Abstract: A method for workload-driven index selections includes receiving a request for a recommended index configuration. The method includes obtaining a plurality of queries executed at the database. The method also includes selecting a set of candidate indexes from the plurality of indexes. The method includes for each respective candidate index of the set of candidate indexes, determining, based on the plurality of queries, a respective workload cost for the respective candidate index. The method also includes selecting, based on the respective workload cost, a first candidate index from the set of candidate indexes for the recommended index configuration. The method includes selecting one or more additional candidate indexes from the set of candidate indexes for the recommended index configuration. The method includes determining that a size of the selected candidate indexes satisfies a size threshold and transmitting the recommended index configuration.

Abstract: Systems, computer program products, and methods are described herein for determining and applying a backup file attribution to files in an electronic network. The present invention is configured to receive data associated with at least one file, wherein the file is stored in at least one data store; and generate at least one file attribute for the file, wherein, in an instance where the file is a tape file type, generate a backup file attribute, wherein, in an instance where the file is not a tape file type, generate a non-backup file attribute, wherein, in an instance where the file is not a type file type and the file comprises a backup feature in the data, generate the backup file attribute, or wherein, in an instance where the file is a tape file type and the file comprises a non-backup feature in the data, generate the non-backup file attribute.

Abstract: Systems and methods involving provision of machine-learning-based prediction of future failure, anomaly, etc. in execution of batch processes are disclosed. In one illustrative implementation, an exemplary method may comprise obtaining historical data from prior execution of one or more batch processes, training a machine learning model to predict one or more future failure(s) and/or future flag(s) in execution of a future batch process, generating and/or collecting descriptive analytics pertinent to execution of the batch processes, and predicting a future failure and/or future flag in execution of the batch processes using the trained machine learning model and/or the descriptive analytics.

Abstract: A method for determining data consistency in a distributed asynchronous architecture is provided. A server receives transaction requests for processing transactions. The server offers one or more services for processing each transaction. Execution of a service for a transaction corresponds to partial processing of the corresponding transaction. The server generates, for each transaction, one or more disposition messages based on the execution of the one or more services. The server aggregates, for each transaction, the generated one or more disposition messages based on a unique identifier of a corresponding transaction and determines a data consistency rate based on the aggregated one or more disposition messages and a set of rules. The set of rules defines one or more standard sequences for executing the one or more services for processing each transaction.

Abstract: An example method includes receiving incident data for historical incidents of downtime or interrupted service. The incident data includes identification information about one or more first computing applications, devices, or services affected by the downtime or the interrupted service. The incident data further includes timing information relating to the historical incidents and version history information of the one or more first computing applications, devices, or services. The method further includes receiving root cause data indicating a cause of the historical incidents and receiving action data indicating a corrective or preventative action taken or to be taken in response to each of the historical incidents. The method further includes training a machine learning algorithm using the incident, root cause, and action data to create a trained model configured to determine a root cause and a new corrective or preventative action for a new incident.

Abstract: Disclosed is an approach for implementing a metadata cache in a virtualization system. A self-adaptive approach is provided to keep compressed and uncompressed entries together in cache. Along with adaptive nature, disclosed is an approach to prioritize critical workloads for the cache.

Abstract: An example medical device includes a device housing configured to be implantable within a patient, the device housing including an internal surface in contact with a voltaic cell of the battery, and a battery external to the device housing and comprising a battery housing configured to be hermetically sealed. The battery is configured to provide electrical power to an electrical component housed within the device housing, and the battery housing is configured to be attached to the device housing. The battery housing includes an internal surface in contact with a voltaic cell of the battery, and an external surface in contact with the biocompatible electrical insulator. an external surface in contact with the biocompatible electrical insulator.

Abstract: Methods and systems are described herein for identifying performance degradation in machine learning models based on comparisons of actual and predicted results. The system may receive predicted and actual results datasets for features within a system with the predicted results being generated by a machine learning model corresponding to the feature. The system may access a hierarchy associated with the features and select a level of the hierarchy having a subset of features. The subset of features may be associated with a target feature. Impact values may then be generated for the subset, where the impact values indicate contributions of the corresponding features to a difference, in the target feature, between predicted and actual results. The system may select a new target feature, from the subset, associated with a highest impact value and may retrain a machine learning model associated with the new target feature.

Abstract: A cooling system can include a two-stage compressor, a variable speed fan and a controller for continuously modulating cooling capacity of the system. The compressor can run in a loaded state and the fan can run at a first speed when a call for cooling calls for maximum system cooling capacity. The compressor can run in the loaded state and the fan speed can decrease to a second fan speed as the called for cooling capacity decreases. The compressor can run in an unloaded state and the fan speed can be increased. The compressor can run in the unloaded state and the fan speed can decrease to a third fan speed as the called for cooling capacity decreases.

