CN1666201B - 用于网络上流的实时分配的方法 - Google Patents
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Abstract
一种用于采样流数据(图1)用于网络(例如,因特网)上的分配的方法包括从流数据源(120)接收流数据(它包括周期性更新的信息项目)。随后,利用基于流数据的总更新率(Ua)和网络容量(C)的采样过程采样流数据的周期性更新的信息项目,从而形成被采样的数据流。或者,可以使用还基于每个周期性更新的信息项目的重要性(Kqmin)和每个周期性更新的信息项目的更新率(Uq)的采样过程采样流数据的周期性更新的信息项目,以形成被采样的数据流。随后,在网络(例如,因特网或专用网络)上分配被采样的数据流。
Description
发明背景
发明领域
本发明一般涉及改变数据的实时分配的方法,尤其涉及用于采样网络上实时分配的流数据的方法。
相关技术描述
许多数据源采用一个或更多媒体中的高速流数据向它们的用户提供大量对时间敏感的内容。这种流数据包括股票报价、其它金融报价、新闻公告、实时气象报告或者其它信息项目。某些流数据还可以包括专用信息项目,诸如位置跟踪系统中对象位置的实时更新。在一般情况中,数据源提供一种或更多种流媒体内容,表示由数据源提供的信息,其中流媒体内容可以至少部分地表示关于该信息的更新的流。媒体可以是流媒体,如在某些上下文中所狭义定义为连续的流而没有设置结束点,但如这里所使用的,它被简单地称作作为流供应的数据,不必是连续的,不必具有限定端并不能提前整体获得。因此,对每几小时仅更新一次的对一个数据的更新仍可以被认为是数据流。
用于将信息从一个数据源发送到信息用户的分配网络常具有有限的容量(例如,有限带宽的信道),它可以阻碍所有用户获得从数据源提供的数据的所有比特。例如,来自一个股票交易所的交易的报价数据可以包括几千个报价,每分钟都改变许多次,导致每秒多个兆字节的流。通常,分配网络的许多部分不能提供带宽来传递用于信息的实时更新的流。例如,如果网络横跨大陆,连接大陆(诸如北美和亚洲),连接国家(诸如美国和印度)或在欠发达地理区域内链接,则网络中某些部分内的带宽会极其有限或者成本太高而不能完全利用。在广域网上直接分配流数据的尝试会压倒该广域网,导致和质量(例如,表示性质和关联)和分配的及时性的劣化。例如,如果由于网络拥塞,每分钟更新一次的报价数据被延迟了10分钟,就将严重劣化数据流的有用性。
常规限制带宽的网络的另一个问题在于它们的容量随时间变化,从而需要考虑动态环境。这种动态环境进一步使在网络上分配流数据的努力复杂化。
因此,本技术领域内还需要一种方法来在网络上分配流数据,它不压倒有限容量的网络,并在相对静态的环境和动态的环境中提供周期性更新的信息项目的及时和高质量的分配。
发明内容
本发明提供一种方法,用于采样流数据以便在网络上实时分配并不压倒网络,按及时方式分配流数据派生出的高质量的采样数据流。本方法还提供用于在动态环境中被采样的数据流的及时和高质量的分配。
一种根据本发明的用于采样包括周期性更新的信息项目的流数据用于网络上的分配的方法实施例包括:首先从流数据源接收流数据。信息项目可以包括股票报价、其它报价、新闻项目、天气信息、位置信息和/或其它变化的信息项目。随后,按基于流数据的更新率和网络容量更低速率采样流数据的周期性更新的信息项目,以生成被采样的数据流。采样率和用于确定采样的算法可以随流而变并可以基于每一个周期性更新的信息项目的相对重要性以及每一个周期性更新的信息项目的更新率。随后,在网络上分配被采样的数据流。网络可以是专用网络,虚拟专用网络,因特网,外联网,内联网,广域网(WAN)等等。
本发明实施例的一个优点在于可以按及时方式并以高质量(即,流数据的较好表示和较高关联)分配信息,因为基于产生流的环境特性或者可选地基于表示每一个周期性更新的信息项目的重要性的因素等采样所接收的流数据。
因此,根据本发明的方法能够在速率有限的网络上进行流数据的分配。用于采样过程的参数可以被动态确定,因此调节采样率以适应动态环境。由于网络负载的变化、计算资源可用性的变化等等,产生流和采样的环境也会变化。
通过参考以下的详细描述将更好地理解本发明的特点和优点,如说明性实施例和附图中所阐述的。
附图概述
图1是其中可以使用本发明实施例的网络系统的框图。
