Search Results

Search found 22 results on 1 pages for 'seasonal'.

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

Page 1/1 | 1 

  • Seasonal Pricing for a Hotel Room

    - by Laykes
    I am trying to manage seasonal prices for hotel rooms. The only way that I can think of doing it would be to use: | DayDate |EndDate | A | B ----------------------------------------------- | 2010/07/1 |2010/07/2 | 200 | 40 | 2010/07/3 |2010/07/4 | 150 | 40 | 2010/07/5 |2010/07/5 | 150 | 50 | 2010/07/6 |2010/07/7 | 200 | 50 | 2010/07/8 |2010/07/9 | 100 | 60 etc.. (table taken from another question). The problem is: I don't want my seasons to be year specific. Seasons for rooms shouldn't change year on year. I don't want my users to have to enter the seasonal information several times. I am also going to have thousands of rooms, so I don't know a way to make this easily manageable. I'm using mysql and php.

    Read the article

  • Isotope.js help: Changing item image after sorting

    - by user3643081
    This is a general question on how to go about building a project I have in mind, and the best way to set off on the right foot. I am fairly new to JS, please be gentle. I want to use isotope.js (or a similar script) to display a page with multiple items (about 30 different plants found in a garden) and the ability to sort them by seasons of the year + "what is most beautiful now" + and "view all" (a total of 6 categories) . On load, or when sorted by either "what is beautiful now" or "view all", I need each item to reflect the image of the current season we are in. When sorted by season, I need those "current" images to switch over to a designated seasonal image of that plant. Therefore, each sortable item will ultimately have 4 different versions with 4 different images in the background ready to surface when plants are sorted. (perhaps 5 if it makes more sense to have a "current" version besides the 4 seasonal versions.) My question: what approach can I take to achieve this effect in a manageable way? Can isotope apply a class to items sorted? Assuming it can: Should each item have 4 inline images, each with a css class, that I then control by using display:inline; and display:none; properties from my stylesheets? (I worry that this approach would significantly increase load times) Would it make more sense to create a blank dummy div who's background I control similarly to the example above -relying mostly on CSS. Or is there some other way involving JS I am overlooking? Any help would be appreciated. Examples of what you suggest would be immensely helpful.

    Read the article

  • SOA Galore: New Books for Technical Eyes Only By Bob Rhubart

    - by JuergenKress
    In my part of the world the weather has taken its seasonal turn toward the kind of cold, damp, miserable stuff that offers a major motivation to stay indoors. While I plan to spend some of the indoor time working my way through the new 50th anniversary James Bond box set, I will also devote some time to improve my mind rather than my martini-mixing skills by catching up on my reading. If you are in a similar situation, you might want to spend some of your time with these new technical books written by our community members: Oracle SOA Suite 11g Administrator's Handbook by Ahmed Aboulnaga and Arun Pareek Oracle SOA Suite 11g Developer's Cookbook by Antony Oracle BPM Suite 11g: Advanced BPMN Topics by Mark Nelson and Tanya Williams SOA & BPM Partner Community For regular information on Oracle SOA Suite become a member in the SOA & BPM Partner Community for registration please visit  www.oracle.com/goto/emea/soa (OPN account required) If you need support with your account please contact the Oracle Partner Business Center. Blog Twitter LinkedIn Mix Forum Technorati Tags: SOA books,BPM books,education,SOA Community,Oracle SOA,Oracle BPM,Community,OPN,Jürgen Kress

    Read the article

  • Add 33 Unique Biomes to Minecraft with the Biomes O’ Plenty Mod Pack

    - by Asian Angel
    Are you tired of looking at the same old biomes in Minecraft? Then add some fresh scenery with the Biomes O’ Plenty mod pack and enjoy a whole new Minecraft world! Biomes included in the mod pack: Birch Forest, Bog, Cherry Blossom Grove, Crag, Deadlands, Dense Forest, Field, Frost Forest, Garden, Glacier, Highland, Mangrove, Marsh, Meadow, Mesa, Mountain, Mystic Grove, Oasis, Ominous Woods, Orchard, Prairie, Quagmire, Rainforest, Savanna, Scrubland, Seasonal Forest, Shrubland, Spruce Forest, Tropics, Tundra, Wasteland, Wetlands, and Woodlands. You can download the mod pack, view the setup instructions, see images of each biome type, and more by visiting the link below. [1.3.2] [MODLOADERMP] Biomes O’ Plenty – Adds 33 Unique Biomes! (SSP/SMP) [via BoingBoing] 8 Deadly Commands You Should Never Run on Linux 14 Special Google Searches That Show Instant Answers How To Create a Customized Windows 7 Installation Disc With Integrated Updates

    Read the article

  • SOA Galore: New Books for Technical Eyes Only

    - by Bob Rhubart
    In my part of the world the weather has taken its seasonal turn toward the kind of cold, damp, miserable stuff that offers a major motivation to stay indoors. While I plan on spending some of that indoor time working my way through the new 50th anniversary James Bond box set, I will also devote some time to improving my mind rather than my martini-mixing skills by catching up on my reading. If you're in a similar situation, you might want to spend some of your time  with these new technical books written by community members: Oracle SOA Suite 11g Administrator's Handbook by Ahmed Aboulnaga and Arun Pareek. Oracle BPM Suite 11g: Advanced BPMN Topics by Mark Nelson and Tanya Williams Oracle SOA Suite 11g Developer's Cookbook by Antony Reynolds and Matt Wright (Coming in December; available for pre-order).

