超级账本白皮书中文版
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AG=AMATEURGRAPH；业余系
MAG=AMATEURGRAPH Member。
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区块链技术白皮书分布式账本智能合约和去中心化应用开发
区块链技术白皮书分布式账本智能合约和去中心化应用开发1. 引言区块链技术随着比特币的发展而逐渐为人们所了解，这项技术具有分布式账本、智能合约和去中心化应用开发等特点，极大地改变了传统的中心化交易模式。
本白皮书旨在深入介绍区块链技术中的分布式账本、智能合约以及去中心化应用开发等关键概念和原理。
2. 分布式账本2.1 概述分布式账本是区块链技术的核心概念之一，它使用点对点网络，将交易记录以区块的形式链式连接起来，并经过加密和验证确认，从而实现交易的透明、可追溯和安全的特性。
2.2 工作原理分布式账本通过共识算法确保节点间的数据一致性，在区块链网络中，每个节点都保存了完整的账本副本，并使用加密算法对交易进行验证和记录。
一旦交易得到验证并被打包成区块，便会广播到所有节点中，同时进行共识验证，确保大部分节点认可并接受该区块，最后被添加进整个区块链中。
3. 智能合约3.1 定义智能合约是基于区块链技术的可编程合约，它能够自动执行、验证和执行合约的交易，并在特定条件满足时自动触发相应的操作。
智能合约主要由代码和数据组成，可以实现去中心化的合约执行。
3.2 实现原理智能合约使用区块链的分布式账本作为存储和执行环境，以及节点的计算能力作为合约执行的基础。
通过使用一种特定的编程语言和编译器，将合约代码转化为字节码，并通过区块链网络进行部署和执行。
当满足合约条件时，智能合约可以自动触发事务的执行。
4. 去中心化应用开发4.1 概述去中心化应用（DApp）是一种基于区块链技术的应用程序，它不依赖于中心化的服务器，而是通过区块链网络中的节点来实现数据存储和交互。
DApp具有去中心化、透明、安全和可靠等特点。
4.2 开发框架为了实现去中心化应用，需要使用特定的开发框架。
目前比较流行的DApp开发框架包括以太坊、EOS等。
这些框架提供了一系列的API 和工具，用于开发智能合约和基于区块链的应用程序。
4.3 开发流程去中心化应用的开发流程包括需求分析、智能合约编写、前端界面设计和测试等步骤。
中国与非洲的经贸合作白皮书(汉英对照版)
中国与非洲的经贸合作前言中国是世界上最大的发展中国家，非洲是发展中国家最集中的大陆，中国和非洲的人口占世界人口三分之一以上。
发展经济和推动社会进步是中国与非洲共同面临的任务。
多年来，在发展过程中，中国与非洲充分发挥双方资源条件和经济结构等方面的互补性，按照平等相待、讲求实效、互惠互利、共同发展的原则，不断加强经贸合作，努力实现互利共赢。
实践证明，中非经贸合作符合双方共同利益，有助于非洲实现联合国千年发展目标，促进了中非共同繁荣和进步。
２０世纪５０年代，中非经贸合作以贸易和对非援助为主。
在双方共同努力下，合作领域不断拓宽，合作内容日益丰富。
特别是２０００年中非合作论坛成立后，双方经贸合作进一步加强和活跃，贸易、投资、基础设施、能力建设全面推进，金融、旅游等领域的合作逐步拓展，形成了多层次、宽领域的格局，处在新的历史起点上。
中非经贸合作是南南合作的重要组成部分，为南南合作注入新的活力，提升了发展中国家在国际政治经济格局中的地位，为推动建立公正合理的国际政治经济新秩序发挥着重要作用。
中国也愿与其他国家和国际组织一道，加强与非洲国家的磋商与协调，共同参与非洲建设，共同推动非洲的和平、发展与进步一、促进贸易平衡发展贸易是中非经贸合作最初的形式。
伴随着中非关系的发展和交往的增多，中非贸易规模日益扩大。
１９５０年，中非双边贸易额仅为１２１４万美元，１９６０年达到１亿美元，１９８０年超过１０亿美元。
２０００年迈上百亿美元台阶后，中非贸易呈现快速增长势头。
２００８年突破了１０００亿美元，其中中国对非洲出口５０８亿美元，自非洲进口５６０亿美元。
２０００年至２００８年，中非贸易年均增长率高达３３.５％，占中国对外贸易总额的比重由２.２％升至４.２％，占非洲对外贸易总额的比重由３.８％升至１０.４％。
２００９年，虽然受国际金融危机影响，中非贸易额下降到９１０.７亿美元，但中国在当年首次成为非洲第一大贸易伙伴国。
随着世界经济复苏，中非贸易呈现良好的恢复发展态势。
大数据标准化白皮书
伯克利云计算白皮书(英文全)
Above the Clouds: A Berkeley View of CloudComputingMichael ArmbrustArmando FoxRean GriffithAnthony D. JosephRandy H. KatzAndrew KonwinskiGunho LeeDavid A. PattersonAriel RabkinIon StoicaMatei ZahariaElectrical Engineering and Computer SciencesUniversity of California at BerkeleyTechnical Report No. UCB/EECS-2009-28/Pubs/TechRpts/2009/EECS-2009-28.htmlFebruary 10, 2009Copyright 2009, by the author(s).All rights reserved.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission.AcknowledgementThe RAD Lab's existence is due to the generous support of the founding members Google, Microsoft, and Sun Microsystems and of the affiliate members Amazon Web Services, Cisco Systems, Facebook, Hewlett-Packard, IBM, NEC, Network Appliance, Oracle, Siemens, and VMware; by matching funds from the State of California's MICRO program (grants 06-152, 07-010, 06-148, 07-012, 06-146, 07-009, 06-147, 07-013, 06-149, 06-150, and 07-008) and the University of California Industry/University Cooperative Research Program (UC Discovery) grant COM07-10240; and by the National Science Foundation (grant #CNS-0509559).Above the Clouds:A Berkeley View of Cloud ComputingMichael Armbrust,Armando Fox,Rean Griffith,Anthony D.Joseph,Randy Katz, Andy Konwinski,Gunho Lee,David Patterson,Ariel Rabkin,Ion Stoica,and Matei Zaharia (Comments should be addressed to abovetheclouds@)UC Berkeley Reliable Adaptive Distributed Systems Laboratory∗/February10,2009KEYWORDS:Cloud Computing,Utility Computing,Internet Datacenters,Distributed System Economics1Executive SummaryCloud Computing,the long-held dream of computing as a utility,has the potential to transform a large part of the IT industry,making software even more attractive as a service and shaping the way IT hardware is designed and purchased.Developers with innovative ideas for new Internet services no longer require the large capital outlays in hardware to deploy their service or the human expense to operate it.They need not be concerned about over-provisioning for a service whose popularity does not meet their predictions,thus wasting costly resources,or under-provisioning for one that becomes wildly popular,thus missing potential customers and revenue.Moreover,companies with large batch-oriented tasks can get results as quickly as their programs can scale,since using1000servers for one hour costs no more than using one server for1000hours.This elasticity of resources,without paying a premium for large scale,is unprecedented in the history of IT.Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services.The services themselves have long been referred to as Software as a Service(SaaS).The datacenter hardware and software is what we will call a Cloud.When a Cloud is made available in a pay-as-you-go manner to the general public,we call it a Public Cloud;the service being sold is Utility Computing.We use the term Private Cloud to refer to internal datacenters of a business or other organization, not made available to the general public.Thus,Cloud Computing is the sum of SaaS and Utility Computing,but does not include Private Clouds.People can be users or providers of SaaS,or users or providers of Utility Computing.We focus on SaaS Providers(Cloud Users)and Cloud Providers,which have received less attention than SaaS Users.From a hardware point of view,three aspects are new in Cloud Computing.1.The illusion of infinite computing resources available on demand,thereby eliminating the need for Cloud Com-puting users to plan far ahead for provisioning.2.The elimination of an