Emc IP Holding Company LLC

Oshry Ben-Harush

Amihai Savir

Assaf Natanzon

Anat Parush Tzur

Shiri Gaber

Omer Sagi

Nimrod Milo

Idan Levy

Stephen J Todd

Avitan Gefen

Or Herman Saffar

Alon Grubshtein

Ran Taig

Raphael Cohen

Shai Harmelin

Nir Goldschmidt

Arik Sapojnik

Anat Parush-Tzur

Brahma Tangella

Daniel S Inbar

Sallie A Paige

Mridul Vinay Garg

Murale Narayanan

Christopher P Barry

Otniel Van Handel

Lena Tenenboim-Chekina

Jeroen Zonneveld

Techniques are provided for automatically detecting disruptive orders for a supply chain. One method comprises obtaining a quote for an order; extracting features from the quote; and applying the extracted features to a disruptive quote machine learning engine that generates an anomaly score indicating a likelihood that the quote will cause a disruption, based on one or more predefined disruption criteria. The disruptive quote machine learning engine may employ an isolation forest algorithm and/or a multi-dimensional anomaly detection algorithm. The disruptive quote machine learning engine may be trained using historical order information comprising part-level information from historical orders and/or a manufacturing production plan comprising an inventory forecast.

Automatic detection of disruptive orders for a supply chain

Techniques are provided for decompression of compressed log data, such as for a real-time viewing of compressed log data. An exemplary method comprises: obtaining a compressed log file comprised of a plurality of compressed log messages, wherein a given compressed log message is comprised of one or more message variables and a message signature corresponding to a message template of the given compressed log message; and presenting a first subset of the compressed log file by translating, in memory, the message signatures of the compressed log messages within the first subset to corresponding message templates using a decompression index that maps a plurality of the message signatures to corresponding message templates. The first subset of the compressed log file may comprise a predefined number of lines surrounding a requested line of the compressed log file. In further variations, at least one additional subset of the compressed log file is precomputed using the disclosed decompression techniques.

Real-time viewing tool for compressed log data

Techniques are provided for correcting sensor data in a multi-sensor environment. An exemplary method comprises obtaining sensor data from a first sensor; applying an anomaly detection technique to detect an anomaly in the sensor data from the first sensor based on additional sensor data from one or more of the first sensor and at least one additional sensor in proximity to the first sensor; and correcting the anomalous sensor data from the first sensor using additional sensor data from one or more of the first sensor and the at least one additional sensor. In some embodiments, additional sensor data from a plurality of neighboring sensors is used to predict the sensor data from the first sensor. The anomalous sensor data is optionally corrected substantially close in time to the detection of the anomaly in the sensor data.

Correction of sensor data in a multi-sensor internet of things environment

Techniques are provided for compression and decompression of log data. An exemplary method comprises: obtaining a log message, wherein the log message comprises a message template and one or more message variables; obtaining a compression index that maps a plurality of message templates to a corresponding message signature; and writing the one or more message variables and a message signature corresponding to the message template of the log message to a log file. A counter may be maintained for each of a plurality of distinct message templates, and a given message signature may be assigned to a particular message template based on a length of the given message signature and a frequency of occurrence of the particular message template. The compression index comprises, for example, a key/value database where the message templates are keys and the corresponding message signatures are values of the key/value database. A decompression index maps message signatures to corresponding message templates.

Real-time compression of log data

Techniques are provided for software license optimization using machine learning-based user clustering. One method comprises obtaining key performance indicators indicating individual usage by a plurality of users of a software product; applying at least one function to the key performance indicators to obtain a plurality of time dependent features; processing the time dependent features using a machine learning model to cluster the users into a plurality of persona clusters; and determining a number of each available license type for the software product for the plurality of users based on the persona clusters. The key performance indicators comprise, for example, user behavioral data with respect to usage of the software product and/or performance data with respect to usage of the software product. One or more policies can be determined for managing an allocation of the available license types for the software product to the plurality of users.

Dynamic optimization of software license allocation using machine learning-based user clustering

Methods and systems for managing the utilization of cloud computing resources are described. The system monitors cloud computing resource utilization for a first set of active jobs to determine real-time utilization data. The system compares the real-time utilization data with historic utilization data to generate a utilization pattern and determines future cloud computing resource utilization for at least one future time period based on the utilization pattern and a second set of scheduled jobs. The system further generates a pricing matrix for utilizing the cloud computing resources during a future time period based on the determined future cloud computing resource utilization. The pricing matrix includes prices associated with utilization of the cloud computing resources for each of the at least one future time period. The system transmits the pricing matrix to one or more devices requesting utilization of the cloud computing resources.

