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公开(公告)号：US6151589A
公开(公告)日：2000-11-21
申请号：US151200
申请日：1998-09-10
摘要： A method for performing continuous auctions over a computer network system consisting of a server/seller and multiple clients/buyers. The seller makes information about the type of sale items, the number of sale items, minimum bid price, time limits for bids to be submitted, and estimated time interval to the next auction decision available to the buyer by displaying it on buyers' computer terminals. Each buyer responds by entering a bid and such bid's duration, within the time limits set by the seller, in to the auction system through buyers' computer terminals. Additionally, a buyer's bid entry time is saved by the system. Determining the response time for present buyers to schedule the next auction. At least one auction winner, whose bid is within bid duration, is selected through a dynamically adjusted customer selection method.
摘要翻译： 一种通过由服务器/卖家和多个客户/买方组成的计算机网络系统执行连续拍卖的方法。 卖方通过在买方的电脑终端上显示销售商品的类型，销售数量，最低投标价格，要提交的投标的时间限制以及下一次拍卖决定的时间间隔， 。 每个买方在买方的电脑终端上通过在卖方设定的时限内输入出价和出价持续时间来进行拍卖。 此外，系统保存买方的出价输入时间。 确定现在买家安排下一次拍卖的响应时间。 通过动态调整的客户选择方法选择至少一个拍卖竞价者，其竞标价格在投标期限内。
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2.发明授权Collaborative caching of a requested object by a lower level node as a function of the caching status of the object at a higher level node 失效作为较高级别节点上对象的缓存状态的函数的由较低级别节点协作缓存所请求的对象
公开(公告)号：US5924116A
公开(公告)日：1999-07-13
申请号：US831237
申请日：1997-04-02
CPC分类号： G06F17/30902
摘要： A method and system of collaboratively caching information to allow improved caching decisions by a lower level or sibling node. In a caching hierarchy, the client and/or servers may factor in the caching status at the higher level in deciding whether to cache an object and which objects are to be replaced. The PICS protocol may be used to pass the caching information of some or all the upper hierarchy down the hierarchy. Furthermore, the caching status information can also be used to direct the object request to the closest higher level proxy which has potentially cached the object, instead of blindly requesting it from the next immediate higher level proxy. A selection policy used to select objects for replacement in the cache may be prioritized not only on the size and the frequency of access of the object, but also on the access time required to get the object if it is not cached. The selection policy may also include a selection weight factor wherein each object is assigned a selection weight based on its replacement cost, the object size and how frequently it is modified. Non-uniform size objects may be classified in ranges of selection weights having geometrically increasing intervals. Multiple LRU stacks may be independently maintained wherein each stack contains objects in a certain range of selection weights. In order to choose candidates for replacement, only the least recently used objects in each group need be considered.
摘要翻译： 协同缓存信息以允许由较低级别或兄弟节点改进的缓存决定的方法和系统。 在高速缓存层次结构中，客户端和/或服务器可以考虑高级别的缓存状态，以决定是否缓存对象以及哪些对象被替换。 可以使用PICS协议将部分或全部上层的缓存信息传递给层次结构。 此外，缓存状态信息还可以用于将对象请求定向到潜在地缓存对象的最接近的较高级代理，而不是盲目地从下一个即时更高级别的代理请求它。 用于选择用于在高速缓存中替换的对象的选择策略可以不仅基于对象的访问的大小和频率，而且还取决于如果没有缓存而获取对象所需的访问时间。 选择策略还可以包括选择权重因子，其中基于其重置成本，对象大小以及修改的频率来为每个对象分配选择权重。 不均匀尺寸的物体可以分类为具有几何增加间隔的选择权重的范围。 可以独立地维护多个LRU堆栈，其中每个堆叠包含在一定范围的选择权重中的对象。 为了选择候选人进行替换，只需要考虑每组中最近最少使用的对象。
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公开(公告)号：US06307965B1
公开(公告)日：2001-10-23
申请号：US09070600
申请日：1998-04-30
IPC分类号： G06K962
CPC分类号： G06F17/30598 , G06F2216/03
摘要： A system and method are provided to analyze information stored in a computer data base by detecting clusters of related or correlated data values. Data values stored in the data base represent a set of objects. A data value is stored in the data base as an instance of a set of features that characterize the objects. The features are the dimensions of the feature space of the data base. Each cluster includes not only a subset of related data values stored in the data base but also a subset of features. The data values in a cluster are data values that are a short distance apart, in the sense of a metric, when projected onto a subspace that corresponds to the subset of features of the cluster. A set of k clusters may be detected such that the average number of features of the subsets of features of the clusters is l.