up-front commitment by Cloud users,thereby allowing companies to start small andincrease hardware resources only when there is an increase in their needs.3.The ability to pay for use of computing resources on a short-term basis as needed(e.g.,processors by the hourand storage by the day)and release them as needed,thereby rewarding conservation by letting machines and storage go when they are no longer useful.We argue that the construction and operation of extremely large-scale,commodity-computer datacenters at low-cost locations was the key necessary enabler of Cloud Computing,for they uncovered the factors of5to7decrease in cost of electricity,network bandwidth,operations,software,and hardware available at these very large economies ∗The RAD Lab’s existence is due to the generous support of the founding members Google,Microsoft,and Sun Microsystems and of the affiliate members Amazon Web Services,Cisco Systems,Facebook,Hewlett-Packard,IBM,NEC,Network Appliance,Oracle,Siemens,and VMware;by matching funds from the State of California’s MICRO program(grants06-152,07-010,06-148,07-012,06-146,07-009,06-147,07-013,06-149, 06-150,and07-008)and the University of California Industry/University Cooperative Research Program(UC Discovery)grant COM07-10240;and by the National Science Foundation(grant#CNS-0509559).of scale.These factors,combined with statistical multiplexing to increase utilization compared a private cloud,meant that cloud computing could offer services below the costs of a medium-sized datacenter and yet still make a good profit.Any application needs a model of computation,a model of storage,and a model of communication.The statistical multiplexing necessary to achieve elasticity and the illusion of infinite capacity requires each of these resources to be virtualized to hide the implementation of how they are multiplexed and shared.Our view is that different utility computing offerings will be distinguished based on the level of abstraction presented to the programmer and the level of management of the resources.Amazon EC2is at one end of the spectrum.An EC2instance looks much like physical hardware,and users can control nearly the entire software stack,from the kernel upwards.This low level makes it inherently difficult for Amazon to offer automatic scalability and failover,because the semantics associated with replication and other state management issues are highly application-dependent.At the other extreme of the spectrum are application domain-specific platforms such as Google AppEngine.AppEngine is targeted exclusively at traditional web applications, enforcing an application structure of clean separation between a stateless computation tier and a stateful storage tier. AppEngine’s impressive automatic scaling and high-availability mechanisms,and the proprietary MegaStore data storage available to AppEngine applications,all rely on these constraints.Applications for Microsoft’s Azure are written using libraries,and compiled to the Common Language Runtime,a language-independent managed environment.Thus,Azure is intermediate between application frameworks like AppEngine and hardware virtual machines like EC2.When is Utility Computing preferable to running a Private Cloud?Afirst case is when demand for a service varies with time.Provisioning a data center for the peak load it must sustain a few days per month leads to underutilization at other times,for example.Instead,Cloud Computing lets an organization pay by the hour for computing resources, potentially leading to cost savings even if the hourly rate to rent a machine from a cloud provider is higher than the rate to own one.A second case is when demand is unknown in advance.For example,a web startup will need to support a spike in demand when it becomes popular,followed potentially by a reduction once some of the visitors turn away.Finally,organizations that perform batch analytics can use the”cost associativity”of cloud computing tofinish computations faster:using1000EC2machines for1hour costs the same as using1machine for1000hours.For the first case of a web business with varying demand over time and revenue proportional to user hours,we have captured the tradeoff in the equation below.UserHours cloud×(revenue−Cost cloud)≥UserHours datacenter×(revenue−Cost datacenter Utilization)(1)The left-hand side multiplies the net revenue per user-hour by the number of user-hours,giving the expected profit from using Cloud Computing.The right-hand side performs the same calculation for afixed-capacity datacenter by factoring in the average utilization,including nonpeak workloads,of the datacenter.Whichever side is greater represents the opportunity for higher profit.Table1below previews our ranked list of critical obstacles to growth of Cloud Computing in Section7.Thefirst three concern adoption,the nextfive affect growth,and the last two are policy and business obstacles.Each obstacle is paired with an opportunity,ranging from product development to research projects,which can overcome that obstacle.We predict Cloud Computing will grow,so developers should take it into account.All levels should aim at hori-zontal scalability of virtual machines over the efficiency on a single VM.In addition1.Applications Software needs to both scale down rapidly as well as scale up,which is a new requirement.Suchsoftware also needs a pay-for-use licensing model to match needs of Cloud Computing.2.Infrastructure Software needs to be aware that it is no longer