【Native Speaker每日综合训练—38系列】【38-18】科技 Big Data

cherry6891

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Part I: Speaker

Big Data Project Susses Us out
What best helps you cope with stress? What happens to you when you die? If you could enhance the DNA of your unborn child, what would you improve?

For the past two months, a project called the “Human Face of Big Data” has been asking smart-phone users these and dozens of other questions.

The project’s goal is to aggregate millions of responses worldwide and see if they provide any insights into our beliefs, rituals and attitudes. More than three million smart-phone users across 100 countries have responded.

While not terribly surprising, some of the results released December 4 are interesting.

Men were more likely to want to boost their unborn child’s intelligence, but women thought the child’s health more important. Older respondents are more likely to relieve stress through work or prayer, while younger people de-stress through music and the arts or friends and family. People between 20 and 50 are more inclined to believe that death is the end of the line. But those under 20 and over 50 are most likely to believe in an afterlife in heaven or hell. One that probably includes their smart phones.

Source:
http://www.scientificamerican.com/podcast/episode/big-data-project-susses-us-out-12-12-06/

【Rephrase 1‘16’‘】

cherry6891

Part II: Speed

Capitalizing on the Power of Big Data for Healthcare

Time2
The increasing digitization of healthcare information is opening new possibilities for providers and payers to enhance the quality of care, improve healthcare outcomes, and reduce costs. Technology advances, regulatory mandates, and government incentives have accelerated the move from paper to digital health records. With information in digital form, healthcare organizations can use available tools and technologies to analyze that information and generate valuable insights.

Bringing together disparate data silos from within an organization can help increase the value of those analyses. The integration of electronic health records (EHRs), medical claims, videos, medical images, scanned documents, and physicians’ notes enables organizations to create a rich, 360-degree view of each patient. Incorporating external sources of data is also key. Integrating social, demographic, environmental, and behavioral information relating to patients allows organizations to discover new correlations that might otherwise have remained hidden.  

Creating a more holistic view of each patient and analyzing a wider array of information will help organizations meet the requirements of emerging healthcare models. For example, an increasing  number of providers are becoming part of Accountable Care Organizations (ACOs), moving toward fee-for-value payment models, and entering into reimbursement contracts through which they are paid for taking care of a whole person or a whole episode of care. These changes require organizations to better coordinate care among multiple providers so they can improve the efficiency of care.[226]

Time3
Healthcare organizations must also analyze internal and external patient information to more accurately measure risk and outcomes.

At the same time, many providers and payers are working to increase data transparency to produce new insights and facilitate research. Some established providers and payers are forming joint ventures, creating integrated delivery networks, and using Health Information Exchanges (HIEs) to share information. Some large pharmaceutical companies are de-identifying data from their  clinical trials, protecting patients’ privacy while making data available to qualified researchers outside the organization.  Legislation in some states has spurred the creation of the All-Payer Claims Database (APCD), which requires all the payers in the state to put claims data into a central repository that can be used to better understand costs, quality, and outcomes.

As all these changes suggest, the era of big data has arrived in healthcare. The volume, variety, and velocity of healthcare data are increasing;  organizations are collecting more data from a wider variety of sources at greater speed every day. Data sources range from the traditional (including  EHRs, medical images, and real-time data from monitoring devices) to  nontraditional (such as patient or plan member feedback from social media). To maximize the value of all this data, organizations must adopt new approaches and deploy solutions that can help deliver meaningful insights at the moment when they can have the greatest impact. Generating New Insights  from Big Data The new health information landscape gives organizations unprecedented opportunities—if they can successfully apply analytics to big data. In addition to improving health outcomes and reducing costs, payers and providers can use new insights to better market products and enhance the experience of patients and plan members. New insights can also help organizations more effectively  communicate with healthcare consumers and encourage healthier lifestyles.[294]

Time4
To realize these benefits, however, new approaches and technologies are required. Organizations need new analytics solutions and robust infrastructures that can handle the volume, variety, and velocity of big  data and generate results rapidly.

