text: local data ={ --Fields for the Steps table --[Field] [Type] --StepNumber Int64 --Text String --Image String --CoordX Int64 --CoordY Int64 Steps = { TUTORIAL_BASIC_MOVEMENT = { {StepNumber=1; Text=[=[Select a unit by left-clicking on it.]=]; Image="T_BasicMovement1.dds"; CoordX=300; CoordY=220;}; {StepNumber=2; Text=[=[Right-click on the map and drag to see where the unit can move. Release the mouse over the desired tile to move the unit.]=]; Image="T_BasicMovement2.dds"; CoordX=320; CoordY=469;}; {StepNumber=3; Text=[=[Be wary of ending your turn on a tile with Miasma (green looking haze), as that will damage your unit.]=]; Image="T_BasicMovement2.dds"; CoordX=220; CoordY=280;}; }; TUTORIAL_BASIC_ATTACK = { {StepNumber=1; Text=[=[To attack an opponent, select your unit by left-clicking on it.]=]; Image="T_Combat1.dds"; CoordX=429; CoordY=426;}; {StepNumber=2; Text=[=[With your unit selected, hover your mouse over your opponent for a combat preview (including all combat bonuses).]=]; Image="T_Combat2.dds"; CoordX=61; CoordY=138;}; {StepNumber=3; Text=[=[If you're sure about attacking after reviewing the combat preview, right-click on the opponent to attack.]=]; Image="T_Combat3.dds"; CoordX=133; CoordY=506;}; }; TUTORIAL_QUEST_LOG_SCREEN = { {StepNumber=1; Text=[=[Quests are missions that you can perform for extra rewards.]=]; Image="T_Quest1.dds"; CoordX=66; CoordY=515;}; {StepNumber=2; Text=[=[Click on a Quest to view its objective.]=]; Image="T_Quest2.dds"; CoordX=395; CoordY=350;}; {StepNumber=3; Text=[=[The Quest Log also has a tab for Victories, where you can review the different ways you can win.]=]; Image="T_Quest2.dds"; CoordX=527; CoordY=483;}; }; TUTORIAL_UNIT_UPGRADES_SCREEN = { {StepNumber=1; Text=[=[This is the basic Unit Upgrade panel. Earn Affinity points to unlock powerful unit upgrades.]=]; Image="T_Upgrade1.dds"; CoordX=10; CoordY=523;}; {StepNumber=2; Text=[=[After you've reached level 1 in any Affinity, you'll be asked to choose your first upgrade.]=]; Image="T_Upgrade2.dds"; CoordX=365; CoordY=350;}; {StepNumber=3; Text=[=[You will always have 2 choices. In this case, we choose additional combat strength.]=]; Image="T_Upgrade3.dds"; CoordX=305; CoordY=309;}; {StepNumber=4; Text=[=[If you reach level 4 in any Affinity (Purity shown here), another powerful upgrade becomes available.]=]; Image="T_Upgrade4.dds"; CoordX=305; CoordY=156;}; }; TUTORIAL_DIPLO_SCREEN = { {StepNumber=1; Text=[=[This is the Diplomacy panel. Here, you can track all things diplomatic. You are represented by the first leader on the panel.]=]; Image="T_Diplo.dds"; CoordX=7; CoordY=387;}; {StepNumber=2; Text=[=[Clicking on any other leader on the panel will open up the Diplomacy screen, where you can trade, negotiate, etc.]=]; Image="T_Diplo.dds"; CoordX=7; CoordY=288;}; {StepNumber=3; Text=[=[Each leader's disposition towards you is displayed below each Sponsor icon.]=]; Image="T_Diplo.dds"; CoordX=7; CoordY=239;}; {StepNumber=4; Text=[=[There is also additional information as well, like your score, Affinity choices, Wonders built, and more.]=]; Image="T_Diplo.dds"; CoordX=298; CoordY=362;}; }; TUTORIAL_VIRTUES_SCREEN = { {StepNumber=1; Text=[=[This is the Virtues panel. You can spend Culture you've earned on bonuses for your faction.]=]; Image="T_Virtues1.dds"; CoordX=93; CoordY=484;}; {StepNumber=2; Text=[=[Once you've earned enough Culture, you can choose a bonus by clicking on a Virtue in any of the available Virtue trees.]=]; Image="T_Virtues2.dds"; CoordX=208; CoordY=295;}; {StepNumber=3; Text=[=[Once you've unlocked enough Virtues in a horizontal or vertical row, you'll activate additional bonuses called "Kickers".]=]; Image="T_Virtues3.dds"; CoordX=152; CoordY=352;}; }; TUTORIAL_COVERT_OPS_SCREEN = { {StepNumber=1; Text=[=[Covert Ops is not available until you've unlocked at least one spy.]=]; Image="T_Covert1.dds"; CoordX=202; CoordY=524;}; {StepNumber=2; Text=[=[Upon receiving your first spy, you'll be prompted to choose a basic bonus, called a National Security Project.]=]; Image="T_Covert2.dds"; CoordX=202; CoordY=414;}; {StepNumber=3; Text=[=[Additionally, you will now have 3 Agents available to assign, either to your cities, or an opponent's.]=]; Image="T_Covert3.dds"; CoordX=202; CoordY=338;}; {StepNumber=4; Text=[=[In your own cities, Agents offer protection against enemy Agents. In an opponent's cities, Agents can attempt Operations.]=]; Image="T_Covert3.dds"; CoordX=453; CoordY=345;}; }; TUTORIAL_COVERT_OPS_SCREEN2 = { {StepNumber=5; Text=[=[Operations range from Very Easy to Hard, and are limited by the Intrigue level of the target City.]=]; Image="T_Covert6.dds"; CoordX=513; CoordY=269;}; {StepNumber=6; Text=[=[Higher Intrigue means having more difficult Operations available to you.]=]; Image="T_Covert6.dds"; CoordX=513; CoordY=83;}; }; TUTORIAL_COVERT_OPS_SCREEN3 = { {StepNumber=7; Text=[=[Monitor the Intrigue in your own cities carefully. If it gets too high, enemy Agents can cause quite a bit of damage.]=]; Image="T_Covert7.dds"; CoordX=138; CoordY=350;}; }; TUTORIAL_TECH_WEB = { {StepNumber=1; Text=[=[Welcome to the Technology Web. You begin in the center of the web, and can choose a research path in any direction.]=]; Image="T_Science1.dds"; CoordX=1; CoordY=314;}; {StepNumber=2; Text=[=[The Tech Web is made up of two types of techs. Branch techs, like "Genetics"...]=]; Image="T_Science2.dds"; CoordX=347; CoordY=434;}; {StepNumber=3; Text=[=[...and Leaf techs, like "Genetic Mapping", which can award Affinity points among other things.]=]; Image="T_Science3.dds"; CoordX=330; CoordY=269;}; {StepNumber=4; Text=[=["Genetic Mapping" contains a red Purity icon, which means you will earn Purity Affinity points when you complete the tech.]=]; Image="T_Science3.dds"; CoordX=370; CoordY=269;}; }; TUTORIAL_TECH_WEB2 = { {StepNumber=5; Text=[=[Lost? You can filter the Tech Web by many different subjects, or search for a specific term.]=]; Image="T_Science4.dds"; CoordX=90; CoordY=490;}; }; TUTORIAL_ORBITAL_LAYER = { {StepNumber=1; Text=[=[To launch an Orbital Unit, select it and click on its launch button. This will bring up the Orbital View.]=]; Image="T_Sat1.dds"; CoordX=6; CoordY=30;}; {StepNumber=2; Text=[=[Now we are in the Orbital View. These are hexes where we have Orbital Coverage, and can potentially launch an Orbital Unit.]=]; Image="T_Sat2.dds"; CoordX=105; CoordY=495;}; {StepNumber=3; Text=[=[These are the hexes that our Orbital Unit would affect, if we launched it.]=]; Image="T_Sat4.dds"; CoordX=278; CoordY=427;}; {StepNumber=4; Text=[=[Our Orbital Unit's effect area is not allowed to overlap with any other's effect area.]=]; Image="T_Sat5.dds"; CoordX=452; CoordY=359;}; }; TUTORIAL_UNIT_UPGRADES_SCREEN_XP = { {StepNumber=1; Text=[=[This is the basic Unit Upgrade panel. Earn Affinity points to unlock powerful unit upgrades.]=]; Image="T_XPUpgrade1.dds"; CoordX=337; CoordY=337;}; }; TUTORIAL_UNIT_UPGRADES_SCREEN_XP2 = { {StepNumber=2; Text=[=[After you've reached level 1 in any Affinity, you'll be asked to choose your first upgrade.]=]; Image="T_XPUpgrade2.dds"; CoordX=455; CoordY=317;}; {StepNumber=3; Text=[=[You will always have 2 choices. In this case, we choose additional combat strength.]=]; Image="T_XPUpgrade3.dds"; CoordX=366; CoordY=86;}; {StepNumber=4; Text=[=[Additional upgrades are unlocked as you earn additional Affinity.]=]; Image="T_XPUpgrade3.dds"; CoordX=578; CoordY=257;}; }; }; } return data