    Read the article

  • Looking for full web based booking system

    - by Sandro Dzneladze
    I've searched around here but could not find any booking system that would suit my need. I hope maybe you can help me out. I've already seen question #11379 over here. Solutions provided there are not helping and scripts not interesting. I'm looking for a booking system that support infinite number of hotels/rooms/pricingschemes. There should be unlimited number of hotel owner/users, who can access control panel and set availability for their hotel. Or create seasonal prices and adjust them. I should be able to have 15% booking fee paid through credit cart. I can work on integration with my system, it just needs to have correct APi/functionality to support this. I love wordpress so if there are some nice plugins for that, I'm open for suggestions. But this is not a must. It should be php/mysql based as I'm not good at anything else :)

    Read the article

  • Why do I get rows of zeros in my 2D fft?

    - by Nicholas Pringle
    I am trying to replicate the results from a paper. "Two-dimensional Fourier Transform (2D-FT) in space and time along sections of constant latitude (east-west) and longitude (north-south) were used to characterize the spectrum of the simulated flux variability south of 40degS." - Lenton et al(2006) The figures published show "the log of the variance of the 2D-FT". I have tried to create an array consisting of the seasonal cycle of similar data as well as the noise. I have defined the noise as the original array minus the signal array. Here is the code that I used to plot the 2D-FT of the signal array averaged in latitude: import numpy as np from numpy import ma from matplotlib import pyplot as plt from Scientific.IO.NetCDF import NetCDFFile ### input directory indir = '/home/nicholas/data/' ### get the flux data which is in ### [time(5day ave for 10 years),latitude,longitude] nc = NetCDFFile(indir + 'CFLX_2000_2009.nc','r') cflux_southern_ocean = nc.variables['Cflx'][:,10:50,:] cflux_southern_ocean = ma.masked_values(cflux_southern_ocean,1e+20) # mask land nc.close() cflux = cflux_southern_ocean*1e08 # change units of data from mmol/m^2/s ### create an array that consists of the seasonal signal fro each pixel year_stack = np.split(cflux, 10, axis=0) year_stack = np.array(year_stack) signal_array = np.tile(np.mean(year_stack, axis=0), (10, 1, 1)) signal_array = ma.masked_where(signal_array > 1e20, signal_array) # need to mask ### average the array over latitude(or longitude) signal_time_lon = ma.mean(signal_array, axis=1) ### do a 2D Fourier Transform of the time/space image ft = np.fft.fft2(signal_time_lon) mgft = np.abs(ft) ps = mgft**2 log_ps = np.log(mgft) log_mgft= np.log(mgft) Every second row of the ft consists completely of zeros. Why is this? Would it be acceptable to add a randomly small number to the signal to avoid this. signal_time_lon = signal_time_lon + np.random.randint(0,9,size=(730, 182))*1e-05 EDIT: Adding images and clarify meaning The output of rfft2 still appears to be a complex array. Using fftshift shifts the edges of the image to the centre; I still have a power spectrum regardless. I expect that the reason that I get rows of zeros is that I have re-created the timeseries for each pixel. The ft[0, 0] pixel contains the mean of the signal. So the ft[1, 0] corresponds to a sinusoid with one cycle over the entire signal in the rows of the starting image. Here are is the starting image using following code: plt.pcolormesh(signal_time_lon); plt.colorbar(); plt.axis('tight') Here is result using following code: ft = np.fft.rfft2(signal_time_lon) mgft = np.abs(ft) ps = mgft**2 log_ps = np.log1p(mgft) plt.pcolormesh(log_ps); plt.colorbar(); plt.axis('tight') It may not be clear in the image but it is only every second row that contains completely zeros. Every tenth pixel (log_ps[10, 0]) is a high value. The other pixels (log_ps[2, 0], log_ps[4, 0] etc) have very low values.

    Read the article

  • Profit : August, 2012

    - by user462779
    August 2012 issue of Profit is now available online. Way back in 2003, I wrote my first feature for Profit. It was titled “Everything You Always Wanted to Know About Application Servers (But Were Afraid To Ask),” and it discussed “cutting-edge” technologies like portals and XML and the brand-new Java Platform, Enterprise Edition (Java EE; we’re now on Java EE 7). But despite the dated terms I used in my Profit debut, I noticed something in rereading that old story that has stayed constant: mid-tier technology is where innovative enterprise IT projects happen. It may have been XML in 2003, but it’s SOA in 2012. While preparing the August issue of Profit was more than just a stroll down memory lane for me, it has provided a nice bit of perspective about what changes and what doesn’t in this dynamic IT industry. Technologies continuously evolve—some become standard practice, some are revived or reinvented, and some are left by the wayside. But the drive to innovate and the desire to succeed are business principles that never go out of fashion. Also, be sure to check out the Profit JD Edwards Special Issue 2012 (PDF), featuring partner profiles, customer successes, and Oracle executive interviews. The Middleware Advantage Three ways a flexible, integrate software layer can deliver a competitive edge Playing to Win Electronic Arts’ superefficient hub processes millions of online gaming transactions every day. Adjustable Loans With Oracle Exadata, Reliance Commercial Finance keeps pace with India’s commercial loan market. Future Proof To keep pace with mobile, social, and location-based services, smart technologists are using middleware to innovate. Spring Training Knowledge and communication help Jackson Hewitt’s Tim Bechtold get seasonal workers in top shape. Keeping Online Customers Happy Customers worldwide are comfortable with online service—but are companies meeting customers’ needs?

