Abstract: The present disclosure involves systems, software, and computer implemented methods for resolving data location for queries in a multi-system instance landscape. One example method includes receiving a request for data for at least one entity that includes a qualified identifier that includes a system tenant qualifier and a local identifier. The system tenant qualifier identifies a system tenant in a multi-system tenant landscape and the local identifier identifies an entity instance of an entity in the system tenant. A routing policy table configured for the multi-system tenant landscape is identified and a cell is located in the routing policy table that corresponds to the entity and the system tenant. A routing policy is determined for routing the request based on the cell. The routing policy is used to determine a target system tenant to which to route the request and the request is provided to the target system tenant.

Abstract: A method for secure debugging in a multitenant cloud environment where an application server maintains a host application shared by multiple tenant users can be implemented. The method can receive a request from a tenant user to debug the host application associated with a tenant user, and responsive to the request, deploy an application runtime environment comprising an application container encapsulating the host application associated with the tenant user and a debugger container encapsulating a debugging software running on the application server. The method can set at least a breakpoint in source code of the host application through a user interface of the debugging software, run the host application associated with the tenant user in the application runtime environment, and evaluate an expression entered through the user interface of the debugging software after the host application associated with the tenant user hits the breakpoint.

Abstract: A collection of multiple software applications may be tested when a patch is issued. However, an emergency patch for a time-sensitive incident may not allow for full regression or functional testing. Provided herein are techniques for performing a multiple application smoke test. Access information, login information, and a success indicator are obtained for each of a plurality of software applications. A test plan including two or more test packages is determined. Each test package indicates a subset of applications and includes access information, login information, and success indicators corresponding to the subset. The test packages are executed in parallel, including authenticating, loading, and validating. Logs are generated and a user interface is provided to present the logs and whether validation of the application interfaces passed or failed, and a failure reason for failed tests.

Abstract: Technologies are described for facilitating data workload capture, workload replay, and comparing the performance of a plurality of database systems. A plurality of requests for database operations are received from one or more database clients. The requests for database operations are executed. For the plurality of requests for database operations, execution context information is generated for a plurality of execution contexts. For the plurality of requests for database operations, a plurality of performance measures are generated. The execution context information and the performance measures are stored in a workload capture store, such as a file. The workload capture file is useable to replay the captured workload at a second database system using at least a portion of the execution context information and at least a portion of the performance measures.

Abstract: Systems and methods include determination of training data instances associated with a respective time periods based on time-series data of each of several metrics, training of a score generator, based on the training data instances, to generate an outlier score, generation of surrogate time-series data of each of the metrics based on the time-series data of each of the metrics, determination of input data instances associated with each one of the respective time periods based on the surrogate time-series data, input of the input data instances to the trained score generator to generate an outlier score for each input data instance, determination of a threshold based on the outlier scores, identification of ones of the training data instances associated with an outlier score greater than the threshold, and identification of an anomaly associated with each of the training data instances associated with an outlier score greater than the threshold.

Abstract: Technologies are described for determining possible routes using pre-processing operations. The pre-processing operations determine possible routes from an origin location to a destination location by dynamically generating representations of the transportation networks at runtime. For example, the pre-processing operations can dynamically determine a geographic area, and/or multiple sub-areas, that covers the origin location and the destination location. Possible routes from the origin location to the destination location can then be determined using only those locations within the geographic area and/or sub-areas. The locations that make up the possible routes can be provided for route optimization that utilizes transportation schedules and/or additional transportation requirements.

Abstract: Provided are systems and methods for creating histograms with distinct value sketches integrated therein and for query processing based on the histograms with distinct value sketches. In one example, the method may include storing a histogram that comprises a representation of a bucket of data from a database and that includes a distinct value sketch with a distinct value attribute that identifies an estimated number of distinct values within the bucket of data, receiving a database query, generating a query execution plan for the database query based on the distinct value attribute of the bucket within the distinct value sketch embedded within the histogram, and executing the database query on the bucket of data from the database based on the generated query execution plan.

Abstract: In one embodiment, the present invention includes a computer-implemented method comprising storing data in an application using an application custom data type and application custom data structure. The data is stored in a database using the application custom data type and the application custom data structure. In one embodiment, a request is sent to access the data from the application to the database. The data is retrieved from the database in response to the request in the application custom data type and the application custom data structure. In one embodiment, the data is sent from the database to a shared memory in the application custom data type and the application custom data structure and the data is retrieved by the application from the shared memory in the application custom data type and the application custom data structure.

Abstract: A method for machine learning based database management is provided. The method may include training a machine learning model to detect an anomaly that is present and/or developing in a database system. The anomaly in the database system may be detected by at least processing, with a trained machine learning model, one or more performance metrics for the database system. In response to detecting the presence of the anomaly at the database system, one or more remedial actions may be determined for correcting and/or preventing the anomaly at the database system. The one or more remedial actions may further be sent to a database management system associated with the database system. Related systems and articles of manufacture are also provided.

Abstract: Systems and methods include determination of an integration style of a first interface between two or more applications, determination of a first message flow of the first interface, the first message flow between two of the two or more applications and associated with an integration domain and the integration style, determination of a first one or more key characteristic values associated with the first message flow, determination of a plurality of integration components associated with the integration domain and the integration style, each of the determined plurality of integration components associated with fulfillment data of each of a plurality of key characteristic values, determination, for each of the plurality of integration components, of an integration score based on fulfillment data associated with the integration component for the first one or more key characteristic values, and determination of one or more of the plurality of integration components to implement the first message flow based on th

Abstract: In an example embodiment, a solution is provided that detects performance degradation of a particular functionality or an overall system problem using machine learning, Mann-Kendall tests, and correlation tests. After a problem has been automatically detected, the first steps of a root cause analysis may be automatically performed, indicating whether, for example non-optimal ABAP coding, a database issue, or hardware or software bottleneck, or some combination thereof. This approach allows a system to rapidly identify a system performance problem and its root cause by combing several data sources. One can see immediately, for example, whether an ABAP code change, an expensive SQL statement, or the combination of both led to an increase in the average response times of a transaction over time.