text:
Auto enthusiasts know that a quality brand is built into each Dodge, and know that only top quality replacement Omni parts will suffice when your car or truck needs fixing. It's not a regular occurrence that you find yourself in need of a replacement Brake Line & Hose to repair your car or truck. You bought your Omni because its reliable transport and a trusted name struck a chord inside you; so why would you settle for anything but the best when it comes to the highest quality auto parts? The vehicle in your driveway symbolizes the best in class compared to all the other vehicles in today's marketplace.
A properly functioning hydraulic brake system will ensure you can stop your car or truck when you want. That means your Dodge Omni brake lines or hoses need to be free of leaks at all times. Hydraulic brake lines connect the brake calipers or wheel cylinders to the master cylinder on your car or truck. Every time you apply the brakes on your Dodge Omni, the brake fluid goes from the master cylinder to each wheel and works its magic to slow down your car. If there's a leak in a brake hose, then you will likely see a large puddle of fluid under your car when you press the brake pedal.
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subset: commoncrawl


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Looking for an easy craft to do with kids? These pretty paper dahlia flowers are so simple to make, you'll wanna create a bunch and use them to decorate your child's room or to put around the house the next time you're having a birthday party or other celebration. The best part: all you need is some colorful paper and tape, which means it's super budget-friendly as well as easy!

To make your paper dahlias, you'll need just a few simple things:

colored paper (at least 10 sheets of 8 1/2" x 11" paper for each large dahlia and 6 sheets each for the smaller dahlia)

card stock paper or cardboard (to make the base circles)

double-sided tape

scissors

round bowls or jars (to make

pencil

paper trimmer (If you don't have a paper trimmer, you can just use scissors to make your squares; but paper trimmers are inexpensive, and will make the project go a lot faster.)

Begin by making the base for the dahlia flower. If you want a large flower, use bowl or jar with a diameter of 4 1/2" to trace a circle onto your card stock or cardboard base. Cut it out with scissors.

Using your scissors or a paper trimmer, cut out squares of paper to make the petals for the dahlia flower. For a large flower, cut squares that are 4" x 4". For a smaller flower, make squares that are 3" x 3".

Next, make the petals of the dahlia flower. Begin by putting a piece of double-stick tape along one side of the square as shown. Then curve the bottom corner as shown to form a cone.

Press the sides together to seal the cone. Your dahlia petal will look like this when you are done.

Make batches of petals at a time--kids will love making a long line of cones, and it will go fast once you get into a rhythm. (You'll need between 50 and 60 petals to make a large dahlia.)

Begin taping the paper cones onto the cardboard circle, flattening the bottom tip as you tape each "petal" down. Leave a bit of space between each cone as shown.

Add another layer of petals to the paper dahlia flower, this time placing the petals on top of the empty spaces so that the petals are layered as shown. Remember to press down on the tips of the cones. (Kids will especially love this part--it's fun, and the results are fast and easy.)

Add the next layer of petals to the dahlia flower.

Continue adding petals until you have completely filled out the paper dahlia flower.

To make a smaller dahlia paper flower, simply use a smaller square and circle. For my flower, I used a 3 1/2" circle for the base and made squares that were 3" x 3" to form the petals.

Keep adding petals to the smaller paper dahlia flower.

As you did with the large dahlia flower, continue adding layers of the petals until all the gaps are filled in.

More Paper Flowers to Make with Kids

How to Make Paper Flowers

How to Make Tissue Paper Flowers

How to Make a Paper Flower Bouquet

Spring Craft for Kids - Flower Wreath


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Product Description
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Items may be available for quick delivery, some items may take longer to special order. After you place your order, customer service will be in touch to provide an estimated date of delivery.


text:
|Message view
|« Date » · « Thread »
|« Date » · « Thread »
|"Vivek Ratan (JIRA)" <j...@apache.org>
|[jira] Updated: (HADOOP-1974) Progress node should cache root object for faster progress computation
|Wed, 03 Oct 2007 03:55:50 GMT
[ https://issues.apache.org/jira/browse/HADOOP-1974?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Vivek Ratan updated HADOOP-1974: -------------------------------- Attachment: 1974_patch02 In my earlier patch, I had forgotten to use just the relative path to the files. That has been fixed in this patch (1974_patch02). > Progress node should cache root object for faster progress computation > ---------------------------------------------------------------------- > > Key: HADOOP-1974 > URL: https://issues.apache.org/jira/browse/HADOOP-1974 > Project: Hadoop > Issue Type: Improvement > Reporter: Vivek Ratan > Assignee: Vivek Ratan > Priority: Minor > Attachments: 1974_patch01, 1974_patch02 > > > In org.apache.hadoop.util.Progress.get(), we walk through the tree of objects to find the root of a node, before figuring out the progress. This approach is not optimized, especially since get() is called frequently. Each Progressnode should cache its root object, and this is easy to do since nodes do not change their parents. > Keeping track of the root node is also useful in synchronization issues. [see HADOOP-1970 for more details]. The root node can be used to synchronize the entire structure for methods that need to traverse the tree in different directions and lock nodes along the way. -- This message is automatically generated by JIRA. - You can reply to this email to add a comment to the issue online.
|View raw message


