes feasible and efficient, instead looking for the needle in the hay stack.

By aggregating and analyzing all visited web pages of our users for discovery, we utilize the “wisdom of crowds”. Our users are our scouts. They bring in their collective intelligence and turn the crawler there where new pages emerge. In addition to instantly indexing all visited web pages our active, community directed crawler is also deriving its crawler start points from discovered pages.

Beyond real time search this is also important to discover and crawl blind spots in the web. Those blind spots are formed by web pages, which are not connected to the rest of the web. Thus they can’t be found just by traversing links.

Distributed discovery also helps indexing the deep web (sometimes also referred to as hidden web). It consists of web pages that are created solely on demand from a database, if a user searches for a specific product or service. But because there are no incoming links from the web, those pages can’t be discovered and crawled by normal search engines, although they start to work on alternate ways to index the hidden web, which is much bigger than the visible web.

Distributed Index & Search

Storing web scale information is not so much of a problem. Expensive are the huge data centers required for answering millions of queries in parallel. The resulting costs of billion dollars can be ommitted can be omitted only with a fully decentralized search engine like FAROO.

Incumbents already envision 10 million servers. A distributed index scales naturally, as more users are also providing the additional infrastructure required for their queries. It also benefits from the increase of hardware ressources, doubling every two years according to Moore’s Law.

Recycling unused computer resources is also much more sustainable than building new giant data centers, which consume more energy than a whole city.

The indexes of all big search engines are distributed across hundreds of thousands computers, within huge data centers. But by distributing the search index to the same to the edge of the network where already both user and content reside, the data have not anymore to travel forth and back to a central search instance, which is consequently eliminated. This prevents not only a single point of failure, but also combines the index distribution across multiple computers with leveraging the geographic proximity normally achieved by spreading multiple data centers across the globe.

Last but not least a distributed index is the only architecture where privacy is system inherent, as opposite to the policy based approaches of centralized search engines where the privacy policy might be subject to changes.

Zooming in from the macroscopic view, every distributed layer has its own challenges again. E.g. for the index peers usually do not behave like they should: They are overloaded, there is user activity, the resource quota is exhausted, they are behind a NAT, their dynamic IP has changed or they just quit.

Those challenges have been perfectly summarized in “The Eight Fallacies of Distributed Computing”. Yet going into all the details and our solutions would certainly go beyond the scope of this post.

Distributed Ranking

A additional benefit is a distributed attention based ranking, utilizing the wisdom of crowds. Monitoring the browsing habits of the users and aggregating those “implicit” votes across the whole web promises a more democratic and timely ranking (important for real time search).

While most real time search engines are using an explicit voting, we showed in our blog post “The limits of tweet based web search” that implicit voting by analyzing visited web pages is much more effective (by two orders of magnitude!).

This also eliminates shortcomings of a Wikipedia like approach where content is contributed in a highly distributed way, but the audit is still centralized. Implicit voting automatically involves everybody in a truly democratic ranking. The groups of adjudicators and users become identical, therefore pulling together for optimum results.

Distributed Bootstrap

The first time when a new peer want to connect to the p2p network, it has to contact to known peers (super peers, root peers, bootstrap peers, rendezvous peers) to learn about the addresses of the other peers. This is called bootstrap process.

The addresses of the known peers are either shipped in a list together with the client software or they are loaded dynamically from web caches.

The new peers then store the addresses of the peers they learned from the super peer. The second time the new peers can directly connect to those addresses, without contacting the super peer first.

But if a peer has been offline for some time, most of the addresses he stored become invalid because they are dynamic IP addresses. If the peer fails to connect to the p2p network using the stored addresses, he starts again the bootstrap process using the super peers.

Scaling
During a strong network growth many peers are accessing to the super peers in order to connect to the p2p network. Then the super peer becomes the bottleneck and a single point of failure in an otherwise fully decentralized system. If super peers become overloaded, no new peers can join the system, which prevents a further network growth.

