Monday, April 24, 2006
WebOS market review
My post last week about XIN, a new contender in the Web OS space, provoked some skeptical comments from ZDNet readers. It wouldn't surprise me if one of the small startups I've mentioned here goes on to become the next Linux So in this post I explain what a Web OS is and why it's of use. I also take a look at the main WebOS vendors.
The OS of course stands for 'Operating System' and here's how Wikipedia defines WebOS:
"More generally, WebOS refers to a software platform that interacts with the user through a web browser and does not depend on any particular local operating system. Such predictions date to the mid-1990s, when Marc Andreessen predicted that Microsoft Windows was destined to become "a poorly debugged set of device drivers running Netscape Navigator." More recently attention has focused on rumors that Google might produce a software platform."
(emphasis mine)
WebOS also happens to be the specific name of a computing research project, which started at the University of California, Berkeley in 1996 and is continuing at other American universities such as Duke. Here's how it's described:
"WebOS provides basic operating systems services needed to build applications that are geographically distributed, highly available, incrementally scalable, and dynamically reconfiguring."
GoogleOS
The WebOS I'm talking about here is the general one. As Wikipedia noted, Google is the most obvious candidate nowadays to build a WebOS. Jason Kottke wrote a famous (in the blogosphere at least) post on GoogleOS back in August 2005. Kottke saw the WebOS as having three parts to it: the web browser as the primary application interface, web apps (like Gmail, etc), and a local server. The third part seems to be the most crucial and the piece largely missing today. Kottke went on to say:
"Aside from the browser and the Web server, applications will be written for the WebOS and won't be specific to Windows, OS X, or Linux. This is also completely feasible, I think, for organizations like Google, Yahoo, Apple, Microsoft, or the Mozilla Foundation to make happen…"
Kottke's post was visionary, but as yet there's no sign of a Google WebOS - or one from Yahoo, Apple, Microsoft and Mozilla for that matter.
Those that are building a WebOS
But there are a number of small startups trying their luck. I've already covered XIN. Others are YouOS, EyeOS, Orca, Goowy, Fold. YouOS got a lot of interest last month, making it to the front page of Digg.
There's also a bit of crossover with Ajax homepages like Netvibes, Pageflakes, Microsoft's Live.com and Google's start page. The key difference from Ajax homepages is that a WebOS is a full-on development platform. The likes of XIN and YouOS are application development platforms that also offer things like file storage. Services like Netvibes and Live.com are more of an interface for web content and 'mini apps' like gadgets (some, like Netvibes and Pageflakes, also offer APIs).
YouOS - a virtual computer
So what is a WebOS again? The developers behind YouOS wrote a manifesto about their work, describing it as an attempt to "bring the web and traditional operating systems together to form a shared virtual computer." They're at pains to point out that a WebOS is different from a traditional computer OS, which is concerned with integrating hardware and software. A WebOS, according to YouOS, is "a liberation of software from hardware". I think this statement gets to the heart of what a WebOS does:
"YouOS is a shared computer that houses your data and applications, but you are the owner of this data and applications."
From a user point of view, of course you still need a traditional OS (like Windows or Linux) on whatever machines you use to access YouOS or another WebOS. But as a user, the OS is no longer your primary concern - it's your data and your apps that you only need to concern yourself with.
What's the best WebOS currently?
To be honest I don't know, but I asked the question in a Digg forum last week and got a great reply from 'automan':
"A webOS that wants to make it should be able to adapt to an open source style of environment. Why would I want to be tied into another "proprietary" image editor or word processor? I think that the webOS that supports containers that you can put your own code into and run will be the ones to survive. […[ I believe that XIN and YouOS have the better model for future development and expansion… YouOS in particular. While it is in no way visually appealing at this point, I believe it has plenty of room to build upon itself to grow in a very good direction."
An open source style makes perfect sense for a WebOS, particularly for the small players wanting to stand a chance against Google and Microsoft. I'll be investigating the above WebOS contenders myself over the next few weeks, so will be in a better position to judge then.
The skeptics
As for developers, a big benefit is that a WebOS theoretically makes it easier to develop apps that work cross-platform. DHTML and Javascript are the main tools to do that, which is where a lot of the skepticism comes from. Take this comment from a ZDNet reader:
"Oh, I wish I wish I wish we could just create a new, standard, simple, clean, cross-platform/write-once/run anywhere, open, programmatic, efficient, robust GUI language that provided the above advantages: 0 administration, 0 risk. Java could've been a contender, but it's a complete mess now; DHTML+Javascript is just evil."