running on bare metal but on VMs.Moreover,itneeds to have billing built in from the beginning.3.Hardware Systems should be designed at the scale of a container(at least a dozen racks),which will be isthe minimum purchase size.Cost of operation will match performance and cost of purchase in importance, rewarding energy proportionality such as by putting idle portions of the memory,disk,and network into low power mode.Processors should work well with VMs,flash memory should be added to the memory hierarchy, and LAN switches and W AN routers must improve in bandwidth and cost.2Cloud Computing:An Old Idea Whose Time Has(Finally)ComeCloud Computing is a new term for a long-held dream of computing as a utility[35],which has recently emerged as a commercial reality.Cloud Computing is likely to have the same impact on software that foundries have had on theTable1:Quick Preview of Top10Obstacles to and Opportunities for Growth of Cloud Computing.Obstacle Opportunity1Availability of Service Use Multiple Cloud Providers;Use Elasticity to Prevent DDOS2Data Lock-In Standardize APIs;Compatible SW to enable Surge Computing3Data Confidentiality and Auditability Deploy Encryption,VLANs,Firewalls;Geographical Data Storage4Data Transfer Bottlenecks FedExing Disks;Data Backup/Archival;Higher BW Switches5Performance Unpredictability Improved VM Support;Flash Memory;Gang Schedule VMs6Scalable Storage Invent Scalable Store7Bugs in Large Distributed Systems Invent Debugger that relies on Distributed VMs8Scaling Quickly Invent Auto-Scaler that relies on ML;Snapshots for Conservation9Reputation Fate Sharing Offer reputation-guarding services like those for email10Software Licensing Pay-for-use licenses;Bulk use saleshardware industry.At one time,leading hardware companies required a captive semiconductor fabrication facility, and companies had to be large enough to afford to build and operate it economically.However,processing equipment doubled in price every technology generation.A semiconductor fabrication line costs over$3B today,so only a handful of major“merchant”companies with very high chip volumes,such as Intel and Samsung,can still justify owning and operating their own fabrication lines.This motivated the rise of semiconductor foundries that build chips for others, such as Taiwan Semiconductor Manufacturing Company(TSMC).Foundries enable“fab-less”semiconductor chip companies whose value is in innovative chip design:A company such as nVidia can now be successful in the chip business without the capital,operational expenses,and risks associated with owning a state-of-the-art fabrication line.Conversely,companies with fabrication lines can time-multiplex their use among the products of many fab-less companies,to lower the risk of not having enough successful products to amortize operational costs.Similarly,the advantages of the economy of scale and statistical multiplexing may ultimately lead to a handful of Cloud Computing providers who can amortize the cost of their large datacenters over the products of many“datacenter-less”companies.Cloud Computing has been talked about[10],blogged about[13,25],written about[15,37,38]and been featured in the title of workshops,conferences,and even magazines.Nevertheless,confusion remains about exactly what it is and when it’s useful,causing Oracle’s CEO to vent his frustration:The interesting thing about Cloud Computing is that we’ve redefined Cloud Computing to include ev-erything that we already do....I don’t understand what we would do differently in the light of CloudComputing other than change the wording of some of our ads.Larry Ellison,quoted in the Wall Street Journal,September26,2008 These remarks are echoed more mildly by Hewlett-Packard’s Vice President of European Software Sales:A lot of people are jumping on the[cloud]bandwagon,but I have not heard two people say the same thingabout it.There are multiple definitions out there of“the cloud.”Andy Isherwood,quoted in ZDnet News,December11,2008 Richard Stallman,known for his advocacy of“free software”,thinks Cloud Computing is a trap for users—if applications and data are managed“in the cloud”,users might become dependent on proprietary systems whose costs will escalate or whose terms of service might be changed unilaterally and adversely:It’s stupidity.It’s worse than stupidity:it’s a marketing hype campaign.Somebody is saying this isinevitable—and whenever you hear somebody saying that,it’s very likely to be a set of businessescampaigning to make it true.Richard Stallman,quoted in The Guardian,September29,2008 Our goal in this paper to clarify terms,provide simple formulas to quantify comparisons between of cloud and conventional Computing,and identify the top technical and non-technical obstacles and opportunities of Cloud Com-puting.Our view is shaped in part by working since2005in the UC Berkeley RAD Lab and in part as users of Amazon Web Services since January2008in conducting our research and our teaching.The RAD Lab’s research agenda is to invent technology that leverages machine learning to help automate the operation of datacenters for scalable Internet services.We spent six months brainstorming about Cloud Computing,leading to this paper that tries to answer the following questions:•What is Cloud Computing,and how is it different from previous paradigm shifts such as Software as a Service (SaaS)?•Why is Cloud Computing poised to take off now,whereas previous attempts have foundered?