Abstract: Among other things, embodiments of the present disclosure relate to distributing the determination of target audience lists across multiple clusters of nodes. Other embodiments may be described and/or claimed.

Abstract: Disclosed herein are system, method, and device embodiments for implementing flow analysis in an integration platform system. An integration platform may provide a declaratively-configurable asset within an integration-application design tool, configure the declaratively-configurable asset based on declarative configuration information via the integration-application design tool, and package the declaratively-configurable asset into an integration application. Further, the integration platform may calculate a statistic associated with the declaratively-configurable asset based on the declarative configuration information during an execution of the integration application, and cause display of a graphic representation of the declaratively-configurable asset and the statistic in a monitoring tool.

Abstract: Systems and techniques are provided for enhanced and flexible ingestion of product-related data from various and diverse data sources. The product-related data is linked to interactions that allow end-user customers to view and manipulate product listings and product data using the ingested information.

Abstract: A system compares two network security specifications expected to implement the same network security policy for a network and identifies possible discrepancies between them. The system generates a representation of relations between subnetworks of the network for each network security specification. The representation efficiently stores permitted connections between subnetworks. The system compares the representations corresponding to the two network security specifications to identify discrepancies across the two network security specifications. If discrepancies are identified across the two network security specifications the system generating a report identifying the discrepancies.

Abstract: The present disclosure is directed to systems and methods for generating an API caching library using a shared resource file. For example, a method may include: receiving, at a first platform, a shared resource file comprising metadata for declaratively deriving an application programming interface (API) caching library for a native application operating on the first platform and a corresponding application related to the native application for a second platform; parsing the shared resource file to extract the metadata at run-time of the native application; declaratively deriving the API caching library based on the extracted metadata, the declaratively deriving the API caching library comprising creating a plurality of objects that represent respective API endpoints of the API caching library; and executing a function of the native application based on at least one of the API endpoints.

Abstract: Disclosed embodiments are a computing system and a computer-implemented method related to minimizing the number of rules/policies needed to be stored to enforce those rules/policies. The minimizing comprising generating adjacency data structures mapping as adjacent pairs of network nodes, which are allowed to communicate with one another according to the plurality rules, and applying them for pruning the rule dataset. This allows an original set of rules/policies to be reduced into a smaller set, which conserves computational resources.

Abstract: A computer-implemented method for dual sequence inference using a neural network model includes generating a codependent representation based on a first input representation of a first sequence and a second input representation of a second sequence using an encoder of the neural network model and generating an inference based on the codependent representation using a decoder of the neural network model. The neural network model includes a plurality of model parameters learned according to a machine learning process. The encoder includes a plurality of coattention layers arranged sequentially, each coattention layer being configured to receive a pair of layer input representations and generate one or more summary representations, and an output layer configured to receive the one or more summary representations from a last layer among the plurality of coattention layers and generate the codependent representation.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing a rich text box to customizable live applications in a cloud collaboration platform. The rich text box component may extend styles and functions performed within a document editor in the cloud collaboration platform to customizable live applications. This extension ensures a consistent user experience where the behavior of text-editing in live application mirrors that of the standardized document editor. Additional functionality, such as mentions and auto-completions, may be performed by the rich text boxes embedded in a live application without adding to the code base of the live application, complicating design decisions, and/or circumventing the security requirements of a live application.

Abstract: A method for testing a web service is disclosed, the method including testing, by a testing application executing on a client computing system, a web service running at least partially on a server computing system. The testing includes directing a web browser of the client computing system to simulate user interaction with the web service to perform a particular operation that includes a particular set of sub-operations. The directing includes issuing one or more requests to perform the particular set of sub-operations. The testing also includes recording test information based on responses received from the web service in response to performing the particular set of sub-operations. The test information includes latency information for at least one of the sub-operations of the particular set. The method also includes presenting, by the client computing system, a user interface having a graphical element depicting an analysis of the recorded test information.

Abstract: A computer-implemented method for enabling a mobile enterprise application of a database system to use a machine learning (ML) service comprises modifying, by a provider of the database system, a mobile enterprise application made available to one or more tenants of the database system to include a native code layer capable of downloading and invoking a trained ML model. One or more servers of the database system receives an upload of the trained ML model and configuration data from a tenant user, wherein the configuration data defines a format of input data and a format of output data of the trained ML model. The one or more servers download the trained ML model to the mobile enterprise application running on a mobile device in response to a request by the native code layer of the mobile enterprise application.