So it seems the jury is out among many people as to how viable a WebOS is. Also a lot of people don't consider a WebOS to be a real operating system, but I think that's semantics and not something worth debating. If you imagine a future when you're accessing your data and apps from multiple devices, the need for a WebOS will become clearer.
The optimists (futurists?)
The reason I'm interested in a WebOS is of course the same reason I'm obsessed with the Web Office - there are so many more opportunities for applications and data running in a networked space, rather than on a single computer or other device. I think we're in the very early stages of WebOS development, but it wouldn't surprise me if one of the small startups I've mentioned here goes on to become the next Linux. A big call perhaps, but we're living and working on the Web more and more every year.
source:http://blogs.zdnet.com/web2explorer/?p=166
Social Networking From Your Cell
source:http://hardware.slashdot.org/hardware/06/04/23/1954252.shtml
Beating Traffic
Woe Is Traffic
Traffic: the commuter's bane. It plagues major city drivers around the globe and shows no sign of letting up.1 In fact, the average U.S. commuter spends about 100 hours a year driving just to work - 20 hours more than a typical year's supply of vacation.2 This personal "daily grind" uses more than 15,000 miles and 1,000 gallons of gas every year, which might not be so bad if much of it wasn't waste: 1.6 million hours and 800 million gallons of gas are wasted every day in traffic jams across the nation. Traffic even affects your health, raising blood pressure, increasing stress, and producing more Type-A personalities.3
Of course, some places are much worse than others. New York tops the list, with Chicago, Newark and Riverside following, albeit at a distance. L.A. comes in at #6 and Houston, where I reside and commute, is #15.4 Other cities, such as Nashville, TN and Kansas City, MO, show up much further down the list, but something tells me that even commuters in those relative traffic havens dedicate significant effort and conversation to 'beating traffic.'
Resources are sometimes available to help in this quest. Houston Transtar provides up to the minute traffic information for all major Houston highways.5 Average traveling speed, construction and accident information are all available at the click of the mouse, but how to avoid the perpetual web of red during the morning and evening rush hours is nowhere to be found. Obvious answers such as public transportation and carpooling are legitimate, but trends show that Americans are meeting the increase in traffic by using such transportation methods less, not more.6 Also, if the online traffic-reporting graphic warns of potential issues, there is no indication of how long they might persist, leaving the traffic conscientious commuter right where he started: guessing.
Tired of the typically inefficient and contradictory workplace chatter on the subject and feeling the pull of a slight worksheet obsession, I set out to statistically analyze my commute in order to determine how I might minimize my time behind the wheel. If there was a way to figure out how to give myself an advantage over the almost 900,000 other Houstonian workers out there (who average a 26.1 minute commute),7 math and a smidgeon of obsessive compulsive disorder had to be essential ingredients. At the very least, I would be able to ascertain just how much of my commute time was up to me - and how much depended on a "higher power" (e.g., weather, school districts, wrecks, etc.).
Gathering Data
From March of 2004 to March of 2005, I recorded my departure and arrival times both to and from work, along with whether school was in or out. Other factors, although most likely important, were excluded to keep the scope of the experiment narrow and measurable.
Driving Data
Every morning, I took note of the time on my car clock as I pulled out of my driveway at the Riata Ranch subdivision of northwest Houston8 and then again as I pulled into the parking garage at my office building close to the north-bound frontage road of Sam Houston Pky and Clay Rd.9 In the evening, I followed the same process in reverse. The morning route 10 and evening route11 differed slightly in length, but data was only recorded when the planned course was followed, allowing for only slight variations.12
School District & Government Data
Being suspicious of the influence of the school session, I collected official 2004-2005 and 2005-2006 calendar data from Cypress Fairbanks Independent School District,13 which covers almost all of my commute route,14 and took note of all full student holidays (i.e., teacher in-service days, but not student early release days).15 I also collected official 2005 and 2006 government holiday information from the city of Houston16 and the US Federal Government,17 but this proved next to useless as I only commuted to work on one city and two federal government holidays.
