Abstract: One embodiment provides a quality of service (QoS) monitoring framework for dynamically binding one or more customer applications to one or more microservices in a dynamic service environment, collecting compliance data and contextual data from the dynamic service environment and one or more hosting environments, and modifying a monitoring infrastructure for the one or more customer applications based on the compliance data and the contextual data.

Abstract: Systems, methods, and computer program products for adaptive, imitation learning and applications to navigational assistance are disclosed. The inventive concepts include: generating a navigational assistance trajectory through a physical environment in response to receiving a request for navigational assistance through the physical environment; and transmitting navigation instructions to a mobile device. The navigation instructions are based at least in part on the navigational assistance trajectory. A user receiving the navigational assistance trajectory and auditory instructions may advantageously navigate through the complex, dynamic physical environment without relying on visual cues.

Abstract: One embodiment provides a method comprising receiving metadata comprising univariate time series data for each variable of a multivariate time series. The method comprises, for each variable of the multivariate time series, applying a hybrid and hierarchical model selection process to select an anomaly detection model suitable for the variable based on corresponding univariate time series data for the variable and covariations and interactions between the variable and at least one other variable of the multivariate time series, and detecting an anomaly on the variable utilizing the anomaly detection model selected for the variable. Based on each anomaly detection model selected for each variable of the multivariate time series, the method further comprises performing ensemble learning to determine whether the multivariate time series is anomalous at a particular time point.

Abstract: Data protection activity time in a backup environment can be predicted with a computer-implemented method. The method includes gathering datasets of data points from a range of backup components of a backup system and constructing input features for a predictive machine learning model, where the input features relate to backup parameters of the backup system. The method also includes training the predictive machine learning model to predict data protection activity times for the backup system by using the gathered datasets and applying the input features to the model. The method also includes deploying the trained predictive machine learning model to predict a data protection activity time of a backup system formed of a set of the backup components.

Abstract: Systems, methods, and computer program products for adaptive, imitation learning and applications to navigational assistance are disclosed. The inventive concepts include receiving a plurality of trajectories, each trajectory describing a path through a dynamic, complex physical environment; receiving a request for navigational assistance through the dynamic, complex physical environment; and in response to receiving the request for navigational assistance: generating a navigational assistance trajectory through the dynamic, complex physical environment, the navigational assistance trajectory being based at least in part on at least one of the plurality of trajectories; generating auditory instructions in accordance with the navigational assistance trajectory; and transmitting the navigational assistance trajectory and the auditory instructions to a mobile device from which the request for navigational assistance was received.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification and information retrieval. One example method of operation may include one or more of monitoring a computing service via various nodes operating on a blockchain, identifying a proposed change to the computing service, storing details of the proposed change in a smart contract, storing the smart contract as a transaction in the blockchain, and validating the proposed change of the smart contract.

Abstract: A method loads training samples and forms training data set from the training samples. The method uses the bidirectional LSTM recurrent neural network that includes one or more input cells and one or more output cells and trains it with the training data set. The method determines a sensitive information and confidence values based on analyzing a text with the trained neural network. The method selects predicted samples from the text, where the sensitive information confidence value corresponding to a one or more predicted samples is above a threshold value, based on determining that a sensitive information accuracy has improved. The method forms a new training data set, where the new training data set comprises the samples and the verified one or more predicted samples based on the verified one or more predicted samples, and trains the previously trained neural network with the new training data set.

Abstract: A computer-implemented method according to one embodiment includes receiving a data object from a first application running on a computing device, a unique identifier (ID) of the data object assigned by the first application, and an access permission for the data object from the first application. The computer-implemented method also includes storing the data object, the unique ID, and the access permission in a data repository in a data distributor layer on the computing device. The computer-implemented method also includes receiving, at an access controller layer of the computing device, a request for the data object from a second application, the request including the unique ID, and retrieving, by the access controller layer, the data object from the data distributor layer using the unique ID in response to the request. The computer-implemented method includes providing, by the access controller layer, the data object to the second application.

Abstract: A method for distributing data among storage devices. The method comprising one or more processors receiving a first graph workload that executes within a networked computing environment. The method further includes identifying data from the first graph workload that is utilized during the execution of the first graph workload that includes a plurality of data packets. The method further includes creating a first graph workload model representative of the graph structure of the first graph workload and determining two or more partitions that are representative of a distribution of the identified data utilized by the first graph workload based, at least in part, on the first graph workload model. The method further includes allocating a plurality of network accessible storage devices among the two or more partitions and copying a first set of data packets of the plurality of data packets to a network accessible storage device.

Abstract: Systems and methods are described for a data breach detection based on snapshot analytics. The described systems and methods identify a plurality of snapshots of a data structure, identify a plurality of leaf nodes of the data structure for each of the snapshots, generate a vector of data attributes for each of the leaf nodes, assign a weight to each of the vectors to produce a set of weighted vectors for each of the snapshots, compute a distance metric between each pair of the snapshots based on the corresponding sets of weighted vectors, and detect an abnormal snapshot among the plurality of snapshots based on the distance metrics.

