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Oasis Supply Edible Frosting Sheets for Cake Top Printing, 7.5 by 10-Inch, 24 Sheets, White
|$42.12 & FREE Shipping on orders over $49. Details
- Enter your model number to make sure this fits.
- Great for quarter sheet Picture Perfect Party Cakes!
- Compatible with all edible printers and systems
- Market rated "best of class" for printability and adaptability to the cakes on which they are used
- Superb Quality Made in USA
- Load into your printer and print your designs.
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Top Customer Reviews
- The image quality on these is fantastic. I'm not sure why... the inks don't seem to soak down into the sheet as much, and the surface doesn't seem as textured/porous as the KopyKake ones.
- They peel off very easy from the backing without needing to put them in the freezer.
- Prime Eligible, free 2 day shipping (or weekday overnight for $3.99). Depending on where you live and what options you choose, KopyKake shipping can be much pricier and slower.
- They are very brittle. They often arrive chipped around the edges or a few cracked entirely.
- They don't seem to blend into the cake icing as good. Given enough time, KopyKake sheets will soften and blend right into the icing. These ones don't, which makes for a weird, unappealing stiff layer on the cake when eating.
- The stock image shown above (as of this writing) is from the KopyKake website, which is misleading, as these are entirely different.
If you find yourself in a pinch and really need frosting sheets, then these are a great price and arrive very quickly. Just be aware of the cons.
Most Recent Customer Reviews
Will continue to buy this product. Performs as expected and is perfect for our business.Published 3 days ago by John W. Aiple
All of the frosting sheets came cracked in the lower right hand corner. wanted a lot of product in order to be able to print out the number of images I needed. Read morePublished 1 month ago by Celeste Dolan
love these. they hold color and show so nice on them i will buy them again.Published 7 months ago by jessica
Wonderful product! Works great and print quality is perfect with my edible printer. Product peels off easily and taste is great.Published 9 months ago by Coley7788
Just opened my sugar sheets and they smell bad!! I thought maybe thats the way they were supposed to smell so still used them. Read morePublished 11 months ago by erika
The dimensions are smaller than anticipated. Full page print does not fit.Published 12 months ago by sondra crummell
Sheets came off the backing while going thru the printer. I had to use several to get one good imagePublished 15 months ago by Amber Palacios


text: After struggling to find a place with crab on the menu in Penzance we drove on to Newlyn. There is a car park a short walk away which seems a rarity here. Great welcome for a large party with friendly staff. Specials board was very tempting. Service was great and the food was fantastic. We tried crab salad, crab soup,... More

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Nance Williamson Celebrity Watchlist
Nance Williamson
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The Good Wife: Tracks Trailer01:24 — Alicia and Cary join forces to defend a former client facing a multi-million dollar lawsuit over ownership rights to his new music. Also, Alicia's nei (more…)
The Good Wife - One Of Those Talks01:40 — After Grace's grades begin to slip, Alicia makes the difficult choice to fire her.
The Good Wife - Artistic Allure01:22 — Lucca agrees to a drink with Rowby, who has expressed his infatuation with her since the beginning of the case.
Watch See all »
Fri Aug 5 11:56pm
Law & Order — Pride(Season 5, Episode 23) SUND
The investigation of a gay councilman's murder involves his roommate (Mitchell Lichtenstein) and a homophobic politician (Daniel Hugh Kelly). Barnett: Robert Joy. Powell: Peter Gerety. Eddie: John Cameron Mitchell. Mrs. Durban: Hallie Foote. Mrs. Crossley: (more…)
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Contact Information
Penny Hydraulics Ltd
Station Road | Clowne | Chesterfield
Derbyshire | S43 4AB | United Kingdom
01246 811475
Winch systems specialist, Raising and Lowering Systems (R & L) have just completed a challenging project for Lloyds Register Group who are a £1 billion-turnover service provider, in the heart of London's financial district. R & L who design and install winch systems for lighting installations, were commissioned to design, supply and install a lighting frame and winch system to raise and lower the huge luminaire in the main entrance atrium of the companies London headquarters.
Lloyd's Register (LR) is a global engineering, technical and business services organisation owned by the Lloyd's Register Foundation, a UK charity dedicated to research and education in science and engineering. Founded in 1760 as a marine classification society, LR now operates across many industry sectors, with over 9,000 employees based in 78 countries.
The company prides itself on its long-standing reputation for integrity, impartiality and technical excellence and their global technology centres and research network which means they are at the forefront of understanding the application of new science and technology to future-proof their clients' businesses.Read more…
Lifting Safety Top of the Agenda at Lloyds Register Group HQRead Article
Manufacturers Must Do More to Fill Substantial Skills GapsRead Article
Top Converters Partner to Develop New Highways CraneRead Article
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Young Engineer Flies the Flag for British ManufacturingRead Article
Swarco & Penny Hydraulics Lifting Highways Safety to New HeightsRead Article
Penny Hydraulics Winners at the Family Business of the Year Awards 2016Read Article
Site Development at Engineering Firm Creates New Jobs for ClowneRead Article
Latest Statistics from the HSE Expose the Need for Further Mechanical Handling in Warehouse EnvironmentsRead Article


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Your Shots: Residents share scenes of San Juan Capistrano
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text: Item is not available Find Another One Others Looked At... Rare, Duck Dynasty Autographed Shotgun SELLER: BSPurser 0 $5,000.00 Remington 870 Express Magnum 28"V.R. Rem... SELLER: Mansfield Sporting Arms LLC 1 $375.00 Remington 870 Express Magnum V.R.28" Rem... SELLER: Mansfield Sporting Arms LLC 1 $325.00 Remington 870 Magnum Express SELLER: Clifton Gross 1 $475.00 REMINGTON MODEL 887 NITRO MAG MAX 4 CAMO... SELLER: Apocalypse Arms 1 $399.99 Remington 870 Wingmaster 12 gauge w/ Ven... SELLER: MNshotgunner 0 $335.00 Guns Shotguns Remington Shotguns Pump Hunting Remington ga Left Hand | 542 | 80 | Lc | 218.9228ms Remington 11-87 12ga Left Hand For Sale from Boyds | Positive feedback: 100% View | Verified Seller | 32 Completed Sales View Sellers Items This item is no longer available. /UserImages/101554/958252743 1667032.jpg Description Shipping and Payment More Info Remington 11-87 Left Handed Premier Super Trap 12Ga Light Contour Capable of handling 12 gauge 2 3/4 inch and 3" shells MODEL 11-87 WARNING: DO NOT INTERCHANGE WITH MODEL 1100 OR MODEL 11-87 SUPER MAGNUM SHOTGUN BARRELS Gauge: 12 Length: 26" VT Choke: Rem Choke³ Excellent gun to have for hunting and even better if your left handed. Varies 3% credit card charge 6% sales tax for Florida Sales $20 GA charge Trades Accepted: Gunsamerica sales email only no phone calls In business since 1943. Transfers, we offer all accessories needed for shooting: cases, holsters, locks, ammo, mags, etc. Appraisals, Gunsmith on staff, Indoor Range & private instruction if needed. Concealed Weapons Class every Sat. Hours of Operation Mon-Fri 11:30-7pm Sat 10-7, Sun 10-4pm Condition: Used, As New Caliber: 12 Gauge Location: FL Share Item Item #: 958252743 Stock No.: 1014095 This item is no longer available. Price: Shipping: See Description (or Arrange local transfer) Accepted Payment Methods: Returns: No Returns Boyds Seller Since: Nov. 2008 Seller Level: Basic This seller is an FFL City: FORT LAUDERDALE State: FL More Items from this Seller ( 18 items total | View All ) Nighthawk Custom Enforcer... $3,000.00 Full Details Add To Cart Beretta Model 84 Cheetah ... $30.00 Full Details Add To Cart Beretta Model 84 Cheetah ... $35.00 Full Details Add To Cart Mc | 15.5423ms -- 101554 Your Recently Viewed Items


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A 30-year-old Waukesha woman stands accused of repeatedly kicking, punching and swearing at her 8-year-old son on multiple occasions.