Analysis
To set up the gathered information, I first organized the variables into inputs and outputs as shown in Table 1.
 | ||
Table 1: Input and Output Variables |
To determine which variables had a statistically significant effect on my commute times, I ran one-way ANOVAs18 on the discrete variables and plotted smoothed graphs of means for the continuous variables.19
Morning Commute ANOVAs
Day of the Work Week
The one-way ANOVA of the morning commute duration versus the day of work week (y1 vs. x1) showed a statistically significant effect.20 The table in the ANOVA output21 and the boxplot below confirm that this effect comes on Fridays, on which there is a significantly shorter commute time:
Source DF SS MS F P
Day of Week 4 544.4 136.1 3.87 0.005
Error 202 7103.2 35.2
Total 206 7647.6
S = 5.930 R-Sq = 7.12% R-Sq(adj) = 5.28%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev ----+---------+---------+---------+-----
1 43 22.209 5.726 (------*------)
2 44 22.886 5.891 (-------*------)
3 47 23.447 6.382 (------*------)
4 39 22.462 7.014 (-------*------)
5 34 18.559 3.855 (-------*-------)
----+---------+---------+---------+-----
17.5 20.0 22.5 25.0
Pooled StDev = 5.930
 | ||
Figure 1. A boxplot of the morning commute time versus the day of the work week. |
Week of the Month
The results from an ANOVA of the week of the month versus the morning commute duration (y1 vs. x2) showed no statistically significant impact, although week 5 has the highest average commute time:
Source DF SS MS F P
Week of Month 4 226.5 56.6 1.54 0.192
Error 202 7421.1 36.7
Total 206 7647.6
S = 6.061 R-Sq = 2.96% R-Sq(adj) = 1.04%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev ---+---------+---------+---------+------
1 52 22.673 7.040 (------*-----)
2 44 21.636 4.760 (-------*------)
3 51 21.706 6.090 (------*------)
4 42 21.048 5.441 (------*-------)
5 18 24.944 7.075 (----------*----------)
---+---------+---------+---------+------
20.0 22.5 25.0 27.5
Pooled StDev = 6.061
 | ||
Figure 2. A boxplot of the morning commute time versus the week of the month. |
Month of the Year
The month of the year versus morning commute time (y1 vs. x3) ANOVA results showed even less of an effect:
Source DF SS MS F P
Month of Year 11 496.5 45.1 1.23 0.269
Error 195 7151.1 36.7
Total 206 7647.6
S = 6.056 R-Sq = 6.49% R-Sq(adj) = 1.22%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev ------+---------+---------+---------+---
1 21 22.476 5.793 (--------*--------)
2 8 22.875 4.121 (-------------*-------------)
3 19 24.053 7.764 (--------*--------)
4 19 22.737 4.053 (--------*--------)
5 19 23.842 5.650 (--------*---------)
6 18 21.722 5.278 (--------*---------)
7 19 18.947 4.441 (--------*--------)
8 23 20.652 5.556 (-------*-------)
9 17 21.824 7.502 (---------*--------)
10 17 21.353 4.182 (--------*---------)
11 16 24.250 9.774 (---------*---------)
12 11 20.545 5.336 (-----------*-----------)
------+---------+---------+---------+---
18.0 21.0 24.0 27.0
Pooled StDev = 6.056
 | ||
Figure 3. A boxplot of the morning commute time versus the month of the year. |
Cypress-Fairbanks ISD
Whether or not the local school district was in session proved to be the greatest measured variable in explaining the morning commute time variation (y1 vs. x6):
Source DF SS MS F P
CyFair 1 774.0 774.0 23.08 0.000
Error 205 6873.6 33.5
Total 206 7647.6
S = 5.791 R-Sq = 10.12% R-Sq(adj) = 9.68%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev -+---------+---------+---------+--------
0 63 19.159 4.646 (------*------)
1 144 23.361 6.222 (----*----)
-+---------+---------+---------+--------
18.0 20.0 22.0 24.0
Pooled StDev = 5.791
 | ||
Figure 4. A boxplot of the morning commute time versus Cypress-Fairbanks ISD Session. |
Evening Commute ANOVAs
Day of the Work Week
While the day of the week proved to have a significant impact on the morning commute, the evening commute showed no such relationship (y2 vs. x1):
Source DF SS MS F P
Day of Week 4 68.5 17.1 0.82 0.516
Error 158 3312.1 21.0
Total 162 3380.7
S = 4.579 R-Sq = 2.03% R-Sq(adj) = 0.00%
Individual 95% CIs For Mean Based on Pooled
StDev
Level N Mean StDev +---------+---------+---------+---------
1 40 22.125 4.333 (--------*--------)
2 40 21.275 5.002 (--------*--------)
3 34 21.706 5.190 (---------*--------)