Abstract: A system includes reception of a request for recovery of a database to a recovery time, determination of a recovery logical timestamp corresponding to the recovery time, identification of database log records associated with a logical timestamp preceding the recovery logical timestamp, application of the identified database log records to the database sequentially according to log sequence number, identification, after applying the identified database log records, of one or more open transactions on the database, identification of a first one or more of the one or more open transactions which are associated with a commit log record associated with a logical timestamp preceding the recovery logical timestamp, and, in response to identification of the first one or more of the one or more open transactions, committing of the first one or more of the one or more open transactions.

Abstract: Provided are systems and methods for auto-completing debriefing processing for a machine learning model pipeline based on a type of predictive algorithm. In one example, the method may include one or more of building a machine learning model pipeline via a user interface, detecting, via the user interface, a selection associated with a predictive algorithm included within the machine learning model pipeline, in response to the selection, identifying debriefing components for the predictive algorithm based on a type of the predictive algorithm from among a plurality of types of predictive algorithms, and automatically incorporating processing for the debriefing components within the machine learning model pipeline such that values of the debriefing components are generated during training of the predictive algorithm within the machine learning model pipeline.

Abstract: A user requests installation of a software package on a server. The server identifies an artifact for the software package installed on a first layer of an overlay file system. Based on dependency information for the artifact, the server identifies one or more additional artifacts that the artifact depends on, installed on additional layers of the overlay file system. Instead of duplicating the software package and its dependencies in storage for the user, the server updates a layer strategy for the user. A layer strategy indicates a set of layers to be used by an overlay file system in generating a logical file system. The updated layer strategy for the user includes both the layer for the artifact of the requested software package and the layers for the additional artifacts that the software package depends on.

Abstract: A method for compressing columnar data may include generating, for a data column included in a data chunk, a dictionary enumerating, in a sorted order, a first set of unique values included in the first data column. A compression technique for generated a compressed representation of the data column having a fewest quantity of bytes may be identified based at least on the dictionary. The compression technique including a dictionary compression applying the dictionary and/or another compression technique. A compressed data chunk may be generated by applying the compression technique to compress the data column included in the data chunk. The compressed data chunk may be stored at a database in a variable-size persistent page whose size is allocated based on the size of the compressed representation of the data column. Related systems and articles of manufacture are also provided.

Abstract: Provided are systems and methods for remotely debugging a software application hosted on a cloud platform. Rather than download and test code locally, a developer may login directly to the cloud platform, and debug the software application in its native cloud environment. In one example, the method may include establishing a communication channel between a remote computing terminal and an application instance hosted on a cloud platform, enabling port forwarding at the remote computing terminal to redirect requests from the remote computing terminal to the application instance hosted on the cloud platform via the communication channel, attaching a debugger to a port at the remote computing terminal that is associated with the communication channel, and forwarding, via the debugger, debugging commands input at the remote computing terminal to the application instance hosted on the cloud platform via the communication channel.

Abstract: Systems, methods, and computer products are described herein for identifying data inconsistencies within database tables associated with an application. A master data inconsistency evaluator receives data including at least one selection parameter within at least one database table. By the master data inconsistency evaluator evaluates the at least one selection parameter by comparing the at least one selection parameter with other database tables associated with the application to identify data inconsistencies. The master data inconsistency evaluator repairs the data inconsistencies to further facilitate an error free transaction.

Abstract: Methods and systems are used for end user creation of trusted integration pathways between different enterprise systems. As an example, a set of identity information associated with an end user is determined. A set of systems and a set of configurable trusted integration pathways that the end user is authorized to create a trusted integration pathway between two systems is determined. A selection of a source system and a destination system from the set of systems is received. A set of information from the source system that is allowed to be shared with the destination system based on pre-defined metadata is identified. A trusted integration pathway between the source system and the destination system is generated, based on a selection of at least a portion of the set of information and the pre-defined metadata.

Abstract: Techniques and solutions are provided for predicting elements of a routing. Such elements include processing resources used in processing a set of inputs, a sequence of processing resources used in processing a set of inputs, operations performed on the inputs, a sequence of the operations, standard values associated with the operations, and how inputs are allocated to processing resources or operations. A machine learning model is trained with a set of inputs and a set of labels for one or more elements of a routing. A set of inputs for inference data is provided to the trained model and a prediction for one of the routing elements is provided. For sequence information, training data can be used to generate a probability model which can be used to obtain an inferred sequence of processing resources or operations.

Abstract: Techniques and solutions are provided for encoding information for sets, including sets whose elements are arranged in a hierarchy. Values are defined for different levels of a hierarchy, where the values increase or decrease from a root of the hierarchy. A flattened representation of the hierarchy is generated by multiplying element values by a level value for a level at which a respective element is located. Values for parent and leaf nodes are defined, and a flattened representation of the hierarchy is generated by multiple elements values by the parent value or the leaf node value, depending on whether a respective elements is a parent or leaf node. Quantity values for a set of elements are encoded by adding a quantity of a given element to a value assigned to elements of a set definition that are present in a set.