首页
外语
计算机
考研
公务员
职业资格
财经
工程
司法
医学
专升本
自考
实用职业技能
登录
外语
It’s Hard to Clean Big Data A)Karim Keshayjee, a Toronto physician and digital health consultant, crunches mountains of data fro
It’s Hard to Clean Big Data A)Karim Keshayjee, a Toronto physician and digital health consultant, crunches mountains of data fro
admin
2015-01-31
28
问题
It’s Hard to Clean Big Data
A)Karim Keshayjee, a Toronto physician and digital health consultant, crunches mountains of data from 500 doctors to figure out how to improve patient treatment. But it’ s a frustrating slog to get a computer to decipher all the misspellings, abbreviations, and notes written in unintelligible medical shorthand.
B)For example, "smoking information is very hard to parse," Keshayjee said. "If you read the records, you understand right away what the doctor meant. But good luck is trying to make a computer understand. There’s ’never smoked’ and ’smoking = 0.’ How many cigarettes does a patient smoke? That’ s impossible to figure out."
C)The hype around slicing and dicing massive amounts of data, or big data, makes it sound so easy: Just plug a library’ s worth of information into a computer and wait for valuable insights to pour out about how to speed up an auto assembly line, get online shoppers to buy more sneakers, or fight cancer. The reality is much more complicated. Data is inevitably "dirty" thanks to obsolete, inaccurate, and missing information. Cleaning it up is an increasingly important and overlooked job that can help prevent costly mistakes.
D)Although techniques are improving all the time, scrubbing data can only accomplish so much. Even when dealing with a relatively tidy set of information, getting useful results can be arduous and time-consuming. "I tell my clients that the world is messy and dirty," said Josh Sullivan, a vice president at business consulting firm Booz Allen who handles data crunching for clients. "There are no clean data sets."
E)Data analysts start by looking for information that’ s out of the norm. Because the volume of data is so huge, they typically hand the job over to software that automatically sifts through numbers and text to look for anything unusual that needs further review. Over time, computers can improve their accuracy in spotting what’ s belongs and what doesn’t. They can also better understand what words and phrases mean by clustering similar examples together and then grading their interpretations for accuracy. "The approach is easy and straightforward, but training your models can take weeks and weeks," Sullivan said.
F)A constellation of companies offer software and services for cleaning data. They range from technology giants like IBM IBM -0.24% and SAP SAP 0.12% to big data and analytics specialists like Cloudera and Talend Open Studio. A legion of start-ups is also trying to get a toehold as data janitors including Trifacta, Tamr, and Paxata.
G)Healthcare, with all its dirty data, is one of the toughest industries for big data technology. Electronic health records make medical information increasingly easy to dump into computers, but there’ s still a lot room for improvement before researchers, pharmaceutical companies and hospital business analysts can slice and dice all the information they want.
H)Keshavjee, the doctor and CEO of InfoClin, a health data consulting firm, spends his days trying to tease out ways to improve patient treatment by sifting through tens of thousands of electronic medical records. Obstacles pop up all the time.
I)Many doctors neglect to note a patient’ s blood pressure in their medical records, something that no amount of data cleaning can fix. Simply determining what ails patients—based on what’ s in their files—is surprisingly difficult for computers. Doctors may enter the proper code for diabetes without clearly indicating whether it’ s the patient who has the disease or a family member. Or they may just enter "insulin" without mentioning the underlying diagnosis because, to them, it’ s obvious.
J)Physicians also use a lot of idiosyncratic shorthand for medications, illnesses and basic patient details. Deciphering it takes a lot of head scratching for humans and is nearly impossible for a computer. For example, Keshavjee came across one doctor who used the abbreviation"gpa." Only after coming across a variation, "gma," did he finally solve the puzzle—they were shorthand for "grandpa" and "grandma."?"It took a while to figure that one out," he said.
K)Ultimately, Keshavjee said one of the only ways to solve the problem of dirty data in medical records is "data discipline." Doctors need to be trained to enter information correctly so that cleaning up after them is less of a chore. Incorporating something like Google’ s helpful tool that suggests how to spell words as users type them would be a great addition for electronic medical records, he said. Computers can learn to pick out spelling errors, but minimizing the need is a step in the right direction.
L)Another of Keshavjee’ s suggestions is to create medical records with more standardized fields. A computer would then know where to look for specific information, reducing the chance of error. Of course, doing so is not as easy as it sounds because many patients suffer from multiple illnesses, he said. A standard form would have to be flexible enough to take such complications into account.
M)Still, doctors would need to be able to jot down more free-form electronic notes that could never fit in a small box. Nuance like why a patient fell, for example, and not just the injury suffered, is critical for research. But software is hit and misses in understanding free-form writing without context. Humans searching by keyword may do a better job, but they still inevitably miss many relevant records.