•What does it take to become a Cloud Computing provider,and why would a company consider becoming one?•What new opportunities are either enabled by or potential drivers of Cloud Computing?•How might we classify current Cloud Computing offerings across a spectrum,and how do the technical and business challenges differ depending on where in the spectrum a particular offering lies?•What,if any,are the new economic models enabled by Cloud Computing,and how can a service operator decide whether to move to the cloud or stay in a private datacenter?•What are the top10obstacles to the success of Cloud Computing—and the corresponding top10opportunities available for overcoming the obstacles?•What changes should be made to the design of future applications software,infrastructure software,and hard-ware to match the needs and opportunities of Cloud Computing?3What is Cloud Computing?Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services.The services themselves have long been referred to as Software as a Service(SaaS),so we use that term.The datacenter hardware and software is what we will call a Cloud.When a Cloud is made available in a pay-as-you-go manner to the public,we call it a Public Cloud;the service being sold is Utility Computing.Current examples of public Utility Computing include Amazon Web Services,Google AppEngine,and Microsoft Azure.We use the term Private Cloud to refer to internal datacenters of a business or other organization that are not made available to the public.Thus,Cloud Computing is the sum of SaaS and Utility Computing,but does not normally include Private Clouds.We’ll generally use Cloud Computing,replacing it with one of the other terms only when clarity demands it.Figure1shows the roles of the people as users or providers of these layers of Cloud Computing,and we’ll use those terms to help make our arguments clear.The advantages of SaaS to both end users and service providers are well understood.Service providers enjoy greatly simplified software installation and maintenance and centralized control over versioning;end users can access the service“anytime,anywhere”,share data and collaborate more easily,and keep their data stored safely in the infrastructure.Cloud Computing does not change these arguments,but it does give more application providers the choice of deploying their product as SaaS without provisioning a datacenter:just as the emergence of semiconductor foundries gave chip companies the opportunity to design and sell chips without owning a fab,Cloud Computing allows deploying SaaS—and scaling on demand—without building or provisioning a datacenter.Analogously to how SaaS allows the user to offload some problems to the SaaS provider,the SaaS provider can now offload some of his problems to the Cloud Computing provider.From now on,we will focus on issues related to the potential SaaS Provider(Cloud User)and to the Cloud Providers,which have received less attention.We will eschew terminology such as“X as a service(XaaS)”;values of X we have seen in print include Infrastruc-ture,Hardware,and Platform,but we were unable to agree even among ourselves what the precise differences among them might be.1(We are using Endnotes instead of footnotes.Go to page20at the end of paper to read the notes, which have more details.)Instead,we present a simple classification of Utility Computing services in Section5that focuses on the tradeoffs among programmer convenience,flexibility,and portability,from both the cloud provider’s and the cloud user’s point of view.From a hardware point of view,three aspects are new in Cloud Computing[42]:1.The illusion of infinite computing resources available on demand,thereby eliminating the need for Cloud Com-puting users to plan far ahead for provisioning;2.The elimination of an up-front commitment by Cloud users,thereby allowing companies to start small andincrease hardware resources only when there is an increase in their needs;and3.The ability to pay for use of computing resources on a short-term basis as needed(e.g.,processors by the hourand storage by the day)and release them as needed,thereby rewarding conservation by letting machines and storage go when they are no longer useful.Figure1:Users and Providers of Cloud Computing.The benefits of SaaS to both SaaS users and SaaS providers are well documented,so we focus on Cloud Computing’s effects on Cloud Providers and SaaS Providers/Cloud users.The top level can be recursive,in that SaaS providers can also be a SaaS users.For example,a mashup provider of rental maps might be a user of the Craigslist and Google maps services.We will argue that all three are important to the technical and economic changes made possible by Cloud Com-puting.Indeed,past efforts at utility computing failed,and we note that in each case one or two of these three critical characteristics were missing.For example,Intel Computing Services in2000-2001required negotiating a contract and longer-term use than per hour.As a successful example,Elastic Compute Cloud(EC2)from Amazon Web Services(AWS)sells1.0-GHz x86 ISA“slices”for10cents per hour,and a new“slice”,or instance,can be added in2to5minutes.Amazon’s Scalable Storage Service(S3)charges$0.12to$0.15per gigabyte-month,with additional bandwidth charges of$0.10to$0.15 per gigabyte to move data in to and out of AWS over the Internet.Amazon’s bet is that by statistically multiplexing multiple instances onto a single physical box,that box can be simultaneously rented to many customers