Abstract: An anomaly service receives log data from nodes in a computing environment, which includes a sequence of information indicative of log messages produced by the nodes. The anomaly service identifies dominant patterns in the sequence of information that are representative of non-anomalous blocks of the log messages. Having identified the dominant patterns, the service is able to extract the non-anomalous blocks from the log data to reveal anomalous blocks that do not fit the dominant patterns. The service may then generate anomaly vectors based on the anomalous blocks, which can be distributed to the nodes to detect anomalies.

Abstract: Techniques described herein propose a ROWID Elimination Rewrite that uses functional dependencies to substitute and/or eliminate ROWID pseudo-columns referenced in a query in order to reduce memory pressure and speed up processing.

Abstract: A machine-learning model may be previously trained with a supervised learning algorithm to identify whether a pair of labels provided as input are similar. A locality sensitive hashing forest (LSH) may be generated for the set of candidate labels. When a user later identifies an input label (e.g., by search query, by interface selection, etc.) the input label may be used to query the LSH forest to identify a subset of the candidate labels. This subset may be used to generate respective pairs comprising the input label, one of the subset candidate labels, and a corresponding feature set generated for the pair. This data may be provided to the model to identify a degree to which the pair of labels are similar. The user may be provided one or more recommendations including similar terms identified from the model's output.

Abstract: Embodiments generate random walks through a directed graph that is represented in a relational database table. Each row of the graph table represents a directed edge in the graph and includes a source vertex and a destination vertex. Each row is further augmented to (a) indicate the number of outbound edges starting from the destination vertex in the row and (b) include an identifier that distinguishes the edge from other outbound edges starting from the same source vertex. An SQL query may be executed on the augmented graph table. Starting from a source vertex (starting vertex or the destination vertex of the previously selected hop) the query randomly selects a row of the graph table representing one of the outbound edges from the source vertex and adds the selected outbound edge as a row in a random walk table that represents the next hop in the random walk.

Abstract: The present disclosure relates generally to a data integration system that integrate an object based data integration tool, such a GUI-based data integration tools, with version control systems using a relational database repository for persistence. Examples of distributed version control systems include Git, Mercurial, and Bazaar, and examples of centralized version control systems include Subversion, CVS etc. in centralized or distributed environments.

Abstract: In accordance with an embodiment, described herein is a system and method for delivery of content based on matching of user profiles with content metadata. The system enables delivery of personalized content, without the overhead of managing segment targeting rules, while providing content publishers or marketers with complete control over such personalization. A recommendation service or application program interface, provided by a computer, cloud computing environment, or other type of computer system, enables receipt and processing of requests, from client devices, for personalized content. A recommendation engine delivers content assets in response to a request from a client device. The recommendation engine determines a content channel and a user identity associated with the request, and then delivers content assets based on rules governing the matching of content asset metadata with the user profile. While content classification evolves over time, so also does the personalization of delivered content.

Abstract: Techniques for on-the-fly adaptation of remembered set data structures are disclosed. Operations include initiating execution of an application thread and a garbage collection process for a heap memory including a first plurality of logical partitions, wherein each logical partition of the first plurality of logical partitions is associated with a remembered set data structure. While the application thread and the garbage collection process are executing, the system determines a set of characteristics. Based on the set of characteristics meeting threshold criteria for adjusting a first remembered set data structure corresponding to a first logical partition, the system identifies a first remembered set configuration corresponding to the first remembered set data structure, creates a replacement remembered set data structure based on the first remembered set configuration, and associates the replacement remembered set data structure with the first logical partition.

Abstract: A parametric constant resolves to different values in different contexts, but a single value within a particular context. An anchor constant is a parametric constant that allows for a degree of parametricity for an API point. The context for the anchor constant is provided by a caller to the API point. The anchor constant resolves to an anchor value that records specialization decisions for the API point within the provided context. Specialization decisions may include type restrictions, memory layout, and/or memory size. The anchor value together with an unspecialized type of the API point result in a specialized type of the API point. A class object representing the specialized type is created. The class object may be accessible to the caller, but the full value of the anchor value is not accessible to the caller. The API point is executed based on the specialization decisions embodied in the anchor value.

