FxGqlC: Added support for DateTime datatype

SELECT convert(string, convert(datetime, '2012-07-13'), 'yyyyMMdd HH:mm:ss') 
-- Formats datetime using a format string, as defined by the .net Framework
--   "Standard Date and Time Format Strings" (http://msdn.microsoft.com/en-us/library/az4se3k1), and 
--   "Custom Date and Time Format Strings" (http://msdn.microsoft.com/en-us/library/8kb3ddd4.aspx)
SELECT datepart(day, '2012-07-13') -- returns 13
-- valid datepart values are: (with examples for '2012-07-12 23:59:50.1234567')
--    year,        yy, yyyy :   2012
--    quarter,     qq, q    :         3  (1 ... 4)
--    month,       mm, m    :         7  (1 ... 12)
--    dayofyear,   dy, y    :       194  (1 ... 366) 
--    day,         dd, d    :        12  (1 ... 31)
--    weekday,     dw, w    :         5  (1 = Sunday ... 7 = Saturday)
--    hour,        hh, h    :        23  (0 ... 23)
--    minute,      mi, n    :        59  (0 ... 59)
--    second,      ss, s    :        50  (0 ... 59)
--    millisecond, ms       :       123  (0 ... 999)
--    microsecond, mcs      :    123456  (0 ... 999999)
--    nanosecond,  ns       : 123456700  (0 ... 999999900)
SELECT dateadd(day, 10, '2012-07-03')
-- returns 2012-07-13
SELECT datediff(day, '2012-07-03', '2012-07-13') 
-- returns 10
SELECT datediff(day, '2012-07-12 23:59', '2012-07-13 00:01') 
-- returns 1, the number of day-boundaries crossed (as in T-SQL)
SELECT datediff(day, '2012-07-13 23:59', '2012-07-13 00:01') 
-- returns 0
SELECT datediff(day, '2012-07-14 23:59', '2012-07-13 00:00')  
-- returns -1
SELECT getdate(), getutcdate() 
-- returns current DateTime in local and UTC/GMT time

Reflections on Digital Interactions: Thoughts from the 2012 NA Faculty Summit

Posted by Alfred Spector, Vice President of Research and Special Initiatives

Last week, we held our eighth annual North America Computer Science Faculty Summit at our headquarters in Mountain View. Over 100 leading faculty joined us from 65 universities located in North America, Asia Pacific and Latin America to attend the two-day Summit, which focused on new interactions in our increasingly digital world.

In my introductory remarks, I shared some themes that are shaping our research agenda. The first relates to the amazing scale of systems we now can contemplate. How can we get to computational clouds of, perhaps, a billion cores (or processing elements)? How can such clouds be efficient and manageable, and what will they be capable of? Google is actively working on most aspects of large scale systems, and we continue to look for opportunities to collaborate with our academic colleagues. I note that we announced a cloud-based program to support Education based on Google App Engine technology.

Another theme in my introduction was semantic understanding. With the introduction of our Knowledge Graph and other work, we are making great progress toward data-driven analysis of the meaning of information. Users, who provide a continual stream of subtle feedback, drive continuous improvement in the quality of our systems, whether about a celebrity, the meaning of a word in context, or a historical event. In addition, we have found that the combination of information from multiple sources helps us understand meaning more efficiently. When multiple signals are aggregated, particularly with different types of analysis, we have fewer errors and improved semantic understanding. Applying the “combination hypothesis,” makes systems more intelligent.

Finally, I talked about User Experience. Our field is developing ever more creative user interfaces (which both present information to users, and accept information from them), partially due to the revolution in mobile computing but also due in-part to the availability of large-scale processing in the cloud and deeper semantic understanding. There is no doubt that our interactions with computers will be vastly different 10 years from now, and they will be significantly more fluid, or natural.

This page lists the Googler and Faculty presentations at the summit.

One of the highest intensity sessions we had was the panel on online learning with Daphne Koller from Stanford/Coursera, and Peter Norvig and Bradley Horowitz from Google. While there is a long way to go, I am so pleased that academicians are now thinking seriously about how information technology can be used to make education more effective and efficient. The infrastructure and user-device building blocks are there, and I think the community can now quickly get creative and provide the experiences we want for our students. Certainly, our own recent experience with our online Power Searching Course shows that the baseline approach works, but it also illustrates how much more can be done.

I asked Elliot Solloway (University of Michigan) and Cathleen Norris (University of North Texas), two faculty attendees, to provide their perspective on the panel and they have posted their reflections on their blog.