    Read the article

  • Convert object to DateRange

    - by user655832
    I'm querying an underlying PostgreSQL database using Pandas 0.8. Pandas is returning the DataFrame properly but the underlying timestamp column in my database is being returned as a generic "object" type in Pandas. As I would eventually like to seasonal normalization of my data I am curious as to how to convert this generic "object" column to something that is appropriate for analysis. Here is my current code to retrieve the data: # get records from db example import pandas.io.sql as psql import psycopg2 # define query to get all subs created this year QRY = """ select i i, i * random() f, case when random() > 0.5 then true else false end t, (current_date - (i*random())::int)::timestamp with time zone tsz from generate_series(1,1000) as s(i) order by 4 ; """ CONN_STRING = "host='localhost' port=5432 dbname='postgres' user='postgres'" # connect to db conn = psycopg2.connect(CONN_STRING) # get some data set index on relid column df = psql.frame_query(QRY, con=conn) print "Row count retrieved: %i" % (len(df),) Thanks for any help you can render. M

    Read the article

  • Databases and Beer

    - by Johnm
    It is a bit of a no-brainer: Include the word "beer" in a subject line of an e-mail or blog post title and you can be certain that it will be read. While there are times this practice might be a ploy to increase readership, it is not the case for this blog post. There is inspiration that can be drawn from other industries to which we, as database professionals, can apply in our industry. In this post I will highlight one of my favorite participants of the brewing industry. The Boston Beer Company started in the 1970s in Boston, Massachusetts. Others may be more familiar with this company through their Samuel Adams Boston Lager and other various seasonal beers. I am continually inspired by their commitment to mastery of the brewing process to which they evangelize frequently in their commercials. They also are continually in pursuit of pushing the boundaries of beer as we know it while working within traditional constraints. A recent example of this is their collaboration with Weihenstephan Brewery of Munich, Germany to produce the soon to be released Infinium beer. This beer, while brewed as an ale, is touted as something closer to something like Champaign - all while complying with the Reinheitsgebot. The Reinheitsgebot is also known as the "German Beer Purity Law" which was originated in 1516. This law states that beer is to consist of water, barley, hops and yeast. That's it. Quite a limiting constraint indeed. and yet, The Boston Beer Company pushed forward. Much like the process of brewing, the discipline of database design and architecture is one that is continually in process and driven by the pursuit of mastery. While we do not have purity laws to constrain us, we have many other types: best practices, company policies, government regulations, security and budgets. Through our fellow comrades, we discuss the challenges and constraints in which we operate. We boil down the principles and theories that define our profession. We reassemble these into something that is complementary to the business needs that we must fulfill. As a result, it is not uncommon to see something amazingly innovative in a small business who is pushing the boundaries of their database well beyond its intended state. It is equally common to see innovation in the use of features available in the more advanced features of databases that are found in large businesses. The tag line for The Boston Beer Company is: "Take Pride In Your Beer.", I would like to offer an alternative and say "Take Pride In Your Database." So, As you pour your next Boston Lager into a frosted glass, consider those who spend their lives mastering the craft of brewing and strive to interject their spirit into everything that you do as a database professional. Cheers!

    Read the article

  • Loop over Json using Jquery

    - by mayanna
    Below is my Json Data received from Ajax response. { "error": { "errorCode": "0001", "errorText": "SUCCESS" }, "responselist": [ { "count": 2, "event": [ { "startDate": null, "eventId": 1234, "eventName": "Interview", "modifiedUser": "User", "eventTypeCode": "1", "eventVenue": null, "eventSpecialInst": "isnsdf", "eventStatusCode": "OP", "eventLangCode": "Eng", "eventDesc": "sdfsadfsd", "fromEmailId": "[email protected]", "rsvpDeadline": 5, "canceledInd": "yes", "canceldEmailText": "sdfasdfasdfasfasdfasdfasdf", "daysToWaitlistLastCall": 5, "daysToReminderAdmin": 6, "daysToReminderEvent": 3, "daysToReminderInvitation": 2, "endDate": null, "venueAddrLine1": null, "venueAddrLine2": null, "venueAddrLine3": null, "cityCode": null, "stateCode": null, "appId": null, "modifiedDate": "2010-12-16", "countryCode": null, "zipCode": null, "user_id": null, "updateFlag": "R" }, { "startDate": null, "eventId": 4321, "eventName": "Seasonal Hiring", "modifiedUser": "User", "eventTypeCode": "1", "eventVenue": null, "eventSpecialInst": "isnsdf", "eventStatusCode": "OP", "eventLangCode": "Eng", "eventDesc": "sdfsadfsd", "fromEmailId": "[email protected]", "rsvpDeadline": 5, "canceledInd": "yes", "canceldEmailText": "sdfasdfasdfasfasdfasdfasdf", "daysToWaitlistLastCall": 5, "daysToReminderAdmin": 6, "daysToReminderEvent": 3, "daysToReminderInvitation": 2, "endDate": null, "venueAddrLine1": "KFC", "venueAddrLine2": "The Forum", "venueAddrLine3": "Koramangala", "cityCode": "Bangalore", "stateCode": "Karnataka", "appId": null, "modifiedDate": "2010-12-16", "countryCode": "India", "zipCode": "560040", "user_id": null, "updateFlag": "R" } ] } ] } Using below code to extract information inside event object. But I am not able to do it. Need guidance. $.ajax({ url:"<%=request.getContextPath()%>/service/showInvitedEvents/21", dataType:"json", success: function(jsonData) { alert("Inside response success"); $.each(jsonData.responselist.event,function(i,item) $.each(Employees,function(i,item) { alert('Iteration is' + i); var teventName = item.eventName; var teventVenue = item.eventVenue; var tstartDate = item.startDate; var tendDate = item.endDate; var tstarend = tstartDate +" - "+ tendDate ; $("#eventTable tbody").append("<tr><td><a id="+teventName+i+" href=<%=request.getContextPath()%>/service/session/1234>"+teventName+"</a></td><td>"+teventVenue+"</td><td>"+tstarend+"</td></tr>"); });

