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With Facebook’s IPO just around the corner, the timing of Google’s latest press blitz should probably be regarded with a healthy dose of suspicion, but the unveiling of the Knowledge Graph is an important step in Google’s journey — and a reaffirmation of values diluted by recent dalliances in social networking. Writing for The Atlantic, Alexis Madrigal perhaps describes it best;

“To me, this update is the epitome of what Google does best. The graph makes the process of Googling something faster, easier, and better. The corporate imperative to keep people searching on Google in the face of renewed competition matches up very nicely with consumers’ desires for the best, fastest search experience. That hasn’t always been the case with the company’s social search integration, so this update feels so refreshing. It’s like a friend in the midst of a midlife crisis returning the Porsche and embracing a trusty new four-door.”

(my emphasis)

Exactly. As I’ve written before, I strongly believe that semantic smarts should be hidden very, very deep, and that semantic technologies are at their best when they quietly and unobtrusively make some existing process better. That’s why I like TripIt so much. It gets travel plans into my calendar faster and more accurately than I could type them, and throws in a whole heap of added value as a byproduct of the data ingest process.

Google’s Knowledge Graph is similar; it works with existing search behaviour, and unobtrusively adds a little extra value.

It’s going to be fascinating to see which direction Google takes this capability, and my latest column for SemanticWeb.com explores that in a little more detail.

Those (like me) based outside the US need to remember that none of this works on sites other than google.com right now.

And if you’ve not seen Google’s introductory video, take a look.

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• Google Goes Back to What It Does Well: Finding Things(readwriteweb.com)

Computer rendering of the Chicago Spire. This is not the current design as of July 12, 2008. (Photo credit: Wikipedia)

We have become accustomed to the simple yet all-powerful search box. ‘Advanced’ search options and arcane query syntaxes have largely been replaced by the learned behaviour of throwing some words at Google*, ignoring the sponsored links, and (usually) finding what we want somewhere in the first 5-10 proper results. A Google search is certainly impressive (especially to those who really remember how poor some of the earlier search engines were), but it remains far from perfect. Do Google’s limitations create a big enough opportunity for others to grab credible market share?

In recent weeks, I’ve received a flurry of information on partial alternatives to Google’s market-dominating search engine. Most appear useful in their own niche, but I doubt even their creators would be surprised to learn that none tempt me to change my Google-powered default search behaviour.

Far more damaging for their prospects, any hope they had of attracting my occasional use is dashed by the very way that they seem to work. They may excel in certain verticals, or in particular types of search, but most make the unfortunate mistake of expecting me to mould my behaviour to them. The pain of remembering how to concoct effective queries for each of these tools far outweighs the gain of their ‘better’ search result, creating a vicious spiral from which they must surely struggle to escape.

Take London-based Sehrch, for example. Behind a name that’s impossible to pronounce or communicate to others (say “Search for that on sehrch,” and 99.99% of those you tell will end up here rather than here) lies an interesting attempt to bring structure to web search, with a little help from data sources like Freebase and DBpedia.

Google could probably find you buildings with 150 floors (just the Chicago Spire, according to both my first page of Google hits and the trusty Wolfram Alpha), but might struggle to find those that were higher. Sehrch finds 14, and they appear in a neat list that’s free of the other stuff that cluttered my Google results. I’m confused that the Chicago Spire (sehrch agrees that it has exactly 150 floors) appears in a search that was quite clearly looking for buildings with more than 150 floors, but the other 13 appear to be valid. I’m not a tall building aficionado, so don’t know how many buildings Sehrch failed to find, but it certainly did better than Google (where the results are a mess, and would require careful reading) or even Wolfram Alpha (which reckons there are two ‘notable’ buildings with more than 150 floors). All of the searches returned a mix of actual buildings, planned buildings, and cancelled buildings.

But — and it’s a big but — both Google and Wolfram Alpha were pretty straightforward to search with normal search behaviours. Sehrch was not.

