专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明授权

US09916472B2 Obfuscation and protection of data rights 有权
公开(公告)号：US09916472B2
公开(公告)日：2018-03-13
申请号：US14805514
申请日：2015-07-22
申请人： International Business Machines Corporation
发明人： Reinhard W. Heckel , Michail Vlachos
IPC分类号： G06F21/62 , G06F21/16
CPC分类号： G06F21/6245 , G06F21/16 , G06F2221/0748
摘要： Embodiments of the present invention disclose a method, computer program product, and system for data obfuscation and right-protection. An initial matrix Xi, represents the initial data set of the application and final matrix Xf is obtained from Xi. The final matrix Xf is obtained by performing one of the following operations Xf=(P(Xi)+E)F; Xf=P(Xi)F+E; and Xf=P(XiF)+E. Where P(.) is a projection operator that projects an input initial matrix in a space having a lower dimension than the input matrix, E represents a noise matrix, and F represents a matrix as a perturbation series. The matrix F is represented as a perturbation series, whose leading term is the identity matrix I, one or more higher-order terms of the perturbation series embedding a secret, multiplicative noise, so as for a matrix multiplied by the matrix F is right-protected.

2. 发明授权

US09594787B2 Identifying relationships between entities using two-dimensional arrays of scalar elements and a block matrix and displaying dense blocks 有权
标题翻译：使用标量元素和块矩阵的二维数组来识别实体之间的关系，并显示密集块
公开(公告)号：US09594787B2
公开(公告)日：2017-03-14
申请号：US15094272
申请日：2016-04-08
申请人： International Business Machines Corporation
发明人： Abderrahim Labbi , Michail Vlachos
IPC分类号： G06F15/18 , G06F17/30 , G06T11/20 , G06F19/24 , G06K9/62 , H03M7/30
CPC分类号： G06F17/30336 , G06F17/30371 , G06F17/30598 , G06F17/30604 , G06F19/24 , G06K9/6223 , G06K9/6253 , G06T11/206 , H03M7/60
摘要： A computer-implemented method for identifying relationships between entities includes accessing a first data structure being a two-dimensional array of scalar elements (e, eij, ekl(i)) representable as a matrix, each of the scalar elements capturing a relationship between two entities; reorganizing the first data structure by clustering the scalar elements separately on each dimension of the two-dimensional array, to obtain a second data structure, representable as a K×M block matrix, wherein each block is a reordered sequence of rows and/or columns of the first data structure; compacting the second data structure by: determining two parallel block sequences, which are the most similar according to a given distance measure, the parallel block sequences being either distinct rows or distinct columns of blocks of the second data structure; and reorganizing the second data structure by merging the two determined sequences into a single block sequence.
摘要翻译：用于识别实体之间的关系的计算机实现的方法包括访问作为可表示为矩阵的标量元素（e，eij，ekl（i））的二维数组的第一数据结构，每个标量元素捕获二者之间的关系实体; 通过在二维阵列的每个维上分别对标量进行聚类来重组第一数据结构，以获得可表示为K×M块矩阵的第二数据结构，其中每个块是行和/或列的重排序列的第一个数据结构; 通过以下方式来压缩第二数据结构：确定与给定距离度量最相似的两个并行块序列，并行块序列是第二数据结构的块的不同行或不同列; 以及通过将两个确定的序列合并成单个块序列来重组第二数据结构。

3. 发明申请

US20180330192A1 Load-Balancing Training of Recommender System for Heterogeneous Systems 审中-公开
公开(公告)号：US20180330192A1
公开(公告)日：2018-11-15
申请号：US15595314
申请日：2017-05-15
申请人： International Business Machines Corporation
发明人： Kubilay Atasu , Celestine Duenner , Thomas Mittelholzer , Thomas Parnell , Charalampos Pozidis , Michail Vlachos
IPC分类号： G06K9/62 , G06F17/18 , G06F17/30 , G06F17/15
CPC分类号： G06K9/6223 , G06F17/153 , G06F17/18 , G06F17/30029
摘要： A method for parallelizing a training of a model using a matrix-factorization-based collaborative filtering algorithm may be provided. The model can be used in a recommender system for a plurality of users and a plurality of items. The method includes providing a sparse training data matrix, selecting a number of user-item co-clusters, and building a user model data matrix by matrix factorization such that a computational load for executing the determining updated elements of the factorized sparse training data matrix is evenly distributed across the heterogeneous computing resources.

