Abstract: The present disclosure relates to computer-implemented methods, software, and systems for detecting outages in a cloud environment. Internal web agents installed at corresponding network segments of a first cloud platform and external web agents installed at an external second cloud platform are configured to perform network calls between themselves and the other web agents. Data from the internal web agents is automatically collected to generate structured data defining network connectivity status corresponding to the network segments of the first cloud platform based on the performed network calls between the internal and external web agents. In response to evaluating the structured data, a health status of network connectivity of the first cloud platform is determined. The health status can be provided to platform services provided by the first cloud platform and/or applications running on the first cloud platform to support managing of lifecycles of entities running on the first cloud platform.

Abstract: According to some embodiments, methods and systems may be associated with enterprise application integration. A formalization platform may facilitate definition of Enterprise Integration Patterns (“EIP”) using a formal representation and defined execution semantics. The formalization platform may also execute model checking to find errors in the formal representation. An implementation platform may receive information from the correctness platform and translate the formal model generated by the correctness platform. The implementation platform may also configure implementation parameters of the translated formal model and simulate the EIP patterns to provide experimental validation. A correctness platform may receive information from the formalization platform and evaluate correctness of the EIP patterns defined via the formalization platform.

Abstract: Systems, methods, and computer media are described for creating efficient error reproduction scenarios. Raw workload capture data can be consolidated based on transactional dependence of the requests in the capture data. The consolidated workload capture data can be stored as a separate data structure that can be accessed to identify requests on which a request of interest in the raw workload capture data is transactionally dependent. For a request of interest (e.g., a request that caused an error), a lightweight error reproduction scenario can be generated that includes the identified transactionally dependent requests and excludes unrelated requests.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

Abstract: Despite the increase of memory capacity and CPU computing power, memory performance remains the bottleneck of in-memory database management systems due to ever-increasing data volumes and application demands. Because the scale of data workloads has out-paced traditional CPU caches and memory bandwidth, one can improve data movement from memory to computing units to improve performance in in-memory database scenarios. A near-memory database accelerator framework offloads data-intensive database operations via or to a near-memory computation engine. The database accelerator's system architecture can include a database accelerator software module/driver and a memory module with a database accelerator engine. An application programming interface (API) can be provided to support database accelerator functionality. Memory of the database accelerator can be directly accessible by the CPU.

Abstract: A computer-implemented method includes representing a plurality of database tables as respective vectors in a multi-dimensional vector space, receiving an indication that a first database table represented by a first vector and a second database table represented by a second vector are related to each other, moving the respective vectors representing the plurality of database tables in the multi-dimensional vector space in response to the indication, and grouping the plurality of database tables into one or more table clusters based on positions of the respective vectors representing the plurality of database tables in the multi-dimensional vector space.

Abstract: Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.

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: Disclosed herein are system, method, and computer program product embodiments for adapting machine learning models for use in additional applications. For example, feature extraction models are readily available for use in applications such as image detection. These feature extraction models can be used to label inputs (such as images) in conjunction with other deep neural network models. However, in adapting the feature extraction models to these uses, it becomes problematic to improve the quality of their results on target data sets, as these feature extraction models are large and resistant to retraining. Approaches disclosed herein include a transfer layer for providing fast retraining of machine learning models.

Abstract: Methods, computer-readable media and systems are disclosed for building, deploying, operating, and maintaining an intelligent dynamic form in which a trained machine learning (ML) model is embedded. A universe of questions is associated with a plurality of output classifiers, which could represent eligibilities for respective benefits. The questions are partitioned into blocks. Each block can be associated with one or more of the classifiers, and each classifier can have a dependency on one or more blocks. An ML model is trained to make inferences from varied combinations of responses to questions and pre-existing data, and determine probabilities or predictions of values of the output classifiers. Based on outputs of the trained model, blocks of questions can be selectively rendered. The trained model is packaged with the question blocks and other components suitably for offline deployment. Uploading collected responses and maintenance of the dynamic form are also disclosed.

Abstract: Methods, systems, and computer-readable storage media for receiving a time-series of data values associated with a plurality of sensors, each sensor generating at least a portion of the time-series of a respective data value, providing a plurality of auto-regression models, each auto-regression model being provided based on a respective first sub-set of the time-series of data values used as input, and a respective second sub-set of the time-series of data values used as training data during a training process, receiving respective data values associated with a time from and generated by each of the plurality of sensors, determining respective predicted values for each of the auto-regression models, and selectively indicating that an anomaly is present in the system based on respective predicted values for each of the auto-regression models, and the respective data values associated with a time.