Recovery
If the whole p2p network breaks down due to a web wide incident and all peers try to reconnect at the same time this leads to a extreme load on the super peers.
This would prevent a fast recovery, as peers would fail to connect but keep tying and causing additional load.
Those problems have been experienced in practice in the Skype network.

Security
Another issue is that the super peers make the whole p2p network vulnerable because of their centralized nature. Both blocking and observing of the whole p2p network become possible just by blocking/observing the few super peer nodes.

FAROO is using a fully distributed bootstrap algorithm, which

*eliminates the super peers as last centralized element, as bottleneck and single point of failure in an otherwise distributed system.

*provides an organic scaling also for the bootstrap procedure.

*ensures a fast recovery in case of a system wide incident.

*makes the p2p network immune to the blocking or monitoring of super peers.

Distributed Update

The distributed system becomes automatically smarter just by the increasing relevance of the collected attention data.
But you may want to refine the underlying algorithms, to improve the efficiency of the p2p overlay, to extend the data model, or to add new functions. And the example of Windows shows that it might be necessary to apply security patches, network wide, frequently and immediately. Updating p2p clients requires a very efficient software update distribution.

10 million peers and 5 Mbyte client software size would require to distribute 50 Terabytes for a full network update. Even for a 100 Mbit/s network connection a central update would last 50 days, if you manage to evenly distribute updates over time.

FAROO is using a distributed, cell division like update instead, where all peers pass on the DNA of a new version to each other within minutes. Of course there is some signature stuff to ensure the integrity of the network.

Divide and Conquer

By consequently distributing every function we ensured a true scalability of the whole system, while eliminating every single point of failure. Our peers are not outposts of a centralized system, but rather part of distributed Cyborg (combining the power of users, algorithms & resources) living in the net.

This is a system which works on a quiet sunny day, but also on a stormy one. It would be even suitable to extreme mobile scenarios, where peers are scattered across a battlefield or carried by a rescue team.

The system recovers autonomously from a disaster, even if there is no working central instance left, the surviving peers find itself, forming a powerful working distributed system again once they awake. If you have seen Terminator reassembling after run over by a truck you get the idea

In biology organisms naturally deal with the rise and falls of its cells, which as simple elements form superior systems. We believe that evolution works in search too, and that the future belongs to multicellulars

Did you know that there are currently over 133,000,000 Blogs?

Its a big number. Now add to that traditional media and journalism sources, and the endless stream of content on Twitter and it is hard not to notice that the media landscape has changed and continues to evolve. Good or bad, this is a reality that makes online search in this space quite important as demand and expectation of news delivery migrates more to an online model. It also makes it apparent that the need for proper filtering and search of media has never been so critical.

At Thoora.com, we believe the key to delivering relevancy in media search comes down to two aspects: aggregation and ranking algorithms.

First, aggregation. Most people have chosen to solve the information overload by visiting a handful of preferred sites, be it a newspaper’s website or a blogging site that hand-selects a few chosen bloggers and posts each day. While this approach certainly addresses the problem of content overload, it also limits exposure to new voices and viewpoints. In some cases, content on these sites are selected by a small group of editors and readers would need to visit multiple websites to see the full dimension of an individual story.

Aggregation and clustering of stories across social and traditional media is key. People want to see a fact-checked article from the AP, a well-researched piece from the Los Angeles Times, a brilliant and insightful post from a blogger, and maybe even the latest real-time reactions from Twitter. In other words, people are looking for the complete picture to each story without having to visit multiple places to watch the story develop.

When we’re talking about a large pool of content, with the goal of giving each voice an equal opportunity to be heard, ranking is critical. Cutting-edge filtering and ranking algorithms can completely automate the process, so there’s no human bias or editorial selection involved. Additionally, these algorithms can factor in the ‘wisdom of crowds’, by considering people (and all of us, not just a select few).