Healthcare organizations are collecting more data, and they intend to analyze data more comprehensively than before. Instead of extrapolating insights from a small sample of claims data, a payer might want to examine all two million rows of data available. Analyzing larger collections of data improves accuracy of results and can help users find unexpected patterns and insights.

Processing large data volumes requires hardware that can deliver outstanding performance. Fortunately, new generations of industry-standard multi-core processors can provide the performance required—often at a much lower cost than yesterday’s big, expensive proprietary systems.

Using industry-standard servers also helps organizations achieve cost-effective scalability. In the past,  organizations traditionally scaled up to accommodate growing data volumes by ripping and replacing one server with a single bigger server. This approach resulted in higher capital and operating costs. Today’s industry-standard servers allow scaling horizontally, so organizations can add systems using smaller, open platforms that are less costly to purchase and maintain

Adopting the right networking and storage solutions is also essential for managing large data volumes and delivering rapid results cost-effectively. Organizations need fast, high-throughput connectivity solutions to reduce data bottlenecks, plus storage solutions that can balance performance, capacity, and cost. Of course, to run queries, conduct  complex analyses, and rapidly generate new insights, organizations need high-performance analytics software. That software must be flexible and scalable enough to support ongoing analytic  endeavors, no matter how big the data or how complex the analysis needs become. Software that enables distributed processing options—including in-memory, in-database, and grid computing models—can help health organizations take advantage of the latest technology advances while providing the scalability for growth and the flexibility for change.[310]

Time5
Variety
To capitalize on the wide variety of data available, organizations need software solutions that can help them capture, integrate, and analyze unstructured data, such as the clinicians’ notes buried in electronic health records. Master data management solutions can help meet an organization’s requirements for integrating data from multiple sources and help ensure the data is reliable.

Healthcare organizations should look for comprehensive data management solutions that enable them to:
• Access critical data independent of systems and platforms
• Produce accurate, correct, and  consistent information from all sources
• Manage data governance initiatives  to conform with compliance and business policies
• Integrate data in a graphical  environment, orchestrate processing, and enable users and organizations to collaborate with other entities
• Centrally manage data from a single, easy-to-use graphical interface console Velocity  Analytics software and the infrastructure on which it runs must also be able to capture and analyze high-velocity data, and deliver timely results. Patient monitoring systems such as those used in ICUs generate critical data at a rapid pace. If organizations can produce insights from that data in real time or near-real time, they can provide those insights to patient care teams at the moments when interventions will have the greatest benefits

Meeting these needs requires moving from a batch processing model to near-real-time processing and reporting. Traditional, batch-oriented approaches are designed to process data on a nightly or weekly basis. To enable faster decision making, organizations need software that can capture data as it comes in and analyze it in close to real time. If healthcare providers can quickly explore high-velocity data, they can promptly identify potential problems and take immediate steps to address them. To help users quickly make sense of high-velocity data, organizations need analytics solutions that incorporate visualization capabilities. Visualization can help users identify patterns, make correlations among disparate data types, and explore data much more quickly and easily than when viewing a spreadsheet. Visual analytics tools that use familiar drag-and-drop functionality and enable access through mobile devices help bring analytics to a wide range of business users. Analytics no longer has to be relegated to back-office specialists.[349]

Time6
Getting Started
Analyzing big data holds tremendous promise for healthcare providers,  payers, and patients. But how should a healthcare organization get started with big data?

1 . Work with business units to  articulate opportunities: Capitalizing on big data opportunities requires an end-to-end strategy in which IT groups are the technical enablers but key executives, business groups, and other stakeholders help set objectives, identify critical success factors, and make relevant decisions. Together, these groups should consider existing problems that have been difficult to address, as well as problems that have never been addressed before because data sources are new or unstructured.