Abstract: A method may include responding to a query to retrieve data from a database by identifying a plurality of query execution plans. An overall robustness value may be determined for each query execution plan. The overall robustness value of a query execution plan may correspond to a sum of individual robustness values for each operator included in the query execution plan. Each operator may have an individual robustness value that corresponds to a first change in a total cost of a query execution plan including the operator relative to a second change in an output cardinality of the operator. One of the plurality of query execution plans may be selected based on the overall robustness value of each of the plurality of query execution plans. The query may be executed by performing a sequence of operators included in the selected one of the plurality of query execution plan.

Abstract: Systems and methods are provided for receiving input data to be processed by an encrypted neural network (NN) model, and encrypting the input data using a fully homomorphic encryption (FHE) public key associated with the encrypted NN model to generate encrypted input data. The systems and methods further provided for processing the encrypted input data to generate an encrypted inference output, using the encrypted NN model by, for each layer of a plurality of layers of the encrypted NN model, computing an encrypted weighted sum using encrypted parameters and a previous encrypted layer, the encrypted parameters comprising at least an encrypted weight and an encrypted bias, approximating an activation function for the level into a polynomial, and computing the approximated activation function on the encrypted weighted sum to generate an encrypted layer. The generated encrypted inference output is sent to a server system for decryption.

Abstract: Techniques for providing an efficient real-time hierarchy based on change events are disclosed. In some embodiments, a computer-implemented method comprises: storing a hierarchy table comprising hierarchy data that represents a snapshot state of a hierarchy tree of entities at a first point in time and having been last updated at the first point in time; receiving one or more user requests to change entity data representing entities of the hierarchy tree; storing one or more change events in a queue based on the user request(s); receiving a query request for the hierarchy tree; in response to the receiving of the query request, generating a query result based on the hierarchy table stored in the database and the change event(s) stored in the queue; and performing a function of an enterprise application platform using the query result.

Abstract: Technologies are described providing adaptive query routing in a replicated database environment. The technologies can be used with a variety of replication protocols. Prepared statements, such as for queries, from a database client can be routed to a source node, or a source node and available replica nodes, when a replica node becomes unavailable. When a replica node becomes available again, a prepared statement can be updated to indicate that the updated prepared statement can be executed at nodes including the replica node that is available again. Prepared statement routing can also be used when a portion of replicated data becomes unavailable at a replica node, but a portion of replicated data remains available.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a set of sensor data from a set of sensors. Each sensor in the set of sensors is configured to sense a physical quantity. Based on the set of sensor data, the program further determines that a particular event has occurred. Based on the determination that the particular event has occurred, the program also sends a set of notifications to a set of subscribers.

Abstract: Data is received that characterizes a computing architecture including at least one web-based server and an associated cryptographic web protocol to be implemented on such computing architecture according to a desired formal specification. Thereafter, a plurality of inattentive variants complying with the web protocol are generated without associated security checks. Messages to and from each inattentive variant are then monitored while executing the associated security checks. At least one security monitor is generated based on the monitored messages that is configured to address security vulnerabilities in the computing architecture relative to the formal specification. At least one generated security monitor can be later deployed in the computing architecture. Related apparatus, systems, techniques and articles are also described.

Abstract: Techniques for predicting failure mode specific reliability characteristics of tangible equipment using parametric probability models are disclosed. In some example embodiments, a computer system receives a model training configuration entered via a user interface, trains a failure curve model for a selected failure mode of a selected equipment model based on the model training configuration at a time indicated by training schedule data, and generates analytical data for the selected failure mode of the selected equipment model using the trained failure curve model. The failure mode corresponds to a specific way in which the equipment model is capable of failing. In some example embodiments, the training of the failure curve model comprises determining a shape parameter and a scale parameter for the failure curve model based on a fitting of failure event data to a continuous probability distribution, and storing the parameters for use in generating the analytical data.

Abstract: Methods and apparatus are disclosed for comparing relevant content of datasets. A hash value is computed over a selected subset of fields to obtain a signature of a dataset, with other fields being disregarded. A hash value can be computed directly for all records of the dataset, or by combining individual hash values for each record. Comparison of the signature with that of other datasets leads to efficient determination whether two datasets match with respect to relevant content in the selected fields. For larger groups of datasets, lists of matched and mismatched datasets can be reported. Optional features include matches insensitive to permutation of the records, or identification of which records in a dataset fail to match.

Abstract: Disclosed herein are system, method, and computer program product embodiments for utilizing an extended cache to access an object store efficiently. An embodiment operates by executing a database transaction, thereby causing pages to be written from a buffer cache to an extended cache and to an object store. The embodiment determines a transaction type of the database transaction. The transaction type can a read-only transaction or an update transaction. The embodiment determines a phase of the database transaction based on the determined transaction type. The phase can be an execution phase or a commit phase. The embodiment then applies a caching policy to the extended cache for the evicted pages based on the determined transaction type of the database transaction and the determined phase of the database transaction.