Abstract: Techniques for persisting user data across secure shell instances are provided. A method includes receiving a first request from a session manager service to establish a connection to a secure shell instance and restore a user block volume with corresponding backup user data. The method may include reserving an empty block volume. The method may also include transmitting a backup data identifier associated with the corresponding backup user data to a backup service and receiving the corresponding backup user data from the backup service. The method may further include providing the corresponding backup user data to the empty block volume to create a restore volume and transmitting a restore volume identifier corresponding to a data center identifier to the session manager service. The method may include receiving a second request to attach the restore volume to a reserved instance, the second request being received from the session manager service.

Abstract: Load Plan Generator (LPG) is a BIAPPS utility for generating ODI load plans based on desired subset of fact tables for loading BIAPPS Data Warehouse. The tool simplifies the configurations process by minimizing the manual steps and configurations and provides a guided list of configurations steps and checklists. The load plan components can include different sets of load plans that will be stitched together by the load plan generator to create one load plan for loading chosen fact groups in the warehouse sourcing from different transaction systems.

Abstract: A computer program product, system, and computer implemented method for application-level redirect trapping and creation of NAT mapping to work with routing infrastructure for private connectivity in cloud and customer networks. The approach disclosed herein generally comprises a method of leveraging a reverse connection endpoint and IP address mapping controller to capture redirection messages from a private cloud or network (e.g., a service consumer network or a service consumer hybrid cloud). This allows at least the IP address mapping controller to manage a cloud networking infrastructure to provide for a service provider network (e.g., a public cloud) to support applications that overcome the isolation requirements of a private cloud or network to perform useful work. For example, without saddling the private cloud or network user with a heavy pre-configuration burden, the approach disclosed herein supports redirection to dynamically determined IP addresses at the private cloud or network.

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: Techniques for stitching application programming interfaces (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: In accordance with an embodiment, systems and methods can provide for load, aggregate, and batch calculation in in a multidimensional database environment. Metadata can be used to identify in which higher dimensions each leaf-level load is used. At such time, the data from the leaf level is aggregated to each of the higher dimensions where it is to be used. Then, the aggregate values are stored in memory until a completed sum (or other calculation) is completed at the higher levels. At such time, the aggregate values can be discarded from memory. The ability of the system to perform a load, aggregate, and batch calculation in one scan is particularly useful in processing very large sets of data, for example in big data environments, where it may not be desirable to load the data for all of the leaf nodes into current memory.

Abstract: Described herein are dialog systems, and techniques for providing such dialog systems, that are suitable for use on standalone computing devices. In some embodiments, a dialog system includes a dialog manager, which takes as input an input logical form, which may be a representation of user input. The dialog manager may include a dialog state tracker, an execution subsystem, a dialog policy subsystem, and a context stack. The dialog state tracker may generate an intermediate logical form from the input logical form combined with a context from the context stack. The context stack may maintain a history of a current dialog, and thus, the intermediate logical form may include contextual information potentially missing from the input logical form. The execution subsystem may execute the intermediate logical form to produce an execution result, and the dialog policy subsystem may generate an output logical form based on the execution result.

Abstract: Techniques are described for generating and applying mini-machine learning variants of machine learning algorithms to save computational resources in tuning and selection of machine learning algorithms. In an embodiment, at least one of the hyper-parameter values for a reference variant is modified to a new hyper-parameter value thereby generating a new variant of machine learning algorithm from the reference variant of machine learning algorithm. A performance score is determined for the new variant of machine learning algorithm using a training dataset, the performance score representing the accuracy of the new machine learning model for the training dataset. By performing training of the new variant of machine learning algorithm with the training data set, a cost metric of the new variant of machine learning algorithm is measured by measuring usage the used computing resources for the training.

Abstract: The embodiments disclosed herein relate to concurrently displaying maps visualizing different subsets of transportation planning data. The concurrently displayed maps are automatically updated based on changes to the transportation planning data. A map locking functionality prevents the locked map from being updated based on changes to the transportation planning data. Other non-locked maps continue to be updated based on the changes to the transportation planning data. The map locking functionality allows for visualizing and comparing different states of the transportation planning data.

Abstract: A method and apparatus for compression units in an index block is provided. A particular leaf block of a database index for a table is generated. The particular leaf block corresponds to a particular range of key values. A compression scheme is determined for a plurality of index entries with key values within the particular range. A compression unit that includes a plurality of compressed index entries is generated by applying the compression scheme to the plurality of index entries. Compression metadata describing the compression scheme is generated. The compression unit and the compression metadata is stored in the particular leaf block. In response to a request, at least a portion of the compression unit is decompressed to access one or more index entries.