2 . Get up-to-speed on technology:   IT groups must solicit information  from peers and vendors to identify  the best software and hardware solutions for analyzing big data in a healthcare context.3 . Develop use cases: Defining and  developing use cases will help organizations focus on the right solutions and create the best  strategies. As part of this process,  IT groups should map out data flows, decide what data to include and what to leave out, determine how different pieces of information relate to one another, identify the business rules that apply to data, consider which use cases require real-time results and which do not, and define the analytical queries and algorithms required to generate the desired outputs.[217]

cherry6891

Part III: Obstacle

Looking for the Needle in a Stack of Needles:

Tracking Shadow Economic Activities in the Age of Big Data
By Manju Bansal on April 28, 2014 | Provided by SAP

The undocumented guys hanging out in the home-improvement-store parking lot looking for day labor, the neighborhood kids running a lemonade stand, and Al Qaeda terrorists plotting to do harm all have one thing in common: They operate in the underground economy, a shadowy zone where businesses, both legitimate and less so, transact in the currency of opportunity, away from traditional institutions and their watchful eyes.

One might think that this alternative economy is limited to markets that are low on the Transparency International rankings (such as sub-Saharan Africa and South Asia, for instance). However, a recent University of Wisconsin report estimates the value of the underground economy in the United States at about $2 trillion, about 15% of the total U.S. GDP. And a 2013 study coauthored by Friedrich Schneider, a noted authority on global shadow economies, estimated the European Union’s underground economy at more than 18% of GDP, or a whopping 2.1 trillion euros. More than two-thirds of the underground activity came from the most developed countries, including Germany, France, Italy, Spain, and the United Kingdom.

Underground economic activity is a multifaceted phenomenon, with implications across the board for national security, tax collections, public-sector services, and more. It includes the activity of any business that relies primarily on old-fashioned cash for most transactions — ranging from legitimate businesses (including lemonade stands) to drug cartels and organized crime.

Though it’s often soiled, heavy to lug around, and easy to lose to theft, cash is still king simply because it is so easy to hide from the authorities. With the help of the right bank or financial institution, “dirty” money can easily be laundered and come out looking fresh and clean, or at least legitimate. Case in point is the global bank HSBC, which agreed to pay U.S. regulators $1.9 billion in fines to settle charges of money laundering on behalf of Mexican drug cartels. According to a U.S. Senate subcommittee report, that process involved transferring $7 billion in cash from the bank’s branches in Mexico to those in the United States. Just for reference, each $100 bill weighs one gram, so to transfer $7 billion, HSBC had to physically transport 70 metric tons of cash across the U.S.-Mexican border.

The Financial Action Task Force, an intergovernmental body established in 1989, has estimated the total amount of money laundered worldwide to be around 2% to 5% of global GDP. Many of these transactions seem, at first glance, to be perfectly legitimate. Therein lies the conundrum for a banker or a government official: How do you identify, track, control, and, one hopes, prosecute money launderers, when they are hiding in plain sight and their business is couched in networked layers of perfectly defensible legitimacy?

Enter big-data tools, such as those provided by SynerScope, a Holland-based startup that is a member of the SAP Startup Focus program. This company’s solutions help unravel the complex networks hidden behind the layers of transactions and interactions.

Networks, good or bad, are near omnipresent in almost any form of organized human activity and particularly in banking and insurance. SynerScope takes data from both structured and unstructured data fields and transforms these into interactive computer visuals that display graphic patterns that humans can use to quickly make sense of information. Spotting of deviations in complex networked processes can easily be put to use in fraud detection for insurance, banking, e-commerce, and forensic accounting.

SynerScope’s approach to big-data business intelligence is centered on data-intense compute and visualization that extend the human “sense-making” capacity in much the same way that a telescope or microscope extends human vision.

To understand how SynerScope helps authorities track and halt money laundering, it’s important to understand how the networked laundering process works. It typically involves three stages.

