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    • 1. 发明授权
    • Near real-time analysis of dynamic social and sensor data to interpret user situation
    • 近实时分析动态社会和传感器数据来解释用户情况
    • US08838516B2
    • 2014-09-16
    • US13413546
    • 2012-03-06
    • Anugeetha KunjithapathamPriyang RathodThomas PhanSimon Gibbs
    • Anugeetha KunjithapathamPriyang RathodThomas PhanSimon Gibbs
    • G06F17/00G06N3/00
    • G06N3/00G06F17/30867
    • In a first embodiment of the present invention, a method of interpreting a situation of a user of an electronic device is provided, comprising: gathering social data related to the user; monitoring one or more physical sensors on the electronic device in order to gather physical data related to the user; mapping structured data in the social data and the physical data to internal data types; extracting features from unstructured social data; identifying attributes related to the features; obtaining values for the identified attributes; interpreting movement of the user by examining the physical data; and detecting a situation of the user by fusing the movement of the user with the identified attributes of the features of the unstructured social data and features of structured social data.
    • 在本发明的第一实施例中,提供了一种解释电子设备用户的情况的方法,包括:收集与用户相关的社交数据; 监视电子设备上的一个或多个物理传感器,以便收集与用户相关的物理数据; 将社会数据中的结构化数据和物理数据映射到内部数据类型; 从非结构化社会数据中提取特征; 识别与特征相关的属性; 获取所识别属性的值; 通过检查物理数据解释用户的移动; 以及通过将用户的移动与非结构化社交数据的特征的已识别属性和结构化社交数据的特征融合来检测用户的情况。
    • 2. 发明申请
    • HEURISTICS-BASED SCHEDULING FOR DATA ANALYTICS
    • 用于数据分析的基于HEURISTICS的调度
    • US20130117752A1
    • 2013-05-09
    • US13353109
    • 2012-01-18
    • Wen-Syan LiThomas Phan
    • Wen-Syan LiThomas Phan
    • G06F9/46
    • G06F9/5066G06F11/3404G06F2209/501G06F2209/508
    • A scheduler may receive a plurality of jobs for scheduling of execution thereof on a plurality of computing nodes. An evaluation module may provide a common interface for each of a plurality of scheduling algorithms. An algorithm selector may utilize the evaluation module in conjunction with benchmark data for a plurality of jobs of varying types to associate one of the plurality of scheduling algorithms with each job type. A job comparator may compare a current job for scheduling against the benchmark data to determine a current job type of the current job. The evaluation module may further schedule the current job for execution on the plurality of computing nodes, based on the current job type and the associated scheduling algorithm.
    • 调度器可以在多个计算节点上接收用于调度其执行的多个作业。 评估模块可以为多个调度算法中的每一个提供公共接口。 算法选择器可以将评估模块与用于多种类型的多个作业的基准数据结合使用,以将多个调度算法中的一个与每个作业类型相关联。 作业比较器可将当前作业与基准数据进行比较,以确定当前作业的当前作业类型。 评估模块可以基于当前作业类型和相关联的调度算法进一步调度当前作业以在多个计算节点上执行。
    • 4. 发明申请
    • DISTRIBUTED MULTI-PHASE BATCH JOB PROCESSING
    • 分布式多相批处理作业处理
    • US20120284719A1
    • 2012-11-08
    • US13099814
    • 2011-05-03
    • THOMAS PHANJINGREN ZHOU
    • THOMAS PHANJINGREN ZHOU
    • G06F9/46G06F9/45
    • G06F9/4843
    • A distributed job-processing environment including a server, or servers, capable of receiving and processing user-submitted job queries for data sets on backend storage servers. The server identifies computational tasks to be completed on the job as well as a time frame to complete some of the computational tasks. Computational tasks may include, without limitation, preprocessing, parsing, importing, verifying dependencies, retrieving relevant metadata, checking syntax and semantics, optimizing, compiling, and running. The server performs the computational tasks, and once the time frame expires, a message is transmitted to the user indicating which tasks have been completed. The rest of the computational tasks are subsequently performed, and eventually, job results are transmitted to the user.