4 33 20.697 4.149 (--------*---------)
5 16 22.875 3.304 (-------------*-------------)
+---------+---------+---------+---------
19.2 20.8 22.4 24.0
Pooled StDev = 4.579
 | ||
Figure 5. A boxplot of the evening commute time versus the day of the work week. |
Week of the Month
Again, the week of the month did not explain the commute time variation (y2 vs. x2):
Source DF SS MS F P
Week of Month 4 86.4 21.6 1.04 0.390
Error 158 3294.2 20.8
Total 162 3380.7
S = 4.566 R-Sq = 2.56% R-Sq(adj) = 0.09%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev ---+---------+---------+---------+------
1 34 21.176 4.496 (-------*-------)
2 39 20.769 4.782 (------*------)
3 42 21.857 4.176 (------*------)
4 35 22.000 4.583 (-------*-------)
5 13 23.462 5.238 (-----------*------------)
---+---------+---------+---------+------
20.0 22.0 24.0 26.0
Pooled StDev = 4.566
 | ||
Figure 6. A boxplot of the evening commute time versus the week of the month. |
Month of the Year
Another change from the morning results, the month of the year proved to have a significant effect, with February, April and November showing the longest evening commute times (y2 vs. x3):
Source DF SS MS F P
Month of Year 11 541.2 49.2 2.62 0.004
Error 151 2839.4 18.8
Total 162 3380.7
S = 4.336 R-Sq = 16.01% R-Sq(adj) = 9.89%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev ----+---------+---------+---------+-----
1 15 21.400 3.418 (------*-------)
2 9 24.222 3.833 (---------*--------)
3 17 20.529 3.319 (-----*------)
4 10 23.700 6.325 (--------*--------)
5 14 20.143 3.416 (------*-------)
6 14 21.357 4.584 (------*-------)
7 14 19.143 4.400 (-------*------)
8 21 21.905 5.078 (-----*-----)
9 14 20.929 4.811 (-------*------)
10 11 20.091 3.590 (--------*--------)
11 16 25.625 4.731 (------*-------)
12 8 20.625 3.021 (---------*---------)
----+---------+---------+---------+-----
18.0 21.0 24.0 27.0
Pooled StDev = 4.336
 | ||
Figure 7. A boxplot of the evening commute time versus the month of the year. |
Cypress-Fairbanks ISD
The school session again showed signification influence, but it was not as strong in the evening as in the morning (y2 vs. x6):
Source DF SS MS F P
CyFair 1 106.2 106.2 5.22 0.024
Error 161 3274.4 20.3
Total 162 3380.7
S = 4.510 R-Sq = 3.14% R-Sq(adj) = 2.54%
Individual 95% CIs For Mean Based on
Pooled StDev
Level N Mean StDev ---------+---------+---------+---------+
0 50 20.400 4.677 (------------*------------)
1 113 22.150 4.434 (--------*-------)
---------+---------+---------+---------+
20.0 21.0 22.0 23.0
Pooled StDev = 4.510
 | ||
Figure 8. A boxplot of the evening commute time versus the Cypress-Fairbanks ISD Session. |
Departure Time Analysis
For the continuous variable of departure time, I plotted smoothed curves of the mean commute time at each minute.
The morning departure time plot shows relatively long commute times until about 7:40AM, at which time a gradual decrease starts that continues in an overall linear fashion for the next hour. After 8:40AM, traffic appears to have only minimal impact. (y1 vs. x4):
 | ||
Figure 9. A smoothed plot of the mean of the recorded morning commute durations versus the home departure time. |
The evening departure time plot shows a peak commute time at about 5:10PM, tapering off linearly through the next two or so hours. Departure times prior to 5:00PM showed erratic results, but it is obvious that traffic played a decreasing role in evening commute time duration moving back through 4:00PM, before which it's influence is noticeable, but slight. (y2 vs. x5):
 | ||
Figure 10. A smoothed plot of the mean of the recorded evening commute durations versus the work departure time. |
 | ||
Figure 11. A close-up of Figure 10. |
I usually leave home at 8:00AM and work at 5:30PM, but a 30 minute delay of each looks like it would shave five minutes off the morning commute and about 2.5 minutes off the evening. Additional half-hour delays bring 2.5 minutes of commute time savings in the evening, but little to no savings in the morning. Slightly earlier departure times appear to result in commute time increases for both trips. Moving back past 4:30 in the evening brings slight improvement in the evening commute, but savings in the morning would most likely require leaving before 6:30AM.
