Abstract: Methods, systems, and computer-readable storage media for receiving, by an auto-pilot platform, one or more log files from an in-memory system, determining, by the auto-pilot platform, occurrence of a first error within the in-memory system based on the one or more logs, wherein the first error is indicated by a first error code within the one or more log files, identifying, by the auto-pilot platform, a first resolution from a resolution repository based on the first error code, the resolution repository including one or more mappings associating error codes to resolutions including associating the first error code with the first resolution, initiating, by the auto-pilot platform, execution of the first resolution, and updating, by the auto-pilot platform, the resolution repository based on execution of the first resolution.

Abstract: Embodiments of the present disclosure pertain to point cloud data. In one embodiment, the present disclosure includes a computer implemented method comprising for compressing and storing point cloud data using a space-filling curve data structure and a dictionary space data structure. In another embodiment, the present disclosure includes applying a query against the data structures to retrieve point cloud data within a range.

Abstract: A method, a system, and a computer program product for detection of potential merge conflicts. One or more changes to one or more source code entries in a software application are received. One or more dependency relationships of the received changes to one or more source code entries are determined and analyzed. A data structure including at least one node corresponding to the received changes is generated. Using the dependency relationships and the generated data structure, one or more conflicting changes are identified.

Abstract: A process for mining existing views to generate join statements is provided herein. A request to generate a join statement may be received, which may include a database object identifier to be part of the join statement. One or more views in the database may be identified, which may include the database object identifier. One or more edges in the one or more views may be identified, which may include the database object identifier. Scores for the one or more edges may be calculated. The edges may be filtered based on their scores according to one or more filtering rules. The filtered edges may be provided. A selected edge may be received, which may be selected from the provided filtered edges. The join statement may be generated in a query language. The join statement may be based on the selected edge. The generated join statement may be provided.

Abstract: Technologies are described for the automated determination of semantic overlap between the texts of different classes for use in machine learning. Specifically, a group of classes can be analyzed to determine how much textual overlap and/or semantic overlap is present between the classes before the classes are used for machine learning modeling. If significant overlap is found between the classes, then the classes can be modified before they are used for machine learning modeling.

Abstract: Techniques and solutions are described for facilitating the use of machine learning techniques. In some cases, filters can be defined for multiple segments of a training data set. Model segments corresponding to respective segments can be trained using an appropriate subset of the training data set. When a request for a machine learning result is made, filter criteria for the request can be determined and an appropriate model segment can be selected and used for processing the request. One or more hyperparameter values can be defined for a machine learning scenario. When a machine learning scenario is selected for execution, the one or more hyperparameter values for the machine learning scenario can be used to configure a machine learning algorithm used by the machine learning scenario.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program determines a scale value based on a plurality of floating point values. The program further scales the plurality of floating point values based on the scale value. The program also converts the plurality of floating point values to a plurality of integer values. The program further determines an integer encoding scheme from a plurality of integer encoding schemes. The program also encodes the plurality of integer values based on the determined integer encoding scheme.

Abstract: A method, a system, and a computer program product for executing a query. A query plan for execution of a query is generated. The query requires access to at least one table stored in a database system. The query includes one or more filter predicates. A filter predicate in the one or more filter predicates is selected. For the selected filter predicate, a plurality of cost function values associated executing a filter evaluation of the selected filter predicate are determined. Filter evaluation of the selected predicate is executed in accordance with at least one determined cost function value in the plurality of cost function values.

Abstract: According to some embodiments, systems and methods are provided, comprising receiving a document at a template module; determining a pair of pixel coordinates for one or more data entry fields in the received document; determining whether the received document matches a stored template based on the determined pair of pixel coordinates; generating a first template when the received document does not match the stored template; applying one or more rules associated with one of the stored template and the first template to the received document; extracting one or more data elements from the received document based on the application of the one or more rules; and returning the extracted one or more data elements. Numerous other aspects are provided.

Abstract: Techniques for provisioning digital content across different tools of different environments of a private cloud architecture are disclosed. In some embodiments, a method performed by a computer system of a private cloud architecture serving an organization comprises: obtaining a raw library of content from an object store, the object store being external to the private cloud architecture; adapting the obtained raw library of content to the private cloud architecture using one or more rules of the organization, the adapting of the obtained raw library of content generating an adapted library of content by applying one or more constraints on use of the obtained raw library of content within the private cloud architecture; and transmitting the adapted library of content to a development environment of the private cloud architecture, to a testing environment of the private cloud architecture, and to a production environment of the private cloud architecture.

Abstract: Techniques are described for detecting the presence of regressions in software, such as software that is being developed and tested in a development environment. Compiled code can be instrumented to introduce annotations to the compiled code. For example, the annotations can be added at the start and end of a code section, where a code section can be specified at any suitable degree of specificity (e.g., a line of code, a function, a component, multiple components, and so forth). When the instrumented code is executed, the annotations generate timing information that describes the execution times of the various annotated code sections. The timing information can be analyzed and compared to previously generated timing information for the code base, and detected differences may trigger actions such as reports, alerts, denied requests to pull code from the source repository, rejected code check-ins, bug reports, and so forth.