Abstract: Methods and systems are provided for providing an insights page that is displayed at a user interface of a computing device in response to a call. The caller who is making a call and/or the callee that is receiving the call can be identified along with one or more relevant organizations. A customer relationship management (CRM) application can then be launched at the computing device, and relevant records that are relevant to the call can be automatically determined at a server system. The relevant records can include records that are determined to be relevant to the call from any organization that a user has access privileges to. The insights page displayed at the user interface of the computing device includes user interface elements for accessing the relevant records. Each user interface element is selectable to open a relevant record that has been determined to be relevant to the call.

Abstract: A method and system for deploying a function in a polyglot serverless elastic runtime execution environment includes receiving function code via a development environment, combining the function code with middleware layers for polyglot serverless elastic runtime execution environment to form an executable function for the polyglot serverless elastic runtime execution environment, and deploying the executable function to the polyglot serverless elastic runtime execution environment.

Abstract: Disclosed herein are systems, methods, and devices that configure components of applications. A computing platform implemented using a server system may be configurable to cause determining an initial configuration of an application component that is configured to enable interactions with a user. The initial configuration is determined based on configuration parameters stored in the computing platform. The computing platform may also cause including a first instance of the application component in a webpage, the first instance being generated based on the initial configuration. The computing platform may also cause receiving a function call from the webpage, the function call being generated based on a user interaction with the webpage. The computing platform may also cause implementing a new instance of the application component responsive to the function call, the new instance comprising at least one change implemented based, at least in part, on the function call.

Abstract: Computing systems, database systems, and related methods are provided for detecting anomalies within a log file. One method involves obtaining log data for test runs executed with respect to a compiled version of executable code for an application platform, filtering the log data based on one or more performance metrics to obtain reference log data, converting the reference log data to a corresponding numerical representation and generating a matrix of the numerical representation. For each line of test log data associated with an update to the executable code, the method converts the line into a numerical representation, determines a difference between the numerical representation and the matrix, and provides an indication of an anomaly when the difference is greater than a detection threshold.

Abstract: A method of distributed smart contract deployment in a multi-tenant environment implemented by a computing device. The method including receiving a smart contract source, converting the smart contract source to a smart contract code for a tenant of the multi-tenant environment, the smart contract code to manage blockchain data transaction validation, and installing the smart contract code at the tenant to enforce logic of the smart contract source at the tenant in the multi-tenant environment.

Abstract: A method is implemented by a computing device to analyze a live stream of log entries to determine new patterns and changes in a number of occurrences of previously identified patterns. The method includes receiving a log entry in a live stream of log entries, determining whether the log entry fits a baseline pattern from one or more previously saved baseline patterns, adding to a count of occurrences for a baseline pattern if the log entry fits that baseline pattern, creating a new baseline pattern for the log entry if the log entry does not fit any of the one or more previously saved baseline patterns, saving the new baseline pattern, generating a snapshot of a pattern hierarchy based on the baseline patterns, comparing the new snapshot to a previously saved snapshot of a pattern hierarchy, and causing a report to be displayed based on a result of the comparing.

Abstract: A test case data set that includes test step data sets is received. The test step data sets include a first test step data set that specifies a dependency data field. Test step objects to be used to execute test steps in a test case are generated based on the test step data sets. The test steps include a first test step to be executed using a first test step object generated based on the first test step data set. The first test step object is used to execute the first test step. The first test step as executed populates values in the dependency data field. The values in the dependency data field are accessed and used in a second test step in the test steps while the second test step is being executed using a second test step object in the test step objects.

Abstract: Computer programming code may be executed via look ahead execution in a virtual machine. The computer programming code may include a first instruction to retrieve data stored in an on-demand computing services environment and a second instruction to transmit the data to a recipient. The first instruction, the second instruction, and the data may be evaluated to determine whether the execution of the computer programming code constitutes acceptable use of the on-demand computing services environment. When it is determined that the execution of the computer programming code does not constitute acceptable use of the on-demand computing services environment, further execution of the computer programming code may be halted.

Abstract: Techniques and structures to prevent exhaustion of a database connection pool, including retrieving data from the database connection pool, monitoring the data to determine whether the connection pool is at risk of an exhaustion condition, analyzing the data to determine whether one or more clients accessing the database connection pool are offenders upon determining that the connection pool is at risk and throttling access to the one or more clients accessing the database connection pool upon determining the one or more clients to be offenders.

Abstract: A training set is created via creating adjacent classified substrings by using character classes to replace corresponding characters in adjacent substrings in each training character string, and associating each pair of adjacent classified substrings and each pair of adjacent substrings with corresponding labels indicating whether corresponding pairs include any token boundary. The system splits input character string into beginning and ending parts and creates classified beginning part by replacing beginning part character with corresponding class and classified ending part by replacing ending part character with corresponding class. The machine-learning model determines probability of token identification, based on training set to determine count of instances that classified beginning part is paired with classified ending part and count of corresponding labels that indicate inclusion of any token boundary.