Oracle International Corporation

Siva Ravada

Ying Hu

Ravi Kothuri

Richard James Anderson, Jr

Daniel Geringer

Jayanta Banerjee

Jayant Sharma

Bhuvan Bamba

Raja Chatterjee

Ravi Kanth V Kothuri

Robert Hanckel

Juan Carlos Reyes Martinez

Roberto Infante Suarez

Baris M Kazar

Ravikanth V Kothuri

Daniel Abugov

Shasank Kisan Chavan

Cheng-Hua Wang

Qingyun Xie

Zazhil Ha Herena Ulloa

Hector Alejandro Saucedo Briseno

Edward Fisher

Zhen Hua Liu

Vikas Arora

Seema Sundara

Aurosish Mishra

Avishkar Misra

Ambuj Kumar Singh

Liujian Qian

William Martinez Cortes

Honglei Zhu

Ning An

Weisheng Xu

Michael Axel Ludwig Horhammer

Zhihai Zhang

Nicole Alexander

David Esparza Alba

Techniques are introduced herein for maintaining geometry-type data on persistent storage and in memory. Specifically, a DBMS that maintains a database table, which includes at least one column storing spatial data objects (SDOs), also maintains metadata for the database table that includes definition data for one or more virtual columns of the table. According to an embodiment, the definition data includes one or more expressions that calculate minimum bounding box values for SDOs stored in the geometry-type column in the table. The one or more expressions in the metadata maintained for the table are used to create one or more in-memory columns that materialize the bounding box data for the represented SDOs. When a query that uses spatial-type operators to perform spatial filtering over data in the geometry-type column is received, the DBMS replaces the spatial-type operators with operators that operate over the scalar bounding box information materialized in memory.

Techniques for in-memory spatial object filtering

Techniques described herein perform spherical PIP analysis by detecting whether a test ray (defined by a test point (TP) and a point (EP) that is external to a spherical polygon) crosses edge arcs ('edges') of the polygon based on relative orientations of vertices of the test ray and edges. A classifier vector (CV) for a test ray is calculated based on the cross-product of the TP and the EP. Using the CV, the orientation of each vertex of the polygon with respect to the test ray is determined. Candidate edges having vertices with opposite orientations with respect to the test ray are identified. Crossing edges are determine by calculating CVs for each candidate edge, and determining orientations of the TP and EP with respect to each candidate edge. A set of crossing edges is determined, where the TP and the EP have opposite orientations with respect to each crossing edge.

Techniques for solving the spherical point-in-polygon problem

Systems, methods, and other embodiments associated with indexing spatial data in a distributed file system are described. A query window defining a portion of a spatial data space is received, and candidate partitions containing the spatial data within the query window are identified. An intermediate partition dataset comprising each of the identified candidate partitions is created. Execution of a distributed function is limited to the identified candidate partitions, and a data structure including at least one record included in the result of the query is generated, and returned as the result of the query over a communication network.

Spatial indexing using resilient distributed datasets

Systems, methods, and other embodiments associated with computerized geo-referencing of images are described. In one embodiment, a method includes extracting features from an image of a location. The method includes determining similarities between the extracted features and known features within vector data for the location. The method includes generating a data structure based upon the similarities. The method includes processing the similarities to identify a match between an extracted feature and a known feature. The method includes assigning geo-location data from the vector data for the known feature to the extracted feature. The method includes identifying a position of an object within the location using the geo-location data.

Computerized geo-referencing for images

Systems, methods, and other embodiments associated with indexing spatial data in a distributed file system using local indexes are described. In one embodiment, a method includes dividing the spatial data into splits. Each of the splits includes a portion of the spatial data and includes additional sub-units that further sub-divide the spatial data. The example method may also include populating index records for the sub-units using metadata extracted from the spatial data. The metadata for each of the sub-units describes sub-unit characteristics of a respective one of the sub-units and split characteristics of one of the splits associated with the respective one of the sub-units. The method includes indexing the spatial data by generating local indexes using the index records with one of the local indexes for each of the splits and generating a global index that identifies the local indexes.

Spatial indexing for distributed storage using local indexes

Systems, methods, and other embodiments are disclosed for identifying features within point cloud data. In one embodiment, point cloud data is read which represents multiple points of at least one point cloud in a multi-dimensional space. Each point in the point cloud data is defined by an attribute value quantifying an attribute of the point and a set of coordinates specifying a location of the point in the multi-dimensional space. The set of coordinates for each point is transformed into a space-filling distance value representing a distance along a space-filling curve. The points are sorted according to the space-filling distance values to generate a sorted order of the points. The points are traversed in the sorted order and output data points are derived, while traversing the points, based on a specified feature criterion. The output data points identify a feature within the at least one point cloud.

System and method providing a scalable and efficient space filling curve approach to point cloud feature generation

Computerized systems, methods, and other embodiments associated with generating virtual map tiles according to an ad hoc specification are described. In one embodiment, a method includes defining, in response to receiving a request through an interface of a map server, virtual map tiles according to an ad hoc specification. The ad hoc specification indicates characteristics of the virtual map tiles including at least a format and map content of the virtual map tiles. The method includes computing the virtual map tiles from at least the map content specified by the ad hoc specification and according to the format indicated by the ad hoc specification. Computing the virtual map tiles includes computing the virtual map tiles in response to receiving the request. The method includes transmitting the virtual map tiles to a client device to cause the viewable region of the electronic map to be displayed on the client device.

