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    Automatic Optimization of Continuous Processes
    4.
    发明申请
    Automatic Optimization of Continuous Processes 审中-公开
    连续过程的自动优化

    公开(公告)号：US20160283254A1

    公开(公告)日：2016-09-29

    申请号：US14665292

    申请日：2015-03-23

    申请人： Dell Software, Inc.

    发明人： Thomas Hill ,  Pawel Lewicki

    IPC分类号： G06F9/445

    CPC分类号： G06F9/44505 ,  G05B19/41865 ,  G06F9/44552 ,  G06Q10/06

    摘要： A system, method, and computer-readable medium are disclosed performing an optimization operation. The optimization operation optimizes continuous processes by identifying process states associated with specific ranges for a limited subset of control parameter inputs. In certain embodiments, the optimization operation states comprise clear, stable, and robust process states. Such an optimization operation provides a simpler and cost effective means to optimize continuous processes. Additionally, such an optimization operation is applicable more rapidly to a wider range of real-world operational issues as they occur regularly in continuous process scenarios.

    摘要翻译： 公开了一种执行优化操作的系统，方法和计算机可读介质。 优化操作通过识别与控制参数输入的有限子集的特定范围相关联的过程状态来优化连续过程。 在某些实施例中，优化操作状态包括清晰，稳定和鲁棒的处理状态。 这种优化操作为优化连续过程提供了更简单和成本有效的手段。 此外，这种优化操作可以在更广泛的现实世界操作问题中更快地适用，因为它们在连续过程情景中经常出现。

    Method for In-Database Feature Selection for High-Dimensional Inputs
    6.
    发明申请
    Method for In-Database Feature Selection for High-Dimensional Inputs 审中-公开

    公开(公告)号：US20170316050A1

    公开(公告)日：2017-11-02

    申请号：US15139672

    申请日：2016-04-27

    申请人： Dell Software, Inc.

    发明人： Thomas Hill ,  Vladimir S. Rastunkov ,  George R. Butler

    IPC分类号： G06F17/30 ,  G06F3/0484

    CPC分类号： G06F16/2428

    摘要： A system, method, and computer-readable medium for performing in-database operations, comprising: presenting an automation interface to a user, the user interface automation interface enabling a user to select one or more key performance indicators; instantiating an in-database processing operation, the in-database processing operation performing feature selection from a high dimensional parameter space; executing at least one database statement within the storage system to derive a subset of diagnostic parameters from the high dimensional parameter space.

    Method for Performing In-Database Distributed Advanced Predictive Analytics Modeling via Common Queries
    7.
    发明申请
    Method for Performing In-Database Distributed Advanced Predictive Analytics Modeling via Common Queries 审中-公开
    通过常见查询执行数据库内分布式高级预测分析建模的方法

    公开(公告)号：US20170046344A1

    公开(公告)日：2017-02-16

    申请号：US14826770

    申请日：2015-08-14

    申请人： Dell Software, Inc.

    发明人： George R. Butler ,  Thomas Hill ,  Vladimir S. Rastunkov

    IPC分类号： G06F17/30 ,  G06N5/04

    摘要： A system, method, and computer-readable medium for performing a distributed analytics operation. The distributed analytics operation uses interface technologies to de-couple an actual data storage technology from an implementation of distributed analytics. Such a distributed analytics operation obviates requirements to deploy specific computer code onto a data storage platform to specifically target that platform for distributed predictive analytics computations.

    摘要翻译： 一种用于执行分布式分析操作的系统，方法和计算机可读介质。 分布式分析操作使用接口技术将实际的数据存储技术与分布式分析的实现相结合。 这种分布式分析操作避免了将特定计算机代码部署到数据存储平台上以特定地针对该平台进行分布式预测分析计算的要求。

    Graph Theory and Network Analytics and Diagnostics for Process Optimization in Manufacturing
    8.
    发明申请
    Graph Theory and Network Analytics and Diagnostics for Process Optimization in Manufacturing 有权
    图形理论和网络分析与制造过程优化诊断

    公开(公告)号：US20160306332A1

    公开(公告)日：2016-10-20

    申请号：US14690600

    申请日：2015-04-20

    申请人： Dell Software, Inc.