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The Dragon Circle were a hate group in the Marvel Comics who appeared as secondary antagonists in Black Panther versus the Klan. They were a society who despite being inspired by the original Ku Klux Klan accepted all races and were said to be more about seeking political power than racial purity.
The Dragon Circle is a cult of unknown origin, who diverged from the Ku Klux Klan (KKK) at some point.
The Dragon Circle somehow evolved into a racially diverse organization, more interested in political power than the Klan's racial and religious views (according to the story, the Klan was also originally more about politics). It was taken over by the Reverend, Addison Blackburn. The Reverend had a number of members of the Dragon Circle infiltrate the KKK to keep him informed of their plans and activities.
(Marvel Premiere#52(fb)) - The Reverend recruited the prison inmate Hector Santiago, whom they outfitted to become Windeagle.
(JungAct#21(fb)) - Angela Lynne investigated the crooked real estate deals in which the Dragon Circle were involved. A gun went off and she was found dead. The Reverend is apparently the culprit of this murder.
(JungAct#19) - Members of the Dragon Circle ambushed, and apparently intended to kill Monica Lynne, as she visited the grave of her sister, Angela. The members were fought off by Monica, and her on-and-off lover, the Black Panther (with the added assistance of Kevin Trublood). The cult members were soon released on bail paid by a wealthy backer (presumably the Reverend).
That night, the home of the Lynne family was unintentionally simultaneously attacked by members of the Dragon Circle, and the KKK. The Black Panther drove off both organizations, who sought to torch the house.
(Jungle Action#20, 21) - Members of the Dragon Circle assaulted Monica and the Black Panther in a local grocery store, which increased the distrust the local citizenry had for the costumed Panther. After recovering, the Panther invaded a meeting of the KKK, who attacked him, and succeeded in overpowering him by strength of numbers. They tied him to a cross which they set afire.
He eventually broke free and escaped, but was hospitalized for a few weeks, recovering from his injuries. He later appeared at a KKK rally, where he was again attacked. This time he fought off his attackers.
(Jungle Action#22) - Jessica Lynne related the story of Caleb Lynne to the Black Panther. Lloyd and Monica, and Kevin Trublood were also present.
(Jungle Action#24(fb)) - The Black Panther questioned Leroy Carter, and then Ambrose Ellis, about Angela's death. Afterwards, he was attacked by Windeagle, who was sent by the Reverend to attack him. The struggle continued for a short time until Dragon Circle members rammed the Panther with their car, and they captured him.
(Jungle Action#24) - The Panther was tied to a water wheel, apparently as part of some kind of James Bond-leave them to die, so they can escape and foil your plans-scheme. The Panther broke freed, but was again subdued by Windeagle and the Dragon Circle
(Marvel Premiere#53(fb)) - The Panther was taken to the Dragon Circle's headquarters, where the Reverend attempted to brainwash him into joining them. T'Challa's will proved to be too strong for them to overcome, but they did succeed in submerging his memories of all of the recent events. He lost all memory of the Dragon Circle, and even his sometime lover Monica, and headed off on a separate mission.
(Black Panther I#14, 15) - Monica Lynne and Kevin Trublood traveled to New York to find the Panther. They were trailed by Windeagle.
(MarvPrem51) - Windeagle attacked the Black Panther at the Wakandan consulate. He was defeated and contained. Monica and Kevin confronted the Black Panther, and helped him realize that part of his memory had been repressed. Windeagle broke free and attacked again, but he was shot and killed by an unidentified sniper.
(MarvPrem52) - After learning the past of Windeagle, the Panther, Trublood, Monica, and Omoro Kaysbe were trailed by members of the Dragon Circle. The Panther picked up on this and led them to a lumberyard, where they defeated the men. The last of the involved members was slain by an unknown sniper. The Panther found a KKK card on the dead cultist, and investigated a KKK rally, where he was confronted and attacked by the mysterious Soul Strangler. The Strangler vanished after a short battle, and the Panther escaped from a mob of angry Klansmen.
(MarvPrem53) - The Reverend declared war on the KKK, but after his meeting with the rest of the Dragon Circle broke up, he was confronted by several Klansmen. They took him deeper into the swamp, and planned to execute him, but reality changed around them, and they seemed to be back in 1867, at the home of Caleb Lynne. Both the Reverend and the Klansmen watched in astonishment as the Panther, who had been trailing the Reverend, battled and defeated the Soul Strangler. The Panther then subdued and brought in the Reverend and the Klansmen. The Reverend and his involvement were exposed, and he was sent to prison. Some members of the Dragon Cult turned evidence on other members, and the cult apparently broke up.


text:
bobble head pigeons
gather to enjoy the feast
from a wrinkled bag
Tags: haiku, life, nature, poems, poetry
Thank you for liking my poetry 'Close', your future visits and likes 🙂
Nice 'pigeon'-haiku. Awesome … photo to go with it.
…bobbled head pigeons ~ great line for lovely haiku ^_^
I love that first line too.
How unusual not to see the golden arches on a discarded bag 🙂
Do you often look for logos on discarded bags, John? 🙂
Stalking Pigeons
Sometime they just jump our at you lol 🙂
Bobble headed is great. Indeed a brown paper bag would be a feast!
They work so hard for their treats!
The really sad thing is when the bag is plastic and the pigeons are injured from the bag! Nicely done esp. bobbleheads!
Pigeons will eat anything… but as wabi says, a plastic bag is probably not the best.
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text:
October 17, 2014 Total Views: 6003 Today: 1 ★★★
Hakata Nakasu Washington Hotel Plaza Fukuoka, A mere 3-minute walk from entertainment complex Canal City, Hakata Nakasu Washington Hotel Plaza provides comfortable rooms with free high-speed wired internet. It features a Japanese and an English breakfast, and laptop PCs can be rented for in-room use. Air-conditioned rooms have an interior in light neutral colours, with an LCD TV, movies on demand at a fee and a fridge. Slippers and free bathroom amenities are provided. The front desk at the Washington Plaza Hotel Nakasu is staffed throughout the day and night, and services offered include in-room massages and laundry. A limited number of parking spaces is available at a charge. Restaurant Sanjusangendo serves a Japanese and a Western breakfast between 07:00 and 09:30 on weekdays, and from 07:00 until 10:00 in weekends and on national holidays. The Hakata Nakasu Washington Hotel Plaza is a 3-minute walk from the Kushida Jinja Shrine, and a 5-minute walk from Nakasu-Kawabata Subway Station. JR Hakata Station is a 15-minute stroll away, and to the Hakata Theatre it takes 4 minutes by car/taxi. (Hakata Nakasu Washington Hotel Plaza Fukuoka hotels booking).


text:
2 bedroom penthouse for sale
Portugal - Baia Da Luz Resort, Rua Alfredo Do Nascime, Praia Da Luz, Lagos
like this property?Call: 03339 395587
Key features:
- 1 to 3 bedroom apartments
- Apartments include furniture
- swimming pools and sports facilities
- 200 metres away from the beach
- underground parking and storage
Full description:
A luxury development with a Roman feel, including cypress, olive and palm trees and fragrant lavender borders, as you walk through the archway you step into a complex where the extensive landscaping is laid out in typical Roman style to complement the architecture, reflecting the history of the area.
Set against the striking backdrop of Rocha Negra, Baía da Luz is located within the town of Praia da Luz, just 6km west of Lagos. Less than 200 metres away from the beach, the development combines a mix of luxury properties with great on-site leisure facilities.
The properties comprise a mix of one, two and three bedroom apartments and three bedroom townhouses with the interiors designed to ensure both a high level of comfort and functionality.
The complex has a village type layout and the apartments are located in three storey clusters, each one having its own security system, pleasant terraces and underground parking with lift access from the basement to each floor.
Each property has all the conveniences of modern day living including satellite television, under floor heating, air conditioning, an electric shutter system on the windows, a fully equipped kitchen fitted with Siemens appliances, white 'Roca' sanitary ware in the bathrooms and a central vacuum system.
Depending on location, prices of freehold property, including furniture, underground car parking and a lockable store.
The main leisure area has two large pools, a children's paddling pool, a children's playground with slide, swings and play areas, a Savannah tennis court and a multi-purpose sports pitch.
The resort also has a beautiful Italian themed restaurant with views over the pool and leisure areas, and a poolside snack bar
Nearest Airports*
- Faro (International), 36.5 miles
*Distances are straight line measurements. This feature is currently in beta.