Waukesha Now will not name the woman to protect the identity of her child. She was charged Tuesday, July 8, in Waukesha County Circuit Court with one felony count of child abuse and misdemeanor disorderly conduct.
If convicted, she could face more than six years in prison and $11,000 in fines.
According to the criminal complaint, Waukesha police responded to a call about domestic abuse on Sunday, July 6. The woman and her son were shouting at one another on a second-floor balcony at an apartment complex.
The woman told police her son suffers from Asperger's syndrome, attention deficit hyperactivity disorder and an anxiety disorder so he needs to be restrained sometimes, but when the woman's father arrived on the scene he offered a different story.
According to the complaint, the woman's father said that his daughter had hit, kicked and screamed profanity at her son on several occasions in the past. Officers spoke to the child, who admitted his mother "kicked him all over," the complaint said.
The woman allegedly told police that she's an alcoholic who needed to "get back on track," and who was also experiencing ongoing problems in her personal life.
Her father said in the complaint that he and his wife had been taking care of the boy for some time, and that during one 12-week stretch when the boy was receiving treatment for some of his cognitive disabilities the woman only visited her son once.
Her initial court appearance is scheduled for Monday, July 21.
More from News and Features
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- Waukesha Community Briefs: Night Out info, food drive and more
- Now You Know: Where were Waukesha's movie theaters?
- New Latin American restaurant Arepanitas opens near downtown Waukesha
- Waukesha newspaper website is changing


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Topic: Media and journalism
(Photo: University of Illinois Press)
March 11, 2015
Craig Lassig /AP Images for College of American Pathologists
March 06, 2015
Call For Stories: Cancer Testing
Kainaz Amaria
March 05, 2015
Guy Raz on Podcasts, Algorithms, And Quiet On The Radio
Daniel McFadden/Sony Pictures Classics
February 20, 2015
Roll Out The Red Carpet: Illinois Is Going To The Oscars
AP Photo
January 29, 2015
Center for Public Integrity Names the "Sugar Daddies of State Politics"
(Photo: Tiffany Jolley)
January 27, 2015
Art Theater Hosts Discussion Panel After Selma Screening
Page 8 of 23 pages ‹ First < 6 7 8 9 10 > Last ›
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David Bald Eagle, Lakota Chief, Musician, Cowboy And Actor, Dies At 97
Writer James Alan McPherson, Winner Of Pulitzer, MacArthur And Guggenheim, Dies At 72
Mysterious And Known As The 'Raven': Scientists Identify New Whale Species
After Coup Attempt, Turkish Government Cracks Down On Media
Witnesses: U.N. Peacekeepers Did Nothing As South Sudanese Soldiers Raped Women
Failed Assassins: Would-be Presidential Shooters In History
Tortoise Wins Again: Slow And Steady Pace Outruns L.A. Fire
WATCH: Marilyn Mosby: 'The System Is In Need Of Reform'
How Jupiter's Red Spot Makes Things High Above It Hot, Hot, Hot
6-Year-Old Boy's Wish To Be A 'Garbage Man' Granted
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ART/BTS wins 2 Tellys
For the second year in a row, the web series brought home both a Silver and Bronze Telly award.
The 21st
21st century radio for the 21st state. Weekdays at 11 am on WILL-AM 580.
2015 Illinois Public Media Annual Report
Our 12-page Annual Report with highlights of Illinois Public Media's activities in 2015.


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For the first time in about 100 years, hybrid and electric-powered vehicles appear to be gaining traction in the new car market.
Read This Article
Summer has officially arrived, which of course means warm weather.
The new 2015 Chevrolet Cruze has been spotted.
With the inception of the fifth-generation Chevrolet Camaro, General Motors has made sure there's practically a unique Camaro for every customer.


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Sexy brunette nurse gets ready for her day at work...
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VideoBash is your first and final stop for free funny videos, games and pictures guaranteed to brighten up your day. The funniest jokes and the most unbelievable pranks caught on tape have been collected in one spot for your viewing pleasure. This is entertainment at its best and no one does it better than VideoBash. These funny clips crackle with humor and they will definitely have you laughing till your sides hurt. Quality is our number one priority and the free funny vids here are the best of the best. There are no mediocre videos here. All the videos, jokes, pranks on VideoBash are the cream of the crop and you'll be hooked from the get go. The best part is you don't have to keep all this good stuff to yourself, you can share all the free entertainment videos with your friends.


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Home Bake: Cakes, muffins, tarts, cheesecakes, brownies and puddings, with foolproof tips from Master Pâtissier Hardcover – 2 Sep 2010
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Choice Magazine, October 2010
'You would be hard-pushed to not want to use every recipe in this book!'
Great British Food, September 2010
'Prepare to drool over this beautiful collection of recipes from the man who made Madonna's wedding cake... '
The Sunday Mirror, 8th May 2011
'Eric Lanlard is the patissier for the A-list - and master of show-stopping treats.'
Foodepedia, September 5, 2010
'Eric Lanlard's new book 'Home Bake' looks too good. So good in fact, it's almost obscene.' --.
Book Description
The recession has seen a marked interest in returning to the simpler, traditional pleasures of baking. Recent research has shown that 85% of Britons have baked cakes at home and 29% say they are baking more than ever before. Recent price rises for raw ingredients including flour, sugar, milk and eggs have also contributed to the consumer's perception that shop-bought cakes and breads have become much more expensive.See all Product Description
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Top Customer Reviews
The 'flour free orange and lavender cake' has without doubt not been tested by eric's team for the home kitchen.