    • 分布式作业处理环境,包括能够接收和处理用户提交的后端存储服务器上的数据集作业查询的服务器或服务器。 服务器识别在作业上完成的计算任务以及完成一些计算任务的时间框架。 计算任务可以包括但不限于预处理,解析,导入,验证相关性,检索相关元数据,检查语法和语义,优化,编译和运行。 服务器执行计算任务,一旦时间段到期,向用户发送一条消息,指示哪些任务已经完成。 随后执行其余的计算任务,最终将作业结果传送给用户。
    • 6. 发明申请
    • SYSTEM AND METHOD FOR AUTOMATING AND SCHEDULING REMOTE DATA TRANSFER AND COMPUTATION FOR HIGH PERFORMANCE COMPUTING
    • 用于自动化和调度远程数据传输和高性能计算的计算的系统和方法
    • US20080178179A1
    • 2008-07-24
    • US11624253
    • 2007-01-18
    • Ramesh NatarajanThomas PhanSatoki Mitsumori
    • Ramesh NatarajanThomas PhanSatoki Mitsumori
    • G06F9/44
    • G06F9/5027
    • The invention pertains to a system and method for a set of middleware components for supporting the execution of computational applications on high-performance computing platform. A specific embodiment of this invention was used to deploy a financial risk application on Blue Gene/L parallel supercomputer. The invention is relevant to any application where the input and output data are stored in external sources, such as SQL databases, where the automatic pre-staging and post-staging of the data between the external data sources and the computational platform is desirable. This middleware provides a number of core features to support these applications including for example, an automated data extraction and staging gateway, a standardized high-level job specification schema, a well-defined web services (SOAP) API for interoperability with other applications, and a secure HTML/JSP web-based interface suitable for non-expert and non-privileged users.
    • 本发明涉及用于在高性能计算平台上支持计算应用的执行的一组中间件组件的系统和方法。 本发明的具体实施方案用于在Blue Gene / L并行超级计算机上部署金融风险应用程序。 本发明涉及将输入和输出数据存储在诸如SQL数据库的外部数据库中的任何应用程序,其中外部数据源和计算平台之间的数据的自动预分段和后期是期望的。 该中间件提供了许多核心功能来支持这些应用程序,例如自动数据提取和登台网关,标准化的高级工作规范模式,与其他应用程序互操作的明确定义的Web服务(SOAP)API,以及 一种适用于非专家和非特权用户的安全的基于HTML / JSP Web的界面。
    • 7. 发明授权
    • Distributed multi-phase batch job processing
    • 分布式多阶段批处理作业处理
    • US08966486B2
    • 2015-02-24
    • US13099814
    • 2011-05-03
    • Thomas PhanJingren Zhou
    • Thomas PhanJingren Zhou
    • G06F9/46G06F9/48
    • G06F9/4843
    • A distributed job-processing environment including a server, or servers, capable of receiving and processing user-submitted job queries for data sets on backend storage servers. The server identifies computational tasks to be completed on the job as well as a time frame to complete some of the computational tasks. Computational tasks may include, without limitation, preprocessing, parsing, importing, verifying dependencies, retrieving relevant metadata, checking syntax and semantics, optimizing, compiling, and running. The server performs the computational tasks, and once the time frame expires, a message is transmitted to the user indicating which tasks have been completed. The rest of the computational tasks are subsequently performed, and eventually, job results are transmitted to the user.
    • 分布式作业处理环境,包括能够接收和处理用户提交的后端存储服务器上的数据集作业查询的服务器或服务器。 服务器识别在作业上完成的计算任务以及完成一些计算任务的时间框架。 计算任务可以包括但不限于预处理,解析,导入,验证相关性,检索相关元数据,检查语法和语义,优化,编译和运行。 服务器执行计算任务,一旦时间段到期,向用户发送一条消息,指示哪些任务已经完成。 随后执行其余的计算任务,最终将作业结果传送给用户。