图2是说明根据本发明的一个实例性实施例的过程中的步骤顺序的流程图。
图3是说明根据本发明的另一个实例性实施例的过程中的步骤顺序的流程图。
图4是说明采样和分配过程的各种阶段的流程图。
具体实施方式
图1是其中可以使用本发明实施例的网络系统100的框图。如图所示,收集数据中心110从各种流数据源120接收数据的流。收集数据中心110包括输入/输出控制(I/O)130以便接收数据流并向它们提供馈送输入处理器132。馈送输入处理器132耦合到用于存储数据的数据存储134,它由馈送输出处理器136读取并发送给I/O 138以便根据参数数据库140中存储的参数传送到各种数据中心150。数据存储134可以是磁存储装置,但在通常情况下,它是短期的快速存储器,用于缓冲馈送输入处理器和馈送输出处理器之间的数据流。用户系统152连接到各数据中心150以接收信息项目。图中,将元件标注以表示相同对象的多个实例。例如,数据源被标注为120(1)、120(2),...,120且用户系统被标注为152(1)、152(2),...,152(M)。
在具体实例中,纽约证券交易所(NYSE)交易的供应商可以将报价和报价更新的流提供到由雅虎操作的收集数据中心。该收集数据中心随后处理该流及其它,用于通过各种容量的网络链接分配给遍布世界的雅虎数据中心。随后,雅虎用户可以将用户的计算机系统(PC,膝上电脑,无线设备,电话等等)引导为链接到雅虎数据中心(通常是最便于访问或者最近的数据中心,但并不严格要求)以获得当前的报价。
收集数据中心110还包括动态负载分析器142,它确定收集数据中心110和数据中心150之间的分配网络的负载因子。在更新参数数据库140中的参数时考虑这种负载因子,如以下更详细地描述的,它用于确定馈送输出处理器136如何采样和/或处理块数据。
参数数据库140保持大量表,它们包含用于处理流的参数的大量记录。某些参数在以下的表中示出,它们说明可在收集数据中心中使用的数据库和/或数据表。
表1列出了与数据中心有关的参数。其中收集中心接收和分配数据流到多个数据中心,可以存在这种数据中心参数表的多个实例。在某些实现中,对应关系是一对一,而在其它实现中,某些数据中心从参数数据库140接收与参数无关的分配。例如,如果收集中心和数据中心之间的链接具有足够的带宽,来自所有被接收的流的所有数据都可以被传递给数据中心,而不需要考虑用于该数据中心的参数表中的参数。某些参数在“SSU”间隔上累积,该间隔是系统统计更新之间的时间。
表2(“项目”表)列出了与信息项目有关的参数。给定的信息项目q是可以被更新的,诸如对报价机符号YHOO的当前报价或者Sunnyvale,CA的天气。项目表中的某些项目的条目可以用于单个项目,诸如对YHOO的报价,但项目表中其它项的条目可以是用于可被更新的相关或不相关项目的组,诸如California内20个县的天气,没有共同特性的项目的混杂收集或者仅部分相关的一些组。通过实例,高容量项目可以被处理成单个项目而低容量项目的组可以分组成为表中的一个“项目”。应理解,这里所用的“信息项目”可以涉及一个项目的组或者超过一个项目的组。在某些实施例中,所有项目中的每一个都可以具有它们自己的组,在某些实施例中,某些项目可以具有它们自己的组,而在其它情况中,所有项目都可以被分组成为其中含超过一个项目的组。在一般的情况中,零或更多的组包括单个信息项目且零或更多组包括超过一个信息项目。
在某些情况中,给定的信息项目从未更新,诸如关于特定事件的新闻文章,但更普通地,信息项目被更新。信息项目的其它实例是一组对象,诸如关于特殊公司的当前评论文章的组,用于国家的不同区域的天气报告的组或者低交易量股票报价的组。虽然组可以包括单个和更多或更少的相关或不相关信息,但它们可以被分组在一起用于在收集数据中心处处理。从未更新的信息项目不需要处理为特别情况,而可以仅被处理为仅在经过某任意长时间段后进行更新的项目。
参数数据库140也可以保持表与流(数据源)相关联,诸如表3。在收集中心接收和分配数据流到多个流的情况下,可以出现这种流参数表的多个实例。在某些实现中,对应关系是一对一,而在其它实现中,某些流由收集中心接收但不采用来自参数数据库140的参数进行处理。例如,来自天气数据源的数据量常比来自金融数据源的量小许多量级,从而所有接收到的天气数据都可以通过收集中心而不处理并不会显著地影响数据中心的拥塞。在某些情况中,在超过一个大数据源的情况下,可以在数据源之间进行某些平衡。
