Abstract: Systems, methods, and techniques for supporting feature customization based on data source capability. A first request is received from an external entity to provision an instance of an application. An application service of a plurality of application services is identified to be implemented to fulfill the first request. A configuration of the application corresponding to the application service is determined. Customization information regarding states of a set of features of the application instance is obtained as a result of determining that a customization is associated with the external entity. A modified configuration is generated for the application based on the customization information and the configuration. An application instance is provisioned to the external entity over the one or more networks having the modified configuration.

Abstract: Systems, methods, and techniques for implementing a defined configuration for an application. A request is received to provision an instance of an application for a external entity. A resource identifier is obtained based on the request. A defined configuration of a plurality of defined configurations for the application is identified based on the resource identifier. A set of features is determined that are specified in the defined configuration. The instance of the application is generated having the set of features. A connection is established with an application resource of a plurality of application resources of the application based on the resource identifier. The instance having the set of features is provisioned over one or more networks to the external entity.

Abstract: Systems, methods, and techniques for supporting feature customization based on data source capability. A first request is received from an external entity to provision an instance of an application. An application service of a plurality of application services is identified to be implemented to fulfill the first request. A configuration of the application corresponding to the application service is determined. Customization information regarding states of a set of features of the application instance is obtained as a result of determining that a customization is associated with the external entity. A modified configuration is generated for the application based on the customization information and the configuration. An application instance is provisioned to the external entity over the one or more networks having the modified configuration.

Abstract: A system includes identification of dimension filters associated with a visualization, identification, from the identified dimension filters, of nested dimension filters associated with a first dimension, generation of a composite filter based on the identified nested dimension filters, transmission of a query to a data source, the query including the composite filter, reception of a result set based on the query from the data source, and display of the visualization presenting the result set on the display.

Abstract: A system includes identification of dimension filters associated with a visualization, identification, from the identified dimension filters, of nested dimension filters associated with a first dimension, generation of a composite filter based on the identified nested dimension filters, transmission of a query to a data source, the query including the composite filter, reception of a result set based on the query from the data source, and display of the visualization presenting the result set on the display.

Abstract: A non-transitory computer-readable storage medium may comprise instructions stored thereon. When executed by at least one processor, the instructions may be configured to cause a backend server to at least receive, from an administrator webserver, an extension, store the extension and an associated timestamp in a repository, the associated timestamp indicating a time at which the extension was received from the administrator webserver, receive a request for the extension from a customer webserver, the request for the extension identifying the extension, and in response to receiving the request for the extension, fetch the extension from the repository, and send the extension to the customer webserver.

Abstract: A non-transitory computer-readable storage medium may comprise instructions stored thereon. When executed by at least one processor, the instructions may be configured to cause a backend server to at least receive, from an administrator webserver, an extension, store the extension and an associated timestamp in a repository, the associated timestamp indicating a time at which the extension was received from the administrator webserver, receive a request for the extension from a customer webserver, the request for the extension identifying the extension, and in response to receiving the request for the extension, fetch the extension from the repository, and send the extension to the customer webserver.

Abstract: Data sets are analyzed to discover trends and determine predictive data models. A data model is determined based on analyzing the data set with a specific algorithm from a set of different analytical algorithms. The data model is stored in a structural file format. The data model is converted into an instantiated object model with the use of a predefined object model. The instantiated object model is converted into an in-database analysis model to score new data within a database system. The scoring is based on the logic in the data model. The in-database analysis model is stored on a database server part of a database system to provide analytical functionality defined in the data model. The new data can be scored with the in-database analysis model. The new data is not extracted from the database system and the scoring is accomplished in-memory.

Abstract: Methods and system for optimizing an execution of a business process are disclosed. In one aspect, a request to execute a business process is received. The business process is executed on multiple threads, which may include multiple computations. The business process is optimized by determining an optimal number of threads for executing the business process by a thread optimization model. From the determined optimal number of threads, the computations in the threads may be distributed or reallocated iteratively by executing an inter-thread computations optimization model. Executing the thread optimization model and the inter-thread computations optimization model optimizes the execution of the business process.

Abstract: An iterative approach to detect outliers may automatically detect outliers in a data set. The iterative approach may include calculating a mean and an average deviation for a list. The iterative approach may further include calculating the deviations of values of a list if the average deviation is not zero. Any values where the calculated deviation of the point or value is greater than the mean may be selected. The selected points may be set as a second list. The second list may be used by the iterative approach until no points are selected for the second list. The previous list is the set of outliers from the data set.

Abstract: Methods and system for optimizing an execution of a business process are disclosed. In one aspect, a request to execute a business process is received. The business process is executed on multiple threads, which may include multiple computations. The business process is optimized by determining an optimal number of threads for executing the business process by a thread optimization model. From the determined optimal number of threads, the computations in the threads may be distributed or reallocated iteratively by executing an inter-thread computations optimization model. Executing the thread optimization model and the inter-thread computations optimization model optimizes the execution of the business process.

Abstract: An iterative approach to detect outliers may automatically detect outliers in a data set. The iterative approach may include calculating a mean and an average deviation for a list. The iterative approach may further include calculating the deviations of values of a list if the average deviation is not zero. Any values where the calculated deviation of the point or value is greater than the mean may be selected. The selected points may be set as a second list. The second list may be used by the iterative approach until no points are selected for the second list. The previous list is the set of outliers from the data set.

Abstract: In one embodiment, a plurality of objects associated with a dataset and a specified number of nearest objects to be identified are received. The received objects are sorted in a structured format. Further, a key object and a number of adjacent objects corresponding to the key object are selected from the sorted plurality of objects, wherein the number of adjacent objects is selected based on the specified number of nearest objects to be identified. Furthermore, distances between the key object and the number of adjacent objects are determined to identify the specified number of nearest objects, wherein the distances are determined until the specified number of nearest objects is identified. Based on the determined distances, the specified number of nearest objects in the dataset is identified for data analysis.

Abstract: Data sets are analyzed to discover trends and determine predictive data models. A data model is determined based on analyzing the data set with a specific algorithm from a set of different analytical algorithms. The data model is stored in a structural file format. The data model is converted into an instantiated object model with the use of a predefined object model. The instantiated object model is converted into an in-database analysis model to score new data within a database system. The scoring is based on the logic in the data model. The in-database analysis model is stored on a database server part of a database system to provide analytical functionality defined in the data model. The new data can be scored with the in-database analysis model. The new data is not extracted from the database system and the scoring is accomplished in-memory.

Abstract: In one embodiment, a plurality of objects associated with a dataset and a specified number of nearest objects to be identified are received. The received objects are sorted in a structured format. Further, a key object and a number of adjacent objects corresponding to the key object are selected from the sorted plurality of objects, wherein the number of adjacent objects is selected based on the specified number of nearest objects to be identified. Furthermore, distances between the key object and the number of adjacent objects are determined to identify the specified number of nearest objects, wherein the distances are determined until the specified number of nearest objects is identified. Based on the determined distances, the specified number of nearest objects in the dataset is identified for data analysis.

Abstract: Various embodiments of systems and methods for performing predictive analysis are described herein. In one aspect, the method includes receiving a command for publishing a chain comprising a plurality of components connected together to perform predictive analysis. Based upon the command, a plurality of procedures corresponding to the plurality of components of the chain is generated. The generated procedures are integrated according to an order of connectivity of the components within the chain. A database object including the integrated procedures is generated. The database object is stored within a database. The stored database object is executable for performing predictive analysis.