Abstract: Techniques for stitching application APIs to communicate between applications are disclosed. Stitching APIs includes transferring data obtained using an API corresponding to a source application and an API corresponding to a destination. A system obtains a mapping file that maps data objects maintained by the source application to data objects maintained by the destination application. When the system receives a data transfer request, the system: (a) identifies the source application and the destination application, (b) identifies the mapping between objects maintained by the source application and objects maintained by the destination application, and (c) identifies the API calls for the source application and the destination application for performing the data transfer. Based on the mapping, the system invokes API calls to a source application and a destination application to transmit the data.

Abstract: Techniques for interacting with users in a discussion environment are disclosed. Upon identifying a question in the discussion environment, a system determines: (a) whether a stored answer has already been associated with the question, (b) whether an answer can be generated by the system using existing information accessible to the system, or (c) whether to contact an expert to answer the question. The system updates the knowledge base by storing the questions and answers, along with user feedback to the questions and answers. Based on the user feedback, the system determines whether to modify existing answers to user-generated questions or to seek answers from additional human experts.

Abstract: The disclosed embodiments relate to a system that produces anomaly-free training data to facilitate ML-based prognostic surveillance operations. During operation, the system receives a dataset comprising time-series signals obtained from a monitored system during normal, but not necessarily fault-free operation of the monitored system. Next, the system divides the dataset into subsets. The system then identifies subsets that contain anomalies by training one or more inferential models using combinations of the subsets, and using the one or more trained inferential models to detect anomalies in other target subsets of the dataset. Finally, the system removes any identified subsets from the dataset to produce anomaly-free training data.

Abstract: System and methods discussed herein are directed to detecting the existence or modification of one or more data assets within a cloud-computing tenancy. The method may include obtaining, by a data management service, a plurality of connection data instances associated with a tenant. Cloud-computing data assets associated with the tenant can be identified from the plurality of connection data instances and presented to the user at a user interface. The user may select which data assets to add to a data catalog managed by the data management service. The data management service may be configured to monitor for new data assets or connections and/or for changes in data and/or connections of previously-selected data assets.

Abstract: The present embodiments relate to data center outage detection and alert generation. An outage detection service as described herein can process near real-time data from various sources in a datacenter and process the data using a model to determine one or more projected sources of a detected outage. The model as described herein can include one or more machine learning models incorporating a series of rules to process near-real time data and offline data and determine one or more projected sources of an outage. An alert message can be generated to provide the projected sources of the outage and other data relevant to the outage.

Abstract: Techniques are described herein for training and evaluating machine learning (ML) models for document processing computing applications using generalized vocabulary tokens. In some embodiments, an ML system determines a set of tokens for non-textual content in a plurality of documents. The ML system generates a fixed-length vocabulary that includes the set of tokens for the non-textual content. The ML system further generates for each respective document in a training dataset of documents, a respective feature vector based at least in part on which tokens in the fixed-length vocabulary occur in the respective document. The ML system trains a ML model based at least in part on the respective feature vector for each respective document in the training dataset.

Abstract: A cloud manager restores a backup of a distributed environment by comparing a stack of a current distributed environment to a backup stack of the backup of the distributed environment. The cloud manager identifies a difference between a first number of nodes in the first set of nodes specified in the first stack and a second number of nodes in the second set of nodes specified in the second stack. The cloud manager restores the backup of the distributed environment by modifying a number of nodes in the current distributed environment based on the identified difference between the first number of nodes and the number of nodes in the second stack.

Abstract: Systems, devices, and methods discussed herein are directed to utilizing patterns and logical entities to identify and maintain relationships between data assets. In some embodiments, a query comprising a logical entity qualifier, one or more pattern identifiers that indicate a pattern, and a data set identifier may be received. The pattern is executed against a data set corresponding to the data set identifier and one or more logical entities are generated based on this execution. A logical entity may be a label that represents a set of one or more data assets in a data set. Assets that share a label can share attributes that are described by the label. The label corresponding to each logical entity may be presented, where each label represents a different set of data assets which share a common trait. In some embodiments, the user may define a pattern by which commonality may be assessed.

Abstract: A method for updating software in one or more servers in a managed system includes bootstrapping of shared secrets to secure communication, using an object store with publicly accessible URLs as a mailbox system between a patch agent locally at the managed system and a patch management server located remotely. The object store provides for indirect and asynchronous communication, allowing the patch management server to manage the updates, whereas the patch agent executes the updates. The patch management server keeps track of update results, and when an update is unsuccessful it performs and supports a remediation process. Both the patch agent and the patch management server keep full logs of the update results to allow for auditing.

Abstract: Techniques for transferring the state information of resources are provided. A gateway receives requests from clients and directs the requests to a resource. The gateway receives a notification that a first resource being accessed by clients will be replaced by a second resource. The first resource completes an initial transfer of state information and receives a terminal instruction. The first resource completes a final transfer of state information, including the terminal instruction, and the gateway redirects traffic, that is addressed to the first resource, to the second resource.

Abstract: Techniques are described for generating an approximate frequency histogram using a series of Bloom filters (BF). For example, to estimate the f1 and f2 cardinalities in a dataset, an ordered chain of three BFs is established (“BF1”, “BF2”, and “BF3”). An insertion operation is performed for each datum in the dataset, whereby the BFs are tested in order (starting at BF1) for the datum. If the datum is represented in a currently-tested BF, the subsequent BF in the chain is tested for the datum. If the datum is not represented in the currently-tested BF, the datum is added to the BF, a counter for the BF is incremented, and the insertion operation for the current datum ends. To estimate the cardinality of f1-values in the dataset, the BF2-counter is subtracted from the BF1-counter. Similarly, to estimate the cardinality of f2-values in the dataset, the BF3-counter is subtracted from the BF2-counter.