参数数据库140可以被设置成常规数据库,但具有较高的响应性。通常,参数数据库140保持每个数据中心每个项目一个项目表。如果一个或更多数据流被分成许多信息项q,则项目表的数目可能会相当大。但是,如果数据库被有效地编入索引,诸如通过使用散列法,可以适应几百万的项目表。在某些实现中,参数数据库140主要或整体存储于快速存储器中,从而它可以被快速访问。参数数据库140可以通过动态负载分析器142或其它过程实时更新,允许采样率的实时调整以应对动态环境,诸如容量变化的网络或者处理量的其它影响。
表1.每个数据中心参数
参数 单位 描述
T0 秒 初始启动的时间
T 秒 当前时间
Tu 秒 最后系统统计更新(SSU)的时间
Iu 秒 SSU间隔
Is 秒 扫描(sweeping)阶段间隔
Ip 秒 如需要,初始化系统统计的时间间隔
Im 秒 扫描阶段中使用的最大非有效时间段
Nsu 字节 所有在先间隔[T0,Tu]中分配的数据
Ns 字节 当前间隔[Tu,T]中放入分配队列中的数据
Ndu 字节 在Tu时保留在分配队列中的数据
Nd 字节 在T时保留在分配队列中的数据
Ua 字节/秒 更新率,由所有源上的Ua=Na/(T-Tu)计算出
C 字节/秒 网络容量,为当前SSU间隔计算为C=(Ns+Ndu-Nd)/(T-Tu)
Cp 字节/秒 从之前的SSU间隔的网络容量
表2.每信息项/组参数
参数 单位 描述
tq 秒 分配队列上设置的最后采样
nqa 字节 所有在先间隔[T0,T]中接收的更新数据
nq 字节 当前间隔[Tu,T]中接收的更新数据
uq 字节/秒 更新率,计算为uq=nq/(T-Tu)
sqa 字节 所有间隔[T0,T]中置于分配队列上的采样数据
sq 字节 从Tu到T置于分配队列上的采样数据
rq 字节/秒 采样率,计算为rq=sq/(T-Tu)
Kqmin 字节/秒 最小采样率
Kqmax 字节/秒 最大采样率
fq 布尔 分配队列指示器
表3.每数据源参数
参数 单位 描述
Nu 字节 在所有在先间隔[T0,Tu]中从源接收到的数据
Na 字节 在当前间隔[Tu,T]中从源接收到的数据
可以在每次过程复位时复位初始启动时间。对于给定信息项目q的最小采样率Kqmin表示q的重要性。例如,Kqmin的初始值可以从配置文件获得并不需要固定,而是可以在每个SSU阶段(以下将描述)如下更新:
Kqmin(新)=Kqmin(旧)*C/Cp,如果Cp<>0
Kqmiin(新)=Kqmin(旧),如果Cp=0
其中信息项的更新率Uq小于其Kqmin值,采样不需要至少是Kqmin,而是可以设置成更低的更新率。对于给定的项目q和给定的数据中心,最大的采样率Kqmax可以计算如下:
Kqmax=uq,如果Ua=0
Kqmax=uq/Ua*C,如果Ua<>0
分配队列指示器fq表示所接收到的数据更新是否将被置于分配队列上。如果接收到的更新不被置于队列上,则将设置fq。如果数据更新将被置于队列上,则将清除fq。
应理解,虽然在这些表中使用特定单位,时间(秒)、数据量(字节)以及数据率(字节/秒),也可以使用其它单位而不背离本发明的范围。
还应理解,术语“采样率”不限于根据计算确定的某些固定的数据传输率,如果这里所述的采样过程应用于数据流,则其更一般地涉及可能存在的传输率。直到诸如动态确定值的采样过程的所有输入都已知才知道采样率或其它率的数值是完全可能的。在多数一般情况中,采样率可以不是被设定或调整的独立变量,而是可以是从属的并由设定其变量和将采样过程应用于被采样数据的结果确定。但是,不管通过“采样率”变量的设定或作为采样过程的副产品来确定采样率,数据都可以被称为具有采样率。
图2是根据本发明的一个实施例说明用于采样流数据的过程200中的步骤顺序的流程图,它包括周期性更新的信息项目,用于在广域网(例如,因特网)上分配。流数据的周期性更新的信息项目可以是任何类型的信息项目,包括但不限于股票或其它金融报价、新闻项目(例如,体育比分或金融信息项目)、商业或金融信息、天气信息或其它类型的周期性更新信息项目,诸如空中交通跟踪信息项目或全球定位系统(GPS)信息项目。此外,周期性更新的信息项目中每一个的周期性更新率可以与流数据的其它信息项目的周期性更新率不同或相同。如上所述,信息项目可以被分组并为项目处理这些组,如这里所描述的。