For example, algorithms should look at the activity (or ’signals’) surrounding each story which articles and posts are generating the most comments, the most tweets, the most sharing and forwarding. These are the hot stories of the day and should appear up top in the rankings. Some search engines are starting to look at votes and links from social bookmarking sites, and while voting and like/dislike are certainly good ways to incorporate the human element into determining relevancy and value; unfortunately we know that voting can often be swayed by successful PR efforts.

At Thoora, we remove the risk of human bias by looking at implicit actions, as opposed to votes or link analysis. We incorporate people’s every day actions — such as comments, tweets, and forwards — into our ranking algorithm to determine which stories are generating the most buzz with minimal distortion.

People can use Thoora to discover the day’s biggest stories by browsing the top ranked stories in key categories (i.e. business, entertainment, sports, lifestyle…) or mine the hidden parts of the blogosphere and news with specific searches. We have indexed more than 100 million blogs and collect content from 720,000 blog posts and articles from 4,500 traditional media sources each day. Our content is Algorithmically Curated and our technology is completely automated, we don’t play favorites. For in the end, just as new voices appear on the web each day, technology will need to continually evolve in order to make sure these voices can be heard, without bias, and without making a too large demand on the reader’s time.

Pic2shop

When our company, Vision Smarts, started working on pic2shop nearly a year ago, there was no barcode reader available for the iPhone. There were a few very successful apps for other platforms, notably ShopSavvy and CompareEverywhere for Android. But on the iPhone, most people thought it was impossible to decode UPCs or EANs, because the iPhone 3G did not have an autofocus camera. Images of the barcodes were hopelessly blurry:

Top: blurry barcode captured by the iPhone 3G
Bottom: barcode captured by an autofocus camera, like the newer iPhone 3GS

Thanks to our expertise in image processing, it seemed like a interesting challenge, and we set out to write the signal processing algorithms that eventually led to the release of the first version of pic2shop in April. It was far from perfect, and not everybody found it easy to use, but it worked! About a month later, we were surprised to see another startup come out with a similar product. Since then, we have been engaged in a competition to make barcode scanning faster and more accurate, and to give our users better search results. That other startup is the maker of the wildly successful RedLaser app, now #1 paid app on the App Store.

While RedLaser is a paid app, pic2shop is free, and we are committed to keeping it free. It seems only fair, since some retailers give us commissions on sales (we display all the results we find, regardless of whether we get a commission or not), and pic2shop is ad-supported. The new version, due in a few days, will feature a fully redesigned user interface and faster scanning. We are also continuously adding more retailers to the pic2shop results, and introducing more convenience functions like emailing the list of scanned items, or searching other sites.

There is now a flurry of barcode apps for the iPhone. Some work only on the newer iPhone 3GS, because it has an autofocus camera. Most of the other apps license the technology from the same two sources. The pic2shop barcode scanner is available as an affordable and easy-to-integrate SDK. Please visit our web site (http://www.visionsmarts.com/products/products.html) for all the details.

Just weeks after pic2shop came out, various companies have started contacting us to include barcode scanning in their own apps. We cannot disclose the specifics, but we are well positioned to predict that the most creative uses of mobile barcode scanning are yet to come!

Concerning pic2shop and shopping apps in general, it is safe to say that they will become mainstream very quickly. Not just for price comparisons, but also for coupons, product traceability, recalls, demonstration videos, etc. Apple (with the iPhone) and Google (with Android) have shown people that smartphones are cool and useful, not just another way to be tethered to the office. Thanks to them, mobile computing is the most exciting domain for developers since the Internet boom.

Benoit Maison
Founder,
Vision Smarts SPRL
Belgium

Some useful links:

pic2shop on the iTunes App Store

Vision Smarts’ Barcode reader SDK

Follow pic2shop on Twitterand facebook

This just in:

Vision Smarts is proud to announce the pic2shop Web Integration Kit, a companion to their free barcode scanning App for iPhone devices. Developed specifically for online retailers, shopping portals and web developers alike, the pic2shop Web Integration Kit offers the ability for their customers to lookup a product by simply scanning a barcode from an iPhone.