Wolfram Alpha took a perfectly realistic plain-text search for “buildings with more than 150 floors” and interpreted it to arrive at a query that the system could understand and operate upon. Sehrch, on the other hand, expected me to build a query from the 130,342 object properties and 249,777 object types that it understands. Frankly, if this search hadn’t been one of the examples, I doubt that I’d have formulated (type:building) (floors>150) correctly.

Extracting meaning and structure from data, and making it available to deliver better search results is a valid and useful thing to be doing. If you want to know about female teenage pop stars from Sweden, Sehrch can give you six. Both Google and Wolfram Alpha might be able to get there too, but I gave up trying to work out how. Sehrch may be returning ‘better’ results, but it’s too different to use.

Wolfram Alpha understands the power of meaning and structure too, but is getting better at hiding the power behind pretty user-friendly queries.

Even Google, the home of brute force computation across the unstructured mess of the Web, recognises the power of meaning and structure, and is doing something about it.

Enter some maths into a Google search box, and you don’t get a list of web pages containing calculators. You get the answer.

Enter a flight code into a Google search box, and you don’t get a list of airline or airport web pages. You get the time the flight is expected to land.

Enter a stock market code into a Google search box, and you don’t get a list of stock exchanges or companies. You get the share price, and a graph showing how it’s changing.

Type ‘showtimes’ into a Google search box, and you get a list of films showing at cinemas near you.

Google is getting better at structure. The company bought Freebase. The company is one of those behind schema.org. It’s investing in WikiData. Google knows that structure and meaning matter, and it’s applying itself to baking both into the search experience with which users are already familiar. Google is getting better, but it’s improving by doing more to anticipate the user’s needs, not by forcing the user to adopt arcane query syntax.

I use Google every day. For some searches, it’s really not (yet) the best place to answer my query. In those situations, I’ll turn to some other tool. Am I going to turn to one like Wolfram Alpha which works in a very different way, but hides that behind a box that typically takes the queries I’m used to typing? Or am I going to turn to one like Sehrch, which works in a very different way and expects me to work in a different way, too?

Sadly for Sehrch, until it finds a way to hide search syntax from the casual user, all its clever search capabilities are going to go unused. And it’s not alone. As I mentioned at the start, I’ve received pitches from a load of similar companies recently. All are interesting. All expect me to change too much without offering enough benefit in return. All therefore, ultimately, fall short.

Structure is good. Meaning is powerful. But I want the computer to infer, discover, reason and suggest. The last thing I want is to go back to typing arcane search syntax. And I very much doubt that I’m alone.

Note: Yes, I know that other big-name search engines like Bing exist and are broadly comparable to Google in scope and capability. But, honestly, they’ve never demonstrated a compelling reason for me to switch away from Google either. Feel free to substitute the name of your favourite mainstream search engine everywhere I wrote ‘Google’.

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Image via Wikipedia

Last week, open standards body OASIS unveiled yet another shiny new standards effort. The OASIS Topology and Orchestration Specification for Cloud Applications (TOSCA) Technical Committee hopes to make it “easier to deploy cloud applications without vendor lock-in,” and to support moving from one cloud to another. The usual suspects — the likes of IBM, CA, and Cisco — are on board. The usual holdouts — Google and Amazon, of course — are not. So what is TOSCA trying to achieve? How does it fit alongside all the dead, dying, or ponderously deliberating cloud standardisation efforts that have gone before? And without the giants of the cloud, is there really any point bothering?

As I’ve probably mentioned before, involvement in various national and international standardisation efforts played a big part in my early career. I went to the working group meetings in odd (but often beautiful) locations. I participated in the conference calls. I engaged on the mailing lists. I drafted and edited and reviewed the documents. I completely buy into the idea that there is a place for foundational standards, developed through consensus-building and maintained for the long haul by organisations that stand apart from the vested interests and their competing agendas.

