公开(公告)号：US20160132359A1

公开(公告)日：2016-05-12

申请号：US14900718

申请日：2014-03-06

Applicant: NEC CORPORATION

Inventor： Masato ASAHARA ,  Kazuyo NARITA ,  Jianquan LIU

IPC: G06F9/50 ,  H04L12/26 ,  H04L29/08

CPC classification number: G06F9/5083 ,  G06F9/5088 ,  G06F11/3419 ,  G06F11/3452 ,  G06F2209/508 ,  H04L41/5009 ,  H04L43/50 ,  H04L67/10

Abstract: An abnormality detection apparatus (2000) handles tasks allocated to a plurality of processing servers (3200) as processing targets in a distribution system (3000) having the processing servers (3200). A history acquisition unit (2020) acquires progress history information which is information regarding progress of the plurality of tasks at a plurality of time point of recording. A target range determination unit (2040) determines a target range. A distribution calculation unit (2060) calculates a task speed distribution which is a probability distribution of processing speeds of the tasks using the progress history information regarding the plurality of tasks. An abnormality determination unit (2080) compares a processing speed of a task to be determined with the task speed distribution to thereby determine whether or not the processing speed of the task to be determined is abnormal.

Abstract translation: 异常检测装置（2000）处理分配给多个处理服务器（3200）的任务作为具有处理服务器（3200）的分发系统（3000）中的处理目标。 历史获取单元（2020）获取作为多个记录时刻的多个任务的进度信息的进度历史信息。 目标范围确定单元（2040）确定目标范围。 分配计算单元（2060）使用关于多个任务的进度历史信息来计算作为任务的处理速度的概率分布的任务速度分布。 异常确定单元（2080）将要确定的任务的处理速度与任务速度分布进行比较，从而确定要确定的任务的处理速度是否异常。

公开(公告)号：US20170316055A1

公开(公告)日：2017-11-02

申请号：US15523729

申请日：2014-12-01

Applicant: NEC Corporation

Inventor： Ting CHEN ,  Kazuyo NARITA

IPC: G06F17/30

Abstract: To reduce the overall computation time of a batch of queries, multiple query optimization in SQL-on-Hadoop systems groups multiple MapReduce jobs converted from queries into a single one, thus avoiding redundant computations by taking sharing opportunities of data scan, map function and map output. SQL-on-Hadoop converts a query into a DAG of MapReduce jobs and each map function is a part of query plan composed of a sequence of relational operators. As each map function is a part of query plan which is usually complex and heavy, disclosed method creates a cost model to simulate the computation time which takes both I/O cost for reading/writing input file and intermediate data and CPU cost for the computation of map function into consideration. A heuristic algorithm is disclosed to find near-optimal integrated query plan for each group based on an observation that each query plan is locally optimal.

公开(公告)号：US20170083670A1

公开(公告)日：2017-03-23

申请号：US15126413

申请日：2015-03-18

Applicant: NEC CORPORATION

Inventor： Yuki KOSAKA ,  Kazuyo NARITA ,  Satoshi MORINAGA ,  Ayumi ENDO ,  Kaori YAMADA ,  Aiko KUMANO ,  Maki KOMAMINE

IPC: G06F19/00

CPC classification number: G06F19/326 ,  G16H50/20

Abstract: A method of extracting a combination of a drug and an adverse event related to the drug includes: for each of positive example combinations, negative example combinations and combinations that are neither positive examples nor negative examples, which are combinations of drug and disease, extracting medical events from medical information data about a patient and generating attribute data based on time-series information about the medical events; and learning a discriminant model based on attribute data of the positive and negative examples; and inputting attribute data corresponding to the combinations that are neither positive examples nor negative examples to the discriminant model to determine scores.

公开(公告)号：US20170053212A1

公开(公告)日：2017-02-23

申请号：US15119070

申请日：2015-02-16

Applicant: NEC Corporation

Inventor： Kazuyo NARITA

IPC: G06N99/00

CPC classification number: G06N20/00

Abstract: Even in circumstances where the size of training data is more than the memory size of a calculator, CD method can be used.A data management apparatus (101) according to the present invention includes a blocking unit (20) which divides training data representing matrix data into a plurality of blocks, and generates meta data indicating a column for which each block holds a value of the original training data, and a re-blocking unit (40) which, when a component of a parameter learned from the training data converges to zero, replaces an old block including an unnecessary column, among the plurality of blocks, with a block from which the unnecessary column has been removed, and regenerates the meta data.

Abstract translation: 根据本发明的数据管理装置（101）包括：分块单元（20），其将表示矩阵数据的训练数据划分成多个块，并生成表示每个块保持原始训练值的列的元数据 数据和重新阻塞单元（40），当从训练数据中学习的参数的分量收敛到零时，将包括多个块中的不必要的列的旧块替换为多个块中的不必要的列 列已被删除，并重新生成元数据。