who will not in general interfere with each others’usage(see Section7).While the attraction to Cloud Computing users(SaaS providers)is clear,who would become a Cloud Computing provider,and why?To begin with,realizing the economies of scale afforded by statistical multiplexing and bulk purchasing requires the construction of extremely large datacenters.Building,provisioning,and launching such a facility is a hundred-million-dollar undertaking.However,because of the phenomenal growth of Web services through the early2000’s,many large Internet companies,including Amazon, eBay,Google,Microsoft and others,were already doing so.Equally important,these companies also had to develop scalable software infrastructure(such as MapReduce,the Google File System,BigTable,and Dynamo[16,20,14,17]) and the operational expertise to armor their datacenters against potential physical and electronic attacks.Therefore,a necessary but not sufficient condition for a company to become a Cloud Computing provider is that it must have existing investments not only in very large datacenters,but also in large-scale software infrastructure and operational expertise required to run them.Given these conditions,a variety of factors might influence these companies to become Cloud Computing providers:1.Make a lot of money.Although10cents per server-hour seems low,Table2summarizes James Hamilton’sestimates[23]that very large datacenters(tens of thousands of computers)can purchase hardware,network bandwidth,and power for1/5to1/7the prices offered to a medium-sized(hundreds or thousands of computers) datacenter.Further,thefixed costs of software development and deployment can be amortized over many more machines.Others estimate the price advantage as a factor of3to5[37,10].Thus,a sufficiently large company could leverage these economies of scale to offer a service well below the costs of a medium-sized company and still make a tidy profit.2.Leverage existing investment.Adding Cloud Computing services on top of existing infrastructure provides anew revenue stream at(ideally)low incremental cost,helping to amortize the large investments of datacenters.Indeed,according to Werner V ogels,Amazon’s CTO,many Amazon Web Services technologies were initially developed for Amazon’s internal operations[42].3.Defend a franchise.As conventional server and enterprise applications embrace Cloud Computing,vendorswith an established franchise in those applications would be motivated to provide a cloud option of their own.For example,Microsoft Azure provides an immediate path for migrating existing customers of Microsoft enter-prise applications to a cloud environment.Table2:Economies of scale in2006for medium-sized datacenter(≈1000servers)vs.very large datacenter(≈50,000 servers).[24]Technology Cost in Medium-sized DC Cost in Very Large DC RatioNetwork$95per Mbit/sec/month$13per Mbit/sec/month7.1Storage$2.20per GByte/month$0.40per GByte/month 5.7Administration≈140Servers/Administrator>1000Servers/Administrator7.1Table3:Price of kilowatt-hours of electricity by region[7].Price per KWH Where Possible Reasons Why3.6¢Idaho Hydroelectric power;not sent long distance10.0¢California Electricity transmitted long distance over the grid;limited transmission lines in Bay Area;no coalfired electricity allowed in California.18.0¢Hawaii Must ship fuel to generate electricity4.Attack an incumbent.A company with the requisite datacenter and software resources might want to establish abeachhead in this space before a single“800pound gorilla”emerges.Google AppEngine provides an alternative path to cloud deployment whose appeal lies in its automation of many of the scalability and load balancing features that developers might otherwise have to build for themselves.5.Leverage customer relationships.IT service organizations such as IBM Global Services have extensive cus-tomer relationships through their service offerings.Providing a branded Cloud Computing offering gives those customers an anxiety-free migration path that preserves both parties’investments in the customer relationship.6.Become a platform.Facebook’s initiative to enable plug-in applications is a greatfit for cloud computing,aswe will see,and indeed one infrastructure provider for Facebook plug-in applications is Joyent,a cloud provider.Yet Facebook’s motivation was to make their social-networking application a new development platform.Several Cloud Computing(and conventional computing)datacenters are being built in seemingly surprising loca-tions,such as Quincy,Washington(Google,Microsoft,Yahoo!,and others)and San Antonio,Texas(Microsoft,US National Security Agency,others).The motivation behind choosing these locales is that the costs for electricity,cool-ing,labor,property purchase costs,and taxes are geographically variable,and of these costs,electricity and cooling alone can account for a third of the costs of the datacenter.Table3shows the cost of electricity in different locales[10]. Physics tells us it’s easier to ship photons than electrons;that is,it’s cheaper to ship data overfiber optic cables than to ship electricity over high-voltage transmission lines.4Clouds in a Perfect Storm:Why Now,Not Then?Although we argue that the construction and operation of extremely large scale commodity-computer datacenters was the key necessary enabler of Cloud Computing,additional technology trends and new business models also played a key role in making it a reality this time around.Once Cloud Computing