    Read the article

  • Keeping dates in order when using date_select and discarding year in Rails?

    - by MikeH
    My app has users who have seasonal products. When a user selects a product, we allow him to also select the product's season. We accomplish this by letting him select a start date and an end date for each product. We're using date_select to generate two sets of drop-downs: one for the start date and one for the end date. Including years doesn't make sense for our model. So we're using the option: discard_year => true To explain our problem, consider that our products are apples. Vendor X carries apples every year from September to January. Years are irrelevant here, and that's why we're using discard_year => true. However, while the specific years are irrelevant, the relative point in time from the start date to the end date is relevant. This is where our problem arises. When you use discard_year => true, Rails does set a year in the database, it just doesn't appear in the views. Rails sets all the years to 0001 in our app. Going back to our apple example, this means that the database now thinks the user has selected September 0001 to January 0001. This is a problem for us for a number of reasons. To solve this, the logic that I need to implement is the following: - If season_start month/date is before season_end month/date, then standard Rails approach is fine. - But, if season_start month/date is AFTER season_end month/date, then I need to dynamically update the database field such that the year for season_end is equal to the year for season_start + 1. My best guess is that I would create a custom method that runs as an after_save or after_update in my products model. But I'm not really sure how to do this. Ideas? Anybody ever had this issue? Thanks!

    Read the article

  • Avoiding seasonality assumption for stl() or decompose() in R

    - by user303922
    Hello everybody, I have high frequency commodity price data that I need to analyze. My objective is to not assume any seasonal component and just identify a trend. Here is where I run into problems with R. There are two main functions that I know of to analyze this time series: decompose() and stl(). The problem is that they both take a ts object type with a frequency parameter greater than or equal to 2. Is there some way I can assume a frequency of 1 per unit time and still analyze this time series using R? I'm afraid that if I assume frequency greater than 1 per unit time, and seasonality is calculated using the frequency parameter, then my forecasts are going to depend on that assumption. names(crude.data)=c('Date','Time','Price') names(crude.data) freq = 2 win.graph() plot(crude.data$Time,crude.data$Price, type="l") crude.data$Price = ts(crude.data$Price,frequency=freq) I want frequency to be 1 per unit time but then decompose() and stl() don't work! dim(crude.data$Price) decom = decompose(crude.data$Price) win.graph() plot(decom$random[2:200],type="line") acf(decom$random[freq:length(decom$random-freq)]) Thank you.

    Read the article

  • Suggestions for opening the Rails toolbox to design a challenge game?

    - by keruilin
    How would you suggest designing a challenge system as part of a food-eating game so that it's automated as possible? All RoR tools, design patterns and logic are at your disposal (e.g., admin consoles, crontab, arch, etc.). Prize goes to whoever can suggest the simplest and most-automated design! Here are the requirements: User has many challenges. Badge has many challenges. (A unique badge is awarded for each challenge won.) Only one challenge can run at a time. Each challenge has a limited number of days that it runs. For example, one challenge can run 3 days, while another runs 7 days. Challenges can be seasonal. For example, "Eat 13 Pumpkins" only runs during the Fall. New challenges are added to the game on an ongoing basis. For example, a new challenge every week. Each challenge has a certain probability of being selected to run. For example, "Eat 10 Pies" challenge has 10% chance of being selected to run. As each new challenge is added to the database, I want the probabilities of running to change dynamically. I want to avoid the scenario where I'm manually updating a database field just to change the probability from 10% to 5%, for example. Challenges act like Easter eggs. Challenge icons pop-up at different places on the webpage. User is awarded a badge for successfully completing a challenge, but only when it's active. There is some wait time between each challenge. Between 1 and 7 days. Which wait time is random, but the probability of the wait time being short is high and the probability of it being a long wait time is low.