    发明人： Thomas Hill ,  Pawel Lewicki

    IPC分类号： G05B19/042 ,  G05B15/02

    CPC分类号： G05B19/0426 ,  G05B15/02 ,  G05B2219/25064 ,  G06F17/10 ,  G06F17/50 ,  G06F17/509 ,  G06Q10/06 ,  G06Q50/04 ,  Y02P90/30

    摘要： A system, method, and computer-readable medium are disclosed for analysis and characterization of manufacturing information such as process trees or genealogies using graph theory. More specifically, using graph theory to analyze manufacturing information of a manufacturing operation allows for deep analysis of relationships between batches or units in a process tree and their closeness or distance, to identify clusters associated with specific quality characteristics or problems, to identify common antecedents of specifically labeled batches (e.g., problem batches), and/or to detect overall desirable or undesirable characteristics of the process tree (e.g., centrality, etc.).

    摘要翻译： 公开了一种系统，方法和计算机可读介质，用于使用图论来分析和表征制造信息，例如过程树或家谱。 更具体地说，使用图论来分析制造操作的制造信息，可以深入分析过程树中的批次或单元之间的关系及其近似或距离，以识别与特定质量特征或问题相关联的集群，以识别常见的前提 特定标记的批次（例如，问题批次）和/或检测过程树的总体期望或不期望的特征（例如，中心性等）。

    Auto Query Construction for In-Database Predictive Analytics
    9.
    发明申请
    Auto Query Construction for In-Database Predictive Analytics 审中-公开

    公开(公告)号：US20170262502A1

    公开(公告)日：2017-09-14

    申请号：US15067643

    申请日：2016-03-11

    申请人： Dell Software, Inc.

    发明人： Vladimir S. Rastunkov ,  Thomas Hill ,  George R. Butler

    IPC分类号： G06F17/30 ,  G06N7/00

    摘要： A system, method, and computer-readable medium for performing an auto-query construction operation for use with a distributed analytics operation. More specifically, in certain embodiments, the auto-query construction operation provides automatically generates SQL code instructions via an auto-query construction user interface (UI) settings in a computational system, such as the Dell Statistica computational system. The auto-query construction operation allows a user to interact with a common interface to provide query information including decision variables, parameters of an analysis and convergence criteria. The query information provided via the UI is automatically transformed to database queries and subsequent computation system operations. Thus, the user experience remains intact whether the analytics is performed in database or within the computation system.

    Adaptive Sampling via Adaptive Optimal Experimental Designs to Extract Maximum Information from Large Data Repositories
    10.
    发明申请
    Adaptive Sampling via Adaptive Optimal Experimental Designs to Extract Maximum Information from Large Data Repositories 有权
    通过自适应优化实验设计进行自适应采样，从大数据存储库中提取最大信息

    公开(公告)号：US20160283524A1

    公开(公告)日：2016-09-29

    申请号：US14666918

    申请日：2015-03-24

    申请人： Dell Software, Inc.

    发明人： Thomas Hill ,  Pawel Lewicki

    IPC分类号： G06F17/30

    CPC分类号： G06F17/30292 ,  G06F17/30306

    摘要： A system, method, and computer-readable medium for extracting the samples from big data to extract most information about the relationships of interest between dimensions and variables in the data repository. More specifically, extracting information from large data repositories follows an adaptive process that uses systematic sampling procedures derived from optimal experimental designs to target from a large data set specific observations with information value of interest for the analytic task under consideration. The application of adaptive optimal design to guide exploration of large data repositories provides advantages over known big data technologies.

    摘要翻译： 一种用于从大数据提取样本的系统，方法和计算机可读介质，以提取关于数据存储库中维度和变量之间的关注关系的大部分信息。 更具体地说，从大数据存储库中提取信息遵循自适应过程，该过程使用从最佳实验设计得出的系统抽样程序，从具有所考虑的分析任务的感兴趣信息值的大数据集特定观察中指定目标。 自适应优化设计的应用指导大型数据存储库的探索提供了超过已知的大数据技术的优势。