Emc IP Holding Company LLC

Dell Products Inc.

Avitan Gefen

Amihai Savir

Ofir Ezrielev

Nadav Azaria

Roi Gamliel

Omer Sagi

Kfir Wolfson

Itay Azaria

Ran Taig

Assaf Natanzon

Nicole Reineke

Shiri Gaber

Shaul Dar

Idan Richman Goshen

Jehuda Shemer

Anat Parush Tzur

Shai Harmelin

Idan Levy

Alon Shitrit

Joseph LaSalle White

Oshry Ben-Harush

Or Herman Saffar

Anil Kumar Koluguri

David Sydow

Maor Sade

Lindsay Braine

Methods and systems for anomaly detection in a distributed environment are disclosed. To manage anomaly detection, a system may include an anomaly detector and one or more data collectors. The anomaly detector may detect anomalies in data obtained from one or more of the data collectors using an inference model and an anomaly threshold. The anomaly threshold may be determined by fitting a normal distribution to output of the inference model when the inference model is exercised across an input range of the inference model. The anomaly threshold may correspond to a portion of the normal distribution. To perform anomaly detection, the inference model may require periodic re-training. Data collected from the one or more data collectors may be used to re-train the inference model as needed. Following anomaly detection and/or inference model re-training, the data may be discarded to remove the data from the anomaly detector.

System and method for memory-less anomaly detection using anomaly thresholds based on probabilities

Methods and systems for anomaly detection in a distributed system are disclosed. To manage anomaly detection, a system may include an anomaly detector and one or more data collectors. The anomaly detector may detect anomalies in data obtained from one or more of the data collectors using an inference model. The inference model may be an autoencoder trained to reconstruct data that is intended to match input data to an extent considered acceptable by the system. To accurately perform anomaly detection, the inference model may require re-training. Data collected from the one or more data collectors may be used to re-train the inference model as needed. Following anomaly detection and/or inference model re-training, the data may be discarded to remove the data from the anomaly detector.

System and method for memory-less anomaly detection using an autoencoder

One example method includes encoding data as a polysaccharide structure, synthesizing the polysaccharide structure to create polysaccharide storage media that comprises the data, and storing the polysaccharide storage media. The example method may also include receiving a read request directed to the polysaccharide storage media, mapping the polysaccharide structure to create a map in response to the read request, traversing the map of the polysaccharide structure to determine an X-base number, and obtaining the data by converting the X-base number to a binary form.

Polysaccharide archival storage

Methods and systems for inference generation are disclosed. To manage inference generation, a system may include an inference model manager and any number of data processing systems. The inference model manager may partition an inference model into portions. Portions of the inference model may be distributed to data processing systems in accordance with an execution plan. The execution plan may include instructions for timely execution of the inference model with respect to the needs of a downstream consumer. The inference model manager may manage execution of the inference model by monitoring the functionality of the data processing systems and dynamically re-assigning and/or re-locating data processing systems in the event that one or more data processing systems becomes unable to execute a portion of the inference model.

System and method for inference generation through dynamic reassignment of inference model portions

Methods and systems for inference generation are disclosed. To manage inference generation, a system may include an inference model manager and any number of data processing systems. The inference model manager may represent an inference model as a bipartite graph. To obtain portions of the inference model, the bipartite graph may be partitioned into portions and the portions may undergo an optimization process. The optimization process may include adding and/or competing for nodes of the bipartite graph in order to increase the stability of the portion with respect to the available computing resources of a corresponding data processing system. The optimization process may continue until all portions achieve stability in a way that reduces necessary communications between the data processing systems. Each portion of the inference model may be distributed to one data processing system so that the data processing systems may collectively generate inferences usable by a downstream consumer.

System and method for inference generation via optimization of inference model portions

Methods and systems for managing storage of data are disclosed. To manage storage of data, images may be stored across a number of storages that provide varying levels of storage performance and have correspondingly varying costs for storing data. To store the images across the storages, the images may be segmented into image segments and a likelihood of each of the image segments being used in the future may be identified. The image segments that are more likely to be used in the future may be stored in higher performance storages while the image segments that are less likely to be used in the future may be stored in lower performance storages. To identify the likelihood of each of the image segments being used in the future, the image segments may be classified based on their membership in one or more areas of interest within the images.

System and method for storage management of images

Methods and systems for inference generation are disclosed. To manage inference generation, a system may include an inference model manager and any number of data processing systems. The inference model manager may represent an inference model as a bipartite graph in order to obtain portions of the inference model. Each portion of the inference model may be distributed to one data processing system so that the data processing systems may collectively generate inferences usable by a downstream consumer. Portions of the inference model may be obtained so that each portion matches the available computing resources of a data processing system throughout the distributed environment. In addition, the portions may be obtained in order to reduce inter-data processing system communications during execution of the inference model.