    Read the article

  • I Didn&rsquo;t Get You Anything&hellip;

    - by Bob Rhubart
    Nearly every day this blog features a  list posts and articles written by members of the OTN architect community. But with Christmas just days away, I thought a break in that routine was in order. After all, if the holidays aren’t excuse enough for an off-topic post, then the terrorists have won. Rather than buy gifts for everyone -- which, given the readership of this blog and my budget could amount to a cash outlay of upwards of $15.00 – I thought I’d share a bit of holiday humor. I wrote the following essay back in the mid-90s, for a “print” publication that used “paper” as a content delivery system.  That was then. I’m older now, my kids are older, but my feelings toward the holidays haven’t changed… It’s New, It’s Improved, It’s Christmas! The holidays are a time of rituals. Some of these, like the shopping, the music, the decorations, and the food, are comforting in their predictability. Other rituals, like the shopping, the  music, the decorations, and the food, can leave you curled into the fetal position in some dark corner, whimpering. How you react to these various rituals depends a lot on your general disposition and credit card balance. I, for one, love Christmas. But there is one Christmas ritual that really tangles my tinsel: the seasonal editorializing about how our modern celebration of the holidays pales in comparison to that of Christmas past. It's not that the old notions of how to celebrate the holidays aren't all cozy and romantic--you can't watch marathon broadcasts of "It's A Wonderful White Christmas Carol On Thirty-Fourth Street Story" without a nostalgic teardrop or two falling onto your plate of Christmas nachos. It's just that the loudest cheerleaders for "old-fashioned" holiday celebrations overlook the fact that way-back-when those people didn't have the option of doing it any other way. Dashing through the snow in a one-horse open sleigh? No thanks. When Christmas morning rolls around, I'm going to be mighty grateful that the family is going to hop into a nice warm Toyota for the ride over to grandma's place. I figure a horse-drawn sleigh is big fun for maybe fifteen minutes. After that you’re going to want Old Dobbin to haul ass back to someplace warm where the egg nog is spiked and the family can gather in the flickering glow of a giant TV and contemplate the true meaning of football. Chestnuts roasting on an open fire? Sorry, no fireplace. We've got a furnace for heat, and stuffing nuts in there voids the warranty. Any of the roasting we do these days is in the microwave, and I'm pretty sure that if you put chestnuts in the microwave they would become little yuletide hand grenades. Although, if you've got a snoot full of Yule grog, watching chestnuts explode in your microwave might be a real holiday hoot. Some people may see microwave ovens as a symptom of creeping non-traditional holiday-ism. But I'll bet you that if there were microwave ovens around in Charles Dickens' day, the Cratchits wouldn't have had to entertain an uncharacteristically giddy Scrooge for six or seven hours while the goose cooked. Holiday entertaining is, in fact, the one area that even the most severe critic of modern practices would have to admit has not changed since Tim was Tiny. A good holiday celebration, then as now, involves lots of food, free-flowing drink, and a gathering of friends and family, some of whom you are about as happy to see as a subpoena. Just as the Cratchit's Christmas was spent with a man who, for all they knew, had suffered some kind of head trauma, so the modern holiday gathering includes relatives or acquaintances who, because they watch too many talk shows, and/or have poor personal hygiene, and/or fail to maintain scheduled medication, you would normally avoid like a plate of frosted botulism. But in the season of good will towards men, you smile warmly at the mystery uncle wandering around half-crocked with a clump of mistletoe dangling from the bill of his N.R.A. cap. Dickens' story wouldn't have become the holiday classic it has if, having spotted on their doorstep an insanely grinning, raw poultry-bearing, fresh-off-a-rough-night Scrooge, the Cratchits had pulled their shades and pretended not to be home. Which is probably what I would have done. Instead, knowing full well his reputation as a career grouch, they welcomed him into their home, and we have a touching story that teaches a valuable lesson about how the Christmas spirit can get the boss to pump up the payroll. Despite what the critics might say, our modern Christmas isn't all that different from those of long ago. Sure, the technology has changed, but that just means a bigger, brighter, louder Christmas, with lasers and holograms and stuff. It's our modern celebration of a season that even the least spiritual among us recognizes as a time of hope that the nutcases of the world will wake up and realize that peace on earth is a win/win proposition for everybody. If Christmas has changed, it's for the better. We should continue making Christmas bigger and louder and shinier until everybody gets it.  *** Happy Holidays, everyone!   del.icio.us Tags: holiday,humor Technorati Tags: holiday,humor