N)Of course, in some cases, what appears to be dirty data, really isn’t. Sullivan, from Booz Allen, gave the example the time his team was analyzing demographic information about customers for a luxury hotel chain and came across data showing that teens from a wealthy Middle Eastern country were frequent guests. "There were a whole group of 17 year-olds staying at the properties worldwide,’ Sullivan said. "We thought, ’ That can’ t be true.’ "
O)But after some digging, they found that the information was, in fact, correct. The hotel had legions of young customers that it didn’t even realize were there, and had never done anything to market to them. All guests under 22 were automatically logged as "low-income" in the company’s computers. Hotel executives had never considered the possibility of teens with deep pockets.? "I think it’s harder to build models if you don’t have outliers," Sullivan said.
P)Even when data is clearly dirty, it can sometimes be put to good use. Take the example, again, of Google’ s spelling suggestion technology. It automatically recognizes misspelled words and offers alternative spellings. It’s only possible because Google GOOG -0.34% has collected millions and perhaps billions of misspelled queries over the years. Instead of garbage, the dirty data is an opportunity.
Q)Ultimately, humans, and not machines, draw conclusions from the data they crunch. Computers can sort through millions of documents, but they can’ t interpret the findings. Cleaning data is just one of step in a long trial and error process to get to that point. Big data, for all its hype about its ability to lift business profits and help humanity, is a big headache. "The idea of failure is completely different in data science," Sullivan said. "If they don’t fail 10 or 12 times a day to get to where they should be, they’re not doing it right."
Data analysts often draw support from software to find out the information different from the common.
选项
答案
E
解析
题干意为数据分析师往往借助电脑软件找出不同寻常的信息。根据题干中的 “Data analysts”可定位至E段前两句 “Data analysts start by looking forinformation that’s out of the norm.Because the volume of data is so huge,they typicallyhand the job over to software that automatically sifts through numbers and text to lookfor anything unusual that needs further review.”,题干提取了这两句的部分信息。
转载请注明原文地址:https://kaotiyun.com/show/axh7777K
0
大学英语六级
相关试题推荐
Choiceblindness:Youdon’tknowwhatyouwant[A]Wehaveallheardofexpertswhofailbasictestsofsensorydiscriminationin
High-performingandvalue-creatingcompanieshavelearnedhowtotietogethertheprinciplesofcustomerpreference,producerec
Thestudyofanimalsinparksandinthewildhasgivenadeeperknowledgeoftheirhabits:theresulthasbeenagreat【C1】_____
A、AfterhegotmarriedtoKaren.B、Afterthefamilymembersweretested.C、AfterKarenpersuadedhimtoseethedoctor.D、After
Overthepastdecade,manycompanieshadperfectedtheartofcreatingautomaticbehaviors—habits—amongconsumers.Thesehab
Overthepastdecade,manycompanieshadperfectedtheartofcreatingautomaticbehaviors—habits—amongconsumers.Thesehab
HIV&AIDS[A]AIDShasnowsurpassedtheBlackDeathonitscoursetobecometheworstpandemicinhumanhistory.Attheendof
HIV&AIDS[A]AIDShasnowsurpassedtheBlackDeathonitscoursetobecometheworstpandemicinhumanhistory.Attheendof
A、Menarestilltheleadersintheirfamily.B、Menandwomenarehavingmorethingsincommonnow.C、Therolesofmenandwomen
随机试题
甲将房屋出租给乙,租期为10年,并签订了合同,乙先支付了5年的租金。但由于乙出国留学,甲一直没有将房屋交给乙居住。后甲因融资需要向丙银行借款150万元,借期为1年,甲将其出租给乙的房屋抵押给丙银行并办理了抵押登记。后因甲到期无力还款,丙银行诉至法院,欲拍卖
A.宫颈上皮内瘤变Ⅰ级B.宫颈上皮内瘤变Ⅱ级C.宫颈上皮内瘤变Ⅲ级(重度不典型增生及局灶原位癌)D.宫颈癌Tb期E.宫颈细胞学为HSIL(高度上皮内瘤变)适于筋膜外子宫全切
避雷针保护范围的大小与它的高度有关。
根据《担保法》的规定,不得抵押的财产有()。
计提固定资产折旧时,下列科目可能涉及的有()。
在短期内,一个国家实现经济增长和充分就业的同时也就实现了物价的稳定。()[2005年真题]
下列有关信用政策的表述中,正确的有()。
人民警察的辞退,是指公安机关对已不具备人民警察条件,不适合在公安机关继续工作的人员,解除其与公安机关任用关系的一种人事行政管理措施。建立人民警察辞退制度的意义是()。
()是指在某一既定时期一个国家内生产的全部社会最终产品和劳务价值的总和,包括消费、投资、政府购买和净出口四个部分。
TheSeptember11terroristattacksontheUnitedStateshaveproducedserious______.
最新回复
(
0
)