4. 发明授权

US10176229B2 Guided keyword-based exploration of data 有权
公开(公告)号：US10176229B2
公开(公告)日：2019-01-08
申请号：US14789182
申请日：2015-07-01
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Abderrahim Labbi , Michail Vlachos , Anastasios Zouzias
IPC分类号： G06F17/30
摘要： A method for guided keyword-based exploration of data stored in a database includes providing, with a processing device, valid query templates for the data, wherein the provided valid query templates include static parts and dynamic parts; selecting those of the provided valid query templates that match a user-provided keyword; generating valid queries from the selected valid query templates using the data; and querying the data using a user-selected valid query selected from the generated valid queries.

5. 发明申请

US20180341657A1 GUIDED KEYWORD-BASED EXPLORATION OF DATA 审中-公开
公开(公告)号：US20180341657A1
公开(公告)日：2018-11-29
申请号：US16053951
申请日：2018-08-03
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Abderrahim Labbi , Michail Vlachos , Anastasios Zouzias
IPC分类号： G06F17/30
CPC分类号： G06F16/2453 , G06F16/2423 , G06F16/24549 , G06F16/24564 , G06F16/2457 , G06F16/24578 , G06F16/248 , G06F16/90324
摘要： A method for guided keyword-based exploration of data stored in a database includes providing, with a processing device, valid query templates for the data, wherein the provided valid query templates include static parts and dynamic parts; selecting those of the provided valid query templates that match a user-provided keyword; generating valid queries from the selected valid query templates using the data; and querying the data using a user-selected valid query selected from the generated valid queries.

6. 发明授权

US09524468B2 Method and system for identifying dependent components 有权
公开(公告)号：US09524468B2
公开(公告)日：2016-12-20
申请号：US14935476
申请日：2015-11-09
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Reinhard Wolfram Heckel , Vasileios Vasileiadis , Michail Vlachos
IPC分类号： G06N5/02 , G06N7/00
CPC分类号： G06N7/005 , G06N5/022 , G06N5/04
摘要： Embodiments include processing a data structure representing a dependency matrix having columns representing respective first components and rows representing respective second components. Aspects include assigning each cell of the matrix a value indicative of the level of dependency or indicative of an unknown dependency of a pair of first and second components forming the cell and assigning each component of the first and second components an affiliation vector indicative of the strength of affiliation of the component to N predefined initial clusters of cells of the matrix. Aspects also include determining a probability model using the affiliations vectors parameters and estimating the parameters of the probability model for a plurality of different numbers of clusters starting from the initial number N of clusters. Aspects further include computing a score for the parameters of the probability model estimated and selecting the parameters of the probability model with the highest computed score.

7. 发明申请

US20160063392A1 METHOD AND SYSTEM FOR IDENTIFYING DEPENDENT COMPONENTS 有权
标题翻译：识别相关组件的方法和系统
公开(公告)号：US20160063392A1
公开(公告)日：2016-03-03
申请号：US14935476
申请日：2015-11-09
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Reinhard Wolfram Heckel , Vasileios Vasileiadis , Michail Vlachos
IPC分类号： G06N7/00
CPC分类号： G06N7/005 , G06N5/022 , G06N5/04
摘要： Embodiments include processing a data structure representing a dependency matrix having columns representing respective first components and rows representing respective second components. Aspects include assigning each cell of the matrix a value indicative of the level of dependency or indicative of an unknown dependency of a pair of first and second components forming the cell and assigning each component of the first and second components an affiliation vector indicative of the strength of affiliation of the component to N predefined initial clusters of cells of the matrix. Aspects also include determining a probability model using the affiliations vectors parameters and estimating the parameters of the probability model for a plurality of different numbers of clusters starting from the initial number N of clusters. Aspects further include computing a score for the parameters of the probability model estimated and selecting the parameters of the probability model with the highest computed score.
摘要翻译：实施例包括处理表示依赖矩阵的数据结构，该依赖矩阵具有表示相应的第一分量的列和表示相应的第二分量的行。方面包括将矩阵的每个单元分配成指示依赖性水平的值或指示构成单元的一对第一和第二分量的未知依赖性，并且将第一和第二分量的每个分量分配给表示强度的附属矢量将组件与N个预定义的矩阵单元格的初始聚类相关联。方面还包括使用关联向量参数来确定概率模型，并从群集的初始数目N开始估计多个不同数量的群集的概率模型的参数。方面还包括计算估计的概率模型的参数的得分，并且选择具有最高计算得分的概率模型的参数。