Abstract: Systems, devices, and methods discussed herein are directed to intelligently adjusting the set of worker nodes within a computing cluster. By way of example, a computing device (or service) may monitor performance metrics of a set of worker nodes of a computing cluster. When a performance metric is detected that is below a performance threshold, the computing device may perform a first adjustment (e.g., an increase or decrease) to the number of nodes in the cluster. Training data may be obtained based at least in part on the first adjustment and utilized with supervised learning techniques to train a machine-learning model to predict future performance changes in the cluster. Subsequent performance metrics and/or cluster metadata may be provided to the machine-learning model to obtain output indicating a predicted performance change. An additional adjustment to the number of worker nodes may be performed based at least in part on the output.

Abstract: Embodiments provide automatic representational state transfer application programming interface (“REST API”) document generation for System for Cross-domain Identity Management (“SCIM”) based resources. For each resource, embodiments identify all SCIM based resource type data files and corresponding schema, where each resource type data file includes resource type definition metadata. Embodiments identify available operations based on the resource type definition metadata and apply HyperText Markup Language (“HTML”) operations corresponding to the metadata based on an available Swagger Operation template engine. Embodiments convert SCIM based schema to Swagger schemas by iterating through each available schema and identifying attributes and preparing a Swagger attribute template engine to generate a Swagger based attribute definition to a Swagger template aggregator. Embodiments then generate Swagger JavaScript Object Notation (“JSON”) corresponding to the Swagger schemas.

Abstract: A method of generating and using a metadata files for integration flows may include analyzing definition files of integration flows to generate metadata files that include summary information for each of the integration flows. The method also includes extracting source-target relationships from the summary information for each of the integration flows and training a model using the plurality of source-target relationships. The method also includes receiving a source element from a current integration flow; providing the source element and characteristics of the current integration flow to the model; and receiving, from the model, recommended target elements to be connected to the source element in the current integration flow.

Abstract: The present embodiments relate to a cloud shell extension framework. A cloud infrastructure (CI) service can invoke a cloud shell from a console to the CI. The service may request that context data to be added for use in a terminal session. The cloud shell extension can include a tool or script that can be used to obtain context data or sample code for preparing an environment specific to the service prior to the user interaction with the environment. The cloud shell extension can allow for a service to register an extension in the cloud shell framework, and their extension can be invoked when a client initiates the cloud shell with the service. The extension can allow for the invoked service to forward context data, such as environment variables, to be set in the terminal session for the client.

Abstract: An email message delivery system is described that provides a fast and reliable managed email delivery service using MTAs and proxy servers. An email message is selected by an MTA for processing. The MTA determines a sender and an intended recipient associated with the email message. The MTA then identifies a pool of usable source internet protocol addresses for the email message based on the sender and selects a particular source IP address from the pool. The MTA then identifies a particular proxy server that is configured to handle the selected particular source IP address and communicates to the particular proxy server, information including the particular source IP address and the destination IP address. The MTA then transmits the email message to the destination IP address using a connection established by the proxy server between the particular source IP address and the destination IP address.

Abstract: Methods, systems, and computer readable media for limiting network function (NF) repository function (NRF) forwarding are disclosed. One example method for limiting NRF forwarding comprises: at a first NF comprising at least one processor: receiving a request message associated with a consumer NF; determining that the request message cannot be serviced by the first NF; inserting or modifying a forwarding limit value in the request message, wherein the forwarding limit value indicates a number of times that the request message can be forwarded; and forwarding the request message including the forwarding limit value toward a second NF.

Abstract: A compiler is capable of compiling instructions that do or do not supply specialization information for a generic type. The generic type is compiled into an unspecialized type. If specialization information was supplied, the unspecialized type is adorned with information indicating type restrictions for application programming interface (API) points associated with the unspecialized type, which becomes a specialized type. A runtime environment is capable of executing calls to a same API point that do or do not indicate a specialized type, and is capable of executing calls to a same API point of objects of an unspecialized type or of objects of a specialized type. When the call to an API point indicates a specialized type, and the specialized type matches that of the object (if the API point belongs to an object), then a runtime environment may perform optimized accesses based on type restrictions derived from the specialized type.