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Students are not required to audition to become a Theatre major but are required to audition for individual productions. Auditions typically take place the first week of Fall and before Thanksgiving holiday break. Auditions are open to everyone, including all Fort Lewis College students, faculty, staff, and the general public. We recommend you check the Call Board in the Theatre Building or contact the Theatre Department AT (970) 247-7089 for the exact date, time and location of the next auditions.
Monday & Tuesday, November 14 & 15, 6:00 pm – 9:00 pm (8-9pm Dance/Movement)
CALLBACKS, WEDNESDAY, NOVEMBER 16, 5:00 - 9:00 pm
Fort Lewis College, MainStage
Times for audition signups will be posted on the Call Board in the Theatre Building or contact the Theatre Department at (970) 247-7089 or email Denise Leslie an appointment.
AUDITION REQUIREMENTS: Prepare a dramatic monologue and a song, together totaling two minutes. The song should not be from the show, but in the style of Gilbert and Sullivan (a "patter" song is acceptable, too). An accompanist will be provided—please bring your own clearly marked copy of the music or you can sing to pre-recorded music (you will not be allowed to sing a cappella).
Or, prepare two contrasting monologues (if you do not wish to be considered for the musical). Suggested reading: A Doll's House.
Note: Rehearsals for A Doll's House will begin Monday, November 28 – December 9, then resume directly after Winter Break on January 9, 2017.
Show dates: A Doll's House – Feb. 10-12, 16-18; Pirates – 3/17-19, 22-25
A Doll's House Play by Henrik Isbsen, Directed by Felicia Meyer
Fort Lewis College Main Stage Theatre
PERFORMANCES: February 10, 11, 12, 16, 17 & 18, 2017*
Rehearsals begin Tuesday, January 12, 2016.
*All performances start at 7:30 pm except for Sunday matinee performance at 2:00 p.m.
Pirates of Penzance Directed by Dennis Elkins
Fort Lewis College MainStage Theatre
PERFORMANCES: March 17, 18, 19, 22, 23, 24 & 25, 2017*
All performances start at 7:30 except for Sunday performances at 2:00 p.m.


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West Indies won the toss and chose to field in the fourth one-day international against Australia at Warner Park here on Friday.
West Indies have made two changes to their line-up. They dropped Andre Fletcher and Sulieman Benn, and brought in Shawn Findlay and Nikita Miller to make their ODI debuts.
Australia made just one change, bringing David Hussey in for his ODI debut for their injured captain Ricky Ponting, whose stiff left wrist has sidelined him for the match, so that Michael Clarke will lead Australia for the first time in an ODI.
Australia have an unbeatable 3-0 lead in the five-match series, after winning the opening match by 84 runs in St. Vincent, the rain-affected second ODI last Friday by 63 runs under the Duckworth-Lewis Method, and the third ODI last Sunday by seven wickets.
West Indies: Chris Gayle (capt), Xavier Marshall, Ramnaresh Sarwan, Shawn Findlay, Shivnarine Chanderpaul, Dwayne Bravo, Denesh Ramdin, Darren Sammy, Nikita Miller, Fidel Edwards Daren Powell
Australia: Michael Clarke (capt), Shaun Marsh, Shane Watson, Michael Hussey, Andrew Symonds, David Hussey, James Hopes, Luke Ronchi, Brett Lee, Mitchell Johnson, Nathan Bracken
Umpires: Asad Rauf, Steve Bucknor, TV Replays: Norman Malcolm, Reserve: Clancy Mack. Match referee: Roshan Mahanama