摘要翻译： 提供了一种系统和方法来通过检测相关或相关数据值的群集来分析存储在计算机数据库中的信息。 存储在数据库中的数据值表示一组对象。 数据值作为表征对象的一组特征的实例存储在数据库中。 特征是数据库的特征空间的尺寸。 每个簇不仅包括存储在数据库中的相关数据值的子集，而且还包括特征的子集。 当集群中的数据值被投影到与集群的特征子集相对应的子空间上时，在度量意义上是短距离的数据值。 可以检测一组k个群集，使得群集的特征子集的特征的平均数量为l。
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4.发明授权
公开(公告)号：US5943667A
公开(公告)日：1999-08-24
申请号：US868244
申请日：1997-06-03
IPC分类号： G06F17/30
CPC分类号： G06F17/3061 , G06F17/30539 , G06F2216/03 , Y10S707/99933 , Y10S707/99934 , Y10S707/99935
摘要： A computer method of removing simple and strict redundant association rules generated from large collections of data. A compact set of rules is presented to an end user which is devoid of many redundancies in the discovery of data patterns. The method is directed primarily to on-line applications such as the Internet and Intranet. Given a number of large itemsets as input, simple redundancies are removed by generating all maximal ancestors, the frontier set, for each large itemset. The set of maximal ancestors share a hierarchical relationship with the large itemset from which they were derived and further satisfy an inequality whereby the ratio of respective support values is less than the reciprocal of some user defined confidence value.The resulting compact rule set is displayed to an end user at some specified level of support and confidence. The method is also able to generate the full set of rules from the compact set.
摘要翻译： 一种从大量数据集中生成的简单而严格的冗余关联规则的计算机方法。 向最终用户提供了一套紧凑的规则，在发现数据模式时缺少许多冗余。 该方法主要针对在线应用，如Internet和Intranet。 给定大量项目集作为输入，通过为每个大项目集生成所有最大祖先（边界集）来消除简单的冗余。 最大祖先的集合与从其导出的大项目集共享分层关系，并进一步满足不等式，由此各个支持值的比率小于某些用户定义的置信度值的倒数。 所产生的紧凑规则集在某些指定的支持级别和置信度下显示给最终用户。 该方法还能够从紧凑集中生成完整的规则集。
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5.发明授权System and method for generating taxonomies with applications to content-based recommendations 有权用于生成基于内容的建议的分类法的系统和方法公开(公告)号：US06360227B1
公开(公告)日：2002-03-19
申请号：US09240231
申请日：1999-01-29
IPC分类号： G06F1700
CPC分类号： G06F17/30873 , Y10S707/99935 , Y10S707/99943 , Y10S707/99944
摘要： A graph taxonomy of information which is represented by a plurality of vectors is generated. The graph taxonomy includes a plurality of nodes and a plurality of edges. The plurality of nodes is generated, and each node of the plurality of nodes is associated with ones of the plurality of vectors. A tree hierarchy is established based on the plurality of nodes. A plurality of distances between ones of the plurality of nodes is calculated. Ones of the plurality of nodes are connected with other ones of the plurality of nodes by ones of the plurality of edges based on the plurality of distances. The information represented by the plurality of vectors may be, for example, a plurality of documents such as Web Pages.
摘要翻译： 生成由多个向量表示的信息的图分类法。 图形分类法包括多个节点和多个边缘。 生成多个节点，并且多个节点中的每个节点与多个向量中的每个节点相关联。 基于多个节点建立树层次结构。 计算多个节点中的多个节点之间的多个距离。 基于多个距离，多个节点中的一个与多个节点中的其他节点通过多个边缘中的一个连接。 由多个向量表示的信息可以是例如多个文档，例如网页。
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公开(公告)号：US06263327B1
公开(公告)日：2001-07-17
申请号：US09522723
申请日：2000-03-10
IPC分类号： G06F1700
CPC分类号： G06F17/30893 , G06F17/30386 , G06F17/3056 , Y10S707/99931 , Y10S707/99936
摘要： A computerized method of online mining of inference rules in a large database. The method is comprised of two stages, a preprocessing stage followed by an online rule generation stage. The pro-processing stage is further defined to be a two step process that involves the generation of large itemsets. The present method defines large itemsets by how the items in the itemsets relate to each other rather than their level of presence. The measure by which itemsets are said to relate to each other is defined by a computed figure of merit, K1. The first substep of the preprocessing stage involves finding those itemsets that possess a minimum computer collective strength of K1. From those found itemsets, a second user supplied input, K2 is used to prune those itemsets with inference strength below K2.