Not only is it far too sweet, it also doesn't fit the size of tin the method asks for and to top it off the flavour of the lavender is totally lost by the spices and very thin sugar syrup. If one is to follow the recipe, it is positively impossible to have a syrup that looks remotely similar to the picture.
Perhaps Eric should have a blog that displays corrections to his recipes and perhaps try them in a home kitchen first - not just at an industrial scale!
It looks very pretty (though I found the photographs of the "method" strangely random and unhelpful).There are sections on sponge cakes, tarts, meringues, muffins and cupcakes, tray bakes, festive recipes, cheesecakes, puddings, pastries and biscuits. The illustrations of luscious baked goods are delightful. Cakes shimmer. Muffins ooze.
It is difficult to put my finger on why I haven't used it. I was certainly keen to get it after reading a review of it on the bakerybits website. But my heart rather sank when it arrived and I saw that it had a celebrity endorsement from Elizabeth Hurley on the front. (Does she look like a cake fiend to you?) It sank further when I kept on reading about "my cafe-patisserie Cake Boy" in the introduction to far too many of the recipes. (I haven't seen any of Eric Lanlard's television shows so don't know is this is par for the course for him.)
I subsequently bought the River Cottage cake handbook and have used that a lot. There's a vast difference between the two books - the River Cottage book is less pretty, the cakes more solid and earthy and less exquisitely decorated, and with a lot more information on adaptable techniques and storage time etc. And certainly no quotes from Elizabeth Hurley on the cover.
I suspect that if you are one of those people who has the patience and skill (and time) to produce beautifully decorated, fashionable cupcakes, you'll get a lot out of this book.Read more ›
Most Recent Customer Reviews
This book is fabulous with delicious recipes and beautiful photographs. Highly recommended. A must have for all kindle bakers! Superb xPublished 9 months ago by Martina
Bought the book before reading the reviews which mention mistakes in the recipes and the first recipe my daughter tried is impossible. Read morePublished 11 months ago by F. Jones
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The Jessie's Cab Toon Revolve attraction, again considered to be the finest fictional attraction at Disneyland
The Jessie's Cab Toon Revolve queue is like a skulk through the bad part of Toontown. The attraction cabs are supposed to look like Bertie the Cab, and while you drive you can revolve them by hugging the headlights.
Avoid Pirate Mode
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Man hangs self, names four people in his suicide note
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A 30-year-old man allegedly committed suicide at his relative's residence Saturday, leaving behind a detailed note naming four people who had allegedly harassed him over repayment of loan. Police have arrested two of those named. According to Dahisar police, Sandeep Patil (30) was found hanging at his sister-in-law's house at around 7 am. He had come to stay there the previous night while his sister-in-law was out of station, they said. "Patil wrote that he had borrowed money from Arun Bhatia, Alok Dubey, Latif Khan and Vikram. He also mentioned their cellphone numbers. They were harassing him, though he told them he was unable to repay the money he had borrowed from them," said ACP Sunil Deshmukh, Dahisar division. By Saturday afternoon, police picked up Bhatia from his Dahisar residence and Khan from his office in Malad. "The four are not professional money lenders but had lent money to Patil as he was going through hard times. Patil, in his suicide note, also mentioned the names of people who had witnessed the alleged harassment," Deshmukh said.
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- Aiming it wrong
- A first, but big step
- Brand Rajini


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Dr. Isabelle Beurroies, Mohammed Boulhout, Dr. Philip L. Llewellyn, Prof. Bogdan Kuchta, Prof. Gérard Férey, Dr. Christian Serre and Dr. Renaud Denoyel Using Pressure to Provoke the Structural Transition of Metal–Organic Frameworks Angewandte Chemie International Edition 49
Version of Record online: 27 AUG 2010 | DOI: 10.1002/anie.201003048
Performance under pressure: Hysteresis of intrusion–extrusion of mercury observed in MIL-53(Cr) particles is interpreted as the transition from large-pore to narrow-pore forms of this metal–organic framework (MOF) material, provoked by the isostatic pressure created by mercury around the porous particles. The observed behavior may be qualitatively explained by a simple energetic model and opens possibilities for applications as dampers or molecular springs.
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Matthew 24:36–44
As a child, I remember hearing in church about the second coming and Jesus returning. Long before the Left Behind series arrived, I heard the mournful strains of "I Wish We'd All Been Ready" around our church campfires.
The deep attention and reverence that Thomas Merton and Abdul Aziz brought to each other's books, traditions and lives undergirded their friendship, and the frank way they explored their similarities and differences enlivened it.
Directed by Clint Eastwood
A plethora of films deal with the afterlife, the shady domain Hamlet calls "that undiscovered country." From them could be built a looming tower of unanswered questions and unreliable answers.
I grew up attending Bible and Baptist
churches; now I generally identify with the emerging church. So I've had quite
a learning curve at the Episcopal seminary where I'm studying. Between
balancing prayer books and hymnals and crash courses in chanting, I've frequently
felt like a stranger in a strange land.
Reflecting on the Benedictus gives us an opportunity to
reflect on the place of memorization and repetition in our formation as people
who read the Bible as if our lives depended on it. Ellen Davis calls reading
the Bible as if our lives depended on it confessional reading. She does not
mean reading the Bible in light of a denominational confession. She means
reading the Bible as an "indispensible word."
LifeWay Research found that 53 percent of 1,000 Protestant pastors polled agreed that sometimes their congregations seem to love America more than God: 59 percent of pastors in the South, as compared to 51 percent in the Midwest and 42 percent in the West (LifeWay Research, June 30).
More Century Marks»


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Schnucks HQ Worker Charged With Stealing $111,000
ST. LOUIS (AP) - A University City woman who worked in the accounting department at Schnucks headquarters in Maryland Heights faces felony theft charges for allegedly stealing more than $111,000 from the supermarket chain.
The St. Louis Post-Dispatch reports that 35-year-old Lolita Martin was charged with removing money from a cash box at the company's main offices. Martin was responsible for overseeing the box, which was used to provide change for the company cafeteria.
The company fired Martin in May amid an internal investigation. Defense attorney Mark Fredman declined comment but said his client plans to plead not guilty.