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Big Data Analysis with Revolution R Enterprise
- Matilda Fitzgerald
- 2 years ago
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1 REVOLUTION WHITE PAPER Big Data Analysis with Revolution R Enterprise By Joseph Rickert January 2011 Background The R language is well established as the language for doing statistics, data analysis, data-mining algorithm development, stock trading, credit risk scoring, market basket analysis and all manner of predictive analytics. However, given the deluge of data that must be processed and analyzed today, many organizations have been reticent about deploying R beyond research into production applications. The main barrier is that R is a memory-bound language. All data used in calculations vectors, matrices, lists, data frames, and so forth all need to be held in memory. Even for modern computers with 64-bit address spaces and huge amounts of RAM, dealing with data sets that are tens of gigabytes and hundreds of millions of rows (or larger) can present a significant challenge. The problem isn t just one of capacity, that is, being simply being able to accommodate the data in memory for analysis. For mission-critical applications, performance is also a prime consideration: if the overnight analysis does not complete in time for the open of business the next day, that s just as much of a failure as an out-of-memory error. And with data set sizes growing rapidly, scalability is also of concern: even if the in-memory analysis completes today, the IT manager still needs the confidence that the production run will complete on time! as the data set grows. Revolution Analytics has addressed these capacity, performance and scalability challenges with its Big Data initiative to extend the reach of R into the realm of production data analysis with terabyte-class data sets. This paper describes Revolution Analytics new add-on package called RevoScaleR, which provides unprecedented levels of performance and capacity for statistical analysis in the R environment. For the first time, R users can process, visualize and model their largest data sets in a fraction of the time of legacy systems, without the need to deploy expensive or specialized hardware. 1
2 Limitations of In-memory Data Analysis As mentioned above, R requires all data to be loaded into memory for processing. This design feature limits the size of files that can be analyzed on a modest desktop computer. For example, in the following line of code, the data frame, mydata, contains 5,000,000 rows and three columns, only a very small subset of the airline data file that will be examined later in this paper. The error message from the R interpreter shows that even this relatively small file cannot be processed in the R environment on a PC with 3 gigabytes of memory. > lm(arrdelay~uniquecarrier+dayofweek, data=mydata) Error: cannot allocate vector of size Mb Of course, a high-end PC or server configured with much more memory and running a 64-bit version of R can go a considerable way in extending the memory barrier. But even when the data can fit into memory, the performance of standard R on large files can be prohibitively slow. Really breaking through the memory/performance barrier requires implementing external memory algorithms and data structures that explicitly manage data placement and movement. [2] RevoScaleR brings parallel external memory algorithms and a new very efficient data file format to R. RevoScaleR Features The RevoScaleR package provides a mechanism for scaling the R language to handle very large data sets. There are three major components to this package: A new file format especially designed for large files, External memory implementations of the statistical algorithms most commonly used with large data sets, and An extensible programming framework that allows R and later C++ programmers to write their own external memory algorithms that can take advantage of Revolution R Enterprise s new Big Data capabilities. RevoScaleR XDF File Format RevoScaleR provides a new data file type with extension.xdf that has been optimized for data chunking, accessing parts of an Xdf file for independent processing. Xdf files store data in a binary format. Methods for accessing these files may use either horizontal (rows) or vertical (columns) block partitioning. The file format provides very fast access to a specified set of rows for a specified set of columns. New rows and columns can be added to the file without re-writing the entire file. RevoScaleR also provides a new R class, RxDataSource, that has been designed to support the use of external memory algorithms with.xdf files. Table 1 lists the key functions for working with Xdf files: 2
3 Table 1-Key Xdf Functions Function rxtexttoxdf rxdataframetoxdf rxdatastepxdf rximporttoxdf rxreadxdf rxxdftotext rxgetinfoxdf rxgetvarinfoxdf rxsetvarinfoxdf rxdatastepxdf Description Import a text data file into an Xdf file Write a data frame in memory to an Xdf file Transform data from an input Xdf file to and output.xdf file Import SAS file or fixed-format text file Read data from an Xdf file into a data frame Export contents of Xdf file to csv file Get information about an Xdf file Get the variable information for an Xdf file: column names, descriptions, factor labels etc. Modify the variable information for an Xdf file, including names, descriptions and factor labels Create a new Xdf file with variable selections and transformations Table 2 lists some of the available low level functions for working with chunks of data. The RevoScaleR: Getting Started Guide presents detailed examples for working with these functions. [1] Table 2 - RxDataSource Functions Function rxopendata rxreadnext rxreadall rxclosedata Description Opens the data file for reading and returns a handle Reads the next chunk of data from the specified file using the data handle Reads all of the data from the file specified by the data handle and creates a data frame Closes the data file specified by the data handle 3
4 Statistical Algorithms The RevoScaleR package also contains parallel external memory implementations of the most common algorithms used to analyze very large data files. These functions, which are listed in Table 3, represent the initial release of the RevoScaleR package. Additional algorithms are planned for subsequent releases. Table 3 - Statistical Functions in RevoScaleR Function rxsummary rxcrosstabs rxcube rxlinmod rxlogit rxpredict Description Computes summary statistics on data in an Xdf file or in a data frame Creates contingency tables by cross-classifying factors in an Xdf file or data frame. This function permits multi-dimensional cross classification Create a list or data frame of cross tabulations in long format Fits linear models from data residing in an Xdf file or data frame. rxlinmodels fits both simple and multiple regression models. Fits binomial, logistic regression models from data residing in and Xdf file or data frame Computes predicted values and residuals for the models created by rxlinmod and rxlogit All of the RevoScaleR statistical functions produce objects that may be used as input to standard R functions. Extensible Programming Framework Advanced R users can write their own functions to exploit the capabilities of the Xdf files and RxdataSource objects. Any statistical or data mining function that can work on chunks of data and be implemented as an external memory algorithm is a candidate for a RevoScaleR implementation. This R programming interface is available in the current release of RevoScaleR; a subsequent release will add a C++ programming interface. RevoScaleR Examples This section illustrates many of the capabilities of the RevoScaleR package through an extended example that uses the moderately large airlines data file: AirlineData87to08. This file contains 4
5 flight arrival and departure details for all commercial flights within the United States for the period October 1987 through April The file contains 123,534,969 rows and 29 columns (variables). It requires 13,280,963 KB (over 13 Gb) to store, uncompressed on disk. Code Block 1 uses the rxoptions function to set the environment. For a more robust computer, the number of cores can also be set appropriately using the rxoptions function. (For the Intel Xeon dual 6-core processor server used in the benchmarks below, setting this value to 12, the number of physical cores, produced optimal results.) Code Block 1 rxoptions(sampledatadir = system.file("sampledata", package="revoscaler"), demoscriptsdir = system.file("demoscripts", package="revoscaler"), blocksperread = 1, reportprogress = 2, rowdisplaymax = -1, memstatsreset = 0, memstatsdiff = 0, numcorestouse = 2, showtransformfn = FALSE) Code Block 2 access the file stored on disk in the Xdf format, displays information about the file and its variables (see Table 4) and produces a summary (Table 5) for two variables: Arrdelay and DayofWeek. Code Block 2 defaultdatadir <- "C:/Users/.../revoAnalytics" dataname <- file.path(defaultdatadir,"airlinedata87to08") rxgetinfoxdf(dataname, getvarinfo=true) # Get summary data rxsummary(~arrdelay:dayofweek,data=dataname) Table 4 Information and Variables in Airline File > rxgetinfoxdf(dataname, getvarinfo=true) Name: C:\Users\... \AirlineData87to08.xdf Number of rows: Number of variables: 29 Number of blocks: 832 Variable Information: Var 1: Year, Type: factor 22 factor levels: Var 2: Month, Type: factor 12 factor levels: January February March April May... August September October November December Var 3: DayofMonth, Type: factor 31 factor levels: Var 4: DayOfWeek, Type: factor 7 factor levels: Monday Tuesday Wednesday Thursday Friday Saturday Sunday Var 5: DepTime, Type: numeric, Storage: float32, Low/High: (0.0167, ) Var 6: CRSDepTime, Type: numeric, Storage: float32, Low/High: (0.0000, ) 5
6 Var 7: ArrTime, Type: numeric, Storage: float32, Low/High: (0.0167, ) Var 8: CRSArrTime, Type: numeric, Storage: float32, Low/High: (0.0000, ) Var 9: UniqueCarrier, Type: factor 29 factor levels: 9E AA AQ AS B6... UA US WN XE YV Var 10: FlightNum, Type: factor 8160 factor levels: Var 11: TailNum, Type: factor factor levels: NA N7298U N7449U N7453U N7288U... N516AS N763JB N766JB N75428 N75429 Var 12: ActualElapsedTime, Type: integer, Low/High: (-719, 1883) Var 13: CRSElapsedTime, Type: integer, Low/High: (-1240, 1613) Var 14: AirTime, Type: integer, Low/High: (-3818, 3508) Var 15: ArrDelay, Type: integer, Low/High: (-1437, 2598) Var 16: DepDelay, Type: integer, Low/High: (-1410, 2601)) Var 17: Origin, Type: factor 347 factor levels: SAN SFO BUR OAK LAX... ROW GCC RKS MKG OTH Var 18: Dest, Type: factor 352 factor levels: SFO RNO OAK BUR LAX... PIR GCC RKS MKG OTH Var 19: Distance, Type: integer, Low/High: (0, 4983) Var 20: TaxiIn, Type: integer, Low/High: (0, 1523) Var 21: TaxiOut, Type: integer, Low/High: (0, 3905) Var 22: Cancelled, Type: logical, Storage: uchar, Low/High: (0, 1) Var 23: CancellationCode, Type: factor 5 factor levels: NA carrier weather NAS security Var 24: Diverted, Type: logical, Storage: uchar, Low/High: (0, 1) Var 25: CarrierDelay, Type: integer, Low/High: (0, 2580) Var 26: WeatherDelay, Type: integer, Low/High: (0, 1510) Var 27: NASDelay, Type: integer, Low/High: (-60, 1392 Var 28: SecurityDelay, Type: integer, Low/High: (0, 533) Var 29: LateAircraftDelay, Type: integer, Low/High: (0, 1407) 6
7 Table 5 Summary of variables ArrDelay and DayofWeek Summary Statistics for: ArrDelay:DayOfWeek (Total: , Missing: ) Name Mean StdDev Mi n Ma x ValidObs ArrDelay:DayOfWeek Statistics by category (7 categories): Ca tegory Means StdDev Mi n Ma x ValidObs ArrDelay for DayOfWeek=Monday ArrDelay for DayOfWeek=Tuesday ArrDelay for DayOfWeek=Wednesday ArrDelay for DayOfWeek=Thurs day ArrDelay for DayOfWeek=Friday ArrDelay for DayOfWeek=Saturday ArrDelay for DayOfWeek=Sunday Next we perform a simple linear regression of ArrDelay against DayOfWeek using the cube option of rxlinmod, the RevoScaleR function to fit linear models. When cube is set to TRUE and the first explanatory variable in the regression model is categorical, rxlinmod uses a partitioned inverse algorithm to fit the model. This algorithm can be faster and uses less memory than the default inverse algorithm. Code Block 3 shows the code for fitting the model and uses the standard R functions to obtain the results of the regression (Table 6) and plot Arrival delay by day of week (Figure 1). Note that the p-values calculated by the standard t-test are extremely small, as would be expected by using such a simple model with such a large data set. Also note that the cube option produces a data frame as output that shows the counts of the data points that went into producing each coefficient (Table 7). Code Block 3 # Fit a linear model with the cube option arrdelaylm1 <- rxlinmod(arrdelay ~ -1+DayOfWeek, data=dataname, cube=true) summary(arrdelaylm1) # Use the standard R function summary arrdelaylm1$countdf # Look at the optput from the cube option # Plot arrival delay by day of week xyplot( ArrDelay ~ DayOfWeek, data = arrdelaylm1$countdf, type = "l", lwd=3,pch=c(16,17), auto.key=true) Table 6 Output of summary rxlinmod.formula(formula = ArrDelay ~ -1 + DayOfWeek, data = dataname, cube=true) Coefficients: Estimate Std. Error t value Pr(> t ) DayOfWeek.Monday E-16 DayOfWeek.Tuesday E-16 DayOfWeek.Wednesday E-16 DayOfWeek.Thursday E-16 DayOfWeek.Friday E-16 DayOfWeek.Saturday E-16 DayOfWeek.Sunday E-16 Residual standard error: on degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: 5.93e+04 on 6 and DF, p-value: < 2.2e-16 7
8 Figure 1 Average Arrival Delay by Day of Week 9 8 ArrDelay Monday Tuesday Wednesday Thursday Friday Saturday Sunday DayOfWeek Table 7 Output from cube option arrdelaylm1$countdf DayOfWeek ArrDelay Counts Monday Tuesday Wednesday Thursday Friday Saturday Sunday The RevoScaleR package also contains a predict function for predicting values of a linear model and computing residuals. The rxpredict function in Code Block4 appends the variables ArrDelay_Pred and ArrDelay_Resid as columns 30 and 31, respectively, to the airlines data file. Figure 2 which shows 9 residual plots of 1000 residuals randomly selected from the file Airlinedata87To08, illustrates how the residuals are available to R for further analysis. Code Block 4 # Predict function computes predicted values and residuals rxpredict(modelobject=arrdelaylm1,data=dataname,computeresiduals=true) par(mfrow=c(3,3)) start <- runif(16,1, ) for (i in 1:9){ residualdf <- rxreadxdf(file=dataname,varstokeep="arrdelay_resid", startrow= start[i],numrows=1000) plot(residualdf$arrdelay_resid)} 8
