Business Objects Software Limited

Sap Se

Alexei Potiagalov

Ivailo Ivanov

Qiang Han

Paul Ekeland

Sarah Menard

Negar Koochakzadeh

Cheong-Wei Chu

Madhu Ahluwalia

Embodiments provide an overview visualization of graph data by rendering a graph's structure based upon different node/edge types, rather than upon a total number of nodes and/or edges. These node/edge types are recognized based upon associated metadata. The small number of different node/edge types of a typical graph reduces components for representation in the overview, simplifying that visualization and facilitating user interaction. A user can switch between the graph overview, and a different view affording node-level details. In analysis and/or querying, switching between the overview and the detailed visualization can afford natural and intuitive control over data granularity. For example the overview visualization of graph data may allow a user to initially filter on particular node types, with the subsequent node-level view permitting exploration of finer details while maintaining the overall context of the original graph. Embodiments may transform graph query results into a tables readily consumed by relational-type databases.

Graph database visualization by node and edge type

A system includes reception, at a first computing node in a cluster of computing nodes, of a request to subscribe to a first message queue of a messaging middleware software component, determination, at the first computing node, of a preferred number of computing nodes of the cluster of computing nodes to listen to the first message queue, determination, at the first computing node and based on data stored in a shared database accessible to each computing node of the cluster, a current number of computing nodes of the cluster which are listening to the first message queue, and, based on a difference between the preferred number and the current number, sending one or more requests to the messaging middleware software component to associate one or more of the computing nodes of the cluster with the first message queue.

Rest gateway for messaging

Methods, systems, and computer program products for visually representing and displaying data are described. The visual representation may be a data animation. A data query may be submitted, a time measurement for processing the query may be obtained, and a sample size of the query may be adjusted based on the time measurement and a frame refresh rate of a data animation. A data animation may be generated based on one or more results of the query.

Methods, apparatus and system for analytics replay utilizing random sampling

Embodiments provide for querying and visualization of query results of graph data. An engine processes graph data to create metadata (e.g., in JSON format) identifying at least different node types and edge types that are present in a graph database. An overview visualization simplifies presentation of graph data by depicting only various different node types and graph types. The overview visualization may form the basis for formulating queries including the metadata, which are then promulgated to the graph database. Returned query results may be visualized as an overview or on a detailed node level, promoting insight and formulation of additional queries including node/edge type metadata. The engine may convert graph data query results into tabular form for consumption by relational database analytical tools. According to particular embodiments, an engine of an in-memory database may be particularly suited to perform graph data visualization, querying, and/or tabular conversion tasks.

Graph database querying and visualization

Methods and apparatus, including computer program products, are provided for spatial analytics. The method may include generating a query including spatial context information, the query structured to include a predicate for the query and the spatial context information, the spatial context information including at least one of feature information or geometric information; and sending the query to a spatial query processor including a database in order to generate a query response in accordance with the predicate and spatial context information. Related systems, methods, and articles of manufacture are also disclosed.

Spatial analytics extensions

A computer-implemented method for finding related datasets includes, for each reference dataset from multiple reference datasets, determining domains and geographies for a user dataset and the reference dataset, obtaining a weighted domain coefficient and a weighted geography coefficient using the determined domains and geographies for the user dataset and the reference dataset, calculating a correlation coefficient between the user dataset and the reference dataset and calculating a semantic filtering coefficient for the user dataset and the reference dataset using the calculated correlation coefficient, the weighted domain coefficient and the weighted geography coefficient.

Semantic filtering in data matching

Various embodiments of systems and methods for providing a secure communication are described herein. A client application generates a Distributed Ruby (DRb) request based on a request received from a user. The obtained DRb request is wrapped to obtain an HTTPS request, which includes the DRb request and one or more authentication information. The generated HTTPS request is forwarded to an HTTPS server, which verifies the HTTPS request based on the authentication information. The HTTPS request is then unwrapped to obtain the DRb request, which is executed by a DRb server to obtain a result of execution of the DRb request.

Secure communication of distributed ruby requests

Disclosed are systems and methods for synchronizing data present in a target data set with data present in a source data set. The data present in the target data set is partitioned to determine a partition definition of the target data set. A target message digest is calculated for the data present in each partition of the target data set. Based upon the partition definition of the target data set, the data present in the source data set is partitioned. A source message digest is calculated for the data present in each partition of the source data set. The source message digest of a partition is compared with the target message digest of a corresponding partition to capture a change in data present in the source data set. Based upon the captured data, the data in the target data set is synchronized with the data in the source data set.

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Richmond, CA, United States of America

Alexei Potiagalov