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KANSAS CITY, Mo. (AP) — The former owner of two Missouri daycare businesses has been sentenced to two years in... More >>
KANSAS CITY, Mo. (AP) — The mother of a 24-year-old man fatally shot by Kansas City police in 2013 has... More >>
CARTHAGE (AP) — Authorities are increasing security at a festival that draws tens of thousands of Vietnamese Catholics to southwest... More >>
JEFFERSON CITY, Mo. (AP) — Missouri Republican Lt. Gov. Peter Kinder said his experience in office and past success in... More >>
COLUMBIA - Getting to the polls can be difficult on Election Day, but COMO Connect will help Columbia residents. The... More >>
COLUMBIA - University of Missouri Interim Chancellor Hank Foley announced Friday he will take over for Interim Athletic Director Wren... More >>
COLUMBIA - Cyclists were out on Friday enjoying new bike lanes along the shoulder of Forum Boulevard. The new lanes... More >>
JEFFERSON CITY - Missouri Department of Transportation announced Friday that due to unforeseen complications, the Missouri River Bridge project will... More >>
KANSAS CITY (AP) — A Kansas man has been charged with killing a woman in a Missouri apartment where officers... More >>
WINONA (AP) — A southern Missouri man has been convicted of killing his wife by giving her Kool-Aid laced with... More >>
OAKVILLE (AP) — St. Louis County police are investigating after more than two dozen break-ins were reported on a single... More >>
ST. LOUIS (AP) — A St. Louis home is damaged after suspects fleeing police crashed into it. KTVI-TV reports... More >>
KANSAS CITY, Mo. (AP) — Kansas City police have found a man dead and another victim critically wounded nearby. ... More >>
COLUMBIA - Opponents and supporters of Proposition 1 met at Columbia's Activity and Recreation Center (ARC) Thursday night to voice... More >>
COLUMBIA – Police arrested one man and are searching for another man in connection with a shooting that killed a... More >>
ST. LOUIS (AP) — A former St. Louis city prosecutor has been sentenced to probation for helping cover up a... More >>
SPRINGFIELD (AP) — An injured bald eagle got a little help from a Missouri State Highway Patrol trooper. The... More >>
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BDP FL maleimide
BDP FL maleimide is a bright and photostable thiol-reactive dye for protein labeling, an ideal replacement for fluorescein for microscopy. BDP FL is a borondipyrromethene dye which has absorption and fluorescence spectra similar... Read More
This study investigates how the overexpression of a specific ORF affects the virulence of E. faecalis. Read More
Viruses can be broadly classified according to whether or not the particle is enveloped – surrounded by a membrane taken from the host cell – or naked. Some naked viruses apparently are more modest than we believed. Read More
Until we have "action figures" of scientists, I do what I can to recognize scientists that have had a large and lasting influence on my academic career. In this blog post, I praise Dr. Moselio Schaechter for his clear and insightful approaches to microbiology. His enthusiasm, breadth of interes... Read More
Unknown fungi isolated from soil sample.I would like to have inputs from the Mycologist in the forum about this fungal colony,its identification,name,species and genus? Read More
DNA microarrays are widely used in biological research and were once thought to be a very promising gene discovery tool. However, since the advent of next generation DNA sequencing, the popularity of DNA microarrays has steadily declined. Compared to the sequencing, microarray behavior is unce... Read More
Wild poliovirus has been detected in the sewers of Brazil and Israel. Fortunately, no cases of poliomyelitis have been reported in either country. Why is poliovirus present in these countries and what are the implications for the eradication effort?
Wild type poliovirus (e.g. not vaccine-deri... Read More
Genomes of non-defective viruses range in size from 2,400,000 bp of dsDNA (Pandoravirus salinus) to 1,759 bp of ssDNA (porcine circovirus). Are even smaller viral genomes possible? The subviral agents called viroids provide an answer to this question.
Viroids, the smallest known pathogens, ar... Read More
As far as I know, mosquitoes do not eat sushi. But mosquito cells have proteins with sushi repeat domains, and these proteins protect the brain from lethal virus infections. Read More
A referenced interactome of host genes and proteins affected by the Bornavirus. KEGG pathways of the host arm of the interactome are also linked from the interactome page. If you have any data to add please use the email link on the page and I will update the various pathways. Read More
In this blog post, I describe how I use the commonly available pGLO plasmid to teach freshman students basic concepts about transformation, receptor recognition, drug resistance mechanisms, and gene regulation. Oh, and fluorescence via GFP! Read More
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A Seminar Report submitted in partial fulfillment of the requirements for the award of the degree of Bachelor of Technology in COMPUTER ENGINEERING by Mr. Tage Nobin Under the guidance of
Prof. L.D. Netak
DEPARTMENT OF COMPUTER ENGINEERING DR. BABASAHEB AMBEDKAR TECHNOLOGICAL UNIVERSITY Lonere-402 103, Tal. Mangaon, Dist. Raigad (MS) INDIA April, 2010
The seminar report entitled Data Quality Management: Methods and Tools submitted by Mr. Tage Nobin (20070653) is approved for the partial fulfillment of the requirement for the award of the degree of Bachelor of Technology in Computer Engineering.
Prof L.D. Netak Guide Dept. of Computer Engineering External Examiner(s) 1. 2. (Name: (Name:
Prof L.D. Netak Head Dept. of Computer Engineering
) )
Place:Dr.Babasaheb Ambedkar Technological University, Lonere. Date: 15/05/2010
This seminar report is a result of intense effort of many people whom I need to thank for making this a reality. I thus express my deep regards to all those who have offered their assistance and suggestions. I am grateful to my seminar guide Prof. L.D.Netak, for making this work possible. No word of thanks is enough for his mentorship, guidance, support and patience. I must acknowledge the freedom he gave me in pursuing topics that I found interesting. His resourcefulness, influence and keen scientific intuition were also vital to the progress of this work and for these I am deeply thankful. Finally, I would like to thank all whose direct and indirect support helped me in completing the seminar in time.
Mr. Tage Nobin (20070653)
Defective data is one of the serious problems pertaining to data world. Business success is becoming ever more dependent on the accuracy and integrity of mission critical data resources. As data volume increases, the question of internal consistency within data becomes paramount, regardless of fitness for use for any external purpose. Different methods and tools are being used for the maintenance of data quality as per the condition and the situation. This paper describes major data quality problems, requirements and common strategies to manage data quality in systems related to data. It also explains the importance of data quality management with special spotlight addressing the management issues of data quality and various methods and tools that can be used and implemented comprehensively in the management of data quality.
1 Introduction 2 What is Data Quality Management 3 Data Quality Definition (Rules and Targets) 3.1 3.2 Importance of Data Quality . . . . . . . . . . . . . . . . . . . . . . . . . Data Quality Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 6 6 7 10 13 13 15 15 17 17 18 19 19 19 21 24
4 Data Quality Management Challenges 5 Design Quality Improvement Process 5.1 Data Quality Management Objective . . . . . . . . . . . . . . . . . . . .
6 Implement Quality Improvement Process (Methods and Tools) 6.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 6.1.2 6.1.3 6.1.4 6.2 6.2.1 6.2.2 6.2.3 Data Profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Cleansing . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Augmentation . . . . . . . . . . . . . . . . . . . . . . . . . . Data Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Auditing Tools . . . . . . . . . . . . . . . . . . . . . . . . . Data Cleansing Tools . . . . . . . . . . . . . . . . . . . . . . . . . Data Migration tool . . . . . . . . . . . . . . . . . . . . . . . . .
Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7 Basic Tools of Data Quality 8 Monitor Data Quality 8.1 Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26 29 29 32
List of Figures
3.1 4.1 5.1 6.1 8.1 Data Quality Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Radial Cycle of Data Quality Process . . . . . . . . . . . . . . . . . . . . Data Quality Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monitor System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 11 14 16 30
Chapter 1 Introduction
SIX HUNDRED BILLION DOLLARS ANNUALLY! Yes that is what poor data quality costs American businesses, according to the Data Warehousing Institute. What about the whole world? Ensuring high level data quality is one of the most expensive and timeconsuming tasks to perform in data warehousing projects. Data quality management is the field being the one in which all kinds of data- raw or processed are managed. It is one of the major fields yet to be rolled on in a serious way. The main topics covered are as follows:
1. What is Data Quality Management?
This is a key first step, as the ability to understand the up-front level of data quality management will form the foundation of the domain rules and processes that will be put in place. Without performing an upfront assessment, the ability to effectively implement a data quality strategy will be negatively impacted. From an ongoing perspective, the data quality management will allow an organization to see how the data quality procedures put in place have caused the quality of the data to improve.
2. Data Quality Definition (Rules and Targets) Once the initial data quality assessment is complete, the second part of the process involves defining what exactly we mean by data quality. From an ongoing perspective, this phase will involve describing the various attributes of a quality data that will make the whole process much easier. Performing trend analyses on the data and the rules in place to ensure the data rules are adhered to and the target is focused on. 3. Data Quality Management Challenges Deploying a data quality management program is not easy; there are significant challenges that must be overcome. Herein we shall discuss the various problems and challenges that may act as an obstacle in the way of quality data. Be it of serious nature or mild one, but these challenges need to be overcome so as to develop a quality data. 4. Design Quality Improvement Processes This phase involves designing of data quality management process architecture. With various ways on the field, we need to choose the best way of managing process that would make the data of finest quality. 5. Implement Quality Improvement Processes (Methods and Tools) Once the designing has been standardized, the next phase of the enhancement process involves the actual implementation of the various methods and tools designed. Since data quality is an iterative process, the rules to manage and regulate will come in handy. 6. Monitor Data Quality The ability to monitor the data quality processes is critical, as it provides the organization with a quick snapshot of the health of the data within the organization. Through analysis of the data quality scorecard results, we can have the information needed to confidently make additional modifications to the data quality strategies 2
in place if needed. Conversely, the scorecards and trend analysis results can also make sure that data quality is being effectively addressed within the organization.
Chapter 2 What is Data Quality Management
First of all we will be dealing with what exactly is 'Data', 'Quality' and 'Management'. As Oxford dictionary defines • Data - "a collection of facts from which conclusions may be drawn" • Quality - "the standard of how good something is measured against other similar things". In context of data it quality may be defined as "fitness for use". • Management - "the action of managing something". In a broad spectrum, data quality management entails the establishment and deployment of roles, responsibilities, policies, and procedures concerning the acquisition, maintenance, dissemination, and disposition of data. Actually the perfect definition depends upon the organization. For, successful data quality management, the solution must include techniques, processes, methods and tools. Data quality management lifecycle must be clearly defined using continuous as well as iterative frameworks. Data quality must be designed into systems using proven engineering principles. Data quality is too often left to chance or given only superficial attention in the design of information systems. While good engineering principles are sometimes applied to software development, data quality is usually left up to the end user. Applying engineering principles to data quality involves understanding the factors that affect the creation 4
and maintenance of quality data. It is helpful to look at data as the output of a data manufacturing process.
Chapter 3 Data Quality Definition (Rules and Targets)
3.1 Importance of Data Quality
First off, data is an essential component of most of today's business processes. In customer facing functions, it is the foundation to managing customer relationships. Without good data quality, it will be difficult to get accurate report metrics, and it will also waste user's time and efforts. Bad data will also incur costs. One of the biggest risks of bad data quality is that it ultimately inhibits adoption, as users get frustrated and lose trust in the data. We need to know the fact that it isn't quality that is expensive; it's the cost of "unquality". Examples of the cost of "unquality" include the cost of sending duplicate promotional materials because customers are duplicated in the database, the opportunity cost of not sending materials to the right customers because the data used to segment customers is flawed, the opportunity cost of not shipping products to a customer because of inaccurate information about inventory levels, and the time spent finding and reconciling data needed to make effective decisions. Data quality is evidenced by valid and reliable data; therefore planning in the early stages about the clear concept of the need and definition is well worth the investment
of time and resources. Data in a database has no actual value (or even quality); it only has potential value. Data has realized value only when someone uses it to do something useful. As mentioned earlier there is no fixed global definition of high quality data. Data quality don't restrict itself to a particular concept, instead differs as domain and application changes. Whatever be the domain, there are some particular parameters which are essential to be gratified in order to make the data a real quality data. Contrary to popular belief, quality is not necessarily zero defects. Quality is conformance to valid requirements.
Data Quality Attributes
To be fit for use, data products must possess all three attributes of quality: 1) Utility - refers to the usefulness of the information for its intended users. 2) Objectivity - refers to whether information is accurate, reliable, and unbiased, and is presented in an accurate, clear, and unbiased manner. 3) Integrity - refers to the security or protection of information from unauthorized access or revision. All the above three attributes may be further defined in terms of seven dimensions of data quality: Relevance- refers to the degree to which our data products provide information that meets the customer's needs. Accuracy- refers to the difference between an estimate of a parameter and its true value. Timeliness- refers to the length of time between the reference periods of the information and when we deliver the data product to customers. 7
Accessibility- refers to the ease with which customers can identify, obtain, and use the information in data products. Interpretability- refers to the availability of documentation to aid customers in understanding and using our data products. This documentation typically includes: the underlying concepts; definitions; the methods used to collect, process, and analyze the data; and the limitations imposed by the methods used. Transparency- refers to providing documentation about the assumptions, methods, and limitations of a data product to allow qualified third parties to reproduce the information, unless prevented by confidentiality or other legal constraints. Completeness- refers to the degree to which values are present in the attributes that require them.
Figure 3.1: Data Quality Attributes
Chapter 4 Data Quality Management Challenges
Data is impacted by numerous processes, most of which affect its quality to a certain degree. It is imperative that the issue of data quality be addressed if the data warehouse is to prove beneficial to an organization. The information in the dataware house has the potential to be used by an organization to generate greater understanding of their customers, processes, and the organization itself. There potential to increase the usefulness of data by combining it with other data sources is great. But, if the underlying data is not accurate, any relationships found in the data warehouse will be misleading. As Wyatt Earp (Data-Base Expert) said Fast is fine, but accuracy is everything. Resolving data quality problems is often the biggest effort in a data mining study. 50-80 percent of time in data mining projects spent on DQ. Because it's (data) in the computer, it doesn't mean its right.