1. In the initial, or placement, stage, launderers introduce their illegal profits into the financial system. This might be done by breaking up large amounts of cash into less-conspicuous smaller sums that are then deposited directly into a bank account, or by purchasing a series of monetary instruments (checks, money orders) that are then collected and deposited into accounts at other locations.

2. After the funds have entered the financial system, the launderer commences the second stage, called layering, which uses a series of conversions or transfers to distance the funds from their sources. The funds might be channeled through the purchase and sales of investment instruments, or the launderer might simply wire the funds through a series of accounts at various banks worldwide.

Such use of widely scattered accounts for laundering is especially prevalent in those jurisdictions that do not cooperate in anti-money-laundering investigations. Sometimes the launderer disguises the transfers as payments for goods or services.

3. Having successfully processed the criminal profits through the first two phases, the launderer then proceeds to the third stage, integration, in which the funds re-enter the legitimate economy. The launderer might invest the funds in real estate, luxury assets, or business ventures.

Current detection tools compare individual transactions against preset profiles and rules. Sophisticated criminals quickly learn how to make their illicit transactions look normal for such systems. As a result, rules and profiles need constant and costly updating.

But SynerScope’s flexible visual analysis uses a network angle to detect money laundering. It shows the structure of the entire network with data coming in from millions of transactions, a structure that launderers cannot control. With just a few mouse clicks, SynerScope’s relation and sequence views reveal structural interrelationships and interdependencies. When those patterns are mapped on a time scale, it becomes virtually impossible to hide abnormal flows.

SynerScope’s relation and sequence views reveal structural and temporal transaction patterns which make it virtually impossible to hide abnormal money flows.

An analysis in Foreign Policy magazine estimates the global shadow economy at about $10 trillion, making this “bazaar republic” the second-largest economy in the world, well ahead of China and just behind the United States. While it would be naïve to think that technology alone can tame this beast, Schneider’s research, included in a report published by consulting firm A.T.Kearney, indicates that increasing the proportion of electronic payments by 10 percent annually for four years would reduce the share of the shadow economy in a nation’s overall GDP by up to 5 percent.

Of course, until transactions become more transparent and enforcement becomes more strenuous, this will remain an uphill battle. The shadow economy has its own unique vocabulary that celebrates the victory of the small individual over the big machine. In Africa and the Caribbean, they call it “System D,” from the French word débrouillards, referring toparticularly effective and resourceful people; in India, they call it “jugaad,” meaning entrepreneurial ingenuity.

Ultimately, the shadow economy means all opportunity, all the time — without a shred of guilt or remorse. That’s why it is so critical to understand how this process works, and how the right policy initiatives and the right tools can help us ensure that “cash” doesn’t become a four-letter word.[1159]

source:
http://www.technologyreview.com/view/526961/looking-for-the-needle-in-a-stack-of-needles-tracking-shadow-economic-activities-in-the/

wnj2611339

thanks,  cherry ~

---

---Speaker
The project called “Human Face of Big Data” has been doing some surveys of smartphone users to collect some insights in terms of believe, rituals and attitudes.
Some of the results are interesting:        
Men wanted to boost the intelligence of their unborn child, while women wanted to improve the child's health. Regarding stress releasing, older respondents chose work or prays, while the younger respondents chose music and the arts or friends and family. People between 20 and 50 believed that death was the end of lives, while people out of the category believed after lives.