UPCs and EANs are the traditional barcodes found on virtually all retail products, from books to cereal boxes. With pic2shop’s unique combination of image processing algorithms and machine learning techniques, looking up a product is as easy as aiming the camera at a barcode. Online retailers and price comparison sites can now offer their customers the means to instantly access product information, or buy in their store, all in real time. The whole process takes only a few seconds.

Shopping portals can capitalize on the mobile shopper interest for barcode scanning at no cost. By adding a feature that is highly valued by users, retailers who have already invested in building a mobile-friendly web site will increase their visibility and return on investment. All that is necessary is inserting a few lines of code into their existing web application to display a “Scan Barcode” button for iPhone-bearing visitors (and Android users too).

The pic2shop Web Integration Kit is perfect for:
* Online retailers
* Price comparison sites
* Product reviews (books, dvds, games, consumer electronics, etc)
* Product information (e.g. nutritional, environmental)
* Gift lists and shopping-oriented social media

When the customer presses the “Scan Barcode” button on the web page, their iPhone launches the pic2shop barcode scanner app.
If pic2shop is not installed, a message is briefly displayed, then the user is automatically taken to the iTunes App Store where they can install pic2shop for free.

If pic2shop is installed, it goes immediately in scanning mode. By aiming the camera at the barcode for one second or two, the UPC or EAN is read (even on older iPhone models). As soon as the code is recognized, pic2shop opens the Safari web browser to search the web application with the barcode digits. The entire processs only takes a few seconds, and is much faster than typing 12 digits or a product name using the on-screen keyboard. And considerably more fun.

Among the API’s most prolific features is its zero cost to implementation, ability to help retailers enhance the shopping experience, and generate buzz about their products. By taking advantage of the iPhone’s custom URL scheme, any app or web page can launch pic2shop, have the user scan a barcode, and then pass the barcode back to the calling web page or app. Vision Smarts has made a web page specific for developers available on their website. For application developers, there is a complete Xcode project that shows how to implement this mechanism in a just few lines of code and an online demo for web developers.

pic2shop Feature Highlights:
* Comfortable and simple User Interface
* Takes only a few seconds
* Compare prices and decide whether to purchase in-store, or to buy online, all in real time
* Allows other iPhone apps and web apps to use pic2shop as a free barcode scanner

“Shopping by barcode is one of those things that make the mobile internet different, and so exciting,” said Benoit Maison, founder of Vision Smarts. “You just can’t do that at your desk. With this new feature of pic2shop, any shopping web site can provide additional value to their users, at no cost. Our objective is to make pic2shop the standard iPhone barcode reader”

Trackle

Pavan Nigam: CEO and Founder

It was about ten months ago when Trackle was launched and I have learnt a lot since then – from the usage of Trackle by our users, and by broader trends in the consumer websphere.

Let me first talk about Trackle.  I have been very impressed by the pace of user adoption.  Our subscriber community has been doubling every couple of months.  The most popular items being tracked remain crime, shopping deals, people (especially their own names), jobs, travel, stocks, sports and real estate.  An average user tracks about ten items which generate about twenty alerts per day.  Most users seem to be happy with getting these alerts via their daily digest – and many of them look forward to it.  On many occasions, when there were delays in our email server, we have received urgent emails from subscribers inquiring about their digest status – a few even called!  Of course, we would rather have subscribers go to trackle.com more often so that they can discover new tracklets and new features that we add regularly.

The most popular tracklets for SMS alerts are sports scores, breaking news, and select social networking events (eg. Facebook posts).  Now comes the surprising part.  I am amazed at how many professionals are using this service.  Marketing and Sales folks are using it for tracking companies – competitors, prospects and customers.  Recruiters are using it to track people.  I know of even a detective agency using it for tracking a variety of items including classified and media site postings.