I also believe that there’s a time and a place for these standardisation efforts. Do it too soon, and we end up ossifying something that needs to be in a state of flux. When you don’t know what the best way to prepare a meal is, it’s too soon to print the recipe book. We need to try different approaches, and we need to be able to throw away the attempts that didn’t work out. More worryingly, standardisation efforts can be used for political ends. They can be little more than a rod with which to beat the (usually dominant) competition. At best a distraction, or a talking shop for those unwilling or unable to just get on and do something. At worst, one amongst a toolchest of dirty tricks in a broader war for hearts, minds, and — ultimately — wallets.

The cloud market is a fascinating place. There are leaders and there are followers. There is innovation, and there is competition. There is agreement, and there is debate. For all the rhetoric, and all the posturing, we really don’t yet know the right answer to many of the cloud’s questions.

Maybe TOSCA and the Open Data Center Alliance and IEEE and the rest are — still — too early, and should be content to let the market thrash out a few more of these issues before anyone tries to write anything down? And when it is time to write some stuff down, let’s make sure we focus on specific, finite, tangible, atomic tasks rather than “the cloud.” As Dave Roberts commented in regard to TOSCA’s scope;

“That goal is so large, that I think it’s probably unbounded. When problems get unbounded, the best you can ever hope to achieve is to solve a large enough subset of the problem that the solution is still interesting. If you can’t achieve that, people ignore the solution because it fundamentally doesn’t help them. There is always an ‘interesting’ part of the problem space that they have to solve a different way, and that undercuts the use of the partial ‘solution.’”

And as for Tosca? Things didn’t end well for her, did they? Might TOSCA’s fate, too, be sealed?

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Carr blogged on Friday, noting that video from the event has been made available on YouTube, and I’ve been steadily working through the material ever since.

Krish followed up on CloudAve with his take, flagging Carr’s presentation as of particular interest. I liked Carr’s presentation too (although prefer our podcast, as I think he went deeper there), and found much to value in most of the other talks as well.

My particular highlights, I think, were three sessions later in the day;

Paul Cheesbrough (CIO at Telegraph Media Group), Francois Blanc (CIO at Valeo), Todd Pierce (SVP & CIO at Genentech) and Andy Beale (CIO at Guardian Media Group) participated in a panel session (embedded below) to discuss their real-world experiences of rolling Google Apps out across large organisations. The key take-aways? Benefit won’t be recognised across the board until six months in, and Executive Assistants need to be kept on-side as their day-to-day work inside people’s calendars is disrupted… and they’re both ‘loud’ and ‘influential.’

Google’s Dave Girouard covered similar issues from the company’s perspective, and is clearly someone to add to my list of podcast targets.

My final highlight was Geoffrey Moore (he of Core, Context, and Chasms), applying some of his broader business ideas to the Cloud. I’d certainly like to explore some of his arguments a little further another day…

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Searching for pizza in Google returns exactly what I’ve become accustomed to; a badly incomplete list of pizza restaurants and take aways. My initial presumption was that True Knowledge would do something pretty similar, maybe with the added smarts to find a few more establishments that didn’t explicitly say ‘pizza’ in their name or description.

I presumed wrongly. Instead, True Knowledge returned a pretty comprehensive list of shops that sell pizza; right down to the pokey little ones attached to petrol stations. I’m impressed by the comprehensiveness, and I’m impressed by the reasoning involved in working out that supermarkets like Netto or Morrisons sell pizza.

Comprehensiveness and computational cleverness aside, the stark differences in interpretation of my intention strike me as interesting; is either more ‘right?’ And if we assume, for a moment, that True Knowledge is the one that got it right, does that matter when practically everyone has become conditioned to expect the interpretation Google used?

I did raise this with William during our call, and some of his thoughts on the matter are worth hearing.

What do you think?

Pizza image by ‘wEnDaLicious,’ shared on Flickr under a Creative Commons license.

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Image via Wikipedia

I mentioned the Semantic Web Gang podcast last week, in the context of our upcoming Live appearance at the Semantic Technology Conference in San Jose next month.