The digital era is changing the human experience. The summit talks and sessions exemplified the new ways in which we interact with devices, each other, and the world around us, and revealed the vast potential for further innovation in this space. Events such as these keep ideas flowing and it’s immensely fun to be part of very broadly-based, computer science community.

Natural Language in Voice Search

Posted by Jakob Uszkoreit, Software Engineer

On July 26 and 27, we held our eighth annual Computer Science Faculty Summit on our Mountain View Campus. During the event, we brought you a series of blog posts dedicated to sharing the Summit's talks, panels and sessions, and we continue with this glimpse into natural language in voice search. --Ed

At this year’s Faculty Summit, I had the opportunity to showcase the newest version of Google Voice Search. This version hints at how Google Search, in particular on mobile devices and by voice, will become increasingly capable of responding to natural language queries.

I first outlined the trajectory of Google Voice Search, which was initially released in 2007. Voice actions, launched in 2010 for Android devices, made it possible to control your device by speaking to it. For example, if you wanted to set your device alarm for 10:00 AM, you could say “set alarm for 10:00 AM. Label: meeting on voice actions.” To indicate the subject of the alarm, a meeting about voice actions, you would have to use the keyword “label”! Certainly not everyone would think to frame the requested action this way. What if you could speak to your device in a more natural way and have it understand you?

At last month’s Google I/O 2012, we announced a version of voice actions that supports much more natural commands. For instance, your device will now set an alarm if you say “my meeting is at 10:00 AM, remind me”. This makes even previously existing functionality, such as sending a text message or calling someone, more discoverable on the device -- that is, if you express a voice command in whatever way feels natural to you, whether it be “let David know I’ll be late via text” or “make sure I buy milk by 3 pm”, there is now a good chance that your device will respond how you anticipated it to.

I then discussed some of the possibly unexpected decisions we made when designing the system we now use for interpreting natural language queries or requests. For example, as you would expect from Google, our approach to interpreting natural language queries is data-driven and relies heavily on machine learning. In complex machine learning systems, however, it is often difficult to figure out the underlying cause for an error: after supplying them with training and test data, you merely obtain a set of metrics that hopefully give a reasonable indication about the system’s quality but they fail to provide an explanation for why a certain input lead to a given, possibly wrong output.

As a result, even understanding why some mistakes were made requires experts in the field and detailed analysis, rendering it nearly impossible to harness non-experts in analyzing and improving such systems. To avoid this, we aim to make every partial decision of the system as interpretable as possible. In many cases, any random speaker of English could look at its possibly erroneous behavior in response to some input and quickly identify the underlying issue - and in some cases even fix it!

We are especially interested in working with our academic colleagues on some of the many fascinating research and engineering challenges in building large-scale, yet interpretable natural language understanding systems and devising the machine learning algorithms this requires.

Using WinForms from a Console application

It is perfectly possible to use WinForms from a Console application.  You just need to add a Reference to System.Windows.Forms.  Thereafter, you can add any code that uses WinForms.  The only pitfall is that you need add the [STAThread] attribute to your Main method.  Otherwise, the OpenFileDialog instantiation will block/deadlock when running on Microsoft .net framework.  On Mono, everything runs fine without the [STAThread] attribute, but to eliminate portability issues, you probably want to add it.

[STAThread]

public static void Main (string[] args)
{
    OpenFileDialog openDialog = new OpenFileDialog ();
    openDialog.ShowDialog ();
}

New Challenges in Computer Science Research

Posted by Jeff Walz, Head of University Relations

Yesterday afternoon at the 2012 Computer Science Faculty Summit, there was a round of lightning talks addressing some of the research problems faced by Google across several domains. The talks pointed out some of the biggest challenges emerging from increasing digital interaction, which is this year’s Faculty Summit theme.

Research Scientist Vivek Kwatra kicked things off with a talk about video stabilization on YouTube. The popularity of mobile devices with cameras has led to an explosion in the amount of video people capture, which can often be shaky. Vivek and his team have found algorithmic approaches to make casual videos look more professional by simulating professional camera moves. Their stabilization technology vastly improves the quality of amateur footage.

Next, Ed Chi (Research Scientist) talked about social media focusing on the experimental circle model that characterizes Google+. Ed is particularly interested in how social interaction on the web can be designed to mimic live communication. Circles on Google+ allow a user to manage their audience and share content in a targeted fashion, which reflects face-to-face interaction. Ed discussed how, from an HCI perspective, the challenge going forward is the need to consider the trinity of social media: context, audience, content.