Abstract: Some embodiments include utilizing a multilink media access control (MAC) address structure to support multilink devices (MLDs) that can operate concurrently in more than one link such as extremely high throughput (EHT) access points (APs) and EHT stations (STA), where the multilink MAC address structure is compatible with legacy devices. An EHT AP can utilize a multilink basic service set (BSS) identification (BSSID) MAC address to communicate with an EHT STA identified by a multilink MAC address. Values of the multilink BSSID and the multilink MAC address of the EHT STA are independent of which of the multiple links are used in the communication. In addition, to utilizing a multilink BSSID, the EHT AP can also support unique link-specific MAC addresses to concurrently support legacy and MLD stations. The EHT STA can also utilize unique link-specific MAC addresses that can be different than the EHT AP's link-specific MAC addresses.

Abstract: In implementations of repeat object blending, a computing device implements a repeat object blending system, which is implemented to receive a digital image depicting a first object and a second object, where the first object is depicted as multiple instances of a repeated base object, and the second object is depicted as multiple instances of a visually different repeated base object. The repeat object blending system can identify visual characteristics of the first object and the second object. The repeat object blending system can then generate an intermediate object by blending one or more of the visual characteristics of the first object and one or more of the visual characteristics of the second object. The resulting intermediate object is a visual representation of the repeated base object blended with the visually different repeated base object.

Abstract: Generally described, the presently disclosed technology utilizes managed Wi-Fi networks pre-installed throughout an MDU property to provide user-specific passphrases that can be used to access the single-SSID wireless network at the property and to provide a cloud portal that can enable convenient access to the functionalities (both by the resident and the manager) provided by the Wi-Fi controller and the Wi-Fi access points. By doing so, the Wi-Fi network management solutions described herein allow the users to experience the benefits of a shared Wi-Fi infrastructure, such as not having to set up and maintain their own Wi-Fi routers, while also allowing them to easily change their Wi-Fi settings from their connected devices.

Abstract: An example method includes receiving incident data for historical incidents of downtime or interrupted service. The incident data includes identification information about one or more first computing applications, devices, or services affected by the downtime or the interrupted service. The incident data further includes timing information relating to the historical incidents and version history information of the one or more first computing applications, devices, or services. The method further includes receiving root cause data indicating a cause of the historical incidents and receiving action data indicating a corrective or preventative action taken or to be taken in response to each of the historical incidents. The method further includes training a machine learning algorithm using the incident, root cause, and action data to create a trained model configured to determine a root cause and a new corrective or preventative action for a new incident.

Abstract: Various embodiments set forth techniques for cache warmup. The techniques determining, by a node, identities of one or more target storage blocks of a plurality of storage blocks managed by a storage system, where the node previously cached metadata corresponding to the one or more target storage blocks; receiving the metadata corresponding to the one or more target storage blocks; and storing the metadata corresponding to the one or more target storage blocks in a cache memory of the node.

Abstract: A system and corresponding computer-implemented method provide secure network access to network-enabled devices. The computer-implemented method creates a user profile, automatically. The user profile is bound to a physical geographic location. The user profile provides access rights to a network. The computer-implemented method assigns, automatically, at least one VLAN to the user profile created. The assigning is based on whether there is an existing user profile bound to the physical geographic location. The computer-implemented method associates, automatically, the user profile created with a network-enabled device.

Abstract: A system and corresponding computer-implemented method perform virtual local area network (VLAN) assignment. The method identifies, automatically, whether a network-enabled device is associated with a previously assigned VLAN for a network. The network-enabled device has passed authentication. The authentication is responsive to a request sent from the network-enabled device to network equipment for access to the network. Based on a result of the identifying, the method automatically (i) selects a VLAN from a pool of dynamically assignable VLANs, (ii) associates the VLAN selected with the network-enabled device, and (iii) instructs the network equipment to assign the network-enabled device to the VLAN selected from the pool.

Abstract: A lithium ion battery is provided that includes: a positive electrode; a negative electrode; a separator comprising a material having a melt temperature of greater than 150° C.; and an electrolyte including an organic solvent and a lithium salt. A method for sterilizing a lithium ion battery is also provided that includes: providing a lithium ion battery (particularly one as described herein); either charging or discharging the battery to a state of charge (SOC) of 20% to 100%; and steam sterilizing the battery to form a sterilized lithium ion battery.

Abstract: Generally described, the presently disclosed technology utilizes managed Wi-Fi networks pre-installed throughout an MDU property to provide user-specific passphrases that can be used to access the single-SSID wireless network at the property and to provide a cloud portal that can enable convenient access to the functionalities (both by the resident and the manager) provided by the Wi-Fi controller and the Wi-Fi access points. By doing so, the Wi-Fi network management solutions described herein allow the users to experience the benefits of a shared Wi-Fi infrastructure, such as not having to set up and maintain their own Wi-Fi routers, while also allowing them to easily change their Wi-Fi settings from their connected devices.

Abstract: Creating a containerized data analytics pipeline, including: creating a data analytics pipeline having a component that is deployed within a container that executes in a container cluster that is external to shared storage accessible by components of the data analytics pipeline; and responsive to detecting that a component of the data analytics pipeline has failed, deploying another instance of the component in a failover container that is configured to support one or more components of the data analytics pipeline.

Abstract: A battery including a conductive housing, a header assembly, and electrode assembly where the electrode assembly includes a solid-state electrolyte. A battery including a conductive housing having an inner surface and an outer surface, a header assembly, an electrode assembly, and an electrically insulative coating on at least a portion of the inner surface of the conductive housing.