Conclusions
Given the above data and analysis, what can be done to improve my commute times? Changing my morning or evening departure time looks promising. The best bet appears to be moving my schedule out a half-hour to 8:30AM and 6:00PM, bringing significant savings (about 7.5 minutes of commute time per day) without getting too far from normal business hours. Spread out over 50 work weeks, that results in a total savings of over 30 hours a year - the equivalent of about a 38% boost to my existing 80 hours of vacation.
Departure time isn't the say-all, however, and making this shift won't always result in a smooth and fast commute. The day of the week in the morning and the month of the year in the evening both have significant impacts, and whether or not school is in session affects both. I could possibly squeeze out a few more minutes of savings by scheduling my vacation days to align with the potentially longest commutes (e.g., non-Friday school days in the months of November, February and April), but the data shows significant variation up and above that described by the measured variables - much likely due to factors outside of the control of the commuter (e.g., weather, wrecks, breakdowns, response to traffic predictions, etc.).22
The commuter may have more control than it appears, however. Adjusting your commute times and rearranging your vacation schedule will probably help in the meantime, but getting cars off the road is the only sure solution - one that is within commuters' sphere of influence.23 It might require punching your "free reign" in the gut, but getting involved in your community by writing your Congressperson or attending city council meetings in promotion/defense of improved mass transit could be the most effective way to "curb" your drive times in the long run.24
Notes
- "Beating Traffic." Mathematical Moments. American Mathematical Society. 2005. Accessed April 2006 from http://www.ams.org/ams/mm31-traffic.pdf. According to the publication, "In the last 30 years while the number of vehicle-miles traveled has more than doubled, physical road space has increased only six percent." ↑
- "Americans Spend More Than 100 Hours Commuting to Work Each Year, Census Bureau Reports." US Census Press Release. March 20, 2005. Accessed April 2006 from http://www.census.gov/Press-Release/www/releases/archives/ american_community_survey_acs/004489.html. ↑
- "Understanding Traffic." Discovery Channel Features. January 30, 2006. Accessed April 2006 from http://www.odeo.com/audio/674920/view. ↑
- "Average Travel Time to Work of Workers 16 Years and Over Who Did Not Work at Home." U.S. Census Bureau: American Community Survey 2003. Accessed April 2006 from http://www.census.gov/acs/www/Products /Ranking/2003/pdf/R04T160.pdf. ↑
- Houston Real-Time Traffic Map. HoustonTranstar.org. Accessed April 2006 from http://traffic.houstontranstar.org/layers/. ↑
- Reschovsky, Clara. "Journey to Work 2000." US Census Bureau. Accessed April 2006 from http://www.census.gov/prod /2004pubs/c2kbr-33.pdf. According to Table 1: Means of Transportation to Work: 1990 and 2000, 2.5% more Americans drove to work alone in 2000 when compared with ten years earlier. All public transportation used saw at least a minor decline. ↑
- "Houston city, Texas: Selected Economic Characteristics: 2004." U.S. Census Bureau: American Fact Finder. Accessed April 2006 from http://factfinder.census.gov/servlet/ADPTable?_bm= y&-geo_id=16000US4835000&-qr_name=ACS_2004_EST_G00 _DP3&-ds_name=ACS_2004_EST_G00_&-_lang=en&-_sse=on. ↑
- Google Local - Cypress N Houston Rd & Riata Ranch Blvd, Houston, TX 77095. Google Maps. Accessed April 2006 from http://maps.google.com/maps?f=q&hl=en&q=Cypress+N+ Houston+Rd+%26+Riata+Ranch+Blvd,+Houston,+TX+77095&om=1. My exact home address is withheld purposely. ↑
- Google Local - W Sam Houston Pky N & Clay Rd, Houston, TX 77041. Google Maps. Accessed April 2006 from http://maps.google.com/maps?f=q&hl=en&q= W+Sam+Houston+Pky+N+%26+Clay+Rd,+Houston,+TX+77041&om=1. Again, the exact details of my office location are purposely omitted. ↑
- My 12.7 mile route to work consists of the following:
a. Proceed .1 miles from home to Riata Ranch Blvd & Cypress N Houston Rd.
b. Proceed west .2 miles on Cypress N Houston Rd.
c. Turn right on Barker Cypress Rd. Proceed .8 miles.
d. Turn right on US-290 E. Proceed 1 mile.
e. Take US-290 ramp. Proceed 6.7 miles.
