Abstract: A displaying method and apparatus for application performance management are disclosed. The method includes obtaining data from a target application; mapping the data into a plurality of dimensions; displaying a plurality of layers corresponding to the plurality of dimensions; and displaying, based on a dimension associated with an object supported by the target application, a visualized node corresponding to the object at a corresponding layer of the plurality of layers.

Abstract: Embodiments leverage local data available from an on-premises Extract, Transfer, and Load (ETL) job, in order to efficiently perform remote processing (e.g., as implemented on the cloud). Connectivity data (e.g., target setup) and ETL logic (e.g., configuring data flattening, pivot transform, and/or data quality transform) is stored locally in a non-transitory computer readable storage medium. In response to receiving data transformed on-premises, the transformed data, connectivity data, and ETL logic are forwarded to a remote location for processing. Some embodiments may also forward the transformed data on to its original target on-premises (e.g., via a local database loader). Particular embodiments may provide hidden, Representational State Transfer (REST)-based loader(s) that duplicate output of the local ETL job. Embodiments conserve developer effort by allowing preparatory local ETL data that is already available on-premises, to be read and used for processing in a remote cloud intelligence system.

Abstract: The disclosure provides a displaying method and apparatus for application performance management. The method comprises: obtaining data from a target application; mapping the data into a plurality of dimensions; displaying a plurality of layers corresponding to the plurality of dimensions; an displaying, based on a dimension associated with an object supported by the target application, a visualized node corresponding to the object at a corresponding layer of the plurality of layers.

Abstract: A system and method of querying data. The method includes transforming first data according to a unified data model. The unified data model has a hierarchical structure with tree nodes and leaf nodes. A leaf node contains a table. The method further includes executing a unified data model query on the first data (having been transformed) to result in second data. The method further includes outputting the second data.

Abstract: A computer implemented system, program product, and method that organizes hierarchical data into a plurality of columns is disclosed. A schema interface is defined for the data and two types of columns, value columns and occurrence columns, are used. Each value column stores the values for a field. Each occurrence column stores the occurrence numbers for a node that is repeatable, or optional, or in a choice group. The hierarchical relationship of the data is jointly preserved by the schema interface and the occurrence numbers in the occurrence columns. A database management system built upon the method is capable of operating at higher efficiency by combining some of the best aspects of relational database and hierarchical database management systems. The computer implemented method also provides a new and efficient method of exchanging data over networks.

Abstract: A computer implemented system, program product, and method that organizes hierarchical data into a plurality of columns is disclosed. A schema interface is defined for the data and two types of columns, value columns and occurrence columns, are used. Each value column stores the values for a field. Each occurrence column stores the occurrence numbers for a node that is repeatable, or optional, or in a choice group. The hierarchical relationship of the data is jointly preserved by the schema interface and the occurrence numbers in the occurrence columns. A database management system built upon the method is capable of operating at higher efficiency by combining some of the best aspects of relational database and hierarchical database management systems. The computer implemented method also provides a new and efficient method of exchanging data over networks.

Abstract: A system and method of querying data. The method includes transforming first data according to a unified data model. The unified data model has a hierarchical structure with tree nodes and leaf nodes. A leaf node contains a table. The method further includes executing a unified data model query on the first data (having been transformed) to result in second data. The method further includes outputting the second data.

Abstract: Embodiments relate to graphical representation and/or automatic generation of an iteration rule in mapping design that is to integrate or transform one or more input data sets into another target data set. The input and output data set can be of flat or hierarchical in nature. In an embodiment, a graphical interface allows users to specify an iteration rule (e.g. JOIN operation in a relational database) in a tree-like structure (e.g. a JOIN tree). The interface allows users to visualize and implement complicated and powerful combinations of multiple data sets, including data sets exhibiting hierarchical structure. Drag-and-drop techniques may be employed to reduce the need for manual typing. Also disclosed are procedures automatically generating an iteration rule based on the data mapping information, thereby reducing a need for manual mapping.

Abstract: A data transform leverages a known hierarchy within a target data structure, in order to improve query and mapping capabilities and enhance performance. Where a target data structure is hierarchical, output data of that target data structure is often built in the document order of the nodes in the structure (from top down and from left to right). Hence, when the data for a child node in the target structure is being built, the data for the parent nodes of the child node has been built. Embodiments utilize this available portion of the target data in the form of target columns, to increase processing efficiency of the transformation process. Use of target columns according to embodiments may also allow powerful and concise expression of mapping logic in the transform, facilitating the use of functions such as selection (e.g. Where clauses), uniqueness (e.g. DISTINCT), ordering (Order By, Group By), and Aggregation.