Сильный konu wide clean dirty html. Образуйте сравнительную и превосходную степени сравнения прилагательных: long, thick, warm, cold, merry, high, weak, strong, heavy, dry, clean, dirty,wide, deep, sad, low, comfortable, cheap, serious

By now, most businesses understand the appeal of using big data analytics. With big data, companies can improve their efficiency, increase productivity, and gain valuable insights that drive their work forward. Few will deny the important role big data now plays in organizations all over the world, but gaining those unique benefits requires having high quality data, something that has become increasingly difficult to do. All too often, the data collected by businesses is filled with mistakes, errors, and incomplete values. This is referred to as dirty data, and it can represent a formidable obstacle to companies hoping to use that data to improve. Dirty data isn’t just a minor issue in the grand scheme of things, either. According to The Data Warehouse Institute (TDWI), dirty data ends up costing U.S. companies around $600 billion every year. To fully address this problem, businesses need to understand what causes dirty data and how best to fix it.

User Errors

Part of the key to using big data analytics most effectively is to have data that is accurate and complete. Unreliable data more often than not leads to businesses coming to the wrong conclusions. The problem is when user error creeps into data sets. One way organizations collect data on their customers is by having them fill out online forms. When filled out fully and correctly, this gives companies lots of information to parse and analyze. When customers leave holes in that data, however, or when they fill it out inaccurately by mistake or on purpose, businesses will find themselves at a severe disadvantage. This is of particular concern with sales and marketing teams who depend on accurate customer information to drive sales. In fact, a recent survey of marketers shows that more than half (60 percent) say the health of their data is unreliable.

Data Linking/Condensing

Other problems with dirty data arise when organizations attempt to link data across different sets. When the sets of data don’t have a unique identifier, linking them can create problems, often popping up in the form of repeated entries that weren’t combined due to minor errors. Or sometimes, data is combined when it shouldn’t be (like when customers with the same name have their information mixed together). These types of dirty data problems most often crop up when businesses employ multiple databases at the same time and try to combine them, or when they are using older technology that can’t keep up with current data demands. The same issues can appear when trying to condense more complex data sets into a more manageable form.

How to Clean Dirty Data

Once a company has identified what causes dirty data, they can go about trying to clean that data up. Such a task isn’t always easy, but once completed, it can be well worth the business’s time, resources, and effort. Data cleaning requires going through the data meticulously, noting where incorrect or absent values could be hurting data accuracy . Obviously, if the data sets are enormous, doing this manually becomes nearly impossible, but luckily, big data algorithms can actually help in cleaning up dirty data. These algorithms have been designed specifically to fix the most common cases of user and collection error. While they may not fix every single mistake or inaccuracy, they do greatly limit the number of errors, making dirty data much cleaner than before.

Preventing Dirty Data

Organizations can also take the proper preparations to prevent dirty data from ever becoming a big problem in the first place. By establishing a trusting relationship with customers (like not filling their emails with spam), people will be less willing to provide inaccurate or false information on any forms they fill out. Companies can also clean up data by updating their systems to ensure they can handle large amounts of data collection and analysis. Businesses with the right technology may even get into , which is like data cleaning but more thorough, involving processes like filtering, decoding, and translating.

Dirty data can pose significant problems to businesses trying to use big data. Much of the time, companies don’t realize they even have a problem until dirty data has become rampant. Taking the steps now to clean data and prevent the issue will go a long way toward helping organizations make the most of the data they collect. Only then will they see the true benefits that big data analytics has to offer.

образуйте сравнительную и превосходную степени сравнения прилагательных: long, thick, warm, cold, merry, high, weak, strong, heavy, dry, clean, dirty,wide, deep, sad, low, comfortable, cheap, serious.

Ответы:

1. Long – longer – the longest 2. Thick – thicker –the thickest 3. Warm – warmer – the warmest 4. Cold – colder – the coldest 5. Merry – merrier – the merriest 6. High-higher – the highest 7. Weak – weaker – the weakest 8. Strong – stronger – the strongest 9. Heavy – heavier –the heaviest 10. Dry- drier – the driest 11. Clean – cleaner – the cleanest 12. Dirty -dirtier –the dirtiest 13. Wide – wider –the widest 14. Deep –deeper – the deepest 15. Sad – sadder – the saddest 16. Low – lower –the lowest 17. Comfortable – more comfortable – the most comfortable 18. Cheap – cheaper – the cheapest 19. Serious – more serious –the most serious

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Vinyl blinds splattered with grease, such as in a kitchen, require a bit more cleaning than blinds that are simply dusty. Since the blinds are vinyl, they may be washed with a mild soap-and-water solution after vacuuming and dusting them to remove any grimy residue that remains.