f. Take Frontage Road Exit to Beltway 8 / FM-529 / Senate Ave. Proceed .7 miles. (I exit here instead of taking the shorter - and most likely faster - freeway to avoid the toll. Yes, I'm cheap and I like spreadsheets.)
g. Turn right on Senate Ave. Proceed 3.1 miles to Clay Rd.
h. Proceed .1 miles to office. ↑ - My 13.0 mile route home consists of the following:
a. From the office, proceed north on Sam Houston Parkway frontage road for 3.1 miles.
b. Turn left on US-290 frontage road. Proceed 1.0 mile.
c. Take US-290 ramp. Proceed 6.8 miles.
d. Take Barker Cypress Rd Exit. Proceed .9 miles, veering right at split.
e. Turn left on Barker Cypress Rd. Proceed .9 miles.
f. Turn left on Cypress N Houston Rd. Proceed .2 miles to Riata Ranch Blvd.
g. Proceed .1 miles to home. ↑ - I occasionally took two variations, one on the way to work and one on the way home. In the morning, I sometimes drove around the south side of the fast food restaurants on the southwest bound frontage road of US-290 to avoid the backup at the light at Senate Ave. In the evening, heading north on Senate Ave, I occasionally continued straight under US-290 to avoid the backup in the left-hand turn lanes. Although the road is not shown on the map, the first left after crossing the US-290 frontage road proceeds about .2 miles, then makes a left turn and dead-ends back into the frontage road. A detail of the US-290 and Senate Ave intersection, which contains both variations, is available from Google Maps: http://maps.google.com/maps?f=q&hl=en&q=US-290 +W+%26+Senate+Ave,+Houston,+TX+77040&ll=29.877 341,-95.564607&spn=0.006549,0.013561&t=h&om=1 ↑
- Cypress-Fairbanks ISD Home Page. CFISD.net. Accessed April 2006 from http://www.cfisd.net/. ↑
- Harris County Appraisal District: Index Map: By School District. HCAD: I-Map Publication Service. Accessed April 2006 from http://www.hcad.org/maps/default.asp. ↑
- As an interesting aside, information was also gathered for surrounding school districts:
a. Houston (http://www.houstonisd.org)
b. Katy (http://www.katyisd.org)
c. Klein (http://www.kleinisd.net)
d. Spring Branch (http://www.springbranchisd.com)
e. Tomball (http://www.tomballisd.net)
f. Waller (http://www.waller.isd.esc4.net)
Analysis indicated that these schedules had no statistically significant impact on my commute, confirming that the effect of the school district schedule is limited to within its own boundaries. ↑ - "Official City Holidays." HoustonTX.gov. 2006. Accessed April 2006 from http://www.houstontx.gov/abouthouston/cityholidays.html. 2005 city holidays confirmed via Mrs. Wilkerson of Houston City's 3-1-1 Helpline, accessible per: "Contact Us." HoustonTX.gov. 2006. Accessed April 2006 from http://www.houstontx.gov/contactus/index.html. ↑
- "2005 Federal Holidays." OPM.gov. Accessed April 2006 from http://www.opm.gov/Fedhol/2005.asp. & 2006 Federal Holidays. OPM.gov. Accessed April 2006 from http://www.opm.gov/Fedhol/2006.asp. ↑
- "ANOVA" stands for ANalysis Of VAriance. For more details on ANOVAs and how/when they are used: "Chapter 12: Introduction to ANOVA." HyperStat Online Textbook. Accessed April 2006 from http://davidmlane.com/hyperstat/intro_ANOVA.html. ↑
- Discrete variables are those whose values are represented in a limited set. For example, the "day of the work week" variable consists of five values ("Monday" through "Friday") and a one-way ANOVA analyzes each to determine if it has a significant impact on the result variation. On the other hand, the "departure time" variable is practically continuous, with as many "categories" as there are minutes, and doesn't lend itself well to ANOVA analysis. ↑
- For each of the ANOVA analyses, the significance level (α) is .05 and the null hypothesis (H0) is that the input variable has no statistically significant influence on the output. When the Pvalue < α, H0 is thrown out. For example, in the case of the day of the work week vs. the morning commute, the Pvalue is .005, which is less than .05. Thus, it is statistically improbable that the results could have occurred at random and, therefore, the day of the week is shown to exert a significant effect on the morning commute duration. ↑
- I used Minitab to run the ANOVAs. The top table of the output lists the output variable (Source), the degrees of freedom (DF), the sum of the squares (SS), the mean of the squares (MS), the Fvalue (F) and the Pvalue (P). The lower table lists the input variables (Level), the number of inputs for each (N), the mean of the inputs (Mean), the standard deviation of the inputs (StDev), and then these mean values graphed with a 95% confidence interval (CI) based on the pooled standard deviation. For more information on interpreting the output of one-way ANOVAs: "How to Read the Output From One Way Analysis of Variance." Jerry Dallal's Tufts Home Page. Accessed April 2006 from http://www.tufts.edu/~gdallal/aov1out.htm. ↑