John Wilkes, Principal Software Engineer, talked about cluster management at Google and the challenges of building a new cluster manager-- that is, an operating system for a fleet of machines. Everything at Google is big and a consequence of operating at such tremendous scale is that machines are bound to fail. John’s team is working to make things easier for internal users enabling our ability to respond to more system requests. There are several hard problems in this domain, such as issues with configuration, making it as easy as possible to run a binary, increasing failure tolerance, and helping internal users understand their own needs as well as the behavior and performance of their system in our complicated distributed environment.

Research Scientist and coffee connoisseur Alon Halevy took to the podium to confirm that he did indeed author an empirical book on coffee, and also talked with attendees about structured data on the web. Structured data is comprised of hundreds of millions of (relatively small) tables of data, and Alon’s work is focused on enabling data enthusiasts to discover and visualize those data sets. Great possibilities open up when people start combining data sets in meaningful ways, which inspired the creation of Fusion Tables. An example is a map made in the aftermath of the 2011 earthquake and tsunami in Japan, that shows natural disaster data alongside the locations of the world’s nuclear plants. Moving forward, Alon’s team will continue to think about interesting things that can be done with data, and the techniques needed to distinguish good data from bad data.

To wrap up the session, Praveen Paritosh did a brief, but deep dive into the Knowledge Graph, an intelligent model that understands real-world entities and their relationships to one another-- things, not strings-- which launched earlier this year.

The Google Faculty Summit continued today with more talks, and breakout sessions centered on our theme of digital interaction. Check back for additional blog posts in the coming days.

Education in the Cloud

Posted by Andrea Held, University Relations

In the last 10 years, we’ve seen a major transition from stand-alone applications that run on desktop computers to applications running in the cloud. Unfortunately, many computer science students don’t have the opportunity to learn and work in the cloud due to a lack of resources in traditional undergrad programs. Without this access students are limited to the resources their school can provide.

So today, we’re announcing a new award program: the Google App Engine Education Awards. We are excited because Google App Engine can teach students how to build sophisticated large-scale systems in the cloud without needing access to a large physical network.

Google App Engine can be used to build mobile or social applications, traditional browser-based applications, or stand-alone web services that scale to millions of users with ease. The Google App Engine infrastructure and storage tools are useful for collecting and analyzing educational data, building a learning management system to organize courses, or implementing a teacher forum for exchanging ideas and practices. All of these adaptations of the Google App Engine platform will use the same infrastructure that powers Google.

We invite teachers at universities across the United States to submit a proposal describing how to use Google App Engine for their course development, educational research or tools, or for student projects. Selected proposals will receive $1,000 in App Engine credits.

If you teach at an accredited college, university or community college in the US, we encourage you to apply. You can submit a proposal by filling out this form. The application deadline is midnight PST August 31, 2012.

Big Pictures with Big Messages

Posted by Maggie Johnson, Director of Education and University Relations

Google’s Eighth Annual Computer Science Faculty Summit opened today in Mountain View with a fascinating talk by Fernanda Viégas and Martin Wattenberg, leaders of the data visualization group at our Cambridge office. They provided insight into their design process in visualizing big data, by highlighting Google+ Ripples and a map of the wind they created.

To preface his explanation of the design process, Martin shared that his team “wants visualization to be ‘G-rated,’ showing the full detail of the data - there’s no need to simplify it, if complexity is done right.” Martin discussed how their wind map started as a personal art project, but has gained interest particularly among groups that are interested in information on the wind (sailors, surfers, firefighters). The map displays surface wind data from the US National Digital Forecast Database and updates hourly. You can zoom around the United States looking for where the winds are fastest - often around lakes or just offshore - or check out the gallery to see snapshots of the wind from days past.

Fernanda discussed the development of Google+ Ripples, a visualization that shows how news spreads on Google+. The visualization shows spheres of influence and different patterns of spread. For example, someone might post a video to their Google+ page and if it goes viral, we’ll see several circles in the visualization. This depicts the influence of different individuals sharing content, both in terms of the number of their followers and the re-shares of the video, and has revealed that individuals are at times more influential than organizations in the social media domain.

Martin and Fernanda closed with two important lessons in data visualization: first, don’t “dumb down” the data. If complexity is handled correctly and in interesting ways, our users find the details appealing and find their own ways to interact with and expand upon the data. Second, users like to see their personal world in a visualization. Being able to see the spread of a Google+ post, or zoom in to see the wind around one’s town is what makes a visualization personal and compelling-- we call this the “I can see my house from here” feature.

The Faculty Summit will continue through Friday, July 27 with talks by Googlers and faculty guests as well as breakout sessions on specific topics related to this year’s theme of digital interactions. We will be looking closely at how computation and bits have permeated our everyday experiences via smart phones, wearable computing, social interactions, and education.

We will be posting here throughout the summit with updates and news as it happens.