----Speed
[Time 2] 1'16''
The digitization of healthcare information helps improve the quality of care, produce better healthcare outcomes, and reduce costs.  Combining data silos within an organization can increase the value of analysis.
Moveover, creating a more holistic view of each patient and analyzing a wider array of information will help organizations meet the requirements of emerging healthcare models.
[Time 3] 0'35''
Healthcare organizations must consider both internal and external patient information to enhance the measurement of risk and outcomes.
Besides, many providers and payers are working to increase data transparency to produce new insights and facilitate research.
Overall, the era of big data has come in healthcare.
[Time 4] 0'56''
Organizations need new analytics solutions and robust infrastructures to better realize the above benefits. They are not only collecting more data, but also trying to analyze data more comprehensively than before. New generations of industry-standard multi-core processors can just provide such outstanding performance, and using industry-standard servers also assist in achieving cost-effective scalability.
It is also essential to adopt the right networking and storage solutions.
[Time 5] 0'32''
Organizations need software solutions to capitalize on the wide variety of data available.
Healthcare organizations should look for comprehensive data management solutions to meet various requirements, and these needs require organizations to move from a batch processing model to near-real-time processing and reporting.
[Time 6] 0'49''
Get started with big data:
1. Work with business units to articulate opportunities to find end-to-end technological strategy.
2. Get up-to-speed on technology to identify the best software and hardware solutions.
3. Develop use cases to focus on the right solutions and create the best strategies.

----Obstacles
[Paraphrase 7] 5’32’’
Underground economic activity is very common, and it is estimated that the total amount of money laundered worldwide occupy a certain percent of global GDP.
Big-data tools, such as those provided by SynerScope could help identify, track, control, and, one hopes, prosecute money launderers by unraveling the complex networks hidden behind the layers of transactions and interactions.
The three stages of the networked laundering process:  illegal profits introducing into the financial system, layering, and integration.
It is virtually impossible to hide abnormal money flows under  SynerScope’s flexible visual analysis.
Global shadow economy is at about $10 trillion.
It is crucial to understand how big-data tools help control the shadow economy.

疏离无罪

占~~~~~~~~

Speaker: A big investigation about 3 questions around the world.

01:15
The increasing digitization of healthcare information can enhance the quality of care and provide more convenient information.

01:13
Sharing patient information can help doing better research.The era of big data has arrived in healthcare.Companies and institution can do more thing with these data,also they need to decelop new tech.

01:30
Organizations need new analytics solutions and robust infrastructures that can handle huge volume of information to achieve its aimed benefits from big data era.

01:29
Organizations need to have a software solution that can deal with huge amount of data and can give out a quick analytics solutions.

00:54
3 things to start a big data healthcare:1 Work with business units to  articulate opportunities.2 Get up-to-speed on technology.3 Develop use case.

06:14
The shadow economy is really big in all the countries around the world.Underground economic activity is a multifaceted phenomenon,which is hard to limited and banned.Cash is the king in this economy,because it is easy to be hidden from the authority.The dirty money need to be laundered to be legal money.
To dectet this illegal process,we need to know the mechanism of laundering process.Then the author intriduce it.It's a kind of networked laundering process.Traditional method to detect illegal transaction is no longer useful.New tool called SynerScope which use big data to analysis this process can collect all the network information and the process to find ou the criminal.As far as we find out the structure of entire networ,the process can not be hidden any more.

vsetteacher

7/7
1:14, 1:39, 1:40，1:41, 1:10, 6:35
Jostling
Hull
Churn out
Spike and trough
Silo
Repository
Query
Relegate
Strenuous
Ravel, Unravel

醒醒Shine

[speaker]
investigation through cellphone: what want to change before you'ar unborn

[time2]
digital healthcare info---> enhance quality of care
   brining outer and inner info. about patient
   multiple providers and efficiency improve

[tim3]
   risk and outcoms measure
big data era of healthcare has come
   good effects, maximize value===>meaningful insights


[time4]
new approaches needed: right networking and storage solutions

[time5]
variety of data: software to manage data

[time6]
how to start with big data?
1- work with business units
2- get up-to-speed on technology

[obstacle]
1 brief intro. of shadow eco. big, multifaceted, cash
2 how to identify? SynerScope
    how the networked laundering process work
    SS: solve it by network angle to detect
3 but not enoough , uphill battle===> critical to understand process and right policy and tools