Figure 4.1: Data Flow Data/information is not static, it flows between data collection and usage process. The main problem herein is that problems can and do arise at all of these stages which makes the need of End-to-End continuous monitoring. This process indeed becomes a herculean task. There are various factors that Influence Data Quality: 1. Data Control 2. Data Age 3. Data Types 4. Device Availability 5. Data Structure 6. Read/Write Management 7. Communication Timing
These factors matters a lot to development of a quality data. Any error up to 1 percent of data may impact findings and result. There are numerous problems which arise in wake of data quality. Some of them are of small nature and some of them big. Whatever be the nature, ignoring any such kind of matter may prove to be costly affair. Some of them listed:
- Much of the raw data is of poor quality. This is because of incorrect data gathering and data operations. This leads to inaccurate assessment of the data. - The above mentioned fact results in data being costly to diagnose and assess. - Consequence of which is the data becomes costly to repair. - Many of the costs involved are hidden and hard to quantify. This makes the assessment a tough task. - Inconsistent data between different systems. Since data flows between different systems, any obstacle in the smooth transition may lead to a total data failure. - Most of the attributes of a quality data are extremely difficult to measure sometimes impossible. - They are of vague nature. The conventional definitions provide no guidance towards practical improvements of the data. - The priority for metadata is undermined. Setting standards for metadata is very important. - Data quality management requires cross-functional cooperation. - It is perceived to be extremely manpower-intensive. - There are various other systematic errors which can be attributed to lack of resources and skills. 12
Chapter 5 Design Quality Improvement Process
The quality of any data statistics disseminated by an agency depends on two aspects: the quality of statistics received, and the quality of internal processes for the collection, processing, analysis and dissemination of data and Meta data.
Data Quality Management Objective
Typical objectives of a data quality management program include: • Eliminate redundant data • Validate data at input • Eliminate false null values • Ensure data values fall within defined domains • Resolve conflicts in data • Ensure proper definition and use of data values • Establish and apply standards. 13
Figure 5.1: Radial Cycle of Data Quality Process
Here in we develop a basic strategy by combining all the above steps: Preliminary Problem Definition, followed by Analysis, Improvement and monitor steps for each problem.
Chapter 6 Implement Quality Improvement Process (Methods and Tools)
Designing the quality management process is clearly not the end of the data quality effort. Just identifying issues does nothing to improve things. The issues need to drive changes that will improve the quality of the data for the eventual users. We see process improvement fundamentally as a way of solving problems. If there is not an apparent or latent problem, process improvement is not needed. If there is any problem, howsoever intangible, one or more processes need to be improved to deal with the problem. Once you sense a problem, good problem solving technique involves alternating between the levels of thought and experience.
1. Profiling 2. Cleansing 3. Data Integration/Consolidation 4. Data Augmentation 15
Figure 6.1: Data Quality Methods
Data Profiling
It can be defined as use of analytical techniques on data for the purpose of developing a thorough knowledge of its content, structure and quality. It is a process of developing information about data instead of information from data. The purpose of these statistics may be to: 1. Find out whether existing data can easily be used for other purposes. 2. Improve the ability to search the data by tagging it with keywords, descriptions or assigning it to a category. 3. Give metrics on data quality, including whether the data conforms to particular standards or patterns. 4. Assess the risk involved in integrating data for new applications. 5. Assess whether metadata accurately describes the actual values in the source database. 6. Develop a master data management process for data governance for improving data quality.
Data Cleansing
Data cleansing or data scrubbing is the act of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database. Used mainly in databases, the term refers to identifying incomplete, incorrect, inaccurate, irrelevant etc. parts of the data and then replacing, modifying or deleting this dirty data. After cleansing, a data set will be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by different data dictionary definitions of similar entities in different stores, may have been caused by user entry errors, or may have been corrupted in transmission or storage. Data cleansing differs from data validation in that validation almost invariably means data is rejected
from the system at entry and is performed at entry time, rather than on batches of data. The process of data cleansing are • Data Auditing: The data is audited with the use of statistical methods to detect anomalies and contradictions. This eventually gives an indication of the characteristics of the anomalies and their locations. • Workflow specification: The detection and removal of anomalies is performed by a sequence of operations on the data known as the workflow. It is specified after the process of auditing the data and is crucial in achieving the end product of high quality data. In order to achieve a proper workflow, the causes of the anomalies and errors in the data have to be closely considered. If for instance we find that an anomaly is a result of typing errors in data input stages, the layout of the keyboard can help in manifesting possible solutions. • Workflow execution: In this stage, the workflow is executed after its specification is complete and its correctness is verified. The implementation of the workflow should be efficient even on large sets of data which inevitably poses a trade-off because the execution of a data cleansing operation can be computationally expensive. • Post-Processing and Controlling: After executing the cleansing workflow, the results are inspected to verify correctness. Data that could not be corrected during execution of the workflow are manually corrected if possible. The result is a new cycle in the data cleansing process where the data is audited again to allow the specification of an additional workflow to further cleanse the data by automatic processing.
Data Augmentation
The term data augmentation refers to methods for constructing iterative algorithms via the introduction of unobserved data or latent variables. In general, however, constructing data augmentation schemes that result in both simple and fast algorithms is a matter of art in that successful strategies vary greatly with the observed-data models 18
being considered.
Data Integration
Data integration involves combining data residing in different sources and providing users with a unified view of these data. This process becomes significant in a variety of situations both commercial (when two similar companies need to merge their databases) and scientific (combining research results from different repositories, for example). Data integration appears with increasing frequency as the volume and the need to share existing data explodes.
It is commonly accepted that data quality tools can be grouped according to the part of a data quality process they cover. Data profiling and analysis assist in detecting data problems. Data transformation, data cleaning, duplicate elimination and data enhancement propose to solve the discovered or previously known data quality problems. Data quality tools generally fall into one of three categories: 1. Auditing 2. Cleansing 3. Migration
Data Auditing Tools
Data auditing tools enhance the accuracy and correctness of the data at the source. These tools generally compare the data in the source database to a set of business rules. When using a source external to the organization, business rules can be determined by using data mining techniques to uncover patterns in the data. Business rules that are internal to the organization should be entered in the early stages of evaluating data 19
sources. Lexical analysis may be used to discover the business sense of words within the data. The data that does not adhere to the business rules could then be modified as necessary. Data Analysis: Activities that enclose the statistical evaluation, the logical study of data values and the application of data mining algorithms in order to define data patterns and rules to ensure that data does not violate the application domain constraints. The set of commercial and research tools that provide data analysis techniques are as follows: Commercial- dfPower ETLQ Migration Architect Trillium WizWhy ResearchPotter's Wheel Ken State University Tool
Data Profiling: Process of analyzing data sources with respect to the data quality domain, to identify and prioritize data quality problems. Data profiling reports on the completeness of datasets and data records organize data problems by importance; output the distribution of data quality problems in a dataset, and lists missing values in existing records. The identification of data quality problems before starting a data cleaning project is crucial to ensure the delivery of accurate information. The following set of commercial and research tools implement data profiling techniques: 20
Commercial- dfPower ETLQ Migration Architect Trillium WizWhy Research- Ken State University Tool
Data Cleansing Tools
Data cleansing tools are used in the intermediate staging area. The tools in this category have been around for a number of years. A data cleansing tool cleans names, addresses and other data that can be compared to an independent source. These tools are responsible for parsing, standardizing, and verifying data against known lists. The data cleansing tools contain features which perform the following functions: • Data parsing (elementizing) - breaks a record into atomic units that can be used in subsequent steps. Parsing includes placing elements of a record into the correct fields. • Data standardization - converts the data elements to forms that are standard throughout the data warehouse. • Data correction and verification- matches data against know lists. • Record matching- determines whether two records represent data on the same subject. • Data transformation- ensures consistent mapping between source systems and data warehouse.