摘要翻译： 一种在大型数据库中在线挖掘推理规则的计算机化方法。 该方法由两个阶段组成，一个预处理阶段，随后是在线规则生成阶段。 前处理阶段被进一步定义为涉及生成大项目集的两步过程。 本方法通过项目集中的项目相互关联而不是其存在级别来定义大项目集。 项目集被称为相互关联的措施由计算出的品质因数K1定义。 预处理阶段的第一个子步骤是找到具有最小计算机集体实力K1的项目集。 从那些找到的项目集中，第二个用户提供输入，K2用于修剪低于K2的推理强度的项目集。
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公开(公告)号：US6092064A
公开(公告)日：2000-07-18
申请号：US964064
申请日：1997-11-04
CPC分类号： G06F17/30613 , G06F17/30327 , G06F17/30539 , G06F17/30864 , Y10S707/954 , Y10S707/956 , Y10S707/968 , Y10S707/99932 , Y10S707/99936
摘要： A computer method of online mining of quantitative association rules consisting of two stages, a preprocessing stage followed by an online rule generation stage. The required computational effort is reduced by the pre-processing stage, defined by pre-processing data to organize the relationship between antecedent attributes to create a heirarchially arranged multidimensional indexing structure. The resulting structure facilitates the performance of the second stage, online processing, which involves the generation of quantitative association rules. The second stage, online rule generation, utilizes the multidimensional index structure created by the preprocessing stage by first finding the areas in the data which correspond to the rules and then uses a merging step to create a merged tree in order to carefully combine interesting regions in order to give a heirarchical representation of the rule set. The merged tree is then used in order to actually generate the rules.
摘要翻译： 一种在线挖掘定量关联规则的计算机方法，包括两个阶段，一个预处理阶段，随后是在线规则生成阶段。 通过预处理阶段来减少所需的计算量，该预处理阶段通过预处理数据来定义，以组织先行属性之间的关系，以创建一个历史性地排列的多维索引结构。 所产生的结构有助于第二阶段的在线处理，其涉及产生定量关联规则的性能。 第二阶段，在线规则生成，利用由预处理阶段创建的多维索引结构，首先查找与规则相对应的数据中的区域，然后使用合并步骤创建合并树，以便仔细地组合有趣区域 命令给出规则集的历史代表性。 然后使用合并的树来实际生成规则。
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8.发明授权System and method for construction of a data structure for indexing multidimensional objects 失效用于构建索引多维对象的数据结构的系统和方法
公开(公告)号：US5781906A
公开(公告)日：1998-07-14
申请号：US660047
申请日：1996-06-06
IPC分类号： G06F17/30
CPC分类号： G06F17/30327 , G06F17/30333 , Y10S707/99931 , Y10S707/99932 , Y10S707/99933 , Y10S707/99943
摘要： An apparatus and a method for constructing a multidimensional index tree which minimizes the time to access data objects and is resilient to the skewness of the data. This is achieved through successive partitioning of all given data objects by considering one level at a time starting with one partition and using a top-down approach until each final partition can fit within a leaf node. Subdividing the data objects is via a global optimization approach to minimize the area overlap and perimeter of the minimum bounding rectangles covered by each node. The current invention divides the index construction problem into two subproblems: the first one addresses the tightness of the packing (in terms of area, overlap and perimeter) using a small fan out at each index node and the other one handles the fan out issue to improve index page utilization. These two stages are referred to as binarization and compression. The binarization stage constructs a binary tree such that the entries in the leaf nodes correspond to the spatial data objects. The compression stage converts the binary tree into a tree for which all but the leaf nodes and the parent nodes of all leaf nodes have branch factors of M. In the binarization stage, a weighting or skew factor is used to achieve flexibility in determining the number of data objects to be included in each of the partitions to obtain a tree structure with desirable query performance. Thus the index tree constructed is not required to be height balanced. This provides a means to trade-off imbalance in the index tree in order to reduce the number of pages which need to be accessed in a query.
