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    IDENTIFYING JOIN RELATIONSHIPS BASED ON TRANSACTIONAL ACCESS PATTERNS
    11.
    发明申请
    IDENTIFYING JOIN RELATIONSHIPS BASED ON TRANSACTIONAL ACCESS PATTERNS 审中-公开
    基于交互访问模式识别加入关系

    公开(公告)号：US20160179921A1

    公开(公告)日：2016-06-23

    申请号：US14578786

    申请日：2014-12-22

    Applicant: Amazon Technologies, Inc.

    Inventor： Anurag Windlass Gupta ,  Timothy Andrew Rath ,  Srinivasan Sundar Raghavan ,  Santosh Kalki

    IPC: G06F17/30

    Abstract: A corpus of information describing queries used to access a transactional data store may be used to identify analytical relationships that are not explicitly defined in a schema or supplied by a user. Join relationships may be identified based on field coincidence in elements of queries in the corpus. Join relationships may be indicative of dimensions and attributes of a dimension. Hierarchy levels for a dimension may be identified based on factors including data type, reference in an aggregating clause, and reference in a grouping clause.

    Abstract translation: 描述用于访问事务数据存储的查询的信息语料库可用于识别在模式中未明确定义或由用户提供的分析关系。 可以基于语料库中的查询元素中的字段重合来识别加入关系。 连接关系可以表示维度的维度和属性。 可以基于包括数据类型，聚合子句中的引用和分组子句中的引用的因素来标识维的层次结构级别。

    DATA INTEREST ESTIMATION FOR N-DIMENSIONAL CUBE COMPUTATIONS
    12.
    发明申请
    DATA INTEREST ESTIMATION FOR N-DIMENSIONAL CUBE COMPUTATIONS 审中-公开
    数据利益估计用于N维计算

    公开(公告)号：US20150370883A1

    公开(公告)日：2015-12-24

    申请号：US14494524

    申请日：2014-09-23

    Applicant: Amazon Technologies, Inc.

    Inventor： Santosh Kalki ,  Srinivasan Sundar Raghavan ,  Timothy Andrew Rath ,  Mukul Vijay Karnik ,  Amol Devgan ,  Swaminathan Sivasubramanian

    IPC: G06F17/30

    Abstract: An online analytical processing system may comprise an n-dimensional cube partitioned into slices, in which each slice may represent data points at the intersections of fixed and variable dimensions. Computation of data points within a slice may be deferred. A dependency graph may be initially constructed, in which the dependency graph is utilized in a subsequent computation. Calculation of data points may be prioritized based on information indicative of a chance that the data points will be accessed.

    Abstract translation: 在线分析处理系统可以包括分割成切片的n维立方体，其中每个切片可以表示固定和可变维度的交点处的数据点。 切片内数据点的计算可能会延迟。 可以最初构建依赖图，其中在随后的计算中使用依赖图。 可以基于指示将访问数据点的机会的信息来优先考虑数据点的计算。

    USE OF DEPENDENCY GRAPHS TO DYNAMICALLY UPDATE N-DIMENSIONAL CUBES
    13.
    发明申请
    USE OF DEPENDENCY GRAPHS TO DYNAMICALLY UPDATE N-DIMENSIONAL CUBES 审中-公开
    使用依赖图来动态更新N维立方体

    公开(公告)号：US20150370882A1

    公开(公告)日：2015-12-24

    申请号：US14494513

    申请日：2014-09-23

    Applicant: Amazon Technologies, Inc.

    Inventor： Santosh Kalki ,  Srinivasan Sundar Raghavan ,  Mukul Vijay Karnik ,  Timothy Andrew Rath ,  Amol Devgan ,  Swaminathan Sivasubramanian

    IPC: G06F17/30

    Abstract: An online analytical processing system may comprise an n-dimensional cube structured using slice-based partitioning in which each slice comprises data points corresponding to a set of dimension values fixed across the slice and a set of dimension values allowed to vary. Slices may be partitioned and replicated across computing nodes. Views of the n-dimensional cube may be partially materialized by determining dependencies between slices. A central data dictionary may maintain information about slices and slice dependencies. Dimensions may be added by adding a new slice without requiring immediate recomputation of existing data points.

    Abstract translation: 在线分析处理系统可以包括使用基于片段的分区结构的n维立方体，其中每个切片包括对应于跨越切片固定的一组维度值的数据点和允许变化的一组维度值。 切片可以跨计算节点进行分区和复制。 可以通过确定切片之间的依赖性来部分地实现n维立方体的视图。 中央数据字典可以维护有关切片和切片依赖关系的信息。 可以通过添加新切片而不需要立即重新计算现有数据点来添加尺寸。

    DATA REPORTING SYSTEM AND METHOD
    17.
    发明申请
    DATA REPORTING SYSTEM AND METHOD 审中-公开

    公开(公告)号：US20190095499A1

    公开(公告)日：2019-03-28

    申请号：US15712917

    申请日：2017-09-22

    Applicant: Amazon Technologies, Inc.

    Inventor： John Payne ,  Yung Haw Wang ,  Mohan Rao Varthakavi ,  Jose Kunnackal John ,  Santosh Kalki ,  Mukul Vijay Karnik ,  Jared Scott Lundell

    IPC: G06F17/30 ,  G06Q40/00 ,  G06Q30/00

    Abstract: A data analysis system determines a set of characteristics of a data set that is provided by a user. In various embodiments, individual characteristics may be statistical measures, analytical insights, data trends, or relationships with other data sets. The data analysis system selects a subset of the characteristics to be presented to the user. In an embodiment, the data analysis system determines a level of importance for each characteristic based at least in part on metadata associated with the data set, and in some embodiments, user preferences provided by the user. In an embodiment, the metadata includes descriptive names, data types, and data characteristics of the data set and of data elements within the data set.

    EFFICIENT DETERMINATION OF JOIN PATHS VIA CARDINALITY ESTIMATION
    18.
    发明申请
    EFFICIENT DETERMINATION OF JOIN PATHS VIA CARDINALITY ESTIMATION 审中-公开
    通过心理估计有效地确定接合点

    公开(公告)号：US20160179894A1

    公开(公告)日：2016-06-23

    申请号：US14578841

    申请日：2014-12-22

    Applicant: Amazon Technologies, Inc.

    Inventor： Anurag Windlass Gupta ,  Timothy Andrew Rath ,  Srinivasan Sundar Raghavan ,  Santosh Kalki

    IPC: G06F17/30

    CPC classification number: G06F17/30498 ,  G06F17/30581

    Abstract: A probabilistic counting structure such as a hyperloglog may be formed during a table scan for each of a selected set of columns. The columns may be selected based on an initial estimate of relatedness, which may be based on data types of the respective columns. An estimated cardinality of an intersection or union of columns may be formed based on an intersection of the probabilistic data structures. A join path may be determined based on the estimated cardinality of an intersection or union of the columns.

    Abstract translation: 对于所选择的列中的每一列，可以在表扫描期间形成诸如超文本记录之类的概率计数结构。 可以基于相关性的初始估计来选择列，其可以基于相应列的数据类型。 可以基于概率数据结构的交集来形成交点或列的并集的估计基数。 可以基于交叉点或列的并集的估计基数来确定连接路径。

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