Abstract: Embodiments include a method of storing digital data in a blockchain by receiving the digital data from a user, deduplicating the data in a deduplication system of the user to form deduplicated data, and encoding the deduplicated data into an array format for storage on a blockchain. A smart contract is deployed for deduplication on the destination side of the data, and the deduplicated data is encoded into a Binary Aligned Map (BAM) format for storage on the blockchain. A process on the destination side stores the deduplicated array data as a next block in the blockchain only if the next block agrees with the smart contract, and the blockchain is stored in system storage comprising hard disk, solid state, tape, or optical devices.

Abstract: A method for managing upgrades of components of clients includes obtaining an upgrade failure prediction request associated with a client of the clients, and in response to obtaining an update failure prediction request: obtaining live data associated with the client, matching the live data with a training data cluster, selecting relevant features associated with processed training data of the training data cluster, generating an upgrade failure prediction using the live data associated with the relevant features and a prediction model, making a determination that the upgrade failure prediction implicates an action is required, and based on the determination, initiating performance of the action.

Abstract: A method for a system. The method may include receiving information regarding a plurality of nodes of the transportation network, a map associated with the transportation network, and a current demand associated with multiple nodes in the transportation network. The method may further include generating, based on the received information, a demand grid related to the transportation network. The method may also include determining a set of service zones based on the received information and the generated demand grid. The method may further include generating, based on the received information and the set of service zones, a model for route optimization. The method may also include determining, based on the model, at least one candidate path for a vehicle associated with the on-demand transportation service.

Abstract: The invention provides seamless journey experience for passenger in multimodal mobility travel using a single ticket for different trip sections and when multimodal operators provide passenger proof-of-transit, smart contracts enable automated fare disambiguation among them. The single truth of passenger travel journey on single ticket in multimodal mobility helps to also develop incentivizations/rewards for passengers, and incentivize modal operator's based on service level agreements in a consortium.

Abstract: An apparatus for providing recommended solutions for a multi-mode transportation problem associated with a multi-mode transportation network. The apparatus may include a memory and at least one processor coupled to the memory. The at least one processor may be configured to receive transportation-related data regarding a plurality of different modes of transportation. The apparatus may further be configured to generate a single-mode data set for each of the plurality of different modes of transportation. The apparatus may also be configured to generate problem-specific harmonized data for the multi-mode transportation network from the received transportation-related data. The apparatus may further be configured to provide a set of recommended solutions for the multi-mode transportation problem based on the problem-specific harmonized data.

Abstract: A kexec-based system update process wherein user-specific data is transferred on reboot of the second kernel. Upon initializing kexec load, buffer memory is assigned to the second kernel and the system loads control pages of fixed size for the second kernel boot, and also loads user-specific data onto extended control pages of variable size. Upon boot of the second kernel, the user-specific data is extracted from the extended control pages and transferred to the corresponding applications.

Abstract: Methods, apparatus, and processor-readable storage media for analyzing time series data for sets of devices using machine learning techniques are provided herein. An example computer-implemented method includes processing time series data from multiple devices; generating at least one data forecast by applying, in response to a request from at least one user, one or more machine learning techniques to at least a portion of the processed time series data; computing one or more qualifying values attributable to the at least one generated data forecast by providing the at least one generated data forecast and the at least a portion of the processed time series data to a conformal prediction framework; and performing one or more automated actions based at least in part on the at least one generated data forecast and the one or more computed qualifying values.

Abstract: Example implementations described herein involve an attribute of an inflow source that is assigned to each trajectory in the camera view, which is used for crowd analysis. The inflow source is estimated using techniques of tracking across cameras. If the inflow source is different even if the flow trajectories are the same direction, the trajectories are visualized in different styles. By using the attribute of the inflow source, it is possible to detect changes and anomalies in the crowd that were difficult to detect using the prior art.

Abstract: In general, embodiments relate to a managing a Redundant Array of Independent Disks (RAID) group. The embodiments include determining a minimum and maximum set of spare disks to allocate to the RAID group, wherein the RAID group comprises a plurality of active members, allocating the minimum number of spare members to the RAID group, allocating an additional spare member to the RAID group, setting a mode of the additional spare member to storage mode, enabling, after the setting, the RAID controller to store data in the plurality of active members and in the additional spare member, wherein the plurality of active members, the minimum number of spare members, and the additional spare member comprise persistent storage.

Abstract: One embodiment provides a computer implemented method of estimating replication completion time. The method includes creating a historical dataset of prior replication data; determining a set of replication parameters to consider; inputting the historical dataset and the set of replication parameters to a replication completion time estimator module; generating a replication completion time prediction based on the historical dataset and the set of replication parameters; and generating a confidence prediction corresponding to the replication completion time prediction.

Abstract: In general, embodiments relate to a managing a Redundant Array of Independent Disks (RAID) group. The embodiments include determining a minimum and maximum set of spare disks to allocate to the RAID group, wherein the RAID group comprises a plurality of active members, allocating the minimum number of spare members to the RAID group, allocating an additional spare member to the RAID group, setting a mode of the additional spare member to storage mode, enabling, after the setting, the RAID controller to store data in the plurality of active members and in the additional spare member, wherein the plurality of active members, the minimum number of spare members, and the additional spare member comprise persistent storage.

