Abstract: Example implementations described herein can be directed to detecting a human disturbance from sensor data streamed from one or more sensors in a network of sensors; processing the detected human disturbance to determine a probability of a chainsaw event (e.g., light chainsaw or dense chainsaw event) and an estimated lead time for the chainsaw event for an area associated with the one or more sensors; and determining, for neighboring sensors to the one or more sensors in the network of sensors, a probability of a change of state to the human disturbance or the chainsaw event for other areas associated with the neighboring sensors.

Abstract: In some examples, a first computing device of a plurality of computing devices may send, to a key management (KM) device, a request for information related to at least one of an active key encryption key (KEK) or a pre-active KEK associated with an entity. The first computing device may receive, from the KM device, an indication that the KM device does not have an active KEK or pre-active KEK. In response, the first computing device may send an instruction to the KM device for creation of a pre-active KEK for the entity. Based on subsequently determining that there are a plurality of pre-active KEKs for the entity available from the KM device, the first computing device may send an instruction to the KM device to activate the pre-active KEK having an earliest creation time, and the first computing device may receive an active KEK for the entity.

Abstract: Example implementations described herein are directed to systems and methods for generation and deployment of automated and autonomous self-learning machine learning models, which can include generating a predictive model and a prescriptive model through an offline learning process at a first system; controlling operations of a second system through deploying the predictive model and the prescriptive model to the second system; and autonomously updating the predictive model and the prescriptive model from feedback from the second system through an online learning process while the prescriptive model and the predictive model are deployed on the second system.

Abstract: In some examples, a computing device of a first network storage receives a virtual machine image and executable instructions for creating a quorum component for a second network storage and a third network storage that replicate data. The computing device implements the virtual machine on the computing device based on the virtual machine image. Further, based on implementing the virtual machine, the computing device executes the executable instructions to create a logical volume on a storage device associated with the computing device, and to create, in the logical volume, at least one target volume for receiving quorum information from the second network storage and/or the third network storage. Connections are established between the virtual machine and the second network storage and the third network storage for the virtual machine to receive quorum information. The virtual machine stores the quorum information in the at least one target volume.

Abstract: In some examples, a computing device stores root metadata data structures (DSs) together in a group root metadata filesystem block, and stores, in a data filesystem block, at least first file data referenced by the first root metadata DS and second file data referenced by a second root metadata DS. The computing device may determine a reference count of the data filesystem block based on the first file data referenced by the first root metadata DS and the second file data referenced by the second root metadata DS. In addition, the computing device may determine a third file data having content that matches the first file data and referenced by a third root metadata DS. The computing device may update the third root metadata DS to reference the first file data, and update the reference count of the data filesystem block based on updating the third root metadata DS.

Abstract: Example implementations described herein are directed to a system with a plurality of sensors associated with a plurality of apparatuses, which involves, for receipt of a hypothesis canvas and meta-data from the plurality of sensors, generating a plurality of meta-pipeline stages, the plurality of meta-pipeline stages having the meta-data and one or more stages, each of the one or more stages being an analytics application or a machine learning application stacked in execution order based on the meta-data and the hypothesis canvas; and executing the plurality of meta-pipeline stages in execution order on the meta-data to generate an analytics model for the system.

Abstract: In some examples, a computing device of a plurality of computing devices at a first geographic location may divide data into a plurality of partitions. For example, at least two computing devices at the first geographic location may maintain a copy of the data of a first partition of the plurality of partitions and may exchange periodic heartbeat communications related to the first partition. The computing device may determine that a computing device at the second geographic location has a lower frequency of access load than the computing devices at the first location that maintain the first partition. The computing device may migrate the data of the first partition to a computing device at the second geographic location to cause at least two computing devices at the second location to maintain the data of the first partition and exchange periodic heartbeat communications related to the first partition.

Abstract: Aspects of the present disclosure involve systems and methods that involve providing a health status from each of a plurality of gateways, the plurality of gateways comprising an active gateway and one or more inactive gateways, the plurality of gateways receiving sensor data, wherein the active gateway transmits the received sensor data to a server; for a detection of a failure of the active gateway based on the health status, selecting an inactive gateway from the one or more inactive gateways to take over transmission of the active gateway; changing a status of the active gateway to inactive; identifying the sensor data received from the active gateway before the failure; and controlling the selected inactive gateway to discard the identified sensor data and resume transmission of the sensor data to the server.

Abstract: In some examples, a computing device is able to communicate with a plurality of data clusters. The computing device may determine a location, format and/or schema of data resource(s) stored in the data clusters upon execution of a process. Metadata may be searched and read to determine the location, format and/or schema of data resource(s) and those data resources may be written to other destinations within the data cluster(s). The locations of the destinations may also be determined at runtime. In some examples, parameters may be configured or specified to drive the execution of one or more operations or processes.

Abstract: In some examples, a computing device may receive a first object. Further, the computing device may be able to communicate with a first network storage system over a network. The computing device may determine, from metadata for the first object, whether the metadata for the first object matches at least one notification rule. Based on determining that the metadata for the first object matches a rule expression of a first notification rule, the computing device may generate a first event notification corresponding to the first object. Further, the computing device may send the first event notification to a first queue specified by the first notification rule. For instance, the first queue may be provided by the first network storage system.