摘要翻译： 一种用于构造多维索引树的装置和方法，其使得访问数据对象的时间最小化并且对数据的偏度有弹性。 这是通过从一个分区开始一次考虑一个级别并使用自上而下的方法，直到每个最终分区可以适合于叶节点内的所有给定数据对象的连续分区来实现的。 通过全局优化方法细分数据对象，以最小化每个节点覆盖的最小边界矩形的面积重叠和周长。 本发明将指数构造问题划分为两个子问题：第一个问题是使用每个索引节点处的小扇形物来解决包装的紧密度（面积，重叠和周长），另一个处理扇出问题 提高索引页面利用率。 这两个阶段被称为二值化和压缩。 二值化阶段构造二叉树，使得叶节点中的条目对应于空间数据对象。 压缩级将二进制树转换为树，除了叶节点和所有叶节点的父节点之外，所有叶节点都具有分支因子M.在二进制化阶段，使用加权或偏斜因子来确定数量的灵活性 的数据对象被包括在每个分区中以获得具有期望的查询性能的树结构。 因此，构建的索引树不需要高度平衡。 这提供了一种权衡索引树中的不平衡的方法，以减少查询中需要访问的页面数量。
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9.发明授权System and method for analyzing streams and counting stream items on multi-core processors 失效用于分析多核处理器上的流和计数流项目的系统和方法公开(公告)号：US08321579B2
公开(公告)日：2012-11-27
申请号：US11828732
申请日：2007-07-26
IPC分类号： G06F15/16
CPC分类号： G06F17/18
摘要： Systems and methods for parallel stream item counting are disclosed. A data stream is partitioned into portions and the portions are assigned to a plurality of processing cores. A sequential kernel is executed at each processing core to compute a local count for items in an assigned portion of the data stream for that processing core. The counts are aggregated for all the processing cores to determine a final count for the items in the data stream. A frequency-aware counting method (FCM) for data streams includes dynamically capturing relative frequency phases of items from a data stream and placing the items in a sketch structure using a plurality of hash functions where a number of hash functions is based on the frequency phase of the item. A zero-frequency table is provided to reduce errors due to absent items.
摘要翻译： 公开了并行流项计数的系统和方法。 将数据流划分为多个部分，并将这些部分分配给多个处理核。 在每个处理核心处执行顺序内核以计算用于该处理核心的数据流的分配部分中的项目的本地计数。 为所有处理核心聚合计数，以确定数据流中项目的最终计数。 用于数据流的频率感知计数方法（FCM）包括从数据流动态地捕获项目的相对频率相位，并且使用多个散列函数将项目放置在草图结构中，其中多个散列函数基于频率相位 的项目。 提供零频率表以减少由于缺少项目导致的错误。
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10.发明授权Method for targeted advertising on the web based on accumulated self-learning data, clustering users and semantic node graph techniques 失效基于积累的自学习数据，聚类用户和语义节点图技术在网络上进行有针对性的广告方法公开(公告)号：US06714975B1
公开(公告)日：2004-03-30
申请号：US08829631
申请日：1997-03-31
IPC分类号： G06F15163
CPC分类号： G06Q30/0244 , G06Q30/02 , G06Q30/0256 , G06Q30/0277
摘要： A method for dynamically placing objects in slots on a web page in response to a current client request for the web page comprises the steps of classifying users into user groups based one or more user-characteristics, accumulating self-learning data based on user click behavior for each user group, matching the current client request with a corresponding user group and scheduling real-time selection of the slots for the objects on the web page based on the self-learning data of the corresponding user group.
摘要翻译： 响应于当前对网页的客户端请求而在网页上的时隙中动态放置对象的方法包括以下步骤：基于一个或多个用户特征将用户分类为用户组，基于用户点击行为累积自学习数据 针对每个用户组，将当前客户端请求与对应的用户组进行匹配，并且基于相应用户组的自学习数据调度对网页上的对象的时隙的实时选择。
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