Abstract: A method and associated systems for optimized orchestration of a data-migration project. A data-migration orchestration system represents a hierarchical organization of each dataset to be migrated as a tree, where each leaf node of the tree represents data to be migrated and where a path between the leaf node and the root node represents a hierarchical directory pathname of sensitive data represented by the leaf node. Each tree is assigned a sensitivity signature that is proportional to the relative sensitivity and access frequency of the dataset represented by that tree. The signatures are organized into clusters as a function of the distances between each signature, and each signature is associated with a soft migration cost specific to that signature's cluster. A soft cost for migrating an application that requires multiple datasets may be determined by adding the migration costs associated with each of the multiple datasets.

Abstract: Embodiments relate to systematic explanation of neural model behavior and effective deduction of its vulnerabilities. Input data is received for the neural model and applied to the model to generate output data. Accuracy of the output data is evaluated with respect to the neural model, and one or more neural model vulnerabilities are identified that correspond to the output data accuracy. An explanation of the output data and the identified one or more vulnerabilities is generated, wherein the explanation serves as an indicator of alignment of the input data with the output data.

Abstract: A computer-implemented method according to one embodiment includes receiving historical two-dimensional (2D) multivariate time series data; transforming the historical 2D multivariate time series data into a three-dimensional (3D) temporal tensor; training one or more deep volumetric 3D convolutional neural networks (CNNs), utilizing the 3D temporal tensor; and predicting future values for additional multivariate time series data, utilizing the one or more trained deep volumetric 3D CNNs.

Abstract: A virtual blockchain configuration may provide a distributed structure that uses a distributed hash configuration to reduce the complexity of blockchain transactions. One example method of operation may comprise one or more of storing a subset of blockchain data in a network device, accessing via the network device a virtual copy of a blockchain, accessing a blockchain block via the virtual copy of the blockchain, and writing blockchain transactions to the blockchain block via the network device.

Abstract: An apparatus includes a cable having two ends and at least two object markers coupled to the cable configured to enable augmented reality (AR) detection of each end of the cable among a plurality of cables. A computer-implemented method using augmented reality (AR) technology includes selecting a cable of interest, identifying an object marker at a first end of the cable of interest, and storing the object marker. The method also includes scanning a plurality of cables, detecting a second instance of the object marker, and identifying a second end of the cable of interest based on the object marker. A computer program product for detecting ends of cables using augmented reality (AR) technology, includes a computer readable storage medium having program instructions embodied therewith. The program instructions executable by a computer to cause the computer to perform the foregoing method.

Abstract: An apparatus includes a cable having two ends and at least two object markers coupled to the cable configured to enable augmented reality (AR) detection of each end of the cable among a plurality of cables. A computer-implemented method using augmented reality (AR) technology includes selecting a cable of interest and identifying an object marker positioned toward a first end of the cable of interest. The method also includes storing the object marker, scanning a plurality of cables, and identifying a second end of the cable of interest based an instance of the object marker positioned toward the second end of the cable of interest. A computer program product for detecting ends of cables using augmented reality (AR) technology includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a computer to cause the computer to perform the foregoing method.

Abstract: A computer-implemented method for detecting anomalies in seasonal multivariate time series data is described. A non-limiting example of the computer-implemented method includes receiving, by a processor, sensor data from a sensor and computing, by the processor, common trends from the sensor data. The method detrends, by the processor, the common trends into detrended data and computes, by the processor, common seasonality from the sensor data. The method deseasonalizes, by the processor, the common seasonality into deseasonalized data. The method obtains, by the processor, remainder components using the detrended data and desesonalized data and identifies, by the processor, remainder components as an anomaly when remainder components are greater than k standard deviations away from zero, where k is a user specified threshold greater than or equal to two.

Abstract: An apparatus includes a cable having two ends and at least two object markers coupled to the cable configured to enable augmented reality (AR) detection of each end of the cable among a plurality of cables. A computer-implemented method using augmented reality (AR) technology includes selecting a cable of interest, identifying an object marker at a first end of the cable of interest, and storing the object marker. The method also includes scanning a plurality of cables, detecting a second instance of the object marker, and identifying a second end of the cable of interest based on the object marker. A computer program product for detecting ends of cables using augmented reality (AR) technology, includes a computer readable storage medium having program instructions embodied therewith. The program instructions executable by a computer to cause the computer to perform the foregoing method.

Abstract: One embodiment provides a method for diagnosing data center incidents including receiving a data center incident report including information technology (IT) device incident information. Augmented reality (AR) is applied for an AR interface for receiving incident evidence information based on the IT device incident information. The incident evidence information is sent to a cognitive analytical process. Using the cognitive analytical process, statistical inference is determined and an incident diagnosis recommendation including analytical results is generated. The analytical results are received by the AR interface for determining a root cause of the incident report.

Abstract: A method and associated systems for a workload-aware thin-provisioning system that allocates physical storage to virtual resources from pools of physical storage volumes. The system receives constraints that limit the amount of storage that can be allocated from each pool and the total workload that can be directed to each pool. It also receives lists of previous workloads and allocations associated with each volume at specific times in the past. The system then predicts future workloads and allocation requirements for each volume by regressing linear equations derived from the received data. If the predicted values indicate that a pool will at a future time violate a received constraint, the system computes the minimum costs to move each volume of the offending pool to a less-burdened pool. It then selects the lowest-cost combination of volume and destination pool and then moves the selected volume to the selected pool.