9 Figure residuals from 4 random locations* * Next, we consider a multiple regression of arrival delay (ArrDelay) on the day of the week (DayOfWeek) and departure time (CRSDepTime). The second line of Code Block 5 constructs the linear model to carry out the regression and illustrates the inline use of the F function which makes a factor variable out of CRSDepTime on the fly as it is being used to construct the linear model. This function illustrates a fundamental design concept of RevoScaleR: the ability efficiently transform data, and create new variables without having to make multiple passes through the file. The cube option produces a table of arrival delay counts by departure time and day of the week. The first five lines of this output are displayed in Table 8. Figure 3 show a plot of arrival delay by departure time and day of the week that is based on these data. Code Block 5 # Multiple Regression arrdelaylm2 <- rxlinmod(arrdelay ~ DayOfWeek:F(CRSDepTime), data=dataname,cube=true) arrdelaydt <- arrdelaylm2$countdf arrdelaydt[1:5,] summary(arrdelaylm2) # Plot data names(arrdelaydt) <- c("dayofweek", "DepartureHour", "ArrDelay", "Counts") arrdelaydt$departurehour <- as.numeric(as.character(arrdelaydt$departurehour)) xyplot( ArrDelay ~ DepartureHour DayOfWeek, data = arrdelaydt, type = "l", lwd=3,pch=c(16,17), main='average Arrival Delay by Day of Week by Departure Hour', layout=c(2,4), auto.key=true) 9
10 Table 8. Output from Cube Option > arrdelaydt[1:5,] Row DayOfWeek X.CRSDepTime ArrDelay Counts 1 Monday Tuesday Wednesday Thursday Friday Figure 3. Plot of Data produced by Cube Option Average Arrival Delay by Day of Wee Sunday Friday Saturday 15 ArrDelay Wednesday Thursday Monday Tuesday DepartureHour Finally, we illustrate the flexible way the RevoScaleR function rxdatastepxdf can be used to generate a file containing a subset of the airlines data and new variables that are transformations of some of the variables in the airlines data file. These variables are created on the fly with the help of the transformfunc parameter to the rxdatastepxdf function. The first six lines of Code Block 6 are a function that defines the new variable. Lines 9 and 10 show the rxdatastepxdf function which reads the airline data file, creates an new file keeping only the variables designated and transforms these variables using the specified transformation function. 10
11 Code Block 6 # Create function to transform data mytransforms <- function(data){ data$late <- data$arrdelay > 15 data$dephour <- as.integer(data$crsdeptime) data$night <- data$dephour >= 20 data$dephour <= 5 return(data)} # The rxdatastepxdf function read the existing data set, performs the # transformations, and creates a new data set. rxdatastepxdf(infile = dataname, outfile="ads2", transformfunc=mytransforms, varstokeep=c("arrdelay","crsdeptime","deptime")) # rxgetinfoxdf(file="ads2",getvarinfo=true) # Run a logistic regression using the new variables logitobj <- rxlogit(late~dephour+night, data="ads2", verbose=true) Table 9 shows the meta-data and data for the newly created file, called ADS2. Table 10 shows the output from perfroming a logistic regression using the newly defined variables. Table 9. Description of data file ADS2 > rxgetinfoxdf(file="ads2",getvarinfo=true) File name: C:\... \ADS2.xdf Number of rows: Number of variables: 6 Number of blocks: 832 Variable information: Var 1: ArrDelay, Type: integer, Low/High: (-1437, 2598) Var 2: CRSDepTime, Type: numeric, Storage: float32, Low/High: (0.0000, ) Var 3: DepTime, Type: numeric, Storage: float32, Low/High: (0.0167, ) Var 4: Late, Type: logical, Storage: uchar, Low/High: (0, 1) Var 5: DepHour, Type: integer, Low/High: (0, 24) Var 6: Night, Type: logical, Storage: uchar, Low/High: (0, 1) Table 10. Output from logistic regression Logistic Regression Results for: Late ~ DepHour + Night Dependent Variable: Late Total independent variables: 3 Number of valid observations: (Number excluded for missings: ) -2*LogLikelihood: e+008 (Residual Deviance on degrees of freedom) Condition number of final VC matrix: Row Coeffs. Value Std. Error t Value Pr(> t ) [ 1,.] (Intercept) [ 2,.] DepHour [ 3,.] Night
12 RevoScaleR Benchmarks In order to provide some idea of the performance that can be reasonably expected from RevoScaleR functions operating on moderately sized data sets, this section provides benchmarks of rxlinmod, rxcrosstabs and rxlogit. All benchmarks were conducted on two platforms: (1) a Lenovo Thinkpad laptop with dual-core, Intel P GHz processor and 3 GB of RAM running Windows 7, and (2) an Intel Xeon X5660 server with GHz CPUs each with 6 cores and 12 threads with 72GB of RAM running Windows Server While no formal comparisons are against other well known statistical systems are included in this analysis, we believe that RevoScaleR s capacity and performance abilities substantially out-perform competitive products. rxlinmod The file AirlineData87to08 contains information on flight arrivals and departure details for all commercial flights within the US from October 1987 to April. It is 13,280,936 KB and contains 123,534,969 rows and 29 columns. Table 4 above provides a summary of the information in the file. The following R code (Code Block 7) runs a simple regression followed by a multiple regression with three explanatory variables. Note that the explanatory variables are categorical. The cube option for rxlinmod ensures the efficient processing of categorical data. Code Block 7 #rxoptions(numcorestouse=12) defaultdatadir <- "C:/Users/.../revoAnalytics" dataname <- file.path(defaultdatadir,"airlinedata87to08") rxgetinfoxdf(dataname,getvarinfo=true) # Simple Regression system.time(delayarr <- rxlinmod(arrdelay ~ DayOfWeek,data=dataName,blocksPerRead=30)) # Multiple Regression system.time(delaycarrierloc <- rxlinmod(arrdelay ~ UniqueCarrier+Origin+Dest,data=dataName,blocksPerRead=30,cube=TRUE)) Table 11 contains the results of the regression benchmarks. Note that the average time for first runs of the simple regression on the laptop is considerably longer than the average time to complete subsequent runs of the same regression. This was most likely due to disk caching on the laptop. This phenomenon was not observed when doing the multiple regression on the laptop and it was not observed at all on the server. Performance on the server was consistent among multiple runs. Other than setting the number of cores to be used by the RevoScaleR compute engine equal to 12, the number of real cores available on the server, no attempt was made to optimize the performance of the server. Table 11 Regression Benchmark Results Average Elapsed Time (seconds) Simple Regression Laptop Server first run NA subsequent runs Multiple Regression
13 rxcrosstabs The file CensusIp2001 contains US census data. It is 17,079,992 KB, and contains 14,583,271 rows and 265 columns. Table 12 contains a portion of the output describing the file CensusIp2001 that is produced by the command rxgetinfoxdf. Table 12 - Header Information and first five columns of CensusIp2001 > rxgetinfoxdf(datafile,getvarinfo=true) File name: C:\... \CensusIp20001.xdf Number of rows: Number of variables: 265 Number of blocks: 487 Variable information: Var 1: rectype 2 factor levels: H P Var 2: year, Census year 14 factor levels: Var 3: datanum, Data set number Type: integer, Low/High: (1, 1) Var 4: serial, Type: integer, Low/High: (1, ) Var 5: numprec, Number of person records following Type: integer 55 factor levels: The following R code (Code Block 8) runs the benchmark. Note that the text string in the third line must include the entire path to the directory containing the file Code Block 8 #rxoptions(numcorestouse=12) # Only run on the Server defaultdatadir <- "C:/Users/... " datafile <- file.path(defaultdatadir,"census5pct2000") # Use rxcrosstabs to compute a 2-way cross tabulation: system.time(agesex <- rxcrosstabs(~f(age):sex,data=datafile, pweights="perwt",blocksperread=25)) # Compute a 4-way cross tabulation system.time(asmc <- rxcrosstabs(~f(age):sex:f(marst):f(condo), data=datafile,pweights="perwt",blocksperread=25)) Table 13 presents the results of the benchmarks. Notice that the first time the 2-way cross-tab was run on the laptop it took and average of seconds to complete, while subsequent runs completed in little over a second. This phenomenon is most likely due to disk caching. Also note that the 4-way cross-tab on the laptop ran slightly faster, on average, than the 2-way cross-tab. This effect is also most likely due to disk caching as the laptop was not rebooted between runs. Table 13 - Results of rxcrosstabs Benchmark Average Elapsed Time (seconds) 2-way cross tab Laptop Server first run NA subsequent runs way cross tab first run NA 13
14 subsequent runs Performance on the server was consistent among multiple runs. No first run effect was observed which is to be expected. Other than setting the number of cores to be used by the RevoScaleR compute engine equal to 12, the number of real cores available on the server, no attempt was made to optimize the performance of the server. rxlogit The file mortdefault contains ten years of mortgage default data (2000 to 2009). It is 234,378 KB, has 10,000,000 and 6 variables (Table 14). Code Block 9 presents function to run the regression and Table 15 contains the benchmark results. Table 14 Header Information and Variables in Mortgage File > rxgetinfoxdf(datafilename,getvarinfo=true) File name: C:\...\mortDefault.xdf Number of rows: Number of variables: 6 Number of blocks: 10 Variable information: Var 1: creditscore, Type: integer, Low/High: (432, 955) Var 2: houseage 41 factor levels: Var 3: yearsemploy, Type: integer, Low/High: (0, 15) Var 4: ccdebt, Type: integer, Low/High: (0, 15566) Var 5: year 10 factor levels: Var 6: default, Type: integer, Low/High: (0, 1) Code Block 9 # Run a logistic regression on the file system.time(logitobj <- rxlogit(default~creditscore + yearsemploy + ccdebt + houseage + year,data=datafilename, blocksperread=2, verbose=1,reportprogress = 1)) Table 15 Results of rxlogit Benchmarks Average Elapsed Time (seconds) Laptop Server Logistic Regression Summary The RevoScaleR package from Revolution Analytics provides external memory algorithms that help R break through the memory/performance barrier. The XDF file format provides an efficient and flexible mechanism for processing large data sets. The new package provides functions for creating XDF files from text files, writing XDF data to text files and data frames and for transforming and manipulating variables during the read/ write process. 14
15 The new package also contains statistical functions for generating summary statistics, performing multi-way cross tabulations on large data sets and for developing linear models and performing logistic regressions. Preliminary benchmarks show that RevoScaleR functions are fast and efficient -- enabling real data analysis to be performed on a million row, 13GB data set on a common dual core laptop. Moreover, the benchmarks show that the performance of RevoScaleR functions scale nicely as computational resources increase. References [1] Revolution Analytics. RevoScaleR: Getting Started Guide, July 19, 2010 [2] Vitter, Jeffry Scott. Algorithms and Data Structures for External Memory. Now Publishers, Inc.: Hannover, MA
16 About Revolution Analytics Revolution Analytics delivers advanced analytics software at half the cost of existing solutions. Led by predictive analytics pioneer and SPSS co-founder Norman Nie, the company brings high performance, productivity, and enterprise readiness to open source R, the most powerful statistics language in the world. In the last 10 years, R has exploded in popularity and functionality and has emerged as the data scientists tool of choice. Today R is used by over 2 million analysts worldwide in academia and at cutting-edge analytics-driven companies such as Google, Facebook, and LinkedIn. To equip R for the demands and requirements of all business environments, Revolution R Enterprise builds on open source R with innovations in big data analysis, integration and user experience. The company s flagship Revolution R product is available both as a workstation and server-based offering. Revolution R Enterprise Server is designed to scale and meet the mission-critical production needs of large organizations such as Merck, Bank of America and Mu Sigma, while Revolution R Workstation offers productivity and development tools for individuals and small teams that need to build applications and analyze data. Revolution Analytics is committed to fostering the growth of the R community. The company sponsors the Inside-R.org community site, local users groups worldwide, and offers free licenses of Revolution R Enterprise to everyone in academia to broaden adoption by the next generation of data scientists. Revolution Analytics is headquartered in Palo Alto, Calif. and backed by North Bridge Venture Partners and Intel Capital. Please visit us at 16
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Company Profile
We take the liberty of introducing ourselves as one of the most successful Construction.
Firms involving in any kind of civil and electromechanical works.
We have been in the construction and maintenance and continue to provide you with the fastest and most reliable solutions to fulfill your needs.
During the last decades, we have carried out many successful projects.
Some of which were : -
It is with great pleasure to inform you that, unlike other companies.
We use our own resources to make our Project implementations possible.
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Ever since its inception, the philosophy at JEFCO remained focused at achieving customer satisfaction at all times.
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For additional information, please visit
Send mail to firstname.lastname@example.com with questions or comments about this web site.


