公开(公告)号：US20170169073A1

公开(公告)日：2017-06-15

申请号：US14967684

申请日：2015-12-14

Applicant: International Business Machines Corporation

Inventor： Mir Hamid Pirahesh ,  Yuanyuan Tian

IPC: G06F17/30

CPC classification number: G06F17/30454 ,  G06F17/30477 ,  G06F17/30958

Abstract: Embodiments of the invention relate to executing graph path queries. A database stores data entities and attributes in node tables and stores links between nodes in an edge table. Edges form a path between a source node and a target node. A source node set is generated and joined with the edge table to produce a first intermediate set. Similarly, a target node set is generated and joined with the edge table to produce a second intermediate set. A result path is generated through a joining of the first and second intermediate paths and application of a length condition.

公开(公告)号：US09449096B2

公开(公告)日：2016-09-20

申请号：US14149422

申请日：2014-01-07

Applicant: International Business Machines Corporation

Inventor： Andrey L. Balmin ,  Bin Bi ,  John Sismanis ,  Yuanyuan Tian

IPC: G06F7/00 ,  G06F17/30

CPC classification number: G06F17/30867 ,  G06F17/30696 ,  G06F17/30864 ,  G06F17/3089 ,  G06Q30/0201 ,  G06Q50/01

Abstract: A computer determines social media influencers in a specific topic. The computer receives a dataset of information on a website, the information including a list of users of the website and a list of content that each user posts, wherein each user is associated with one or more other users. The computer identifies a plurality of variables associated with the dataset, wherein the plurality of variables represent the information of the dataset on the website. The computer executes a topic specific search based on the plurality of variables, the topic search providing at least another list of users representing influencers in a specific topic.

Abstract translation: 一台电脑确定一个特定主题的社会媒体影响者。 计算机接收网站上的信息数据集，该信息包括网站的用户列表和每个用户发布的内容的列表，其中每个用户与一个或多个其他用户相关联。 计算机识别与数据集相关联的多个变量，其中多个变量表示网站上数据集的信息。 计算机基于多个变量执行主题特定搜索，主题搜索提供代表特定主题中的影响者的至少另一用户列表。

公开(公告)号：US09400767B2

公开(公告)日：2016-07-26

申请号：US14108812

申请日：2013-12-17

Applicant: International Business Machines Corporation

Inventor： Andrey Balmin ,  Severin A. Corsten ,  John A McPherson, Jr. ,  Shirish Tatikonda ,  Yuanyuan Tian

IPC: G06F15/16 ,  G06F17/10 ,  G06F17/30 ,  G06F17/50

CPC classification number: G06F17/10 ,  G06F17/30958 ,  G06F17/509

Abstract: Embodiments relate to subgraph-based distributed graph processing. An aspect includes receiving an input graph comprising a plurality of vertices. Another aspect includes partitioning the input graph into a plurality of subgraphs, each subgraph comprising internal vertices and boundary vertices. Another aspect includes assigning one or more respective subgraphs to each of a plurality of workers. Another aspect includes initiating processing of the plurality of subgraphs by performing a series of processing steps comprising: processing the internal vertices and boundary vertices internally within each of the subgraphs; detecting that a change was made to a boundary vertex of a first subgraph during the internal processing; and sending a message from a first worker to which the first subgraph is assigned to a second worker to which a second subgraph is assigned in response to detecting the change that was made to the boundary vertex of the first subgraph.

Abstract translation: 实施例涉及基于子图的分布式图处理。 一方面包括接收包括多个顶点的输入图。 另一方面包括将输入图划分成多个子图，每个子图包括内部顶点和边界顶点。 另一方面包括将一个或多个相应子图分配给多个工人中的每一个。 另一方面包括通过执行一系列处理步骤来启动多个子图的处理，包括：在每个子图内部处理内部顶点和边界顶点; 检测在内部处理期间对第一子图的边界顶点的变化; 以及响应于检测到对所述第一子图的所述边界顶点所做的改变，将从所述第一子图分配给第一子图的消息发送到分配了第二子图的第二工作者。

公开(公告)号：US09396164B2

公开(公告)日：2016-07-19

申请号：US14058338

申请日：2013-10-21

Applicant: International Business Machines Corporation

Inventor： Berthold Reinwald ,  Shirish Tatikonda ,  Yuanyuan Tian

IPC: G06F17/16

CPC classification number: G06F12/0223 ,  G06F17/16 ,  G06F2212/251

Abstract: Embodiments of the invention relate to sparsity-driven matrix representation. In one embodiment, a sparsity of a matrix is determined and the sparsity is compared to a threshold. Computer memory is allocated to store the matrix in a first data structure format based on the sparsity being greater than the threshold. Computer memory is allocated to store the matrix in a second data structure format based on the sparsity not being greater than the threshold.

Abstract translation: 本发明的实施例涉及稀疏性驱动的矩阵表示。 在一个实施例中，确定矩阵的稀疏度并将稀疏性与阈值进行比较。 分配计算机存储器以基于稀疏度大于阈值的第一数据结构格式存储矩阵。 分配计算机存储器以基于不大于阈值的稀疏度将第二数据结构格式存储在矩阵中。

公开(公告)号：US09286044B2

公开(公告)日：2016-03-15

申请号：US14317016

申请日：2014-06-27

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Matthias Boehm ,  Douglas Burdick ,  Berthold Reinwald ,  Prithviraj Sen ,  Shirish Tatikonda ,  Yuanyuan Tian ,  Shivakumar Vaithyanathan

IPC: G06F9/44 ,  G06F9/45

CPC classification number: G06F8/445 ,  G06F8/443 ,  G06F8/45 ,  G06F8/452 ,  G06F9/4881

Abstract: Hybrid parallelization strategies for machine learning programs on top of MapReduce are provided. In one embodiment, a method of and computer program product for parallel execution of machine learning programs are provided. Program code is received. The program code contains at least one parallel for statement having a plurality of iterations. A parallel execution plan is determined for the program code. According to the parallel execution plan, the plurality of iterations is partitioned into a plurality of tasks. Each task comprises at least one iteration. The iterations of each task are independent.

Abstract translation: 提供了MapReduce之上的机器学习程序的混合并行化策略。 在一个实施例中，提供了用于并行执行机器学习程序的方法和计算机程序产品。 接收到程序代码。 程序代码包含至少一个具有多个迭代的并行的语句。 确定程序代码的并行执行计划。 根据并行执行方案，将多个迭代划分为多个任务。 每个任务包括至少一个迭代。 每个任务的迭代是独立的。