Vacuum First

Vacuuming the blinds first helps remove cobwebs and debris that are clinging to the slats. An upholstery brush attachment helps loosen materials clinging to the blinds. Close the blinds in one direction, such as turning the lowest part of each blind to face you, then vacuum the blinds, including the framework, holding the blinds to the window. Open the blinds and close them so they face the other way, such as in a position that the highest part of each blind faces you, and vacuum them once again. The process is the same with vertical blinds, except the left or right side of the blinds faces in as you open or close them.

Dusting the Blinds

Vacuuming removes a lot of the loose, dry debris, but may not remove it all. Dust the blinds with a feather duster or wipe them with a microfiber dust mitt or a blind-cleaning wand to pick up even more dust. Dust the corners and edges of the window sill and frame as well; otherwise, their dust may end up on the blinds. Wipe down both sides of the blinds by opening them first one way, then the other, as when you were vacuuming.

Wet-Cleaning in Place

Dusting and vacuuming blinds doesn"t remove greasy buildup, but soapy water will. Add a squirt or two of mild dishwashing liquid to a bucket of warm water. Close the blinds so the slats face down toward you. Roll up old towels to catch water on the window sill and on the floor in front of the window; then dip a sponge into the soapy water and wipe each slat individually, starting at the top. Re-dip the sponge into the water every slat or two; then wring most of the water out of the sponge to avoid drips. Wipe the blinds again with a soft, slightly damp cloth afterwards. Close the blinds the other way and wipe them down again with the soapy water, followed by the damp cloth.

Bathtub Treatment for Dirty Blinds

The bathtub may be used to clean blinds that seem too grimy to clean in place. Raise the blinds as high as possible; then remove them from the holder keeping them in place. You may have to slide away a cover on the holder to allow the blinds to come out. Place them in the tub and add enough water to cover the blinds, along with a few squirts of dish soap and a gallon container of white vinegar to cut grease. Swish the blinds around, wiping any greasy areas you spot with a sponge, then drain the tub. Rinse them with a shower wand, or by dumping cups of water over them in the tub. Hold them up over the tub to allow most of the water to drip off; then hang them back up, extending them fully. Close them in one direction and dry them with a soft cloth; then close them the other way and dry the exposed side of the blinds. Wipe down the windowsill or floor as well if any water dripped from the blinds.

About the Author

Kathy Adams is an award-winning journalist and freelance writer who traveled the world handling numerous duties for music artists. She writes travel and budgeting tips and destination guides for USA Today, Travelocity and ForRent, among others. She enjoys exploring foreign locales and hiking off the beaten path stateside, snapping pics of wildlife and nature instead of selfies.

Doormats not only welcome guests to our home, they provide a place for those who are about to enter to wipe the dirt off their shoes. However, if you don"t clean your doormat regularly, you"ll probably end up tracking dirt inside anyway. Shaking and vacuuming the mat will remove most of the dirt, but to really get it clean, give it a disinfecting wash with soapy water. Synthetic mats with rubber backing stand up to a good scrub and often come out looking good as new.

Removing Dust

1

Shake the doormat out while standing outdoors. Beating the mat against the sidewalk can help dislodge ground-in dirt.

2

Vacuum the doormat to remove any dirt that didn"t come out from shaking. Any type of vacuum will work, from the shop-vac in the garage to a small handheld variety.

3

Shake and vacuum the mat weekly to help keep it clean and prevent stains from forming.

Hand Washing

1

Add 1 teaspoon of antibacterial liquid soap to a bucket of water if you have a synthetic mat. Swish the water to make suds.

2

Dip a nylon-bristled scrub brush into the soapy water and scrub the doormat in a circular motion. If the mat is particularly stained, allow the soap to sit for 10 minutes before rinsing. If working indoors, scrub the mat in the bathtub to prevent making a mess.

3

Rinse the doormat with a garden hose if working outdoors or the shower if working in the tub. Hang the doormat over a railing or clothesline to dry.

Things You Will Need

• Vacuum cleaner
• Antibacterial liquid soap
• Bucket
• Nylon-bristled scrub brush
• Garden hose (optional)
• Disinfectant spray (optional)

Tips

• Disinfect your doormat after vacuuming by spraying it with a disinfectant spray. Test the spray on the back of the mat if you"re unsure about colorfastness.
• Clean the floor on which the doormat was sitting to rid accumulated dirt and bacteria that was trapped below the mat.

About the Author

S.R. Becker is a certified yoga teacher based in Queens, N.Y. She has a Master of Fine Arts in creative writing and has worked as a writer and editor for more than 15 years. Becker often writes for "Yoga in Astoria," a newsletter about studios throughout New York City.