Abstract: The present embodiments relate to a CI replication service that can replicate domain data from IDCS control plane to data plane and to all subscribed regions of a domain. For instance, the CI replication service can provide replication of required resources of a domain for AuthN and AuthZ from an IDCS local region to other regions for high availability (e.g., to improve latency). The CI replication service can replicate the resources from a domain's home region to all subscribed regions for local availability of data for workloads running in those regions. Further, when a new region is subscribed for a domain, then the service can bootstrap that domain's data from home region before enabling that region for the domain.

Abstract: Embodiments autonomously test a logical model for inconsistencies. For example, metadata descriptive of a logical model can be received, where the logical model includes an abstraction for a database schema, the database schema is implemented at a database, and the database schema includes a fact table and a dimension table. Logical queries can be automatically generated including at least first and second logical queries based on the retrieved metadata, where the first and second logical queries target a logical object of the logical model. At least the first and second logical queries can be issued to a server that hosts the logical model, where, at the server, the first and second logical queries are translated to first and second database queries, and the first and second database queries target at least a fact table and a dimension table from the database schema. Query results received from execution of the first and second database queries can be compared.

Abstract: Methods, systems, and computer readable media for application programming interface (API) related groupings involving common API framework (CAPIF) are disclosed. One example method for using an API exposure function (AEF) group comprises: at a CAPIF node including at least one processor: receiving an AEF group creation message for creating an AEF group associated with an API invoker, wherein the AEF group creation message includes AEF group information indicating one or more service APIs usable by an API invoker; storing the AEF group information; determining, using the AEF group information, service API information associated with the AEF group for the API invoker; and providing the service API information to the API invoker.

Abstract: Disclosed is a method, system, and computer program product for implementing adaptive business workflows, where the context is coordinated with a hierarchy. The adaptive workflow may include conditional stages that are also coordinated with the hierarchy.

Abstract: Systems, devices, and methods of the present invention detect affective argumentation in text. In an example, an application executing on a computing device accesses text comprising fragments. The application creates a discourse tree from the text. The discourse tree includes nodes, each nonterminal node representing a rhetorical relationship between two of the fragments and each terminal node of the nodes of the discourse tree is associated with one of the fragments. The application matches each fragment that has a verb to a verb signature, thereby creating a communicative discourse tree. The application determines whether the communicative discourse tree represents text that includes affective argumentation by applying a classification model trained to detect affective argumentation to the communicative discourse tree.

Abstract: Embodiments learn and visualize a software component and interface. Software code including a series of events can be received, where the series of events include calls to a software component and interface under test. Supplemental code can be injected into the received code. The software code with the injected supplemental code can be executed, where the executed supplemental code generates state data for the software component and interface. Based on the generated state data, a state diagram visualization can be generated that visualizes states and state transitions for the software component and interface under test given the series of events.

Abstract: Techniques are disclosed for implementing a multi-tenant framework for microservices in a microservices-based application. A distributed computing system can execute a cluster on which the microservices-based application is hosted. The microservices-based application can include at least one microservice that incorporates the multi-tenant framework. The multi-tenant framework includes modules configured to provide multi-tenant functionality for the microservice. The microservice may receive a request containing tenant context data at an interface. A first module of the multi-tenant framework can extract the tenant context data from the request. In response to the request, the microservice may generate microservice data. A second module of the multi-tenant framework can use the extracted tenant context data to obtain a data store connection for the tenant. The microservice can use the data store connection to access the tenant's data store and store the microservice data.

Abstract: Techniques are described herein for using machine learning to learn vector representations of DNS requests such that the resulting embeddings represent the semantics of the DNS requests as a whole. Techniques described herein perform pre-processing of tokenized DNS request strings in which hashes, which are long and relatively random strings of characters, are detected in DNS request strings and each detected hash token is replaced with a placeholder token. A vectorizing ML model is trained using the pre-processed training dataset in which hash tokens have been replaced. Embeddings for the DNS tokens are derived from an intermediate layer of the vectorizing ML model. The encoding application creates final vector representations for each DNS request string by generating a weighted summation of the embeddings of all of the tokens in the DNS request string. Because of hash replacement, the resulting DNS request embeddings reflect semantics of the hashes as a group.