Generating virtual map tiles according to an ad hoc specification

Systems, methods, and other embodiments are disclosed for correcting errors in the geo-spatial locations of acquired image data. In one embodiment, acquired aerial or satellite image data is segmented to generate extracted boundary data. The extracted boundary data represents boundaries of features of a portion of the Earth's surface, but at incorrect geo-spatial coordinates. The extracted boundary data is matched to expected boundary data derived from ground truth data. The expected boundary data represents boundaries of the features at correct geo-spatial coordinates. Adjustment parameters are generated that represent a geo-spatial misalignment between the extracted boundary data and the expected boundary data. Metadata in a header of the acquired image data is modified to include the adjustment parameters. The adjustment parameters may be applied to the acquired image data to generate corrected image data at correct geo-spatial coordinates.

System and method providing automatic alignment of aerial/satellite imagery to known ground features

Techniques and systems for processing within-distance queries are provided. A query for geometry objects within a query distance of a query geometry is received. An in-memory R-tree (IMR-tree) is generated for the query geometry. The IMR-tree includes nodes corresponding to edges of the query geometry. An R-tree index for a plurality of candidate geometries is accessed. At least one node of the R-tree index is processed by: generating an expanded bounding geometry based on the query distance, and using the IMR-tree to determine a topological relationship between the expanded bounding geometry and the query geometry. When the expanded bounding geometry intersects the query geometry, if at least one within-distance test is satisfied, the candidate geometries associated with the selected node are added to a result set. Otherwise, if the selected node is a non-leaf node of the R-tree index, child nodes of the selected node are processed.

Methods for processing within-distance queries

An optimized method of processing queries requesting a description of a spatial relationship between a test geometry and a query geometry, such as points, lines, polygons, and collections thereof, is disclosed. A first part of the method finds a first spatial relationship between a minimum bounding rectangle (MBR) of the test geometry and an In-Memory R-tree (IMR-tree) built to describe the query geometry. If the first relationship does not specify the requested description, then a second part of the method uses the IMR-tree of the query geometry to find a second spatial relationship between the test geometry itself and the query geometry. Optimizations are applied to the first part and to the second part. Optimizations in the second part depend on the test geometry.

Methods for query processing of topological relationships among complex spatial objects

A method and apparatus for querying a database table containing point spatial data and without indexes is provided. A request for point spatial data in the table includes a query window provided by the user and describing an area of interest in which the user desires the point spatial data contained therein. The query window is tiled to create interior tiles and boundary tiles. A first query is formed to determine the point spatial data contained in the interior tiles. A second query is formed to determine the point spatial data contained within the boundary tiles and also within the query window. The second query includes a function that tests to determine whether the point spatial data within a boundary tile also lies within the query window. The first and second queries are executed in part on an enhanced data storage device and the results joined and returned to the user in answer to the request.

LIDAR model with hybrid-columnar format and no indexes for spatial searches

Systems, methods, and other embodiments associated with index-based optimization of geometric figured-related queries are described. In one embodiment, a method includes receiving two points selected from a corpus of spatial data. A hierarchical index on the data is accessed to choose candidate nodes. The index is a hierarchical arrangement of nodes arranged in paths from root node entries to leaf node entries such that each node is contained in all nodes in a path leading to the node. The method includes determining a spatial relationship between the two points and the candidate nodes in the index. The candidate nodes are a proper subset of the nodes in the index, such that the spatial relationship is not determined between the two points and some non-candidate nodes. A candidate node is selected based on the determined angles for processing related to construction of a geometric figure describing the spatial data.

Index-based optimization of convex hull and minimum bounding circle queries

Techniques for history enabling a table in a database system so that past versions of rows of the history-enabled table are available for temporal querying. The table is history enabled by adding a start time column to the table and creating a history table for the history-enabled table. The start time field's value in a row of the history-enabled table indicates when the contents of the row last changed. The rows of the history table are copies of rows of the history-enabled table that have been deleted or updated. The rows include end time fields whose values indicate when the row was updated or deleted. A history table's row thus indicates a period in which the history table's row was in the history-enabled table. Temporal queries are performed on a view which is the union of the history-enabled table and the history table.

Database system that provides for history-enabled tables

A method, system, and computer program product for implementing a multi-column/multi-row constraint. Lock techniques prevent access collisions even when two or more concurrently running software applications are attempting to insert rows into the same relational database table (subject to the same multi-column/multi-row constraints). The method commences by receiving (e.g., from a first process) a first row to be inserted into a relational database table where the row comprises at least two constrained columns corresponding to the multi-column/multi-row constraint, then receiving (e.g., from a second process) a second row to be inserted into the same table. Using column locks, the method recognizes the potential for conflicting row insertions, and manages locks to prevent conflicting access grants to the two constrained columns of the existing database table. The evaluation of the multi-column/multi-row constraint is performed over the first row to be inserted and a row or rows of the relational database table.

Implementing a multi-column/multi-row constraint in a relational database table

Techniques are described for memory-efficient spatial histogram construction. A hierarchical spatial index has leaf nodes and non-leaf nodes, each leaf node representing a bounding region containing a spatial object, each non-leaf node representing a bounding region at least partially containing one or more spatial objects. A plurality of selected nodes is selected from the plurality of non-leaf nodes. The plurality of selected nodes includes an ancestor of each leaf node. For each particular node in the plurality of selected nodes, a weight is determined. The weight is based on the number of spatial objects contained within the bounding region of the particular node. A spatial partitioning of the plurality of selected nodes is determined. A spatial histogram is generated based on the spatial partitioning of the weights of the plurality of selected nodes.

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Nashua, NH, United States of America

Siva Ravada