网络可以是任何类型的网络,包括公共网络、专用网络、因特网(即,全球互连网络或网络)、内联网、外联网、虚拟个人网络(VPN)、非TCP/IP网络或无线网络。本技术领域内的熟练技术人员将理解,与流数据相比,这些网络的容量可以是有限的。例如,可以以1兆字节/秒(MBs)的速率提供流数据,而网络的容量可以仅仅是20-50千字节每秒(KBs)。在这点上,网络的有限容量要求按速率有限的方式分配流数据。
如步骤210所述的,从流数据源接收流数据。流数据源可以是股票交易、商业或商业信息流数据源,且周期性更新的信息项可以是金融报价(例如,指数报价或个股报价)。
接着,流数据的周期性更新的信息项目以一采样率进行采样,该采样率基于流数据的总更新率(Ua)和网络容量(C),如步骤220中描述的。换句话说,采样率被计算为Ua和C的函数。流数据的周期性更新的信息项目的采样形成被采样的数据库,它基本上是所接收的流数据的子集。在变化环境中,可以例如按至少每一或两分钟的速率使用本技术领域内熟练技术人员已知的方法动态地确定Ua和C。在这种情况中,由于采样率基于动态确定的Ua和C,所以采样率本身响应于Ua和C的变化动态地进行调整。
在具体实施例中,从全世界的50个股票交易所收集全世界的金融报价数据并将其从收集中心(诸如,雅虎的加州数据中心)分配到多个数据中心(诸如,遍布世界的雅虎数据中心)。在该实例中,在高峰期,Ua的典型值将从几个KBs到超过1MBs。从加州的收集中心到美国东海岸的数据中心的带宽可以从100KBs到400KBs,而从收集中心到印度或中国的数据中心的带宽可以从10KBs到80KBs。每个数据中心的采样率都可以动态地调整以符合从收集中心到这些数据中心的网络带宽。
基于表示周期性更新的信息项目中的每一个的重要性的因子(Kqmin)和/或每个周期性更新的信息项目的单个更新率(Uq),通过采样流数据的周期性更新的信息项目可以增加被采样数据流的关联。可以根据在先经验(例如,通过确定哪些周期性更新的信息项目被频繁访问或被认为更受欢迎,由此用户使用哪些数据中心)预先确定用于周期性更新的信息项目中的每一个的Kqmin。作为实例,在基于美国的数据中心的周期性更新的信息项目是金融报价的情况下,用于频繁访问的NYSE报价的Kqmin可以大于用于其它国家股票交易所的不频繁访问的报价的Kqmin。对于这里所描述的其它参数,Kqmin可以涉及单个信息项目,诸如特定公司的当前报价,或者涉及项目组,诸如对特定公司证券的几个变体的当前报价,或者无关项目,其中至少当确定诸如Kqmin的参数应用于采样过程时项目的组被处理为一个项目。
Kqmin是一最小速率,如果用于信息项目的更新速率至少是该速率时,流数据将按该最小速率发送,而不考虑网络的带宽。只要了解了当前的揭示内容,本技术领域内的熟练技术人员将理解,用于周期性更新的信息项目的Kqmin值可以取决于数据中心,其中采样数据流将被分配给该数据中心。例如,如果数据中心主要服务印度用户,则印度的金融报价将比中国的金融报价更重要。但是,如果数据中心主要服务中国用户,则中国的金融报价会比印度的金融报价更重要。但是,例如,来自NYSE和NASDAQ的金融报价会是很重要的,不管数据中心所覆盖的地理区域。
数据中心的典型用户可以是访问来自网络服务器的两个网站或网页的个人,该网络服务器通过从数据中心获得的数据构建网站或网页。例如,用户可以是雅虎用户,使用网络浏览器或其它因特网客户端来访问包含新闻、报价和其它信息的MyYahoo!网页。
为了适应变化的环境,采样率可以基于动态确定的Ua、C、Kqmin和Uq值,以允许采样的数据流按及时方式被分配,而不倾覆网络,并具有动态调整以适应环境中的变化的采样率。此外,可以为每个周期性更新的信息项目单独调整采样率,因此按典型的方式用不同的更新率精确地采样周期性更新的信息项目。
作为实例,在高峰买卖时间期间,Ua约是1MBs,而可用的网络带宽C对东海岸数据中心约是200KBs对印度数据中心约是40KBs。对于诸如Dow Jones工业平均或NASDAQ指数的指数,收集中心用400字节/秒的更新率Uq接收这些流。收集中心可以用每个股票符号(诸如IBM或YHOO)100到400字节/秒的更新率Uq接收每个单独的证券报价,而较不常交易的股票可以具有每秒仅几字节的更新率。