Abstract: Embodiments utilize trained query performance machine learning (QP-ML) models to predict an optimal compute node cluster size for a given in-memory workload. The QP-ML models include models that predict query task runtimes at various compute node cardinalities, and models that predict network communication time between nodes of the cluster. Embodiments also utilize an analytical model to predict overlap between predicted task runtimes and predicted network communication times. Based on this data, an optimal cluster size is selected for the workload. Embodiments further utilize trained data capacity machine learning (DC-ML) models to predict a minimum number of compute nodes needed to run a workload. The DC-ML models include models that predict the size of the workload dataset in a target data encoding, models that predict the amount of memory needed to run the queries in the workload, and models that predict the memory needed to accommodate changes to the dataset.

Abstract: An aspect of the present disclosure facilitates resolving tickets in a multi-tenant environment. In one embodiment, a server receives a ticket for a tenant from a ticketing system and then determines a gross job representing a class of jobs suitable for resolution of the received ticket. The server then identifies a set of values for a set of system parameters characterizing the computing resources serving the tenant. The server selects a target job based on the combination of the determined gross job and the set of values for the set of system parameters. The selected target job is then executed to cause resolution of the ticket for the tenant. According to another aspect, the server performs the above noted actions automatically without manual intervention, in response to adding of the ticket into the ticketing system.

Abstract: In accordance with an embodiment, described herein are systems and methods for providing tiered data storage in cloud infrastructure environments. A data storage service (block store) is adapted to automatically adjust the manner by which the data for a data volume or block volume (data/block volume), associated with a cloud instance, can be stored to meet the requirements of a performance tier. For example, responsive to selection of a particular performance tier, the storage of the data/block volume can be allocated between a first type of data storage associated with a first performance characteristics; and a second type of data storage associated with a second performance characteristics. A graphical user interface enables configuring data/block volumes to use particular performance tiers, and/or to support automatic tuning.

Abstract: The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.

Abstract: Systems and methods for improving question-answering recall for complex, multi-sentence, convergent questions. More specifically, an autonomous agent accesses an initial answer that partly answers a question received from a user device. The agent represents the question and the initial answer as discourse trees. From the discourse trees, the agent identifies entities in the question that are not addressed by the answer. The agent forms an additional discourse tree from an additional resource such as a corpus of text. The additional discourse tree rhetorically connects a non-addressed entity with the answer. The agent designates this discourse tree as an imaginary discourse tree. When combined with the initial answer discourse tree, the imaginary discourse tree is used to generate an improved answer relative to existing solutions.

Abstract: A data receiver circuit includes a summer circuit configured to receive an input signal that encodes multiple data symbols, and combine the input signal with a feedback signal to generate an equalized input signal, which is used to generate a clock signal. The data receiver circuit also includes multiple data slicer circuits that sample, using the clock signal and multiple voltage offsets, the equalized input signal to generate multiple samples of a particular data symbol. A precursor compensation circuit included in the data receiver circuit may generate an output value for the particular data symbol using the multiple samples. The data receiver circuit also includes a post cursor compensation circuit that generates the feedback signal using at least one of the multiple samples and a value of a previously received sample.

Abstract: Techniques herein train a multilayer perceptron, sparsify edges of a graph such as the perceptron, and store edges and vertices of the graph. Each edge has weight. A computer sparsifies perceptron edges. The computer performs a forward-backward pass on the perceptron to calculate a sparse Hessian matrix. Based on that Hessian, the computer performs quasi-Newton perceptron optimization. The computer repeats this until convergence. The computer stores edges in an array and vertices in another array. Each edge has weight and input and output indices. Each vertex has input and output indices. The computer inserts each edge into an input linked list based on its weight. Each link of the input linked list has the next input index of an edge. The computer inserts each edge into an output linked list based on its weight. Each link of the output linked list comprises the next output index of an edge.

Abstract: Techniques for providing, to a resource on a private network of a service provider, access to a resource on a private network of a customer. Service to customer (S2C) resources deployed on a cloud infrastructure to facilitate the access. Whereas IP address ranges may overlap between private networks and/or private IP addresses may be used in one or more of the private networks, the S2C resources enable the data exchange between the private networks. For example, the S2C resources translate between IP addresses such that data within each private network uses IP addresses that can be properly processed by the private network.

Abstract: A framework for performing drift analysis with respect to database and enterprise applications. For a product for which drift analysis is to be performed, a set of one or more parts of the product are determined. Current state information for the set of one or more parts is obtained by crawling an environment in which the product is deployed. Current state information obtained for the set of one or more parts is compared with a baseline for the product. The comparison aids to identify one or more differences between the current state information and information in the baseline for the set of one or more parts. The information in the baseline for the set of one or more parts is indicative of a state of the set of one or more parts when the baseline was generated. A report indicative of the one or more differences is generated and output.