System and method for execution of inference models across multiple data processing systems

Methods and systems for anomaly detection in a distributed environment are disclosed. To manage anomaly detection, a system may include an anomaly detector and one or more data collectors. The anomaly detector may detect anomalies in data and classify the anomalies based on magnitudes of anomalies using an inference model. Different magnitudes of anomalies may be keyed to different action sets in response to the presence of anomalies in data. To perform anomaly detection, the inference model may require re-training. Data collected from the one or more data collectors may be used to re-train the inference model as needed. Following anomaly detection and/or inference model re-training, the data may be discarded to remove the data from the anomaly detector.

System and method for memory-less anomaly detection using anomaly levels

Methods and systems for obtaining synthesized images are disclosed. To obtain synthesized images, an imaging system may obtain areas of interest associated with an image. The areas of interest may be keyed to various processes and/or services. A downstream consumer of the services may retrieve the areas of interest to perform further analysis. In order to more easily interpret the areas of interest, the areas of interest may be displayed to the downstream consumer as part of a synthesized image. The synthesized image may include the areas of interest and a synthesized environment. The synthesized environment may be intended to emulate an environment in which the downstream consumer may expect to find the areas of interest. By doing so, the downstream consumer may be able to understand and contextualize the areas of interest more easily than if viewing the areas of interest alone.

System and method for procedural reconstruction of synthesized images

Methods and systems for identifying areas of interest in an image and management of images are disclosed. To manage identification of areas of interest in an image, subject matter expert driven processes may be used to identify the areas of interest. The identified areas of interest may be used to establish plans to guide subsequent use of the image. The identified areas of interest may also be used to establish plans to cache portions of the image to speed subsequent use of the image.

System and method for image exploration using areas of interest

Techniques are provided for adaptive sensor position determination for multiple mobile sensors. One method comprises obtaining a spatio-temporal representation of sensor measurements, from multiple mobile sensors, wherein the spatio-temporal representation comprises multiple layers each corresponding to a different point in time, wherein a given layer comprises multiple positions, and wherein each position in the given layer corresponds to a possible location for at least one of the multiple mobile sensors in an environment; applying the spatio-temporal representation to an environment state prediction model that generates a prediction of at least one future sensor measurement value for multiple positions in the spatio-temporal representation; applying the predictions of the at least one future sensor measurement value to a sensor position determination model that determines a new position for each of one or more of the multiple mobile sensors; and initiating a movement of the one or more of the multiple mobile sensors to the new position.

Adaptive sensor position determination for multiple mobile sensors

Techniques are provided for automatic discovery of data records. One method comprises obtaining data records each corresponding to a different item and comprising features extracted from a data source, wherein the data records identify related items identified using a collaborative filter that relates items based on user preferences; generating an item network comprising multiple nodes each corresponding to a different item, where two nodes are connected by an edge based on: (i) an item type of the two nodes, (ii) a ratio of numerical values associated with the two nodes, and/or (iii) a pairwise configuration similarity score for the two nodes; clustering the nodes into node clusters based on topological properties of the item network; and identifying items related to a given item that (i) share an edge with the given item and (ii) are in a node cluster comprising a node of the given item.

Automatic discovery of related data records

A method for alleviating data poisoning in an edge computing resource includes receiving a numeric value from an Internet of Things (IoT) unit and associating the numeric value with a cluster selected from a plurality of clusters in accordance with a suitable clustering algorithm such as a k-means clustering algorithm. In at least some embodiments, the numeric value comprises a poisoned numeric value including an adversarial component injected by an adversary to negatively impact a trained model of a cloud-based artificial intelligence engine. Rather than permitting the injected adversarial component to corrupt the AI engine, a cluster with which the numeric value is associated is sampled in accordance with a probability distribution of the cluster to obtain a surrogate for the poisoned numeric value. The surrogate may then be provided as an input to an inference module of the AI engine to generate a prediction.

Cluster resampling for alleviation of data poisoning on the edge

A method, computer program product, and computing system for processing historical input/output (IO) performance data associated with one or more storage objects of a storage system. A plurality of IO modeling systems may be trained using the historical IO performance data. Modeling performance information may be determined for the plurality of IO modeling systems across the historical IO performance data. A forecast score may be determined for each IO modeling system based on the modeling performance information for the plurality of IO modeling systems. A subset of the plurality of IO modeling systems may be selected based upon the forecast score for each IO modeling system. The at least one IO modeling system may be trained using the historical IO performance data. IO performance data may be forecasted using the at least one trained IO modeling system from the subset of the plurality of IO modeling systems.

System and method for modeling and forecasting input/output (IO) performance using adaptable machine learning

One example method includes transcribing a portion of the audio component to create a transcription file that includes text, searching the text of the transcription file and identifying information in the text that may include personal information, defining a textual window that includes the information, evaluating the text in the textual window to identify personal information, and masking the personal information in the audio component of the recording. The personal information may be masked with information of a non-personal nature.

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Tel Aviv, Israel

Avitan Gefen