Source data rendered as a string of hexadecimal data representing a set of Extended Binary Coded Decimal Interchange Code (EBCDIC) data, and a data layout description defining a record in the source data that includes a plurality of fields, are obtained. Respective hexadecimal lengths of the fields based on a source data length of each field and a source datatype of each field are determined. Hexadecimal sub-strings are extracted from the hexadecimal string based on the hexadecimal lengths and source datatypes of the fields. At least some of the hexadecimal sub-strings are converted to a target format. The sub-strings are output in the target format.

A function is decomposed into a plurality of function shares. The function returns a Boolean result based on whether an input y satisfies a query on a data set. The function shares hide the function from non-collaborating entities that separately execute the function shares. Each of the functions shares are sent to one of a plurality of servers having a same data set. The function shares are executed on the data set at the servers to obtain a respective plurality of shares. A conditional disclosure of secrets operation is simulated on the shares and the input y. The conditional disclosure of secrets operation uses a secret known to at least one of the servers, and further uses a source of randomness shared between the servers. A Boolean value corresponding to the Boolean result is returned based on the conditional disclosure of secrets operation returning the secret.

Various embodiments of the present invention address technical challenges related to software testing and make substantial technical improvements to improving the computational efficiency and operational reliability of test automation platforms, as well as to the operational reliability of software applications that are tested using the software application platforms. Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing efficient and techniques for visual software test management using captured test case data entities, annotation-based test case data entities, and dynamic test case data entity cloning.

Systems and methods are described for implementing attribute aware, relationship-based access control by receiving a query to access a relational database from a user, determining if a rule of the relational database is applicable to the query, determining one or more relationships associated with the query based at least in part on the rule, and modifying the query by adding an extra join operation to the query based at least in part on the rule and the one or more relationships. Further, when a type of the rule is row level, a where clause filter is added to the modified query to enforce a condition of the rule, and when the type of the rule is field level, a case column is added to the modified query and a select clause is added to the modified query to wrap the query. The modified query is processed to produce a result.

In various examples, searching of data—including real-world data, simulation data, system under test (SUT) data, and/or map data—may be executed using a query engine configured to compile detailed binary code from high-level declarative queries for searching the data to identify scenarios or engineering artifacts of interest. A user may identify a behavior or scenario of interest, define the behavior or scenario in a descriptive and/or declarative manner—including implicit indications of temporal or spatial relationships—and the query engine may then compile an explicit procedural description that may be used to search the data for one or more instances and/or variations of the defined scenario or computational representation of an engineering artifact under investigation. Once the scenarios are identified, behaviors of the machine may be observed, criteria with respect to the machine performance may be evaluated, and/or test coverage with respect to the scenario type may be collected.

A processing system including at least one processor may perform a method including receiving a data request, executing a request fulfillment module to determine at least one information model and at least one executable flow associated with the data request, determining that at least one combining module is to be applied to the data request based on the at least one information model and the at least one executable flow, applying the at least one combining module to the data request, and generating a data pipeline to transmit data to a target that initiated the data request, wherein the data pipeline is generated in accordance with the at least one combining module that is applied.

A relational database management system (RDBMS) accepts a workload comprised of one or more queries against a relational database. The RDBMS evolves a default cost profile into a plurality of cost profiles using fixed or dynamic evolution, wherein each of the cost profiles captures one or more cost parameters for the workload. The cost profiles are represented by a multi-dimensional matrix that has one or more dimensions, and each of the dimensions represents one of the cost parameters. The RDBMS dynamically determines which of the cost profiles is an optimal cost profile for the workload by mapping the cost profiles to the workload using a random walk scoring algorithm or a biased walk scoring algorithm that searches the multi-dimensional matrix to identify the optimal cost profile. The RDBMS selects and performs one or more query execution plans for the workload based on the optimal cost profile for the workload.

Systems and methods include determination of a first logical page number of a first database page to be persisted, identification of a first blockmap page associated with the first logical page number, determination, from the first blockmap page, of a block number associated with the first logical page number, determination that the block number is an object key, determination, in response to the determination, that the block number is an object key, determination of a first object key to associate with the first logical page number, and writing of the first database page to the object store using the first object key.

A data funnel interface with adjustable paths guides end-users to information in a relational table. The interface consists of one or more categories of user data derived from the set of user attributes in the database table. Each category of user data consists of human readable data in the table that conveys values relevant to the end-user. Selecting a data item from one category establishes a logical relationship with the next set of data in an unselected category. The end-user has random access to the categories of data and he or she is always free to choose a data item that either builds on an existing pathway or starts a new one. In the interface, alongside each category is a visual cue that indicates the status of an end-user’s data selection. Upon selecting a data item from each category in the interface, the system displays a Continue button to transfer the control to a window object that displays table information. The interface and its updated display of categories of data is generated automatically.

A method of identifying causal relationships includes receiving data comprising a set of values corresponding to one or more variables, and generating a list of candidate causal models of relationships between or within the variables. The list is ranked based on a likelihood of each candidate causal model, wherein the likelihood includes at least a correlation value. The method further includes receiving feedback identifying a candidate causal model and a change in rank of the candidate causal model, re-ranking the list based on the feedback, and displaying the re-ranked list. The method generates an intervention comprising a suggested modification corresponding to a variable of a selected causal model among the candidate causal models in the re-ranked list, receives additional data corresponding to the variable of the suggested modification and evaluates the additional data to determine whether the likelihood of the selected causal model has changed.