The amount of information on the web has become so overwhelming and so multi dimensional (news, blogs, media sites, social networks and more), that it can be incredibly time consuming to stay on top of this varied information – and, that assumes that you have the time to do that in the first place.  Our product team is devising new features to simplify some of these tasks.

In the websphere, the most astonishing trend has been Twitter.  For a company that most of us had not heard about a year ago, it is amazing to see its pervasiveness.  It has become synonymous with real time search – yet another term that was not in the mainstream lexicon a year ago.  Now, everyone is tweeting – from Larry King to Paris Hilton to Kobe Bryant.  And, so many (so called) real time search companies are trying to mine these mounds of data to get to the few hidden gems of information.

Of course, the fundamental flaw is that the gems are far and few between since the bulk of the tweets are mundane and frivolous.  For now, Twitter appears to be the domain of Marketing and PR folks who are running all kinds of experiments to gain experience in extracting value from these billions of tweets.

The other major trend is the promotion of APIs by social networks such as Facebook and LinkedIn.  Companies are exploring ways of integrating these services in their own offerings along with extracting value from the immense community generated information that became accessible through these APIs.

I am convinced that there is a tremendous value proposition in mining an aggregation of all this data – whether it comes from tweets, Facebook posts, blogs, news sources, media sites and more.  And, I am sure that some startups somewhere in Silicon Valley are already working on that!

Source: Trackle CEO blog here:

Get ready for a shocker – the vast majority of online real estate sites are a waste of time for a real life home buyer. If you were buying a home in your town of choice and you knew there were 10 homes that met your criteria – would you use a site that only showed 6 of them?

Sadly the vast majority of consumers have no idea that when they’re on the major Real Estate search sites this is exactly what’s going on.

I’m not going to name names in this post, but the bottom line is that virtually all the real estate vertical search players as well as the real estate sections of the top horizontal search engines get their home listings via broker/agent syndication feeds.  What this mean is that in order for these search engines to have complete coverage, they have to get every agent or broker to upload every new listing, every day. And as a result, no matter how terrific their User Interface (UI) is or how much other information they throw around the properties on their site – they are missing big swaths of current listings that consumers want and need.

They’ll deny it, they’ll tell you that they have “92%” of the listings – but what this really means is:

They know there are x number of active Multiple Listing Services (MLS) listings in the country;

Their database has y # of total properties;

x/y = 0.92;

That is very different from having 92% of the actual MLS listings that are active in any given market!

What it means is that they have a ton of other stuff in the database … foreclosures … homes that were already sold … strange hybrid listings … and so on.

The truth is that only those websites owned by, or operated on behalf of, an actual Real Estate Brokerage or Agent have access to the full MLS data set.  There is one exception, Realtor.com, which by virtue of its relationship with NAR gets access to the vast majority of MLS listings.

So here’s a tip - there are two real estate data acronyms to look for that will help you as a home buyer determine if you’re likely looking at the complete MLS data set.  One is IDX (Internet Data Exchange) and the other is VOW (Virtual Office Website).  These types of data sets typically (although sadly not always, more like 9 out of 10 times) mean you are viewing almost everything available from the local MLS.  Look for them in the home listings themselves or at the footer of the real estate websites you’re using.

“Wait a second,” you say … “isn’t your company Roost.com a vertical search site subject to the same limitations?”

Well I’m glad you asked! If you poke under the hood, Roost is actually a hosted network of individual Broker and Agent websites with a smart search engine on top.  That allows us to do a lot of cool things, but most importantly for this discussion it means that when you’re searching on Roost, you’re actually looking at a Broker or Agent’s website which has access to the good stuff.

Go ahead … give us a try … click here.

Alex Chang
CEO of Roost

It is hot!  So hot that Google legitimized it with their recent update.  Buzz is building on social search like never before, as this handy trend graph from BlogPulse indicates:

But what is social search?