This month’s show was recorded yesterday, and is now available. During the conversation, Gang members dig into the two hot stories of the moment; the launch of Wolfram Alpha and Google’s apparent embracing of semantics with their ‘Rich Snippets.’

Have a listen, and see what you think.

The Semantic Web Gang podcasts are sponsored by Talis. Show notes are available.

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Amid mounting international concern that the guidance lasers aboard Jeff Bezos‘ new Floating Amazon Cloud Environment would interfere with Rudolph‘s sense of direction, sources close to the Amazon Web Services team tell me that they’ve been forced to alter priorities and switch attention to an early release of the next product on their roadmap.

Today sees the release of Amazon‘s latest web service; the Hadoop-powered Elastic MapReduce;

“Using Amazon Elastic MapReduce, you can instantly provision as much or as little capacity as you like to perform data-intensive tasks for applications such as web indexing, data mining, log file analysis, machine learning, financial analysis, scientific simulation, and bioinformatics research. Amazon Elastic MapReduce lets you focus on crunching or analyzing your data without having to worry about time-consuming set-up, management or tuning of Hadoop clusters or the compute capacity upon which they sit.”

The company’s press release quotes VP for Product Management & Developer Relations, Adam Selipsky, who notes;

“Some researchers and developers already run Hadoop on Amazon EC2, and many of them have asked for even simpler tools for large-scale data analysis. Amazon Elastic MapReduce makes crunching in the cloud much easier as it dramatically reduces the time, effort, complexity and cost of performing data-intensive tasks.”

MapReduce was brought to prominence by Google, and is one of the principal techniques at that company’s disposal in enabling them to break massive data sets into manageable chunks suitable for cost-effective processing on the commodity hardware for which they are known. The abstract for a Google research paper on the topic outlines the value proposition reasonably succinctly;

“MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper.

Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program’s execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system.

Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google’s clusters every day.“

Hadoop is a Yahoo!-nurtured Open Source equivalent to Google’s MapReduce, managed as a project of the Apache Software Foundation, and reputedly scalable to handle many petabytes of data distributed across thousands of CPUs.

As Adam noted in the press release, customers (such as the New York Times and Netflix) are already using Hadoop on Amazon’s Web Services. Today’s announcement makes it easier to cost-effectively and transparently commission (and decommission) the required compute resources. This is the ‘elasticity’ referred to in the new service’s name, and is an increasingly important aspect of the current generation of Cloud-based compute services; much of the economic value proposition lies in only using (and therefore paying for) the resources you actually need to complete a task. If demand increases, the number of (virtual) machines available should rapidly increase to cope, and they should shut back down just as rapidly when the demand passes;

“Amazon Elastic MapReduce enables you to use as many or as few compute instances running Hadoop as you want. You can commission one, hundreds, or even thousands of instances to process gigabytes, terabytes, or even petabytes of data. And, you can run as many job flows concurrently as you wish. You can instantly spin up large Hadoop job flows which will start processing within minutes, not hours or days. When your job flow completes, unless you specify otherwise, the service automatically tears down your instances.“

Elastic MapReduce is currently available only for data centres in Amazon’s US region (so non-US customers can use the service; they just have to be able/willing to transfer the data beyond their borders), and is priced in addition to existing EC2 instances with Elastic MapReduce on a $US0.10 per hour ‘small’ instance costing a further $US0.015 per hour (yes, 1 and a half cents per hour) and on a $US0.80 per hour ‘extra large’ instance costing a further $US0.12 per hour.

Elastic MapReduce is another nice example of slow, incremental improvement to Amazon’s core Web Services offer.

It remains to be seen, as developers get down to using it for real, whether it’s pitched as a low-end disruptor that simply rounds out another piece of the emerging AWS whole, or if it’s a viable competitor in its own right to the recently announced Cloudera which sees taking Hadoop to mainstream enterprise customers as its raison d’etre;

“Cloudera can help you install, configure and run Hadoop for large-scale data processing and analysis. Get Cloudera’s Distribution for Hadoop and start working with Big Data today.“

Update: Amazon’s Jeff Barr provides a lot more detail in a post to the AWS Blog.