Abstract: An anomaly with a disk array enclosure (DAE) of a set of DAEs connected to a host server is detected. Each DAE is chained to another DAE by first and second chains and includes a serial attached SCSI (SAS) expander and a peer SAS expander. The SAS expander in a DAE forms part of the first chain. The peer SAS expander in the DAE forms part of the second chain. Upon detection, DAE log collection is triggered to obtain logs for storage in a repository, separate from the DAEs. The logs are tagged with an anomaly class of a set of anomaly classes. For each anomaly class, a number of logs stored in the repository is limited to a threshold number.

Abstract: Systems and methods described herein can involve establishing a blockchain network between a plurality of organizations, each organization representing a different mode of transport and/or transit oriented service providers; deriving multi-modal features from first data sources of the plurality of organizations in the blockchain and second data sources off the blockchain; constructing a multi-modal network that integrates the different mode of transports from the plurality of organizations from the derived multi-modal features; executing smart contracts between the plurality of organizations across the blockchain in accordance with service level agreements generated from reinforcement learning through the multi-modal model derived from the multi-modal network; and providing, through a micro-application, access to multi-modal transportation services based on the executed smart contracts.

Abstract: Methods, apparatus, and processor-readable storage media for automatically allocating device resources using machine learning techniques are provided herein. An example computer-implemented method includes determining values for multiple parameters from data obtained from one or more devices; generating at least one device component failure prediction by applying one or more machine learning techniques to at least a portion of the determined values; computing one or more qualifying values attributable to the at least one generated device component failure prediction by providing the at least one generated device component failure prediction to a conformal prediction framework; and automatically allocating one or more resources to at least a portion of the one or more devices based at least in part on the at least one generated device component failure prediction and the one or more computed qualifying values.

Abstract: A method for managing upgrades of components of clients includes obtaining an upgrade failure prediction request associated with a client of the clients, and in response to obtaining an update failure prediction request: obtaining live data associated with the client, matching the live data with a training data cluster, selecting relevant features associated with processed training data of the training data cluster, generating an upgrade failure prediction using the live data associated with the relevant features and a prediction model, making a determination that the upgrade failure prediction implicates an action is required, and based on the determination, initiating performance of the action.

Abstract: A set of restore jobs are created to restore a backup. The backup may be maintained across a set of storage devices connected to a backup server. The set of storage devices have different levels of performance and each restore job is responsible for restoring from a respective storage device. Information describing arrangements of processors and memory units is reviewed to create a set of heterogeneous domains, each domain including a processor and memory unit. Performance characteristics, including latency and bandwidth measurements, of the heterogeneous domains are examined. The heterogeneous domains are ranked based on their performance characteristics. The restore jobs are assigned to the heterogeneous domains by successively matching a restore job associated with a storage device having a lowest level of performance relative to other unmatched storage devices with a heterogeneous domain having a highest level of performance relative to other unmatched heterogeneous domains.

Abstract: Embodiments include a method of storing digital data in DNA storage by receiving the digital data from a user, deduplicating the data in a deduplication system of the user to form deduplicated data, and encoding the deduplicated data into a DNA string into a format for storage on a blockchain. A smart contract is deployed for deduplication on the destination side of nucleotide sequences comprising the DNA string, and the deduplicated nucleotide sequences are encoded into a Binary Aligned Map (BAM) format for storage as metadata on the blockchain. A process on the destination side synthesizes the deduplicated nucleotides for storage in the DNA storage, and stores the deduplicated nucleotides in the DNA storage as a next block in the blockchain only if the next block agrees with the smart contract.

Abstract: Embodiments include a method of storing digital data in a blockchain by receiving the digital data from a user, deduplicating the data in a deduplication system of the user to form deduplicated data, and encoding the deduplicated data into an array format for storage on a blockchain. A smart contract is deployed for deduplication on the destination side of the data, and the deduplicated data is encoded into a Binary Aligned Map (BAM) format for storage on the blockchain. A process on the destination side stores the deduplicated array data as a next block in the blockchain only if the next block agrees with the smart contract, and the blockchain is stored in system storage comprising hard disk, solid state, tape, or optical devices.

Abstract: Embodiments for storing digital data in DNA storage by receiving input file data, encoding the input file data into an oligonucleotide sequence to produce sequence data through a transformation of text to binary to Base_3 encoding, organizing the Base_3 sequence data into chunks of a defined chunk size, and storing the chunks in a block of a blockchain. The oligonucleotide sequence may comprise metadata for the input file data that refers to actual data stored in the DNA storage, where the actual data is formed by synthesizing the oligonucleotide sequence in a DNA synthesis process. The chunks may be stored in the blockchain only if the block agrees with a smart contract defined for the oligonucleotide sequence.

Abstract: A set of restore jobs are created to restore a backup. The backup may be maintained across a set of storage devices connected to a backup server. The set of storage devices have different levels of performance and each restore job is responsible for restoring from a respective storage device. Information describing arrangements of processors and memory units is reviewed to create a set of heterogeneous domains, each domain including a processor and memory unit. Performance characteristics, including latency and bandwidth measurements, of the heterogeneous domains are examined. The heterogeneous domains are ranked based on their performance characteristics. The restore jobs are assigned to the heterogeneous domains by successively matching a restore job associated with a storage device having a lowest level of performance relative to other unmatched storage devices with a heterogeneous domain having a highest level of performance relative to other unmatched heterogeneous domains.