    Read the article

  • 3 Ways to Make Steam Even Faster

    - by Chris Hoffman
    Have you ever noticed how slow Steam’s built-in web browser can be? Do you struggle with slow download speeds? Or is Steam just slow in general? These tips will help you speed it up. Steam isn’t a game itself, so there are no 3D settings to change to achieve maximum performance. But there are some things you can do to speed it up dramatically. Speed Up the Steam Web Browser Steam’s built-in web browser — used in both the Steam store and in Steam’s in-game overlay to provide a web browser you can quickly use within games – can be frustratingly slow on many systems. Rather than the typical speed we’ve come to expect from Chrome, Firefox, or even Internet Explorer, Steam seems to struggle. When you click a link or go to a new page, there’s a noticeable delay before the new page appears — something that doesn’t happen in desktop browsers. Many people seem to have made peace with this slowness, accepting that Steam’s built-in browser is just bad. However, there’s a trick that will eliminate this delay on many systems and make the Steam web browser fast. This problem seems to arise from an incompatibility with the Automatically Detect Proxy Settings option, which is enabled by default on Windows. This is a compatibility option that very few people should actually need, so it’s safe to disable it. To disable this option, open the Internet Options dialog — press the Windows key to access the Start menu or Start screen, type Internet Options, and click the Internet Options shortcut. Select the Connections tab in the Internet Options window and click the LAN settings button. Uncheck the Automatically detect settings option here, then click OK to save your settings. If you experienced a significant delay every time a web page loaded in Steam’s web browser, it should now be gone. In the unlikely event that you encounter some sort of problem with your network connection, you could always re-enable this option. Increase Steam’s Game Download Speed Steam attempts to automatically select the nearest download server to your location. However, it may not always select the ideal download server. Or, in the case of high-traffic events like big seasonal sales and huge game launches, you may benefit from selecting a less-congested server. To do this, open Steam’s settings by clicking the Steam menu in Steam and selecting Settings. Click over to the Downloads tab and select the closest download server from the Download Region box. You should also ensure that Steam’s download bandwidth isn’t limited from here. You may want to restart Steam and see if your download speeds improve after changing this setting. In some cases, the closest server might not be the fastest. One a bit farther away could be faster if your local server is more congested, for example. Steam once provided information about content server load, which allowed you to select a regional server that wasn’t under high-load, but this information no longer seems to be available. Steam still provides a page that shows you the amount of download activity happening in different regions, including statistics about the difference in download speeds in different US states, but this information isn’t as useful. Accelerate Steam and Your Games One way to speed up all your games — and Steam itself —  is by getting a solid-state drive and installing Steam to it. Steam allows you to easily move your Steam folder — at C:\Program Files (x86)\Steam by default — to another hard drive. Just move it like you would any other folder. You can then launch the Steam.exe program as if you had never moved Steam’s files. Steam also allows you to configure multiple game library folders. This means that you can set up a Steam library folder on a solid-state drive and one on your larger magnetic hard drive. Install your most frequently played games to the solid-state drive for maximum speed and your less frequently played ones to the slower magnetic hard drive to save SSD space. To set up additional library folders, open Steam’s Settings window and click the Downloads tab. You’ll find the Steam Library Folders option here. Click the Add Library Folder button and create a new game library on another hard drive. When you install a game in Steam, you’ll be asked which library folder you want to install it to. With the proxy compatibility option disabled, the correct download server chosen, and Steam installed to a fast SSD, it should be a speed demon. There’s not much more you can do to speed up Steam, short of upgrading other hardware like your computer’s CPU. Image Credit: Andrew Nash on Flickr     

    Read the article

  • Short Season, Long Models - Dealing with Seasonality

    - by Michel Adar
    Accounting for seasonality presents a challenge for the accurate prediction of events. Examples of seasonality include: ·         Boxed cosmetics sets are more popular during Christmas. They sell at other times of the year, but they rise higher than other products during the holiday season. ·         Interest in a promotion rises around the time advertising on TV airs ·         Interest in the Sports section of a newspaper rises when there is a big football match There are several ways of dealing with seasonality in predictions. Time Windows If the length of the model time windows is short enough relative to the seasonality effect, then the models will see only seasonal data, and therefore will be accurate in their predictions. For example, a model with a weekly time window may be quick enough to adapt during the holiday season. In order for time windows to be useful in dealing with seasonality it is necessary that: The time window is significantly shorter than the season changes There is enough volume of data in the short time windows to produce an accurate model An additional issue to consider is that sometimes the season may have an abrupt end, for example the day after Christmas. Input Data If available, it is possible to include the seasonality effect in the input data for the model. For example the customer record may include a list of all the promotions advertised in the area of residence. A model with these inputs will have to learn the effect of the input. It is possible to learn it specific to the promotion – and by the way learn about inter-promotion cross feeding – by leaving the list of ads as it is; or it is possible to learn the general effect by having a flag that indicates if the promotion is being advertised. For inputs to properly represent the effect in the model it is necessary that: The model sees enough events with the input present. For example, by virtue of the model lifetime (or time window) being long enough to see several “seasons” or by having enough volume for the model to learn seasonality quickly. Proportional Frequency If we create a model that ignores seasonality it is possible to use that model to predict how the specific person likelihood differs from average. If we have a divergence from average then we can transfer that divergence proportionally to the observed frequency at the time of the prediction. Definitions: Ft = trailing average frequency of the event at time “t”. The average is done over a suitable period of to achieve a statistical significant estimate. F = average frequency as seen by the model. L = likelihood predicted by the model for a specific person Lt = predicted likelihood proportionally scaled for time “t”. If the model is good at predicting deviation from average, and this holds over the interesting range of seasons, then we can estimate Lt as: Lt = L * (Ft / F) Considering that: L = (L – F) + F Substituting we get: Lt = [(L – F) + F] * (Ft / F) Which simplifies to: (i)                  Lt = (L – F) * (Ft / F)  +  Ft This latest expression can be understood as “The adjusted likelihood at time t is the average likelihood at time t plus the effect from the model, which is calculated as the difference from average time the proportion of frequencies”. The formula above assumes a linear translation of the proportion. It is possible to generalize the formula using a factor which we will call “a” as follows: (ii)                Lt = (L – F) * (Ft / F) * a  +  Ft It is also possible to use a formula that does not scale the difference, like: (iii)               Lt = (L – F) * a  +  Ft While these formulas seem reasonable, they should be taken as hypothesis to be proven with empirical data. A theoretical analysis provides the following insights: The Cumulative Gains Chart (lift) should stay the same, as at any given time the order of the likelihood for different customers is preserved If F is equal to Ft then the formula reverts to “L” If (Ft = 0) then Lt in (i) and (ii) is 0 It is possible for Lt to be above 1. If it is desired to avoid going over 1, for relatively high base frequencies it is possible to use a relative interpretation of the multiplicative factor. For example, if we say that Y is twice as likely as X, then we can interpret this sentence as: If X is 3%, then Y is 6% If X is 11%, then Y is 22% If X is 70%, then Y is 85% - in this case we interpret “twice as likely” as “half as likely to not happen” Applying this reasoning to (i) for example we would get: If (L < F) or (Ft < (1 / ((L/F) + 1)) Then  Lt = L * (Ft / F) Else Lt = 1 – (F / L) + (Ft * F / L)  