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Everyone who wants to do so should have had their chance to read the Open Cloud Manifesto by now, and to see the list of companies putting their names to it. As expected, Microsoft, Google and Amazon are not there.

Reuven Cohen of Enomaly is one of those involved in bringing the Manifesto to fruition, and I spoke to him on Thursday evening last week to hear more.

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The paperback edition of Nick’s book has just been published, and includes an additional chapter that introduces ‘The Cloud 20′; representative examples of the different ways in which companies big and small, new and old are engaging with the Cloud Computing phenomenon.

I was delighted when Nick agreed to speak with me over the weekend, and the resulting podcast has just been released so you can listen in on our conversation.

As well as talking about the book, which most readers of this blog have doubtless already read, we looked more broadly at some of the trends shaping consumer and corporate attitudes to the Cloud. How does the current economic climate, for example, affect the rate of adoption of utility computing or of Cloud-based applications such as those from Google?

Have a listen, and let us know what you think…

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In my recent cull of subscriptions to print media, BusinessWeek had no difficulty whatsoever in avoiding the chop. It consistently offers a useful and timely perspective on events in the world around me, and (subjectively) seems to intelligently consider the tech perspective on things more often than some of its competitors.

Last September, the BusinessWeek.com site rolled out the beta of a new service; Business Exchange. Closely linked to stories in the magazine and features on BusinessWeek.com, the Business Exchange is a fledgling social network within which members can access background material on stories, submit additional resources of their own, and comment on the content they find. Business Exchange links frequently feature prominently at the end of articles in the magazine, but I’m quite surprised at how rarely I find myself explicitly directed back into the magazine from the site. For a quick introduction, see this video of a recent presentation by BusinessWeek Executive Editor, BusinessWeek.com Editor in Chief (and active twitterer), John Byrne.

According to BusinessWeek’s Director of User Participation, Ron Cassalotti, over 1,000 topics have been approved on the site since it opened four months ago. One of those (the Semantic Web, of course) was started by me, but I also consume and contribute content across a range of other topics relevant to my business interests. Much of this activity is relatively passive, but the site also offers the ability to grow a network of like-minded fellow members, to flag items of interest, and to comment on content shared by others.

User profile and topic pages are visible to non-members, and also rank highly in Google; as Ron demonstrated by showing me how much higher my Business Exchange profile page ranks than my far older LinkedIn profile.

Topics are suggested by BusinessWeek staff and by members of the site, and I get the impression that topics tend to be approved if the topic is in-scope (for BusinessWeek readers) and actively discussed out on the open Web. Ron tells me that there is no formal taxonomy for topics, which certainly makes it more straightforward for his team to adapt to evolving member interests and the shifting nature of the News. The lack of a formal taxonomy raises issues of its own, of course. I, for example, followed both ‘Cloud Computing‘ and ‘Cloud Computing Research.’ The former was proposed by Business Exchange member Ralph Perry, and the latter by BusinessWeek Senior Writer Stephen Baker in gathering background content to inform articles in the magazine. For a while I simply cross-posted content to both, but the numbers would suggest that Stephen’s topic is ‘winning,’ and attracting the eyeballs. Presumably at some point a back-end process (or one of Ron’s team) will make a decision to simply merge the two topics?

The network – and its features – are clearly still evolving, and there’s a way to go. I do find myself on the site most days, though, exhibiting web site visiting behaviour that I thought I’d left behind years ago in favour of my RSS reader.

And today? I find that I’m the Business Exchange’s Featured User, there for all the world to see… 

Thanks to Ron and the team, and if you keep up the good work I’ll keep coming back.

My next step, of course, is to move from being Featured User on the Business Exchange to getting my work printed in BusinessWeek itself. Then my social network-sceptical mum really will be impressed!

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