• House-holding - combining individual records that have the same address. • Documenting - documenting the results of the data cleansing steps in the meta data. Data cleaning: The act of detecting, removing and/or correcting dirty data. Data cleaning aims not only at cleaning up the data but also to bring consistency to different sets of data that have been merged from separate databases. Sophisticated software applications are available to clean data using specific functions, rules and look-up tables. In the past, this task was done manually and therefore subject to human error. The following set of commercial and research tools implement data cleaning techniques: Commercial- DataBlade dfPower ETLQ ETI*DataCleanser Firstlogic NaDIS QuickAddress Batch Sagent Trillium WizRule Research- Ajax Arktos FraQL
Duplicate elimination: The process that identifies duplicate records (referring to the same real entity) and merges them into a single record. Duplicate elimination processes are costly and very time consuming. They usually require the following steps: (i) to standardize format discrepancies; (ii) to translate abbreviations or numeric codes; (iii) to perform exact and approximate matching rules and (iv) to consolidate duplicate records. The set of commercial and research tools that provide duplicate elimination techniques is presented below: Commercial- Centrus Merge/Purge ChoiceMaker DataBlade DeDupe dfPower DoubleTake ETLQ ETI*DataCleanser Firstlogic Identity SearchvServer MatchIT Merge/Purge Plus ResearchAjax Flamingo Project
Data enrichment (also known as data enhancement): The process of using additional information from internal or external data sources to improve the quality of the input data that was incomplete, unspecific or outdated. Postal address enrichment, geocoding and demographic data additions are typical data enrichment procedures. The set of commercial and research data enrichment tools are listed below: Commercial- DataStage dfPower ETLQ Firstlogic NaDIS QuickAddress Batch Sagent Trillium Research- Ajax
Data Migration tool
The third type of tool, the data migration tool, is used in extracting data from a source database, and migrating the data into an intermediate storage area. The migration tools also transfer data from the staging area into the data warehouse. The data migration tool is responsible for converting the data from one platform to another. A migration tool will map the data from the source to the data warehouse. There can be a great deal of overlap in these tools and many of the same features are found in tools of each category.
Data transformation: The set of operations (schema/data translation and integration, filtering and aggregation) that source data must undergo to appropriately fit a target schema. Data transformations require metadata, such as data schemas, instance-level data characteristics, and data mappings. The set of commercial and research tools that can be classified as data transformation tools is the following: Commercial- Data Integrator DataFusion DataStage dfPower ETLQ Hummingbird ETL Firstlogic Informatica ETL SQL Server Trillium Research- Ajax Arktos Clio FraQL Potter's Wheel TranScm
Chapter 7 Basic Tools of Data Quality
1. Fishbone Diagram Fishbone diagrams are diagrams that show the causes of a certain event. Common uses of the Fishbone diagram are product design and quality defect prevention, to identify potential factors causing an overall effect. Each cause or reason for imperfection is a source of variation. Causes are usually grouped into major categories to identify these sources of variation.
2. Flow Chart A flowchart identifies the sequence of activities or the flow of materials and information in a process. There is no precise format, and the diagram can be drawn simply with boxes, lines, and arrows. Flowcharts help the people involved in the process understand it much better and more objectively by providing a picture of the steps needed to accomplish a task.
3. Histogram and bar chart 26
Histograms provide clues about the characteristics of the parent population from which a sample is taken. Patterns that would be difficult to see in an ordinary table of numbers become apparent. Bar Chart is series of bars representing the frequency, e.g. number of times yes/no.
• Displays large amounts of data that are difficult to interpret in tabular form. • Shows centering, variation, and shape. • Illustrates the underlying distribution of the data. • Provides useful information for predicting future performance.
4. Scatter diagram It is a plot of two variables showing whether they are related. • Supplies the data to confirm a hypothesis that two variables are related. • Provides both a visual and statistical means to test the strength of a relationship. • Provides a good follow-up to cause and effect diagrams. 5. Run Chart Run charts show the performance and the variation of a process or some quality or productivity indicator over time in a graphical fashion that is easy to understand and interpret. They also identify process changes and trends over time and show the effects of corrective actions.
• Monitors performance of one or more processes over time to detect trends, shifts, or cycles. • Allows a team to compare performance before and after implementation of a solution to measure its impact. • Focuses attention on truly vital changes in the process.
6. Control Chart Control charts, also known as Shewhart charts or process-behaviour charts, in statistical process control are tools used to determine whether or not a manufacturing or business process is in a state of statistical control.
7. Process chart It is an organized way of recording all the activities performed by a person, by a machine, at a workstation, with a customer, or on materials. Codes can be applied such as for operations, transport, inspection, delay, storage against e.g. Numbered steps, time, distance and step description.
Chapter 8 Monitor Data Quality
Monitoring data quality is an important sub-aspect of the Data Quality Life Cycle. It is based on the specified goals and rules and therefore on the current quality level obtained after the initial analysis carried out on the basis of data profiling and the initial cleansing of your data. Monitoring is not an end in itself but more or less serves as a sensor for data quality weaknesses, before they make themselves felt in the destination system. We can undertake the monitoring function by orienting itself towards the defined data quality initiatives and general instructions as well any changes which may be required.
Monitoring System
We can develop a simple 3-step monitoring system. This is a model for monitoring the process of data quality. Ultimately, data quality monitoring are based on a well-understood set of metrics which provides important knowledge about the value of the data in use. First of all these metrics need to be in right order. Data quality must be tracked, managed, and monitored if it is to improve business efficiency and transparency. Therefore, being able to measure and monitor data quality throughout the lifecycle and compare the results overtime is an essential ingredient in the proactive management of ongoing data quality improvement and data governance.