- Some have even suggested chaos theory and driver psychology as ways to best model traffic behavior. More information on chaos theory and traffic: "Chaos and your everyday Traffic Jam." FailedSuccess.com. Accessed April 2006 from http://www.failedsuccess.com/index.php?/ weblog/comments/traffic_jam_causes/. More information on driver psychology: Groegera, J. A. and Rothengatter, J. A. "Traffic psychology and behaviour." Transportation Research Part F: Traffic Psychology and Behaviour. Volume 1, Issue 1, August 1998, Pages 1-9. Accessed April 2006 from http://dx.doi.org/10.1016/S1369-8478(98)00007-2. ↑
- "Understanding Traffic." Discovery Channel Features. January 30, 2006. Accessed April 2006 from http://www.odeo.com/audio/674920/view. Every subway train takes 1,000 cars off the road. Every bus, 40 cars. ↑
- "Critical Relief for Traffic Congestion." PublicTransportation.org. Accessed April 2006 from http://www.publictransportation.org/pdf/reports/congestion.pdf. Public transportation stands to improve commute times more than departure time adjustment. "The Benefits of Public Transportation: An Overview." PublicTransportation.org. Accessed April 2006 from http://www.publictransportation.org/reports/asp/pub_benefits.asp. Public transportation brings unparalleled reliability and consistency. ↑
Scientists find brain cells linked to choice
LONDON (Reuters) - If choosing the right outfit or whether to invest in stocks or bonds is difficult, it may not be just indecisiveness but how brain cells assign values to different items, scientists said on Sunday.
Researchers at Harvard Medical School in Boston have identified neurons, or brain cells, that seem to play a role in how a person selects different items or goods.
Scientists have known that cells in different parts of the brain react to attributes such as colour, taste or quantity. Dr Camillo Padaoa-Schioppa and John Assad, an associate professor of neurobiology, found neurons involved in assigning values that help people to make choices.
"The neurons we have identified encode the value individuals assign to the available items when they make choices based on subjective preferences, a behaviour called economic choice," Padoa-Schioppa said in a statement.
The scientists, who reported the findings in the journal Nature, located the neurons in an area of the brain known as the orbitofrontal cortex (OFC) while studying macaque monkeys which had to choose between different flavours and quantities of juices.
They correlated the animals' choices with the activity of neurons in the OFC with the valued assigned to the different types of juices. Some neurons would be highly active when the monkeys selected three drops of grape juice, for example, or 10 drops of apple juice.
Other neurons encoded the value of only the orange juice or grape juice.
"The monkey's choice may be based on the activity of these neurons," said Padoa-Schioppa.
Earlier research involving the OFC showed that lesions in the area seem to have an association with eating disorders, compulsive gambling and unusual social behaviour.
The new findings show an association between the activity of the OFC and the mental valuation process underlying choice behaviour, according to the scientists.
"A concrete possibility is that various choice deficits may result from an impaired or dysfunctional activity of this population (of neurons), though this hypothesis remains to be tested," Padoa-Schioppa.
source:http://news.scotsman.com/latest.cfm?id=610912006How To Set Up A Load-Balanced MySQL Cluster
source:http://developers.slashdot.org/article.pl?sid=06/04/23/1333219
Google Violates Miro's Copyright?
source:http://yro.slashdot.org/article.pl?sid=06/04/23/1331246
Abandoned Games
source:http://games.slashdot.org/article.pl?sid=06/04/23/139228
High DPI Web Sites
One area of Web design that is going to become more important in the coming years is high DPI. For those of us working on WebKit, this will also become an issue for WebKit applications and for Dashboard widgets.
What is DPI?
DPI stands for “dots per inch” and refers to the number of pixels of your display that can fit within an inch. For example a MacBook Pro has a 1440×900 resolution on a 15 inch screen. Screens exist for laptops, however, that have the same physical size (15 inches) but that cram many more pixels into the same amount of space. For example my Dell XPS laptop has a 1920×1200 resolution.