    Read the article

  • Entity Attribute Value Database vs. strict Relational Model Ecommerce question

    - by Dr. Zim
    It is safe to say that the EAV/CR database model is bad. That said, Question: What database model, technique, or pattern should be used to deal with "classes" of attributes describing e-commerce products which can be changed at run time? In a good E-commerce database, you will store classes of options (like TV resolution then have a resolution for each TV, but the next product may not be a TV and not have "TV resolution"). How do you store them, search efficiently, and allow your users to setup product types with variable fields describing their products? If the search engine finds that customers typically search for TVs based on console depth, you could add console depth to your fields, then add a single depth for each tv product type at run time. There is a nice common feature among good e-commerce apps where they show a set of products, then have "drill down" side menus where you can see "TV Resolution" as a header, and the top five most common TV Resolutions for the found set. You click one and it only shows TVs of that resolution, allowing you to further drill down by selecting other categories on the side menu. These options would be the dynamic product attributes added at run time. Further discussion: So long story short, are there any links out on the Internet or model descriptions that could "academically" fix the following setup? I thank Noel Kennedy for suggesting a category table, but the need may be greater than that. I describe it a different way below, trying to highlight the significance. I may need a viewpoint correction to solve the problem, or I may need to go deeper in to the EAV/CR. Love the positive response to the EAV/CR model. My fellow developers all say what Jeffrey Kemp touched on below: "new entities must be modeled and designed by a professional" (taken out of context, read his response below). The problem is: entities add and remove attributes weekly (search keywords dictate future attributes) new entities arrive weekly (products are assembled from parts) old entities go away weekly (archived, less popular, seasonal) The customer wants to add attributes to the products for two reasons: department / keyword search / comparison chart between like products consumer product configuration before checkout The attributes must have significance, not just a keyword search. If they want to compare all cakes that have a "whipped cream frosting", they can click cakes, click birthday theme, click whipped cream frosting, then check all cakes that are interesting knowing they all have whipped cream frosting. This is not specific to cakes, just an example.

    Read the article

  • Evaluating points in time by months, but without referencing years in Rails

    - by MikeH
    FYI, There is some overlap in the initial description of this question with a question I asked yesterday, but the question is different. My app has users who have seasonal products. When a user selects a product, we allow him to also select the product's season. We accomplish this by letting him select a start date and an end date for each product. We're using date_select to generate two sets of drop-downs: one for the start date and one for the end date. Including years doesn't make sense for our model. So we're using the option: discard_year => true When you use discard_year => true, Rails sets a year in the database, it just doesn't appear in the views. Rails sets all the years to either 0001 or 0002 in our app. Yes, we could make it 2009 and 2010 or any other pair. But the point is that we want the months and days to function independent of a particular year. If we used 2009 and 2010, then those dates would be wrong next year because we don't expect these records to be updated every year. My problem is that we need to dynamically evaluate the availability of products based on their relationship to the current month. For example, assume it's March 15. Regardless of the year, I need a method that can tell me that a product available from October to January is not available right now. If we were using actual years, this would be pretty easy. For example, in the products model, I can do this: def is_available? (season_start.past? && season_end.future?) end I can also evaluate a start_date and an end_date against current_date However, in setup I've described above where we have arbitrary years that only make sense relative to each other, these methods don't work. For example, is_available? would return false for all my products because their end date is in the year 0001 or 0002. What I need is a method just like the ones I used as examples above, except that they evaluate against current_month instead of current_date, and past? and future months instead of years. I have no idea how to do this or whether Rails has any built in functionality that could help. I've gone through all the date and time methods/helpers in the API docs, but I'm not seeing anything equivalent to what I'm describing. Thanks.