对于东海岸数据中心,对于NYSE,NASDAQ指数,Kqmin可以是200字节/秒(即,按平均,发送所接收的所有其它采样),对于某些单独的NYSE,NASDAQ或AMEX股票报价(诸如IBM或YHOO),Kqmin可以从40到100字节/秒,并且对于所有其它的股票报价,Kqmin可以是10到40字节/秒。对于某些不常变化的报价机符号,当该报价机符号的更新率低于Kqmin时,采样率可以比Kqmin更低。
对于印度数据中心,对NYSE,NASDAQ指数,Kqmin也可以是200字节/秒,但对在收集中心处用40到400字节/秒的更新率接收到的某些印度股票报价,Kqmin可以从40到200字节/秒。
在网络上分配采样的数据流,如步骤230所述。可以使用诸如TCP/IP传输的常规方法实现采样数据流的分配。
根据Ua、C以及可选的Kqmin和/或Uq,较少更新的周期性更新的信息项目可以以被认为不够的速率被采样,以便在网络上分配。在这种情况中,可以选择这种较少更新的信息项目,用于在网络上散布,如步骤240中所述。以下,所选择的周期性更新的信息项目(例如,较少更新的信息项目)的散布被称作“SWEEP(扫描)”步骤(或阶段)。
图3是根据本发明说明用于采样流数据的过程300中步骤顺序的流程图,该流数据包括周期性更新的金融报价,用于在因特网上分配。过程300包括从流数据源接收流数据,如步骤310中所述的。
随后,如步骤320中所述的,根据动态确定的流数据的总更新率(Ua)、动态确定的网络容量(C)、每个周期性更新的信息项目的预先确定的重要性(Kqmin)以及每个周期性更新的信息项目的动态确定的更新率(Uq),使用采样率采样数据流的周期性更新的金融报价中的每一个。
周期性更新的金融报价的采样形成采样的数据流,如步骤320中所述的。随后,在因特网上分配采样的数据流,如步骤330中所述的。在过程300中,所选择的周期性更新的金融报价(例如,数据流中较少更新的金融报价)也在网络上被散布(参见步骤340)。
图4是说明实现上述采样、分配和散布步骤的过程的各种阶段的流程图。如图所示,过程以初始化阶段(阶段1)开始,通常由被初始化的服务器或用于开始过程的其它原因触发。开始该过程的时间(或合适该阶段完成)这里被称作“方法开始时间”。在阶段1中,初始化存储器中(in-memory)的数据库,计算必要的系统和报价统计并开始用于SSU阶段和SWEEP阶段(都将在以下详细描述)的开始,如下所述。特别是,存储器中的数据库和To被初始化并从配置文件获得Iu,Is,Ip,Im和Kqmin的值。
可以从配置文件获得用于Tu,Nu,Na,Ua,Nsu,Ndu,Ns,Nd,C,Cp,t0,nqa,nq,uq,sqa,sq,rq,Kqmax和fq的值。或者,过程可以包括附加阶段(阶段1A),其中在Ip的时间段内所有接收到的流数据都被置于分配队列中,而不采样,并且该过程被监控以确定用于这些变量的合适值,它们不是从配置文件获得的。
同样在阶段1中,开始SSU阶段计时器和扫描阶段计时器(以下说明)。计时器间隔分别是Iu和Is。在该阶段中,过程包括开始必要的线程以连续从分配队列获得数据并将其发送到网络并按需要更新系统统计Nsu、Ndu、Ns和Nd。
阶段2中,流数据被采样且采样的数据流在网络上被分配。该阶段中的活动是通过周期性更新的信息项目的接收而触发的,周期性更新的信息项目诸如对金融报价的更新。在该阶段中,所接收的流数据被如下采样:
1.获得当前系统时间T。
2.用接收到的周期性更新的金融报价更新重写现存的数据,设置fq。
3.计算uq。如果uq<=Kqmin,则将周期性更新的金融报价更新置于分配队列上,设置tq=T,清除fq,并进行到阶段2的步骤5。否则,继续到阶段2的步骤4。
4.计算Kqmin,如果uq<=Kqmin,则将周期性更新的金融报价更新置于分配队列上,设置tq=T,清除fq。
5.更新其它报价统计(即,nq,sq,nqa,sqa)和系统变量(Na,Ns)。
阶段3(称作“SSU阶段”)由SSU计时器的期满触发。在该阶段中,几种方法和金融报价统计被动态确定(或者使用昂贵网络和环境监控技术计算或确定)或动态更新,如下所述。特别是,所计算的或确定的量包括C,并可以包含Kqmin。更新值包括CP,Tu,Nu,Nsu,Ndu。随后,复位SSU计时器并将阶段变成阶段2。