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login with
tired of waking up at 4am
Little man is still waking at 4 am. Between 3 & 4. Uhggggg! He never used to be like this!
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So frustrating sometimes!
He usually has a diaper change, some water in his bottle, and teething tablets for tylnol for his teeth. He seems restless when he wakes up
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I am 23, raising Malachi who's 2&1/2, Elianna who is 1, & our newest addition Isaiah! Born April 15, 2016❤
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Coal-fired Plants in the USA - North & South Dakota
Antelope Valley
Location: ND
Operator: Basin Electric Power Coop
Configuration: 2 X 450 MW
Operation: 1984-1986
Fuel: lignite
Boiler supplier: CE
T/G supplier: Westinghouse
EPC: Stearns-Rogers
Quick facts: AVS is located 7mi northwest of Beulah ND. It is part of a $4bn energy complex that includes the Great Plains Synfuels Plant and the Freedom Mine owned by North American Coal Co. This is the largest lignite mine in the USA and has two 120ydł draglines and a fleet of 200t overburden trucks.
Photograph courtesy of Basin Electric Power Coop
Ben French
Location: SD
Operator: Black Hills Power
Configuration: 1 X 25 MW
Operation: 1961 (ret)
Fuel: subbituminous coal, natural gas
Boiler supplier: B&W
T/G supplier: AC
Quick facts: Bem French PS was built in Rapid Cty and operated for over 50yrs before being taken offline in Aug 2012. A $3.5mn demolition project began in Jul 2015. The 200ft tall stack was a local landmark. The power station was named for J.B. "Ben" French, who served as Black Hills Power's first president from 1941 to 1957 and as company chairman until 1972.
Photograph courtesy of DiGeronimo Companies
Big Stone
Location: ND
Operator: Otter Tail Power Co
Configuration: 1 X 475 MW
Operation: 1975
Fuel: subbituminous coal
Boiler supplier: B&W
T/G supplier: WH
EPC: Bechtel
Quick facts: Big Stone is jointly-owned by Otter Tail Power Co (53.9%), NorthWestern Energy (23.4%), and Montana-Dakota Utilities (22.7%). A new baghouse was installed in 2007. In Mar 2013, the owners began a $405mn dry FGD and SCR retrofit project at Big Stone. The new equipment began testing in mid-2015. Sargent & Lundy was project engineer and Graycor Industrial was lead civil contractor on the project. B&W was in charge of required boiler mods.
Photograph courtesy of Otter Tail Power Co
Coal Creek
Location: ND
Operator: Great River Energy
Configuration: 2 X 550 MW
Operation: 1979-1981
Fuel: lignite
Boiler supplier: CE
T/G supplier: GE
EPC: Black & Veatch
Quick facts: This is North Dakota's largest power plant. Ground was broken in the fall of 1974. Coal Creek Station burns 7.5-8mn tpy of lignite supplied by the adjoining Falkirk Mine. Electricity is delivered to customers over a 436mi HVDC power line. The boilers are 205ft tall. Since the power plant was built, approximately $200mn has been invested in environmental equipment. Most fly ash is sold and in 2003, GRE built an 85,000t fly ash storage dome to ensure that an adequate supply is available throughout the year. Since Mar 2007, Coal Creek has also supplied waste heat for the operations of the new 50mn gpy Blue Flint Ethanol plant built next to the power station.
Photograph courtesy of Great River Energy
Location: ND
Operator: Otter Tail Power Co
Configuration: 1 X 450 MW
Operation: 1981
Fuel: lignite
Boiler supplier: B&W
T/G supplier: WH
EPC: Bechtel
Quick facts: Coyote has a baghouse and dry aqueous carbonate FGD scrubber, one of the first of its kind. Fuel is from Dakota Westmoreland Corp's Beulah Mine, a 9,000ac surface mine complex located 75mi northwest of Bismarck. It currently produces lignite from three active pits. Coyote is jointly owned by Otter Tail Power Co (35%), Northern Municipal Power Agency (30%), Montana-Dakota Utilities (25%) and NorthWestern Energy (10%).
Photograph courtesy of powerofcoal.com
Location: ND
Operator: Montana-Dakota Utilities
Configuration: 1 X 40, 1 X 75 MW
Operation: 1954-1963
Fuel: bituminous coal, lignite, natural gas
Boiler supplier: B&W, Riley
T/G supplier: GE
EPC: Ebasco
Quick facts: Heskett Unit-2 cost $10.5mn. It was retrofit with a CFB boiler in 1987 and has accumulated the most operating hours of any CFB boiler in the USA. The plant is named for R M Heskett, the founder of Minnesota Northern Power Co, a predecessor of Montana-Dakota Utilities.
Photograph courtesy of powerofcoal.com
Leland Olds
Location: ND
Operator: Basin Electric Power Coop
Configuration: 1 X 216 MW, 1 X 440 MW
Operation: 1966-1975
Fuel: lignite
Boiler supplier: B&W
T/G supplier: GE, BBC
EPC: RW Beck, Burns & Roe
Quick facts: This plant is named after Leland Olds, a pioneering advocate of public power and Chairman of the Federal Power Commission from 1940-1949. In Sep/Oct 2009, a new low-pressure turbine rotor was installed at Leland Olds-2 replacing damaged equipment. Workers also installed overfire air systems in the boiler during the outage.
Photograph courtesy of Basin Electric Power Coop
Location: ND
Operator: Great River Energy
Configuration: 1 X 188 MW
Operation: 1967 (ret)
Fuel: subbituminous coal, lignite
Boiler supplier: FW, CE
T/G supplier: GE
EPC: Pioneer, Black & Veatch
Quick facts: Stanton power station has two boilers (the second added in 1982) and a single T/G set. In 2006, the plant completed a 853-day record run. Fuel burn is about 850,000 tpy. A new dry FGD system allows the plant to use both lignite and subbbituminous coal and meet applicable air quality regulations. The Stanton plant was retired on 25 Feb 2017. Demolition is scheduled to begin in 2018.
Photograph courtesy of Great River Energy
Data: industcards, Platts UDI World Electric Power Plants Data BaseUpdated 01-Mar-2017