Managing utilization of cloud computing resources

Flexible scheduling for backup jobs includes backup policies that allow for flexibility in scheduling execution time, and an automated data driven backup job execution scheduler. The scheduler balances load on the customer's and data protection system's resources using a dynamic pricing calculator that takes into account the real-time and predicted near-future status of the protection environment. The backup job scheduler includes a user interface that enables users to specify user-customized backup policies that enable flexibility in scheduling execution times and transparency in pricing.

Flexible scheduler for backups

Techniques are provided for sensor data compression in a multi-sensor Internet of Things environment. An exemplary method comprises obtaining sensor data from a plurality of sensors satisfying one or more of predefined sensor proximity criteria and predefined similar sensor type criteria; applying an image-based compression technique to the sensor data to generate compressed sensor data; and providing the compressed sensor data to a data center. The image-based compression technique comprises a discrete cosine transform technique, a video compression technique, and/or an auto-encoder deep learning technique that utilizes one or more over-fitted bidirectional recurrent convolutional neural networks. The sensor data is optionally normalized prior to being applied to the image-based compression technique.

Sensor data compression in a multi-sensor internet of things environment

Application data is received from a plurality of monitored applications. The application data is parsed into a plurality of features describing an operation of the plurality of monitored applications. A counter associated with at least one of the plurality of features is incremented. A system health is derived for the plurality of monitored applications from the counter.

System operational analytics

A report cost estimation module is used with cloud storage systems that store and process client data. Reports on the cloud stored client data take a significant amount of CPU, memory, storage and networking resources. A data analysis model dynamically estimates pricing for client data reports according to resource consumption by identifying a cluster group for the report and using the designated regression model for the cluster group. The data analysis can store the estimated and actual report costs and improve the estimated report costs using machine learning algorithms and crowed sourcing techniques. The report price is accurately provided to the client before running the report and thus allowing the customer to carefully manage a client report budget.

System and method for price estimation of reports before execution in analytics

At least part of an analytic process is executed on one or more data sets. Execution of the analytic process is performed within an analytic computing environment. During the course of execution of the analytic process, a data structure is generated comprising data structure elements. The data structure elements represent attributes associated with execution of the analytic process. Value is assigned to at least a portion of the data structure elements. The data structure generated during execution of the analytic process may be stored in an accessible catalog of other data structures generated during execution of other analytic processes.

Data value structures

Methods and apparatus are provided for multi-site time series data analysis. An exemplary method comprises obtaining a plurality of vectors over a period of time, wherein each vector comprises a local health score from each of a plurality of distributed sites that perform operational analytics on time-series data; determining a distribution of the vectors during a training phase; and calculating a global health score for the plurality of distributed sites for a given vector during a prediction phase based on a distance of the given vector from a center of mass of the distribution. Feedback is optionally provided to individual sites, such as an indication of a local health score of an individual site relative to the global health score, and adjustments to thresholds and/or parameters used by the individual site.

Methods and apparatus for multi-site time series data analysis

Data compression optimization based on client clusters is described. A system identifies a cluster of similar client devices in a group of client devices, by comparing data compression factors that correspond to each client device in the group of client devices. The system identifies a relationship between data compression factors corresponding to the cluster and data compression ratios corresponding to the cluster. The system identifies a client device, in the cluster, which corresponds to a data compression ratio that is inefficient relative to other compression ratios corresponding to other client devices in the cluster. The system outputs a data compression recommendation for the client device, based on data compression factors corresponding to the client device and the identified relationship between the data compression factors corresponding to the cluster and the data compression ratios corresponding to the cluster.

Data compression optimization based on client clusters

Example embodiments of the present invention relate to a method, an apparatus, and a computer program product for predictive behavioral analytics for information technology (IT) operations. The method includes collecting key performance indicators from a plurality of data sources in a network. The method also includes performing predictive behavioral analytics on the collected data and reporting on results of the predictive behavioral analytics.

Method and apparatus for predictive behavioral analytics for IT operations

Described are techniques that determine cumulative skew curves. A first model is determined that generates a predicted destination cumulative skew curve for a specified data set in a destination data storage system having a destination data movement granularity. The predicted destination cumulative skew curve is predicted by the first model in accordance with one or more inputs including a source cumulative skew curve for the specified data set in a source data storage system that uses a source data movement granularity. The source cumulative skew curve for the specified data set is determined based on observed data. First processing is performed using the first model. The first model generates as an output the predicted destination cumulative skew curve. The first processing includes providing the one or more inputs to the first model. Also described is how to generate the first model.

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Kibutz Galon, Israel

Oshry Ben-Harush