阶段4(称作“SWEEP阶段”)由扫描计时器触发。在该阶段中,扫描存储器中的数据库将分配在由Im标记的最后持续时间内已在流数据中被更新但仍未在因特网上分配的任何周期性更新的金融报价。满足该标准的该金融报价和其它信息项目被称作“所选择的周期性更新的金融报价”。在该阶段中,获得当前的系统时间T,扫描存储器中的数据库并检索周期性更新的金融报价。随后,对其中T-tq>=Im以及fq被设置的每个报价,金融报价被分配且fq被清除。随后,将扫描计时器复位并将阶段变成阶段2。
根据本发明的方法可以通过软件和硬件而有效地实现,例如具有存储器中的数据库的FreeBSD操作系统上的模块化的C/C++编程语言库。但是,本技术领域内的熟练技术人员将理解,其它编程语言(诸如Java,Peri,Visual Basic以及Pascal)以及任何其它操作系统(诸如Windows,Solaris和Linux)可用来实现本发明。
应理解,可以在本发明的实施中采用这里描述的本发明实施例的各种可选方案。所附权利要求书旨在限定本发明的范围,且从而覆盖该权利要求书及其等效物内的方法。
Claims (19)
1.一种用于采样包括周期性更新的信息项目的流数据用于网络上的实时分配的方法,其特征在于,所述方法包括:
接收步骤,用于从一个或多个流数据源接收流数据;
采样步骤,用于利用至少部分基于流数据的周期性更新的信息项目的基于动态确定的总更新率(Ua)以及要向其中分配数据的网络的基于动态确定的容量(C)的采样过程,来采样流数据,从而形成被采样的数据流;以及
分配步骤,用于在网络上分配被采样的数据流。
2.如权利要求1所述的方法,其特征在于,在采样步骤中,采样过程进一步基于每一个周期性更新的信息项目的预定重要性(Kqmin)的采样过程,以形成被采样的数据流。
3.如权利要求2所述的方法,其特征在于,在采样步骤中,采样过程进一步基于每一个周期性更新的信息项目的基于动态确定的更新率(Uq)的采样过程,以形成被采样的数据流。
4.如权利要求1所述的方法,其特征在于,进一步包括,在采样步骤中,采样过程进一步基于每一个周期性更新的信息项目的基于动态确定的更新率(Uq)以及每一个周期性更新的信息项目的预定重要性(Kqmin),以形成被采样的数据流。
5.如权利要求3和4中任意一个所述的方法,其特征在于,基于动态确定的总更新率(Ua)、基于动态确定的网络的容量(C)和基于动态确定的每个周期性更新的信息项目的更新率(Uq)是在至少每个预定时间间隔时被动态确定的。
6.如权利要求1所述的方法,其特征在于,进一步包括:
散布步骤,用于在分配步骤之后,在网络上散布所选的周期性更新的信息项目。
7.如权利要求1所述的方法,其特征在于,进一步包括:
在分配步骤期间,在专用网络上分配被采样的数据流。
8.如权利要求1所述的方法,其特征在于,进一步包括:
在分配步骤期间,在网络上分配被采样的数据流。
9.如权利要求1所述的方法,其特征在于,进一步包括:
在分配步骤期间,在公共网络上分配被采样的数据流。
10.如权利要求1所述的方法,其特征在于,所述周期性更新的信息项目是周期性更新的金融报价信息项目。
11.一种用于采样包括周期性更新的信息项目的流数据用于网络上的分配的方法,其特征在于,所述方法包括:
接收步骤,用于从一个或多个流数据源接收流数据;
采样步骤,用于使用采样过程来采样流数据,其中该采样过程是基于:
流数据的周期性更新的信息项目的基于动态确定的总更新率(Ua);
基于动态确定的网络的容量(C);
每一个周期性更新的信息项目的预定重要性(Kqmin);以及
基于动态确定的每一个周期性更新的信息项目的更新率(Uq),
从而形成被采样的数据流;
分配步骤,用于在网络上分配被采样的数据流;以及
散布步骤,用于在网络上散布所选择的周期性更新的信息项目。
12.如权利要求11所述的方法,其特征在于,所述周期性更新的信息项目是周期性更新的金融报价信息项目。
13.如权利要求11所述的方法,其特征在于,基于动态确定的总更新率(Ua)、基于动态确定的网络的容量(C)和基于动态确定的每一个周期性更新的信息项目的更新率(Uq)是在至少每个预定时间间隔时被动态确定的。
14.如权利要求11所述的方法,其特征在于,每一个周期性更新的信息项目的预定重要性(Kqmin)是基于执行条件而动态确定的。