Abstract: In some examples, a system receives a user input to create a classification to use for a first data set. In addition, the system may receive a user input to associate the classification with the first data set as reference data. The system may determine a classification association with the first data set based in part on comparing a first classification model corresponding to the reference data with a second classification model of a second data set, the second classification model being determined based at least in part on a plurality of data properties of the second data set. Further, the system may determine a user curation result with respect to the classification association with the first data set, and may update the classification model for the first data set based at least in part on the user curation result.

Abstract: In some examples, a first computing device associated with a first site may receive a certificate of a second computing device associated with a second site that is different from the first site. The first computing device may send, to the second computing device, a credential of a user associated with the second site. In addition, the first computing device may send to the second computing device, a certificate of the first computing device. Furthermore, based at least on authentication of the credential of the user by the second computing device, trusted communications may be established between the first computing device and the second computing device.

Abstract: A server system provides one or more element applications configured to render document elements, including generating visualization data representations for generating the document elements, in particular on the basis of data obtained from one or more data sources; executing a graphical user interface for access by a user on one or more client computers, each client computer or user thereof being associated with a respective tenant of one or more tenants, wherein the client computers are communicably connected with the server system via one or more communication networks; wherein one or more document applications are provided for generating electronic documents upon request of a respective client computer or user thereof for being displayed and/or for printing purposes, the one or more document applications being executed on the one or more client computers and/or on one or more servers of the server system.

Abstract: In some examples, a first computing device of a plurality of computing devices may be a member of a first partition group. For example, the first partition group may correspond to a first partition of multiple partitions of a metadata database. The first partition group may include at least one other computing device. Furthermore, each of the plurality of computing devices may maintain at least one partition of the metadata database so that the metadata database is partitioned into multiple partitions distributed across the computing devices. The first computing device may determine a first time threshold for a heartbeat communication to be sent to the other computing device(s) in the first partition group, and may select a random time before the first time threshold. The first computing device may send, based on the selected random time, the heartbeat communication to the other computing device(s) in the first partition group.

Abstract: Systems and methods described herein involve establishing a secure hypertext transfer protocol (HTTPS) connection between a media server and one or more cameras selected by a client device through a web browser application; establishing a websocket secure (WSS) connection between the media server and the web browser application of the client device; receiving, at the media server, media chunks of live video from the selected one or more cameras through the HTTPS connection; and transmitting, from the media server, the media chunks of live video received from the selected one or more cameras to the web browser application of the client device through the WSS connection.

Abstract: In some examples, a computer system may receive a plurality of chunks of data of a data object. The system may compress the plurality of chunks of data to obtain a plurality of compressed chunks, and may determine whether the plurality of compressed chunks together are less than a threshold size. Based on determining that the plurality of compressed chunks together are less than the threshold size, the system may add, to respective entries in a map data structure, respective sizes of the plurality of compressed chunks. In addition, the system may compact the map data structure by combining values in at least two of the respective entries, and may store the plurality of compressed chunks and the compacted map data structure.

Abstract: In some examples, a system may receive, from a user device, a user request for a storage operation related to storage of data at a storage. An update may be added to an update queue to persist the storage operation prior to sending a response to the user device indicating performance of the storage operation. For instance, adding the update to the update queue may include sending a request to cause the update to be added to the update queue based on specifying a type of the storage operation and/or a state to be achieved by the storage operation. Additionally, information about the update may be sent to another processor for adding the update to another update queue managed by the other processor. Subsequently, the update may be obtained from the update queue and processed to perform the storage operation.

Abstract: In some examples, a computing device may communicate with a plurality of network storage systems, such as a first network storage system provided by a first service provider employing a first storage protocol and a second network storage system provided by a second service provider employing a second storage protocol different from the first storage protocol. The computing device receives a first object, and determines, for the first object, a first remote bucket at the first network storage system and a second remote bucket at the second network storage system. The computing device may add a synchronization event to a queue for replicating the first object to the first remote bucket and the second remote bucket. Based on consuming the synchronization event from the queue, the computing device replicates data and metadata of the first object to the first remote bucket and the second remote bucket.

Abstract: In some examples, a first computing device may receive a first request from a second computing device. For example, the first computing device may be able to communicate with a plurality of database nodes, each database node maintaining a portion of a database distributed across the plurality of database nodes. Further, the first computing device may maintain a local cache of a subset of information maintained in the database. The first computing device may send a second request based on the first request to a first database node to cause the first database node to change the data in the database. In addition, the first computing device receives, from a message queue of a plurality of distributed message queues, a cache update message based on the change to the data in the database. The first computing device may update the local cache based on the cache update message.

Abstract: In some examples, a computing device may store a first snapshot of a state of data in a first partition at a first point in time. The computing device may create a second partition and a third partition to each receive a portion of the data, the second partition and the third partition each including a metrics schema, and may determine information for the metrics schemas based on information in the first snapshot. During the determining of the information for the metrics schemas, the computing device may receive a write to the first partition. The computing device may update the first partition based on the write and may add a split update command to a data structure based on the write. In addition, the computing device may update at least one of the metrics schemas in the second partition or the third partition based on the split update command.