According to different industry voices, social search …

“… involves combining social graph information with pure algorithmic search results.“

“… combines traditional algorithm-driven technology with online community filtering.“

“… helps you find more relevant public content from your broader social circle.“

“… is information retrieval, way finding tools informed by human judgment.“

These definitions are quite broad and varying, and the result is that so many solutions have come under the banner of “social search”. However, one thing common across these diverse set of tools and services is this: they’ve all used collective intelligence (wisdom of the crowds, if you will) in some way to improve what they present to users in the search process.

My company, Zakta, is also a recent entrant in “social search”, and we refer to Zakta as a personal and social Web search engine.  Our aim is to improve informational searches on the Web.What prompted me to write this post was the recent Google announcement on social search.  Our small community of users felt that Google was encroaching on Zakta’s turf, and I thought I should help clarify where Zakta fits.

First, Zakta has no turf – Google dominates all Second, we are trying to add value to the informational search experience of users through a comprehensive solution framework, so we don’t get into feature battles with giants that we don’t have a chance of surviving (as it is, I’ve been called “Nuts!” to start Zakta at this time, and having my tiny company enter into a feature race with the giants should surely bring me the label “Stupid” too – something I’d very much like to avoid!).

Here’s a personal framework that I’ve used to understand the social search space myself and to steer the design and development of Zakta.

On the X-axis, I plot the Personal (focus is on the individual) versus Communal (focus is on the community as a whole) continuum.  On the Y-axis, I plot the nature of information that users interact with, in terms of whether it is Disorganized (focus has been on mere collection of information) versus Organized (focus is on curation of digital information).

Using this framework, I’ve mapped a handful of social search services and tools that I’m somewhat familiar with. So, admittedly, both this framework and my characterization of these services in this framework are based on my personal viewpoint.  I’d welcome comments for improvement, or other viewpoints.  I hope you find this framework a useful tool to make sense of what is happening with this growing space that is simply called “social search”.

Now I can put Zakta into this context. As portrayed in this framework, Zakta is a personal Web search engine because it provides tools to deliver a personal search engine experience that puts the searcher in control.

Zakta is also a social Web search engine in many distinct ways:

• It enables a searcher to collaborate with people they trust to find, collect, organize and share information on topics of interest
• It enables a searcher to connect to others they trust and discover information relevant to their interests from the recommendations made by their trust-network
• It enables a searcher to benefit from the contributions of the community of Web users in the form of published Zakta Guides on topics of interest
• It enables a searcher to gain from the ongoing relevance ranking improvements that happen behind the scenes that take into account the signals of recommendation expressed by not only the user’s trust-network, but also the community as a whole not just on Zakta, but elsewhere on the Web

As you can see, Zakta is not as much about finding what your social network has been saying.  Rather it is all about empowering you personally and helping you benefit from your trusted network as well as the community at large to improve your own Web search experience and discover useful information on an ongoing basis on topics of your interest.

As always, I’d love to get your feedback!

As we launched we have received some questions about who we are and what we are trying to accomplish. One of the first was Scott Lowe from scottlowe.org – one of the bloggers we’ve selected for our category Virtualization. I took the chance to give Scott a sense of what we are trying to accomplish that I think fits well in an initial description of our service on our blog. So without further ado here’s the body of the email – hope it gives you a good sense of what makes us different from most other blog communities on the web:

Hi Scott –

My marketing manager informed me about ur having questions about trakkrz.com via twitter. Hopefully I can give you a vision of what we’d like trakkrz to be as it grows:

A place where people passionate about a topic can find what is being said by the top bloggers, trakk their posts, comment together and direct people to real people writing real, current articles using their expertise.