Abstract: The current document is directed to a query-as-a-service system (“QAAS system”) that collects enormous volumes of data from network-connected entities, referred to as “Things” in the phrase “Internet of Things,” persistently stores the collected data and provides a distributed-query-execution engine that allows remote clients to continuously execute queries against the collected data. In a described implementation, both the raw data and query results are persistently stored in the QAAS system, with the raw data stored for significantly longer periods of time. Query results generated by the query-processing engine are securely transmitted to QAAS remote clients for distribution to file systems, storage.

Abstract: Systems, devices, and methods of the present invention calculate a rhetorical relationship between one or more sentences. In an example, a computer-implemented method accesses a sentence comprising a plurality of fragments. At least one fragment includes a verb and a words. Each word includes a role of the words within the fragment. Each fragment is an elementary discourse unit. The method generates a discourse tree that represents rhetorical relationships between the sentence fragments. The discourse tree includes nodes including nonterminal and terminal nodes, each nonterminal node representing a rhetorical relationship between two of the sentence fragments, and each terminal node of the nodes of the discourse tree is associated with one of the sentence fragments. The method matches each fragment that has a verb to a verb signature, thereby creating communicative discourse tree.

Abstract: Embodiments relate to configuring artificial-intelligence (AI) decision nodes throughout a communication decision tree. The decision nodes can support successive iteration of AI models to dynamically define iteration data that corresponds to a trajectory through the tree.

Abstract: First, the system obtains time-series sensor data. Next, the system identifies missing values in the time-series sensor data, and fills in the missing values through interpolation. The system then divides the time-series sensor data into a training set and an estimation set. Next, the system trains an inferential model on the training set, and uses the inferential model to replace interpolated values in the estimation set with inferential estimates. If there exist interpolated values in the training set, the system switches the training and estimation sets. The system trains a new inferential model on the new training set, and uses the new inferential model to replace interpolated values in the new estimation set with inferential estimates. The system then switches back the training and estimation sets. Finally, the system combines the training and estimation sets to produce preprocessed time-series sensor data, wherein missing values are filled in with imputed values.

Abstract: Region summaries of database data are stored in persistent memory of a storage cell. Because the region summaries are stored in persistent memory, when a storage cell is powered off and data in volatile memory is not retained, region summaries are nevertheless preserved in persistent memory. When the storage cell comes online, the region summaries already exist and may be used without the delay attendant to regenerating the region summaries stored in volatile memory.

Abstract: Fairness of a trained classifier may be ensured by generating a data set for training, the data set generated using input data points of a feature space including multiple dimensions and according to different parameters including an amount of label bias, a control for discrepancy between rarity of features, and an amount of selection bias. Unlabeled data points of the input data comprising unobserved ground truths are labeled according to the amount of label bias and the input data sampled according to the amount of selection bias and the control for the discrepancy between the rarity of features. The classifier is then trained using the sampled and labeled data points as well as additional unlabeled data points. The trained classifier is then usable to determine unbiased classifications of one or more labels for one or more other data sets.

Abstract: Techniques for generating and evaluation predictive models to optimize computing applications are described herein. In some embodiments, a system generates, using a model, a first set of scores for a plurality of accounts, wherein a score in first set of scores for a respective account characterizes a priority of interacting with the respective account relative to other accounts in the plurality of accounts, wherein the model defines a set of criteria for scoring accounts, wherein each criterion in the set of criteria is associated with one or more feature values and a weight. The system may receive user input that modifies the set of criteria. Responsive to the user input the system may update the model and generate, using the updated model, a second set of scores for the plurality of accounts. Actions may be prioritized/executed based at least in part on the account scores.

Abstract: An example framework and method facilitate enhancing resiliency of a computing environment. The example method includes dynamically discovering or accessing first status information related to a first metric associated with a first resource of the computing environment; selectively sharing the first status information among one or more computing environment resources, including the first resource, resulting in shared status information; and using the shared status information to selectively initiate one or more responses by the one or more resources in accordance with the shared status information. In a specific implementation, metrics managers may manage metrics and initiate remediation actions when certain criteria are met; the resources can selectively quiesce or unquiesce when the certain criteria are met or when their status changes accordingly; and a resiliency manager may coordinate communications between metrics managers and computing resources.