Abstract: Disclosed herein are system, method, and computer program product embodiments for analyzing contextual symbol information for document processing. In an embodiment, a language model system may generate a vector grid that incorporates contextual document information. The language model system may receive a document file and identify symbols of the document file to generate a symbol grid. The language model system may also identify position parameters corresponding to each of the symbols. The language model system may then analyze the symbols using an embedding function and neighboring symbols to determine contextual vector values corresponding to each of the symbols. The language model system may then generate a vector grid mapping the contextual vector values using the position parameters. The contextual information from the vector grid may provide increase document processing accuracy as well as faster processing convergence.

Abstract: Technologies are described for restarting an application while maintaining data in memory (e.g., using shared memory). For example, shared memory can be associated with an application. The shared memory can also be associated with a holder process to maintain the shared memory from the time the application stops to the time the application starts again. When the application starts, the shared memory can be associated with the started application. In addition, restart of in-memory databases can be provided using shared memory. For example, in-memory data can be maintained when a database process or database management system stops and starts (e.g., during a restart).

Abstract: Techniques and solutions are described for transferring metadata between software applications. A translation framework stores metadata models in a standard format. Source applications can cause metadata models to be stored in the translation framework in the standard format. A target application can receive metadata models, or elements thereof, from the translation framework, which metadata models can be optionally converted from the standard format to a target format used by the target application. The translation framework can store annotations to a metadata model in the form of custom metadata types. The custom metadata types can be used by one or both of the target application and a source application. Source applications can cause values to be stored for annotations they support. Target applications can read values for annotations they support. Source applications and target applications do not write or read, respectively, unsupported annotations.

Abstract: Disclosed herein are system, method, and computer program product embodiments for mitigating similar and/or duplicate data. An embodiment operates by a computing device receiving a first and second code segment and parsing the first and second code segments into a first and second abstract syntax tree (AST), respectively. Thereafter, the computing device generates a first and a second normalized. AST corresponding to the first and second ASTs, respectively, based on a normalization rule such that the first and second normalized ASTs correspond to a first and second normalized code segment. The computing device then derives analytical information of the first and second normalized ASTs based on the first and second normalized code segments.

Abstract: Methods, systems, and computer-readable storage media for receiving workload data, the workload data including queries executed within a distributed database system over a period of time, defining windows, each window including a time slice within the period of time, generating a hypergraph for each window, each hypergraph including vertices and hyperedges and being generated based on a sub-set of queries and weight functions, partitioning each hypergraph into blocks, for each shard in a set of shards, determining a set of ratings, each rating in the set of ratings being based on a weight of a respective share with respect to a respective block, and assigning each shard in the set of shards to a block in the set of blocks based on the set of ratings for the respective shard, the shard being assigned to a block, for which a maximum rating is provided in the set of ratings.

Abstract: Technologies are described for performing automated data integration, reconciliation, and/or self-healing using machine learning. For example, data integration can be checked using a reconciliation procedure. The number of times that the reconciliation is performed can be determined dynamically by a machine learning model. For each iteration, reconciliation can be performed to check integrated data against source data. If any reconciliation errors are found, then self-healing operations can be performed. Results of the reconciliation can be output. The reconciliation results can be used to update the machine learning model so that the machine learning model can dynamically adjust the number of iterations to perform based at least in part on reconciliation results.

Abstract: A cloud computing environment may have a landscape space for singleton applications including a SAAS UAA component to receive a subscription request associated with a user and a platform SAAS application. A user system registry may indicate spaces in which the user is registered, a route proxy agent may route communications via a first secure communication channel in accordance with information in the user system registry, and a route service broker may handle binding requests. The environment may also include a first system space for first system microservices with a first system onboarding application that receives provisioning application information via the route proxy agent and the secure communication channel. First backend microservices may similarly receive application router information, and a first route service shared instance clone may provide binding requests to the route service broker. A second system space for second system microservices may similarly be provided.

Abstract: The present disclosure involves systems, software, and computer implemented methods for a communication-efficient secret shuffle protocol for encrypted data based on homomorphic encryption. A service provider and multiple clients participate in a secret shuffle protocol of randomly shuffling encrypted client-specific secret input values. The protocol includes generation and exchange of random numbers, random permutations and different blinding values. A last protocol step includes using homomorphism, for each client, to perform computations on intermediate encrypted data to homomorphically remove a first blinding value and a second blinding value, to generate a client-specific rerandomized encrypted secret input value. As a result, the client-specific rerandomized encrypted secret input values are generated in an order that is unmapped to an order of receipt, at the service provider, of the encrypted secret input values.

Abstract: A query is received at a database execution engine. A query plan including a sub plan structured as a directed acyclic graph is determined by the database execution engine. A set of trees characterizing the sub plan is generated by the database execution engine and using the directed acyclic graph. The set of trees include a first tree and a second tree, the first tree including at least one leaf characterizing a memory store operation and the second tree including a root characterizing a memory access operation. The set of trees are stored for use in execution of the query at run time. Related systems, methods, and articles of manufacture are also described.