8. 发明授权

US10528578B2 Method and device for data mining on compressed data vectors 有权
公开(公告)号：US10528578B2
公开(公告)日：2020-01-07
申请号：US14387139
申请日：2013-04-24
申请人： International Business Machines Corporation
发明人： Nikolaos Freris , Francesco Fusco , Michail Vlachos
IPC分类号： G06F16/2458 , G06F16/23
摘要： A method for data mining on compressed data vectors by a certain metric being expressible as a function of the Euclidean distance is suggested. In a first step, for each compressed data vector, positions and values of such coefficients having the largest energy in the compressed data vector are stored. In a second step, for each compressed data vector, the coefficients having not the largest energy in the compressed data vector are discarded. In a third step, for each compressed data vector, a compression error is determined in dependence on the discarded coefficients in the compressed data vector. In a fourth step, at least one of an upper and a lower bound for the certain metric is retrieved in dependence on the stored positions and the stored values of the coefficients having the largest energy and the determined compression errors.

9. 发明授权

US10147103B2 System and method for a scalable recommender system using massively parallel processors 有权
公开(公告)号：US10147103B2
公开(公告)日：2018-12-04
申请号：US15468308
申请日：2017-03-24
申请人： International Business Machines Corporation
发明人： Celestine Duenner , Thomas Parnell , Charalampos Pozidis , Vasileios Vasileiadis , Michail Vlachos
IPC分类号： G06F12/00 , G06Q30/02 , H04L29/08 , G06F13/16 , G06N3/04 , G06F9/30 , G06F9/52 , H04L12/801 , G06Q50/00
摘要： Methods and apparatus are provided to determine entities and attributes dependencies for creating recommendations of items or entities using a highly scalable architecture. For example, a user may be recommended an item if a probability model of the method determines that the user relates to the item although the user has no contact to the item before the method is performed. The methods and apparatus provide a data structure representing a matrix having rows representing entities and columns representing attributes of the entities. Each entity of the entities of the data structure may include a user and each attribute of the attributes of the data structure may include an item. A cell of the matrix may be formed by a component pair including an entity and an attribute. In this manner, the methods and apparatus provide an efficient way for processing the probability model.

10. 发明申请

US20170024575A1 OBFUSCATION AND PROTECTION OF DATA RIGHTS 有权
标题翻译： OBFUSCATION和数据权利的保护
公开(公告)号：US20170024575A1
公开(公告)日：2017-01-26
申请号：US14805514
申请日：2015-07-22
申请人： International Business Machines Corporation
发明人： Reinhard W. Heckel , Michail Vlachos
IPC分类号： G06F21/62
CPC分类号： G06F21/6245 , G06F21/16 , G06F2221/0748
摘要： Embodiments of the present invention disclose a method, computer program product, and system for data obfuscation and right-protection. An initial matrix Xi, represents the initial data set of the application and final matrix Xf is obtained from Xi. The final matrix Xf is obtained by performing one of the following operations Xf=(P(Xi)+E)F; Xf=P(Xi)F+E; and Xf=P(XiF)+E. Where P(.) is a projection operator that projects an input initial matrix in a space having a lower dimension than the input matrix, E represents a noise matrix, and F represents a matrix as a perturbation series. The matrix F is represented as a perturbation series, whose leading term is the identity matrix I, one or more higher-order terms of the perturbation series embedding a secret, multiplicative noise, so as for a matrix multiplied by the matrix F is right-protected.
摘要翻译：本发明的实施例公开了一种用于数据混淆和权限保护的方法，计算机程序产品和系统。初始矩阵Xi表示应用的初始数据集，并且从Xi获得最终矩阵Xf。通过执行以下操作之一获得最终矩阵Xf =（P（Xi）+ E）F; Xf = P（Xi）F + E; Xf = P（XiF）+ E。其中P（。）是投影运算符，其投影矩阵在具有比输入矩阵更小的维度的空间中，E表示噪声矩阵，F表示作为扰动序列的矩阵。矩阵F被表示为扰动序列，其前导项是单位矩阵I，嵌入秘密乘法噪声的扰动序列的一个或多个高阶项，以及乘以矩阵F的矩阵，保护。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式