was“off the ground,”new application opportunities and usage models were discovered that would not have made sense previously.4.1New Technology Trends and Business ModelsAccompanying the emergence of Web2.0was a shift from“high-touch,high-margin,high-commitment”provisioning of service“low-touch,low-margin,low-commitment”self-service.For example,in Web1.0,accepting credit card payments from strangers required a contractual arrangement with a payment processing service such as VeriSign or ;the arrangement was part of a larger business relationship,making it onerous for an individual or a very small business to accept credit cards online.With the emergence of PayPal,however,any individual can accept credit card payments with no contract,no long-term commitment,and only modest pay-as-you-go transaction fees.The level of“touch”(customer support and relationship management)provided by these services is minimal to nonexistent,butthe fact that the services are now within reach of individuals seems to make this less important.Similarly,individuals’Web pages can now use Google AdSense to realize revenue from ads,rather than setting up a relationship with an ad placement company,such DoubleClick(now acquired by Google).Those ads can provide the business model for Wed2.0apps as well.Individuals can distribute Web content using Amazon CloudFront rather than establishing a relationship with a content distribution network such as Akamai.Amazon Web Services capitalized on this insight in2006by providing pay-as-you-go computing with no contract: all customers need is a credit card.A second innovation was selling hardware-level virtual machines cycles,allowing customers to choose their own software stack without disrupting each other while sharing the same hardware and thereby lowering costs further.4.2New Application OpportunitiesWhile we have yet to see fundamentally new types of applications enabled by Cloud Computing,we believe that several important classes of existing applications will become even more compelling with Cloud Computing and contribute further to its momentum.When Jim Gray examined technological trends in2003[21],he concluded that economic necessity mandates putting the data near the application,since the cost of wide-area networking has fallen more slowly(and remains relatively higher)than all other IT hardware costs.Although hardware costs have changed since Gray’s analysis,his idea of this“breakeven point”has not.Although we defer a more thorough discussion of Cloud Computing economics to Section6,we use Gray’s insight in examining what kinds of applications represent particularly good opportunities and drivers for Cloud Computing.Mobile interactive applications.Tim O’Reilly believes that“the future belongs to services that respond in real time to information provided either by their users or by nonhuman sensors.”[38]Such services will be attracted to the cloud not only because they must be highly available,but also because these services generally rely on large data sets that are most conveniently hosted in large datacenters.This is especially the case for services that combine two or more data sources or other services,e.g.,mashups.While not all mobile devices enjoy connectivity to the cloud100% of the time,the challenge of disconnected operation has been addressed successfully in specific application domains, 2so we do not see this as a significant obstacle to the appeal of mobile applications.Parallel batch processing.Although thus far we have concentrated on using Cloud Computing for interactive SaaS,Cloud Computing presents a unique opportunity for batch-processing and analytics jobs that analyze terabytes of data and can take hours tofinish.If there is enough data parallelism in the application,users can take advantage of the cloud’s new“cost associativity”:using hundreds of computers for a short time costs the same as using a few computers for a long time.For example,Peter Harkins,a Senior Engineer at The Washington Post,used200EC2 instances(1,407server hours)to convert17,481pages of Hillary Clinton’s travel documents into a form more friendly to use on the WWW within nine hours after they were released[3].Programming abstractions such as Google’s MapReduce[16]and its open-source counterpart Hadoop[11]allow programmers to express such tasks while hiding the operational complexity of choreographing parallel execution across hundreds of Cloud Computing servers.Indeed, Cloudera[1]is pursuing commercial opportunities in this space.Again,using Gray’s insight,the cost/benefit analysis must weigh the cost of moving large datasets into the cloud against the benefit of potential speedup in the data analysis. When we return to economic models later,we speculate that part of Amazon’s motivation to host large public datasets for free[8]may be to mitigate the cost side of this analysis and thereby attract users to purchase Cloud Computing cycles near this data.The rise of analytics.A special case of compute-intensive batch processing is business analytics.While the large database industry was originally dominated by transaction processing,that demand is leveling off.A growing share of computing resources is now spent on understanding customers,supply chains,buying habits,ranking,and so on. Hence,while online transaction volumes will continue to grow slowly,decision support is growing rapidly,shifting the resource balance in database processing from transactions to business analytics.Extension of