Why does this matter?
Consider a Web page that is designed for an 800×600 resolution. Let’s say we render this Web page such that the pixels specified in CSS (and in img tags and such on the page) map to one pixel on your screen.
On a screen with 1920×1200 resolution the Web site is going to be tiny, taking up <>
Now this may not be a huge problem yet, but as displays cram more and more pixels into the same amount of space, if a Web browser (or any other application for that matter) naively continues to say that one pixel according to the app’s concept of pixels is the same as one pixel on the screen, then eventually you have text and images so small that they’re impossible to view easily.
How do you solve this problem?
The natural way to solve this “high DPI” problem is to automatically magnify content so that it remains readable and easily viewable by the user. It’s not enough of course to simply pick a pleasing default, since the preferences of individuals may vary widely. An eagle-eyed developer may enjoy being able to have many open windows crammed into the same amount of space, but many of us would like our apps to remain more or less the same size and don’t want to have to squint to read text.
The full solution to this problem therefore is to allow your user interface to scale, with the scale factor being configurable by the user. This means that Web content has to be zoomable, with the entire page properly scaling based off the magnification chosen by the user.
What the heck is a CSS px anyway?
Most Web site authors have traditionally thought of a CSS pixel as a device pixel. However as we enter this new high DPI world where the entire UI may be magnified, a CSS pixel can end up being multiple pixels on screen.
For example if I set a zoom magnifcation of 2x, then 1 CSS pixel would actually be represented by a 2×2 square of device pixels.
This is why a pixel in CSS is referred to as a relative unit, because it is a unit whose value is relative to the viewing device (e.g., your screen).
CSS 2.1 describes how a the px unit should be rescaled as needed.
http://www.w3.org/TR/CSS21/syndata.html#length-units
What’s wrong with zooming?
Zooming an existing Web page so that it can be more easily viewed has a number of immediate benefits. Text remains readable. Images don’t become so tiny that they can’t be viewed.
Doing naive zooming, however, will result in a Web site that - when scaled - looks much worse. (Try looking at what happens to images in Internet Explorer for Windows when you change the OS DPI setting from 96 to 120 for example.) Several factors come into play here.
For example, with text you don’t want or need to “zoom” it. In other words, you aren’t going to take the actual pixels for each character and scale them like you’d scale an image. Instead you simply use a larger font size. This will allow text to have a higher level of detail on high DPI displays and ultimately look more and more like the text you might see in a printed book.
For images, you first and foremost need a good scaling algorithm. You’d like for the image to look about as good as it did on a lower DPI display when rendered at the same physical size. However, the problem with scaling of existing images is that all you’ve done is maintained the status quo, when instead you could be designing a Web site that looks *even better* on these higher DPI displays.
How can I make images look better?
Consider a common Web site example: the use of images to do UI elements like buttons with rounded corners and fancy backgrounds. Let’s say the Web designer uses a 50×50 pixel image for the button. The rounded corners and background may look reasonably nice on a lower DPI display and even continue to look nice when the image is scaled by 2x but rendered at the same physical size on a higher DPI display.
What if you could use a 200×200 image instead? Or, even better, what if you used an image format that hadn’t hard-coded all of its pixel information in the first place? The use of either a higher resolution image (with more detail) or of a scalable image format allows for the creation of images that would look *better* when rendered on the higher DPI display.
Enter SVG
Safari actually supports PDF as an image format (the hands of the clock Dashboard widget are an example of this). However other browsers do not support this format. The agreed-upon standard for scalable graphics on the Web is SVG.
SVG stands for Scalable Vector Graphics and is an XML language for describing two-dimensional images as vector graphics. Describing graphics in this fashion allows for the creation of images that will look better on high DPI displays when rendered at the same physical size.
Our goal with WebKit is to make SVG a first-class image format, so that it can be used anywhere you might use a PNG, a GIF or a JPG. In other words, all of the following should be possible:
div {
background-image: url(tiger.svg)
}
li {
list-style-image: url(bullet.svg)
}
Our current thinking regarding SVG images used this way is that they would be non-interactive (in other words you can’t hit test elements inside the SVG’s DOM). It’s debatable whether or not script execution should be allowed when SVG is used this way.
These are some issues we’d like to hammer out, since we view this use of SVG as being very different from SVG included explicitly in a compound XHTML document or included via the use of an