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Philips I966 Aurora Specifications : Philips has launched its new smartphone I966 Aurora on October 2014. The smartphone is now available to purchase at the price of Rs.36,000 from leading shopping portals and retail outlet. The smartphone comes in one color option which is Black. The smartphone gives competition to Nokia X Plus and LG L60i Dual.
Lets take a look at the Philips I966 Aurora Specifications and Features.
Philips I966 Aurora Specifications
Display: 5.5 Inch IPS LCD Capacitive Touchscreen with 16M Colors
Resolution: 2560 x 1440 pixels
Dimensions: 153 x 74 x 8 mm
Pixel Density: 534 ppi
Primary Camera: 20.7 MP with Autofocus, LED Flash and Digital Zoom
Secondary Camera: 8.0 MP
Video Recording: Yes
Operating System: YunOS 3.0
Processor: 2.5 GHz Quad Core
Ram: 3 GB
Weight: 140 gms
Dual Sim: No
Internal Memory: 32 GB
Expandable Memory: No
Bluetooth: Yes v4.0
Wifi: 802.11 b/g/n
WLAN Support: No
USB Support : Yes
Sensors: Accelerometer, Proximity
Battery Capacity: 3000 mAh Li-Ion
Total Standby Time (2G): Up to 390 hrs
Total Standby Time (3G): Up to 370 hrs
Total Talk Time (2G): Up to 15 hrs
Total Talk Time (3G): Up to 13 hrs
Network Band : GSM
Network (2G): GSM 800 / 900 / 1900 MHz
Network (3G): HSDPA 900 / 2100 MHz
Network (4G): LTE 700 / 2600 MHz
Philips I966 Aurora Review
The smartphone comes with 5.5 inch IPS LCD touchscreen with 16M Colors and has resolution of 2560 x 1440 pixels.
The smartphone has inbuilt memory of 32 GB Which is not expandable via SD Card.
The smartphone has 20.7 MP of rear camera that records HD videos at the framerate of 1080p@30FPS and it does have 8.0 MP of Front Facing camera for video calling.
Operating System
Philips I966 Aurora runs on YunOS 3.0 operating system based on Android os and it does support dual sim functionality.
The smartphone has good processing speed of 2.5 GHz Quad Core and has 3 GB of ram which is not sufficient to run every apps on this device.
The smartphone gives battery life of 3000 mAh which gives standby time of upto 390 hrs and talktime of upto 15 hrs.
The connectivity options include in this smartphone are Bluetooth v4.0, GPS, Micro-USB, Wi-Fi 802.11 and GPRS/EDGE.
Philips I966 Aurora Features
- IPS LCD Touch Screen
- YunOS 3.0 OS
- 3G Connectivity
- Predictive Text Input
Philips I966 Aurora Price
The price of the smartphone according to country are given below-
Price in USA:- $ 600 Approx
Price in India: Rs 36,000 /-
Price in UK: £ 400 Approx
Philips I966 Aurora Release Date
The smartphone is now launched and available to purchased in leading online stores and retailers.
Philips I966 Aurora Manual
The smartphone manual will be uploaded as soon as it is available.
For more updates on the latest news on Philips I966 Aurora Specifications price and availability on ecommerce site stay updated to this blog.