Here’s our strategy for doing so:

* Make sure spam doesn’t get into the system – if we only select blogs with true people behind them that really care about their field then spam will never get in – don’t just follow some scraped RSS feed for the topic and post anything that whizzes by – if those bloggers start to spam we’ll simply remove them – we find that almost anything of significant importance on a topic is mentioned fairly quickly by the top bloggers in a topic.
* Highlight the best of what is already good content – give users the ability to “trakk” what they consider to be the best content for each topic – let people see what is the most trakked today, this week and all time – this way people can see the truly exceptional articles at a glance and then go visit them to get the full scoop – this will take time and building the community but we’re committed to doing just that.
* Share revenue – here is a tough one that we’ll continue to refine and improve – because we want people who really care to drive the community, what better place to get them than our currently selected bloggers and what better way to motivate them than sharing any revenues we generate (I know they’re tiny now – but we can only hope ) – therefore as people sign up you’ll see user’s adsense code showing up in our source code – along with our own ads.
* Be transparent – we give followed bloggers access to trakkrz-analytics.com so they can see just how much traffic we’re getting, how many visitors we’re sending their way, what keywords are “popping” and more – it’s built on piwik, the opensource google analytics alternative
* Make it fun with some interesting photos and videos from flickr and youtube – seeing what is popular via other, non-blog media we think gives a little unique flavor to the web and sometimes adds perspective to the written blogged view.

Really I’d try to say that we’re trying to be like the love-child of Digg and Alltop – a commentable place but that has that added filter of editorially selected blogs.

Thanks everyone for your interest in trakkrz.com. We look forward to your participation in what we hope will be a wonderful community to support bloggers who provide great content for the rest of us.

Todd Hogan, Founder – trakkrz.com

The Melody Catcher

“What’s that song stuck in my head?”

Introduction:

Massive and ever increasing volumes of music are available online. As long as titles or names of composers, performers or artists are known, it’s easy to find related information. If only the melody is remembered, however, common search engines fail to provide any results. Many ideas and concepts of melody finders or Music Information Retrieval (MIR) Systems have been described. For a good overview see: http://en.wikipedia.org/wiki/Music_information_retrieval#MIR_applications With the Melody Catcher, we aim to make melody searching accessible for the broad public.

The Concept of the Melody Catcher
The Melody Catcher system (MC) is based on a novel approach to reach its goal, a better disclosure of online audio sources. The matching technique focuses on monophonic and well defined online audio sources rather than the more complex polyphonic audio sources. In addition, the system improves and combines aspects of existing melody search systems, optimizing the user interface, increasing error tolerance and improving the efficiency and accuracy of search results.

The use of monophonic MIDI files as the basis for matching is not new as such. Some of the existing melody search systems use such MIDI files in their database. However, in contrast to the MC, no such system includes arbitrary sources from the Internet.

Several online music collections use monophonic (a.k.a. single voice) MIDI files. Such collections can be incorporated automatically into the MC’s matching system with its own web crawler. In addition, there are thousands of sites offering collections of polyphonic MIDI files. These files are already accessible by MC by text only search. In the future, we will improve the MC algorithm to include searching for polyphonic MIDI files. We will also propose guidelines for content authors how to create polyphonic MIDI files for an optimal inclusion into MC’s database.

Furthermore, many sites offering ring tones for cell phones provide monophonic MIDI files for pre-listen functionality. We have found that these sites are an immense source of popular melodies. Other ring tone file formats such as RTTTL can easily be converted by the web crawler for inclusion into the database.

Currently there are some 10 melody finders on line. Evaluations learned, however, that their overall results still are disappointing. In most cases the wanted song is not found or at best low in the results.

The Melody Catcher is based upon a different approach.

Its matching technique is focused on monophonic and well defined on-line audio sources, like MIDI audio files. This is leading to far better results than systems focusing on polyphonic audio sources and in most cases showing the wanted song in top of the results. Although partly based upon its own database the system is mainly dased upon on on-line sources of such audio files, found by a web crawler. In the results the user can play the the wanted melody and find its title, but he is also linked back to the source site of that melody. In this way the system can be of help to discloses sources of MIDI files, containing the wanted melody and in most cases also similar songs. On the site under about one can read more about its history.

Best Regards

Dr. Jan L. van Os
The Melody Catcher