Abstract: Techniques are disclosed for implementing, by a computer infrastructure orchestration service (CIOS) changes to a first service. The CIOS may manage a declarative infrastructure provisioner to deploy infrastructure resources and artifacts based at least in part on declarative instructions. CIOS may detect a first modification to a first service (e.g., a sidecar application). CIOS may identify that configuration data of a second service references (e.g., via a git submodule) the first service. In response to identifying a second modification to the second service, CIOS may present, via a graphical interface, changes comprising the first modification to the first service. User input may be received indicating acceptance or rejection of the changes of the first modification. CIOS may then implement the changes to the first service according to the user input. For example, CIOS may ensure that the first service, as modified, is deployed alongside the second service in subsequent deployments.

Abstract: A database server stores compressed units in data blocks of a database. A table (or data from a plurality of rows thereof) is first compressed into a “compression unit” using any of a wide variety of compression techniques. The compression unit is then stored in one or more data block rows across one or more data blocks. As a result, a single data block row may comprise compressed data for a plurality of table rows, as encoded within the compression unit. Storage of compression units in data blocks maintains compatibility with existing data block-based databases, thus allowing the use of compression units in preexisting databases without modification to the underlying format of the database. The compression units may, for example, co-exist with uncompressed tables. Various techniques allow a database server to optimize access to data in the compression unit, so that the compression is virtually transparent to the user.

Abstract: In one implementation, a data transformation engine is employed to analyze an incoming JSON schema syntax and use a “best effort” model to convert a plurality of JSON schemas to other types of schemas such as an XML schema. The model is configured to be adaptable and dynamic in order to convert as much of the JSON as possible. In addition, the conversion tool is configured to transform schemas to other technologies and allow for conversion errors within one or more error thresholds. Further, the system may be distributed standalone with no dependencies to other implementations, such as BPM implementations.

Abstract: In accordance with an embodiment, the system supports bottom-up aggregation in a multidimensional database computing environment. A dynamic flow, coupled with a data retrieval layer or data fetching component, which in some environments can incorporate a kernel-based data structure, referred to herein as an odometer retriever, or odometer, that manages pointers to data blocks, contains control information, or otherwise operates as an array of arrays of pointers to stored members, enables bottom-up aggregation of cube data which, for example with pure aggregating queries, provides considerable run time improvement.

Abstract: Embodiments employ an inference method for neural networks that enforces deterministic constraints on outputs without performing post-processing or expensive discrete search over the feasible space. Instead, for each input, the continuous weights are nudged until the network's unconstrained inference procedure generates an output that satisfies the constraints. This is achieved by expressing the hard constraints as an optimization problem over the continuous weights and employing backpropagation to change the weights of the network. Embodiments optimize over the energy of the violating outputs; since the weights directly determine the output through the energy, embodiments are able to manipulate the unconstrained inference procedure to produce outputs that conform to global constraints.

Abstract: Techniques for determining and presenting invoice criticality information for an invoice on a graphical user interface (GUI) are disclosed. Invoice criticality represents an urgency and/or significance associated with an invoice. An urgency is determined based on a difference between (a) a predicted amount of time for completing processing of the invoice and (b) an amount of time currently remaining until the target completion date. A significance is determined based on a predicted impact level resulting from missing a target completion date for the invoice. A GUI displays invoice criticality information associated with one or more invoices. The GUI presents a suggested action for resolving a predicted hold for a particular invoice. The GUI presents a suggested action based on a current and/or impending processing stage for a particular invoice. The GUI presents correlation information indicating relationships between invoice characteristics, processing times, and/or impact levels.

Abstract: A system and method for facilitating establishing a secure connection between a client application and a content provider. An example method includes employing a security gateway to authenticate a client for communications therewith; maintaining, for the client, security credentials for a data provider via a security configuration module, wherein the security credentials are associated with a description of data, which is associated with a data provider; using the gateway to determine which of the security credentials to use to fulfill the request message received by the security gateway from the client based on the request; and employing the selected security credentials to selectively retrieve data from and deliver the data to the client application. The example method may further include generating the request message when a User Interface (UI) control displayed in a UI display screen of a browser client is selected or activated.

Abstract: Embodiments establish a pool of tunnel connections using a secure protocol. A pool of tunnels can be initiated from endpoint connection managers to cloud connection managers, where a request is received from the endpoint connection managers by the cloud connection managers. A request from a cloud client to communicate with a secure computing device using a first of the endpoint connection managers is received at a first of the cloud connection managers. One of the pool of tunnels that is connected to the first endpoint connection manager is identified. The identified tunnel is configured to connect the cloud client and the first endpoint connection manager.

Abstract: The disclosed system produces synthetic signals for testing machine-learning systems. During operation, the system generates a set of N composite sinusoidal signals, wherein each of the N composite sinusoidal signals is a combination of multiple constituent sinusoidal signals with different periodicities. Next, the system adds time-varying random noise values to each of the N composite sinusoidal signals, wherein a standard deviation of the time-varying random noise values varies over successive time periods. The system also multiplies each of the N composite sinusoidal signals by time-varying amplitude values, wherein the time-varying amplitude values vary over successive time periods. Finally, the system adds time-varying mean values to each of the N composite sinusoidal signals, wherein the time-varying mean values vary over successive time periods.