    Read the article

  • Master Data Management and Cloud Computing

    - by david.butler(at)oracle.com
    Cloud Computing is all the rage these days. There are many reasons why this is so. But like its predecessor, Service Oriented Architecture, it can fall on hard times if the underlying data is left unmanaged. Master Data Management is the perfect Cloud companion. It can materially increase the chances for successful Cloud initiatives. In this blog, I'll review the nature of the Cloud and show how MDM fits in.   Here's the National Institute of Standards and Technology Cloud definition: •          Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction.   Cloud architectures have three main layers: applications or Software as a Service (SaaS), Platforms as a Service (PaaS), and Infrastructure as a Service (IaaS). SaaS generally refers to applications that are delivered to end-users over the Internet. Oracle CRM On Demand is an example of a SaaS application. Today there are hundreds of SaaS providers covering a wide variety of applications including Salesforce.com, Workday, and Netsuite. Oracle MDM applications are located in this layer of Oracle's On Demand enterprise Cloud platform. We call it Master Data as a Service (MDaaS). PaaS generally refers to an application deployment platform delivered as a service. They are often built on a grid computing architecture and include database and middleware. Oracle Fusion Middleware is in this category and includes the SOA and Data Integration products used to connect SaaS applications including MDM. Finally, IaaS generally refers to computing hardware (servers, storage and network) delivered as a service.  This typically includes the associated software as well: operating systems, virtualization, clustering, etc.    Cloud Computing benefits are compelling for a large number of organizations. These include significant cost savings, increased flexibility, and fast deployments. Cost advantages include paying for just what you use. This is especially critical for organizations with variable or seasonal usage. Companies don't have to invest to support peak computing periods. Costs are also more predictable and controllable. Increased agility includes access to the latest technology and experts without making significant up front investments.   While Cloud Computing is certainly very alluring with a clear value proposition, it is not without its challenges. An IDC survey of 244 IT executives/CIOs and their line-of-business (LOB) colleagues identified a number of issues:   Security - 74% identified security as an issue involving data privacy and resource access control. Integration - 61% found that it is hard to integrate Cloud Apps with in-house applications. Operational Costs - 50% are worried that On Demand will actually cost more given the impact of poor data quality on the rest of the enterprise. Compliance - 49% felt that compliance with required regulatory, legal and general industry requirements (such as PCI, HIPAA and Sarbanes-Oxley) would be a major issue. When control is lost, the ability of a provider to directly manage how and where data is deployed, used and destroyed is negatively impacted.  There are others, but I singled out these four top issues because Master Data Management, properly incorporated into a Cloud Computing infrastructure, can significantly ameliorate all of these problems. Cloud Computing can literally rain raw data across the enterprise.   According to fellow blogger, Mike Ferguson, "the fracturing of data caused by the adoption of cloud computing raises the importance of MDM in keeping disparate data synchronized."   David Linthicum, CTO Blue Mountain Labs blogs that "the lack of MDM will become more of an issue as cloud computing rises. We're moving from complex federated on-premise systems, to complex federated on-premise and cloud-delivered systems."    Left unmanaged, non-standard, inconsistent, ungoverned data with questionable quality can pollute analytical systems, increase operational costs, and reduce the ROI in Cloud and On-Premise applications. As cloud computing becomes more relevant, and more data, applications, services, and processes are moved out to cloud computing platforms, the need for MDM becomes ever more important. Oracle's MDM suite is designed to deal with all four of the above Cloud issues listed in the IDC survey.   Security - MDM manages all master data attribute privacy and resource access control issues. Integration - MDM pre-integrates Cloud Apps with each other and with On Premise applications at the data level. Operational Costs - MDM significantly reduces operational costs by increasing data quality, thereby improving enterprise business processes efficiency. Compliance - MDM, with its built in Data Governance capabilities, insures that the data is governed according to organizational standards. This facilitates rapid and accurate reporting for compliance purposes. Oracle MDM creates governed high quality master data. A unified cleansed and standardized data view is produced. The Oracle Customer Hub creates a single view of the customer. The Oracle Product Hub creates high quality product data designed to support all go-to-market processes. Oracle Supplier Hub dramatically reduces the chances of 'supplier exceptions'. Oracle Site Hub masters locations. And Oracle Hyperion Data Relationship Management masters financial reference data and manages enterprise hierarchies across operational areas from ERP to EPM and CRM to SCM. Oracle Fusion Middleware connects Cloud and On Premise applications to MDM Hubs and brings high quality master data to your enterprise business processes.   An independent analyst once said "Poor data quality is like dirt on the windshield. You may be able to drive for a long time with slowly degrading vision, but at some point, you either have to stop and clear the windshield or risk everything."  Cloud Computing has the potential to significantly degrade data quality across the enterprise over time. Deploying a Master Data Management solution prior to or in conjunction with a move to the Cloud can insure that the data flowing into the enterprise from the Cloud is clean and governed. This will in turn insure that expected returns on the investment in Cloud Computing will be realized.       Oracle MDM has proven its metal in this area and has the customers to back that up. In fact, I will be hosting a webcast on Tuesday, April 10th at 10 am PT with one of our top Cloud customers, the Church Pension Group. They have moved all mainline applications to a hosted model and use Oracle MDM to insure the master data is managed and cleansed before it is propagated to other cloud and internal systems. I invite you join Martin Hossfeld, VP, IT Operations, and Danette Patterson, Enterprise Data Manager as they review business drivers for MDM and hosted applications, how they did it, the benefits achieved, and lessons learned. You can register for this free webcast here.  Hope to see you there.

    Read the article

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

1