compute-intensive desktop applications.The latest versions of the mathematics software packages Matlab and Mathematica are capable of using Cloud Computing to perform expensive evaluations.Other desktop applications might similarly benet from seamless extension into the cloud.Again,a reasonable test is comparing the cost of computing in the Cloud plus the cost of moving data in and out of the Cloud to the time savings from using the Cloud.Symbolic mathematics involves a great deal of computing per unit of data,making it a domain worth investigating.An interesting alternative model might be to keep the data in the cloud and rely on having sufficient bandwidth to enable suitable visualization and a responsive GUI back to the human user.Offline image rendering or3D animation might be a similar example:given a compact description of the objects in a3D scene and the characteristics of the lighting sources,rendering the image is an embarrassingly parallel task with a high computation-to-bytes ratio.“Earthbound”applications.Some applications that would otherwise be good candidates for the cloud’s elasticity and parallelism may be thwarted by data movement costs,the fundamental latency limits of getting into and out of the cloud,or both.For example,while the analytics associated with making long-termfinancial decisions are appropriate。
金融科技行业中超级账本技术的使用教程与金融业务应用案例
金融科技行业中超级账本技术的使用教程与金融业务应用案例超级账本技术（Hyperledger）是一种基于区块链技术的开源项目，该项目旨在通过提供一个可靠、可扩展的平台来构建跨行业的分布式账本解决方案。
金融科技行业作为一种应用区块链技术的行业，也可以充分利用超级账本技术来改进金融业务流程及安全性。
本文将介绍超级账本技术的使用教程，并提供一些金融业务应用案例。
使用超级账本技术的前提是具备一定的区块链技术基础，了解区块链的基本概念，如分布式账本、共识机制和智能合约等。
下面是超级账本技术的使用教程：1. 安装与配置超级账本技术环境超级账本技术是一个开源项目，可以从官方网站上下载并安装到本地环境中。
在安装完成后，需要进行一些基本的配置，例如设置节点的身份和权限、配置网络拓扑等。
2. 创建网络与通道在超级账本技术中，网络由多个节点组成，节点可以是Peer节点、Orderer节点或CA节点。
首先需要创建一个网络，然后在网络中创建一个或多个通道，通道用于不同节点之间的交流与数据传输。
通过创建通道，可以将不同节点连接在一起，形成一个分布式的账本网络。
3. 定义链码与智能合约在超级账本技术中，链码（Chaincode）是一种特殊的智能合约，用于定义业务逻辑和数据模型。
可以使用Go或Java等编程语言编写链码，并将其部署到网络中的节点上。
链码可以对账本数据进行读写操作，实现业务流程的自动执行。
4. 执行交易与查询操作在超级账本技术中，交易是指对账本数据进行读写操作的过程。
可以通过调用链码提供的函数来执行交易，例如转账、存证等操作。
另外，超级账本技术还提供了强大的查询功能，可以根据特定条件查询账本中的数据，并返回查询结果。
5. 实现隐私与加密保护在金融科技行业中，数据的隐私与安全性至关重要。
超级账本技术提供了多种方式来加强隐私与加密保护，例如使用身份验证、访问控制、加密等技术手段。
通过合理配置，可以保证账本中数据的机密性和完整性，防止未授权的访问与篡改。
小年祝福_日常祝福语_
小年祝福20xx小年篇一一、当您看见这信息时，幸运已降临到你头上，财神已进了您家门，荣华富贵已离您不远祝福您朋友：小年快乐!二、小年来到喜临门，送你一只聚宝盆，装书装本装学问，装金装银装财神，装了健康装事业，装了朋友装亲人，时时刻刻都幸福，平平安安交鸿运!三、毛主席说：拜年、祝福不是资产阶级的专利，我们无产阶级也要拜，就是拜得晚一点也不怕。
无非拱拱手，说些吉利话嘛，红包那些东西，腐朽得很，消磨意志。
四、小年到了，想想没什么送给你的，又不打算给你太多，只有给你五千万：千万快乐!千万要健康!千万要平安!千万要知足!千万不要忘记我!小年祝福祝语贺语小年祝词五、说一句恭喜发财，答一句全家安康;说一句朋友祝福语万事如意，答一句工作顺利。
今天就是说吉祥话儿的日子，今天就是顺心的日子。
六、感谢你的关怀，感谢你的帮助，感谢你对我做的一切……请接受我新春的祝愿，祝你平安幸福。
七、小年快乐!万事大吉!合家欢乐!财源广进!吉祥如意!花开富贵!金玉满堂!福禄寿禧!恭喜发财!八、一年四季，即将岁末，新年新气息，说一声恭喜，五路财神运财来;赌一下运气，好事情排队来;饮一杯美酒，今后笑口常开;开一朵春花，新春大家乐开怀!九、新的一年，祝好事接二连三，心情四季如春，生活五颜六色，七彩缤纷，偶尔八点小财，烦恼抛到九霄云外!请接受我十全十美的祝福。
祝免年春节快乐!十、小年圣旨到：从今起你的烦恼失意扫进“回收站”，新建一个“小年开心文件夹”，写一篇“快乐”文档，放一幅“如意”幻灯，用CAD绘出美好小年，祝你小年快乐吉祥!十一、让我告诉你七种活得开心的方法：多关心我;多想我;多照顾我;多疼我;多见我;多发信息给我;最重要的就是认识到一个十二、友情是香喷喷的大米饭，热腾腾的涮火锅，火辣辣的二锅头。
又馋了吧，小年喝一盅吧!十三、小年里，孙悟空翻着筋斗云送来一把“铁扫帚”，猪八戒扛着“烦恼清洁剂”来敲门，沙僧送来一个“万事如意”灶台，唐僧对您行合掌礼：阿弥陀佛，小僧这厢有礼了，小年吉祥，略表心意!十四、春节送你个福，送福祝福全是福，有福藏福家家福，享福见福时时福，金福银福处处福，大福小福天天福，接福纳福年年福，守福祈福岁岁福。
区块链技术白皮书模板
区块链技术白皮书模板1. 引言在这个数字化时代，区块链技术以其去中心化、透明度高以及安全性强的特点，成为了诸多行业的热点关注。
本篇白皮书旨在介绍一个针对某特定领域的区块链技术解决方案，为读者提供全面的信息，并激发对该领域中潜在机遇的兴趣。
2. 领域背景在此部分，将详细介绍所涉及的领域的现状和问题。
通过对该领域的分析，将引出使用区块链技术解决这些问题的合理性和必要性。
3. 技术概述3.1 区块链基础在此部分，将对区块链技术的核心原理进行详细的介绍，包括分布式账本、去中心化、共识机制等基本概念。
3.2 应用场景在此部分，将列举适用于该领域的区块链应用场景，并详细描述其运作方式和优势。
3.3 技术架构在此部分，将提供一个具体的技术架构图，并解释各个组成部分的功能和关系。
4. 解决方案在此部分，将详细介绍使用区块链技术解决该领域问题的方案。
通过详细的案例和技术流程图，向读者展示方案的可行性和优势。
5. 实施计划5.1 发展阶段在此部分，将详细介绍该方案的发展阶段，并说明每个阶段实际操作的目标和措施。
5.2 时间规划在此部分，将列出实施该方案的时间规划表，并解释关键节点的意义和相关工作的安排。
5.3 风险评估在此部分，将对实施过程中可能出现的风险进行评估，并提出相应的风险控制措施。
6. 市场前景在此部分，将详细阐述该方案在当前市场环境下的前景，包括市场规模、增长预测等相关数据和分析。
7. 总结在此部分，将对全文进行简要的总结，并再次强调该方案的优势和潜在机遇。
8. 参考文献此部分列出了白皮书中所引用的所有参考资料。
至此，本篇区块链技术白皮书模板的编写已完毕，希望能帮助读者更好地了解和掌握该领域相关信息。
如读者对该方案感兴趣，可进一步联系我们获取更多详细信息。
区块链专业术语中英文对照表
窃听者 电子商务服务器…的密钥 椭圆曲线数字签名算法保障 用于节点的 Eigentrust++技术 电力成本 电力消耗与目标难度 Electrum 钱包
区块链
English ellipticcurve multiplication Emercoin(EMC)
区块链
English FEC field programma blegatearray(FPGA) Financial disintermediation fintech fork attack forks fraud proofs full nodes G generating generation transaction generator point genesis block
解码为 16 进制 深网 解码原始交易 通缩货币 授权股权证明机制 滞期费 拒绝服务攻击 分离块 确定性钱包 去中心化交易所 难度位 难度调整 难度目标 数字公正服务 数字货币 分布式哈希表
自治系统运行环境
区块链
English
中文
Distributed Ledger Technology（DLT） 分布式账簿技术 domain name service(DNS) double-spend attack double spend Dogecoin DoS(denial of service) attack 域名服务(DNS) 双重支付攻击 双花 狗狗币 拒绝服务攻击 权益代表证明机制/DPOS 算法 （POS 基础上的改良） 双重目标 双重目的挖矿 尘额规则（极其小的余额）
区块链
English compressed private keys compressed public keys computing power connections consensus Consensus Ledger consensus attacks consensus innovation consensus plugin Confidential Transactions conting block headers with converting compressed keys to converting to bitcoin addresses conversion fee consortium blockchains counterparty protocol Counterparty
区块链能做什么不能做什么
区块链能做什么不能做什么[目前区块链投融资领域泡沫明显，投机炒作、市场操纵甚至违规违法等行为普遍，特别是涉及公开发行交易的Token项目。
政府有关部门应加强监管，防范金融风险]区块链最早作为比特币的底层技术2008年由中本聪提出。
但比特币的脚本语言缺乏图灵完备性（Turingcompleteness），使用的UTXO （unspenttransactionoutput，未使用交易输出）模型难以支持复杂的状态操作。
为此，布特林2013年提出了以太坊（Ethereum）。
以太坊是一个基于账户模型的区块链系统，脚本语言具有图灵完备性，目标是实现萨博1994年提出的智能合约（smartcontract）并支持分布式应用（decentralizedapplication，简称是DApp）。
随着2014年美国R3公司创立和2015年Linux基金会发起超级账本（Hyperledger）项目，区块链受到了越来越多主流机构的重视。
比如，高盛2016年讨论了区块链在共享经济、智能电网、房地产保险、股票市场、回购市场、杠杆贷款交易以及反洗钱（anti-moneylaundering，AML）和“了解你的客户”（knowyourcustomer，KYC）中的应用。