Figure 8.1: Monitor System
This paper on Data Quality Management System expresses the basic tasks of the management in the field of techniques, tools and improvement of data quality. Organizations seeking relief from data problems often turn to technology for help. This is not the most effective solution. Data quality is a behavioral problem, not a technology problem. To solve the data quality problem organizations need to change user behavior. A comprehensive program based on prevention, detection, correction and accountability is required. Deploying a data quality management program is not an easy task, but the rewards are enormous. Deploying a disciplined approach to managing data as an important asset will better position an organization to improve the productivity of its information workers and to better serve its customers. Strong frameworks and process are imperative for controlling data quality and for managing data, the most important asset of an organization. Additional validation procedures such as exception analysis and data reconciliation ensure high success rates in migrationrelated initiatives. The challenges associated with data quality control initiatives can be effectively handled by implementing the recommended framework and process to control data quality. Maintaining data quality is no longer an option, particularly in today's competitive and regulatory climate. With this in place, the six-phase program can be effectively pursued for the management of data quality.
[1] Thomas Korner "Handbook on Data Quality Assessment Methods and Tools", 2005, 3th Edition.
[2] Yang W. Lee "Total Data Quality Management: The case of IRI",2001
[3] Suzanne M. Embury "Data Quality Control",1999
[4] Theodore Johnson "Data-Quality& Data-Cleaning-An-Overview",2006
[5] http://www.sap.com/management/data_quality_management/index.epx
[6] http://en.wikipedia.org/wiki/data_quality.html
[7] http://www.tricare.mil/imp/GuidelinesOnDataQualityManagement.html
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Pictured: The John Hench's BMW Toon Twist attraction during construction
The John Hench's BMW Toon Twist queue is like a tour into Toontown. The attraction bmws are just like Barnie the Cab, and as you drive you can twist them by yanking the tires.
Run from Zombie Mode
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Skydiving.com provides a network skydiving location providing services to Brookline, along with various towns throughout Hillsborough County and the whole entire state of New Hampshire. Just what are you waiting for? Call our team now at 1-800-617-7948 for additional information. Remember to talk to our team about ongoing skydiving discounts in the Brookline area!
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Wine Kloud is dynamic start up company looking to make some noise in the wine industry. Where the wine industry meets technology. Over 1000 wine shops nationwide offer "daily wine deals" in their emai…
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Opus is a social media start-up aimed at integrating the fine art industry with the rest of the world, and to make it more accessible to the general public by acting as the first ever online art excha…
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Join Date: Jan 2010
Posts: 646
Location: Montebello, Ca
News Credits: 2
[This] is going from a huge ball joint to a flat round post unless your good at cutting or you've got the cajones too possibly mess up.
First, you need an extra Skullgrin:
Next, turn him over (you don't even have to transform him), and follow the disassembly as shown:
Here are the wrists (which basically just pop out once you take off the tank treads:
This is the wrist unaltered:
His wrist (truth be told) looks really ugly inside. It looks great and you can't see the butchered ball joint, but cutting plastic from the bottom of the joint and cutting the middle top half off then shaving the post to fit the original fist size so if I ever sell it I can put it back too normal . . . .
Now that I've scared people away from it, its simple for an moderate Radicon. Just take your time. Remember to shave the post between the fist and the ball joint then trim a bit of the ball joint closest to the fist. Cut the top of the ball joint off so it fits snugly in the fist. Close it up slowly. Make sure it doesn't resist. Finally enjoy the new hands!
[Protector] still transforms:
Just don't force it in; ease it in nicely. It will snap in and twist the wrist a bit to get the roof to snap together.
Originally Posted by zephon
Inspired by what he did, I copied the exact formula he used!
I had to modify the peg on Sidearm though so that the new hands can grip him:
Last edited by Superquad7; 03-22-2011 at 02:23 AM..
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Former Israeli Army Chief: Only Nuking Iran Can Halt Uranium Enrichment
Underground Site Can't Be Destroyed With Bunker Busters
Western officials familiar with the situation say that previous estimates on Iran's Fordow enrichment site, underground near the major city of Qom, is a little more "underground" than anyone had anticipated, and that GBU-28 bunker-busters can't destroy it.
Former Israeli Army Chief of Staff Dan Halutz, the only official quoted in the report, says that the site is totally immune from conventional attack and that "the only way to finish off Iran's nuclear program is by using the nuclear option."
Not that Halutz was endorsing nuking a site on the outskirts of a city of 1.5 million people. Rather, he expressed hope that Netanyahu was "not mad enough to think about it." So far there has been no indication from sitting Israeli officials on that.
The revelation is going to make Israel's push to war a lot harder, especially with the admission that Iran's civilian enrichment program's uranium has indeed been going to civilian purposes, and not military ones.
Last 5 posts by Jason Ditz
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- US-Backed Syrian Rebels Kill 38 Troops in Aleppo Tunnel Bomb - July 22nd, 2016
- Red Crescent Recover 14 Executed Bodies From Army-Held Benghazi District - July 22nd, 2016


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corporate case assistant jobs near Santa Monica, CA
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Case Manager, Clinical Services
Exclusive Healthcare - Los Angeles, CA
Clinically oriented Registered Nurse (RN) to work in a concierge case manager role alongside Nurse Practitioners/Physician Assistants as an integral part of the...
Administrative Assistant - Multifamily
The Laramar Group - Los Angeles, CA
Administrative Assistant - Multifamily*. Administrative Assistant – Multifamily*. Represents Laramar when necessary e.g. small claims, evictions, case...
Legal Assistant / Paralegal
University of La Verne - Los Angeles, CA
At least five years of experience in a law firm, university, or corporate law department as an officially designated legal assistant and/or paralegal....
HR Administrative Assistant
System One - Manhattan Beach, CA
We are currently looking for Human Resources Generalist to join our Corporate Organization on a contract to hire basis to provide administrative support to the...
Legal Secretary/Administrative Assistant- Glendale
Allstate - Glendale, CA
Monitor and maintain compliance with proprietary case management system. With more than 1200 employees, the Allstate Department of Law and Regulation is among...
Legal Office Assistant/ Client Intake Specialist
Chase Law Group - Manhattan Beach, CA
Legal Assistant Job Purpose:. Confirming case status with attorney. Inputting information into file database and case management software;...
Analyst, Paralegal Business & IP Litigation
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Paralegal Certificate (ABA-certified legal assistant program). Self-starter, detailed-oriented, strong analytical, organizational, and case management skills,...
Lecturer, Management and Organization
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Possible ranks include Lecturer, Assistant Professor, Associate Professor and Full Professor of Clinical Management and Organization....
Medical Records Field Tech
HealthPort - Los Angeles, CA
Upload scanned charts to our corporate data center using your high-speed internet connection at home. Must have the ability to maneuver a 45lb equipment case in...
Front Office Medical Assistant- Surgical Center
Prime MSO- Surgical Facility - Encino, CA
The *Front Office Assistant*. Enter case data into Vision Scheduling Program. We own and operate six surgical facilities throughout the greater Los Angeles,...