小蘑菇开始打怪

谢谢Cherry~~~
----------------

speaker:

scientists found up a search on people’s smart phone to collect data

by analyzing the data, scientists can know the notion, ideas and believes of different group of people with different age

time2:

use the data collected from different aspects, the hospital can provide a more rich healthcare to patients

the great outcome of the advanced tech

time3:

some improvement of the introduction of big data into healthcare

the privacy problem

time4:

the challenges of managing the big amount of data both to the methods and to the hard device

time5:

healthcare organization should look for comprehensive data management solutions

in order to achieve the management, what health care organization should do on the aspects of device

time6:

how to get started with big data

1, work with business units to articulate opportunities

2, get up-to-speed on technology

time7:

the shadow economy accounts for a big part of the economy

the new way of using big data analyzing to detect the illegal money

three steps os the networked laundering process

the importance of shadow eco

ffffionabear

感谢Cherry妹纸~~！！
————感谢！！！嘿~你的作业~不，是你的作业~(￣_,￣ ) ~~~#作业天天见~~#~~~进击的阅读小分队~~~\(^o^)/~——————————————————
[speaker]
The "human face of big data" project do the research on the hypothesis that we can control the gene of our offsprings.The results are interesting.Men are more care for babies' intelligence,while women more focus on health.And people between 20-50 believed death is the end of live,while others believe there is hell or heaven after life.
[speed]
1'38
The technology development,regulatory mandates and government incentives have accelerated the pace of health care records digitizing.That might not only benefit the patients by 360-degress monitoring their live and diseases,but also benefit the research of exploring diseases.
2'02
Healthcare organizations should also analyze the big data more accurately to measure the risk and outcomes.What's more,providers relatived are working hard to enhance data transparency and widen the data resources to support further research.
2'29
To utilize the big data,the healthcare organization need new technologies an d approches.For example,they need stanard serve to make the process more effective,and they also need right networking and storage solutions to accelerate the speed of data searching.
2'33
Healthcare organizations are also need software solutions which help them analyzing the big data instantly.It seems that analytics may be relegated to background specialists in the future.
1'08
To get started,the healthcare organizations should first,work with business units,asking them to set successful factors for them.And second,let IT groups spare no effort to create suitable hardware and software for healthcare system.
[obstacle]
6'57
main idea:There is a so-called "shadow economic" in the age of Big Data,those "dirty money",which came from drug cartels and organized crime ,are easily laundered by oversea bank system.But using the big-data tool SynerScope,the authorities might be easy to track and halt money laundering.
process of SynerScope running:
1.before entering the financial system: launderers might reorganizing the assets or dividing it into small pieces.
2.in the financial system: the launderers start layering and separating the assets worldwide.
3.intergration: making the fund re-enter the real economy
solution:The shadow economy is running without any remorse,it's another word of opportunity for launderers,so it's hard to crackdown.  

czgygz

Speaking:
results ofa project called the “Human Face of Big Data”

Time2 02:29
1.The increasing digitization of healthcare information is opening new possibilities for providers and payers to enhance

the quality of care, improve healthcare outcomes, and reduce costs.
2.Bringing together disparate data silos from within an organization can help increase the value of those analyses.
3.Creating a more holistic view of each patient and analyzing a wider array of information will help organizations meet

the requirements of emerging healthcare models.

Time3 01:41
1.internal&external infos are both needed
2.impriove of data transparancy
3.the big data era has come & what organizations must do to maximize the value of all shis data and improve health

outcomes and reduce costs.

Time4 02:21
new approoaches and tech are required
data are intended to be ananlyzed more comprehensively before
some harsdware is required
cost-effeective scalability can be improved from two aspects: industry-standard servers  and the right networking and

storage solutions

Time5 02:00
some software solutions are needed with some requirement for these softwares to match
a batch processing model--->near-real-time processing and reporting.(real time, visualisation)

Time6 01:45
how?1.work with business units 2.tech improvemence 3.develop use cases.

Time7 09:47
status quo of shadow economy and its impact
how money are launderied(3 stages)
feature of cash and its relation with the shadow enomoy