中国区块链技术和产业发展论坛2016年10月发布的《中国区块链技术和应用发展白皮书（2016）》讨论了区块链在金融服务、供应链管理、文化娱乐、智能制造、社会公益和教育就业等领域的应用场景。
2009年1月，比特币网络上线标志着区块链应用落地。
但从那时至今近10年时间里，除了加密货币（cryptocurrency）发行和交易之外，区块链没有得到大规模应用。
截至2018年10月31日，CoinMarketCap网站统计了全球范围内的2086个加密货币和15545个加密货币交易所，全体加密货币的市值约2035亿美元（其中比特币市值占比为54%），过去24小时交易量约106亿美元；但DappRadar网站统计了以太坊及其上1137个分布式应用，发现过去24小时活跃用户数只有12521人，其中只有2个分布式应用的24小时活跃用户数超过或接近1000人，而且比较活跃的分布式应用集中在游戏、博彩和加密资产交易等与实体经济关系不大的领域。
1带翻译
1、1、金融用语：受取手形：应收票据；外貨ポジジョン：外汇头寸；公定歩合：法定贴现率；最割引率：再贴现率；つなぎ融資：过渡性融资；変動為替レート：浮动汇率2、股市用语：上げ幅：升幅；先安：看跌；そこを割る：跌破最低大关；持ち合い：暂告平息；軟調：疲软3、缩略语：ADBゕジゕ開発銀行（亚洲开发银行）；CIEC 国際経済協力会議（国际经济合作会议）；GA TT関税貿易一般協定（国际关税和贸易总协定）；FAO 国連食料農業機構（联合国粮农组织）；IMF 国際通貨基金（国际货币基金组织）；JICA国際協力事業団（日本国际事业协力团）OPEC石油輸出国機構（石油输出国组织）；UNDP国連開発計画（联合国开发计划署）一、经济类文章○円の国際化変動相場制の第二の不均衡は、日本の貿易収支の大幅な黒字である。
確かに第二次石油ショックの直後こそ貿易収支は赤字またはわずかな黒字だったが、数年後には大幅黒字が復活している。
79年の第二次石油ショックの後も、79－80年こそ、貿易収支は２０億ドル前後の赤字、経常収支は大幅の赤字だったが、81年以降再び黒字を増やし、83年には経常収支も黒字となった。
85年、86年は貿易収支各461億ドル、828億ドル、経常収支は各350億ドル、492億ドルの黒字である。
普通ならとっくに円高となってよさそうだが、そうならなかったのは、日本からゕメリカへ莫大な金利稼ぎの長期資本が流出したからである。
この時期の貿易黒字の急増は、日本の輸出努力とともに、ゕメリカのドル高のあおりを受けたと見るのが正しいだろう。
かつては通貨レートは貿易の動きに依存していたが、現在では、資本収支の動きが大きな影響を持つようになっている。
それだけに、貿易摩擦問題が通貨レート調整によって解決する見通しは少ない。
むしろ日本側は、経常収支の黒字を対外投資の推進に振り向け、円高を避けてきた。
实行浮动汇率制度的第二个不均衡问题是日本的贸易收支顺差大幅度。
超级账本Hyperledger白皮书(中文版)
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四个白皮书的主要内容
四个白皮书的主要内容最近，一些组织发布了四种白皮书，他们涵盖了改善体育运动、建立有效的数字基础设施、加强分布式账本技术和解决应用程序跨越本地与分布式网络的问题。
下面列出的就是这四种白皮书的主要内容。
1.改善体育运动：这篇白皮书详细介绍了如何通过建立分布式数据存储、分析和使用体育统计数据，优化和实现体育运动。
它还详细阐述了实现此目标的方法，包括建立优先技术框架，开发可靠的应用程序，建立可靠的网络环境，遵守退役竞技员的道德准则，分类传统以及虚拟体育竞技，以及创新的新服务和应用程序。
2. 建立有效的数字基础设施：本白皮书主要探讨如何实现基于区块链技术的数字基础设施，以及如何改善基于区块链技术的数字城市和实体城市中存在的解决方案。
它还介绍了如何利用区块链技术实现有效的社会和经济管理，以及如何确保账本公司的开放性和公平性。
3.加强分布式账本技术：这篇白皮书详细介绍了如何利用区块链技术快速可靠地实现分布式账本技术。
它也介绍了微观经济学模型，通过使用哈希函数，实现高度可靠和安全的分布式账本技术，缩短分布式账本实现时间，以及建立可扩展的、低成本的分布式账本技术。
4.解决应用程序跨越本地和分布式网络的问题：这篇白皮书探讨了在基于区块链技术的应用程序开发过程中，如何克服在本地与分布式网络之间的跨越问题，以及如何利用计算机视觉（CV）、智能合约和大数据技术来解决应用程序跨越本地与分布式网络问题。
它也提出了一些基本原则，即提供弹性、伸缩性和可扩展性，以及性能与经济效益的平衡，从而改变了本地和分布式网络应用程序的架构。
总之，这四种白皮书的主要内容涉及改善体育运动和建立有效的数字基础设施、加强分布式账本技术以及解决应用程序跨越本地与分布式网络的问题。
它们提供了详细的解决方案和技术框架，可以帮助企业和机构构建基于区块链技术的新型数字社会和经济模型。
缔结公平合法的交易，以及按照可持续发展和创新精神建立新的业务模型，这些白皮书都可以成为制定这些政策的有益准则。
Documentum 5技术白皮书-prient
地址：北京市上地信息路26号中关村创业大厦1009 邮编：100085电话：（8610）82898762~65传真：（8610）82898762~65 - 400蓬天合作伙伴资料Docuemntum 5技术白皮书蓬天信息系统（北京）有限公司目 录企业内容管理 (3)内容应用 (4)创建企业内容管理模块 (5)无处不在的内容管理 (6)对内容生命周期的管理 (6)创建内容应用 (13)系统架构 (14)D OCUMENTUM ECM平台的四个层面 (14)内容存储库和服务层面 (15)Documentum内容存储库 (15)内容对象 (16)Content Server (17)Content Server扩展 (21)接口层 (27)Documentum基础类（Documentum Foundation Classes） (28)Documentum业务对象架构（Business Object Framework） (28)Web Services (29)基于标准的接口 (30)客户端层 (30)基于微软Windows的客户端应用 (31)Documentum基于Web的客户端应用 (32)与企业级应用的内容服务集成 (34)企业平台基础 (35)开放性 (36)可扩展性 (36)可伸缩性 (36)可靠性 (37)安全性 (37)可移植性 (38)全球性 (38)全面性 (40)企业内容管理商业信息以多种形式存在：文本、电子表格、图像、XML文件、网页、视频、音频、电子邮件、即时消息和固定内容（比如报告、记录和扫描的图片）。
从工程制图、制造流程到中间市场和销售介绍，非结构化内容对于企业顺畅高效运行是非常重要的。
一个企业内容管理系统提供面向非结构化信息的管理，它依照用户定义的规则提供文档的创建、管理、处理、分发、归档，系统在内容间建立关联，允许同一个内容被用于不同的上下文和翻译文档中。
增加了智能化，创建分类和元数据，以使搜寻和恢复更为迅速高效。
浪潮财务白皮书
浪潮财务白皮书(总23页) -CAL-FENGHAI.-(YICAI)-Company One1-CAL-本页仅作为文档封面，使用请直接删除浪潮财务白皮书***********浪潮财务白皮书信息管理中心目录总账模块....................................................................................... 错误!未定义书签。
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bnb白皮书范文
bnb白皮书范文
百度百科白皮书（Baidu Whitepaper）是百度公司发表的一份详细介绍其技术、产品或服务的文献。
本篇白皮书将具体介绍百度百科的背景、特点、发展历程以及未来发展方向。
一、背景
二、特点
2.大数据支持：百度百科由百度拥有的大量数据资源支持，用户通过百度可以轻松找到百科词条，在传播度和受众范围上具有巨大优势。
3.与引擎关联：百度百科与百度引擎关联紧密，提供更准确的知识结果，并且有助于更好地满足用户需求。
三、发展历程
1.创立与上线：百度百科由百度创始人李彦宏提议，2024年成立，并于同年5月20日上线试运行。
初期，百度百科主要依靠百度的推广，逐渐吸引了大量用户的参与。
3.知识付费模式：为了更好地保证百科内容的质量，百度百科于2024年推出了知识付费模式，诚邀专业人士提供优质的知识内容，并采用知识付费的方式进行激励和分配。
四、未来发展方向
1.打造权威知识库：百度百科将进一步提高词条审核和内容质量的管理，加强对专业知识的聚集和输出，建立权威的中文知识库。
2.加强大数据应用：百度百科将进一步整合百度公司的大数据资源，提供更准确和个性化的知识推荐和结果。
3.拓展国际化发展：百度百科将积极推动国际化发展，在全球范围内提供中文知识的服务和传播，为更多的用户提供中文知识的文化窗口。
总结：。
永洪Z-Dashboard白皮书
对前后数据的对比中。如今年的收入与去年同期收入的对比，今年实现的利润与去年的利润对比等等。 如通过仪表盘可以清晰的反映出，今年的收入与去年相比，完成了多少。在期末的时候，还可以清晰 的反映出，今年收入与上一年收入的对比情况等等。为此仪表盘的一个重要应用，就在于数据之间的 对比。 在目标考核中，也有不小的用处。在实际工作中，为了对员工进行绩效考核，往往会为用户设置不同 的目标。如会为销售员设置销售目标等等。在这种情况下，就可以仪表盘来直观的反映出销售人员的 实际业绩与目标之间的关系。
永洪科技 Z-Dashboard 白皮书 3 数据展现
Yonghong Dashboard 可以在各种浏览器上实现多种类型报表的制作。除了基本的报表创建，修改，编辑和 删除的功能以外，还支持通过各种数据筛选和钻取，使用各种数据展现方式，突出数据中关键字段。
3.1
展示数据组件
数据展示组件是一组可交互式的图形，可以进行某些计算，数据排序、汇总和合并等。所进行的计算与数据 跟数据排列有关，可以动态地改变它们的版面布置，以便按照不同方式分析数据，也可以重新安排行号、列 标和页字段。每一次改变版面布置时，数据展示组件会立即按照新的布置重新计算数据。如果原始数据发生 更改，则可以更新组件。
2.2
统计分析
得益于我们的跨粒度计算，所有的计算都会被以最优化的方案转化为库内计算，从而获取最好的性能。各种 常见的汇总函数，以及几乎所有的统计函数都支持。为了更好的理解数据，提供了丰富的数理统计分析方法， 这些方法包括指标描述统计量分析、相关分析、方差分析、回归分析、数据抽样、时间序列预测等。这些功 能从简单到复杂，可以提供对政府和商业用户关键性能指标（KPI 指标）以及其影响因素的全面分析，从而 实现更科学的决策管理。
Filecoin白皮书(中文版)
（a)介绍 Filecoin 网络，概述这个协议以及 详细介绍几个组件。
（b)形式化去中心化存储网络（DSN)的计 划与内容，然后构建 Filecoin 作为一个 DSN。
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（c)介绍一种叫“复制证明”的新型存储 存储矿工通过提供存储空间赚取令牌
6. 7.
有效的工作量证明（Proof-of-Work）：我 们展示了如何基于“时空证明”来构建有效 的工作量证明来应用于共识协议。旷工们不 需要花费不必要的计算来挖矿，但相反的必 须存储数据于网络中。
最后，矿工们能参与到区块链新区块的锻造。矿 工对下一个区块链的影响与他们在网络中当前存 储使用量成正比。
Filecoin 协议由四个新型组件组成
（f)讨论用例，如何连接其他系统以及如 1.
何使用这个协议。
去中心化存储网络(Decentralized Storage Net
注意：Filecoin 是一项正在进行的工作。 正在进行积极的研究，本文的新版本将会出 现在 https://filecoin.io
图一是使用了术语定义之后的 Filecoin 协议草 图，伴随着一个例子如图 2 所示
8.
1.2 协议概述
Filecoin 协议是构建于区块链和带有原生令牌 的去中心化存储网络。客户花费令牌来存储 数据和检索数据，而矿工们通过提供存储和 检索数据来赚取令牌。
Filecoin DSN 分别通过两个可验证市场来处 理存储请求和检索请求：存储市场和检索市 场。客户和矿工设定所要求服务的价格和提 供服务的价格，并将其订单提交到市场。
1. 用户为数据存储和检索支付令牌
2. 3.
4. 5.
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