15.如权利要求11所述的方法,其特征在于,进一步包括,在分配步骤期间,在专用网络上分配被采样的数据流。
16.如权利要求11所述的方法,其特征在于,进一步包括,在分配步骤期间,在网络上分配被采样的数据流。
17.如权利要求11所述的方法,其特征在于,进一步包括,在分配步骤期间,在公共网络上分配被采样的数据流。
18.一种用于采样包括周期性更新的信息项目的流数据用于网络上的实时分配的方法,其特征在于,所述方法包括:
从一个或多个流数据源接收流数据;
利用基于流数据的周期性更新的信息项目的基于动态确定的总更新率(Ua)以及要向其中分配数据的网络的基于动态确定的容量(C)的采样过程,来采样流数据,从而形成被采样的数据流;
在网络上分配被采样的数据流;
在扫描间隔内确定是否任何周期性更新的信息项目由流数据更新但没有在采样步骤中被采样并在分配步骤中被分配;以及
在网络上分配确定步骤中确定的信息项目,而不依赖于将排除扫描间隔内信息项目的分配的至少一个采样过程。
19.如权利要求18所述的方法,其特征在于,所述周期性更新的信息项目是周期性更新的金融报价信息项目。
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US10/132,974 US7305483B2 (en) | 2002-04-25 | 2002-04-25 | Method for the real-time distribution of streaming data on a network |
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- 2002-04-25 US US10/132,974 patent/US7305483B2/en not_active Expired - Lifetime
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- 2003-04-25 AU AU2003225197A patent/AU2003225197A1/en not_active Abandoned
- 2003-04-25 KR KR1020047017184A patent/KR100899044B1/ko active IP Right Grant
- 2003-04-25 EP EP03721911.0A patent/EP1540536B1/en not_active Expired - Lifetime
- 2003-04-25 CN CN038150670A patent/CN1666201B/zh not_active Expired - Fee Related
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- 2003-04-25 JP JP2004500218A patent/JP2005524150A/ja active Pending
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WO2003091915A1 (en) | 2003-11-06 |
EP1540536A1 (en) | 2005-06-15 |
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US7305483B2 (en) | 2007-12-04 |
AU2003225197A1 (en) | 2003-11-10 |
JP2005524150A (ja) | 2005-08-11 |
US20030204585A1 (en) | 2003-10-30 |
KR20050007335A (ko) | 2005-01-17 |
EP1540536B1 (en) | 2016-01-06 |
US7849128B2 (en) | 2010-12-07 |
CN1666201A (zh) | 2005-09-07 |
KR100899044B1 (ko) | 2009-05-25 |
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