Emc IP Holding Company LLC

Omer Sagi

Amihai Savir

Avitan Gefen

Ran Taig

Nimrod Milo

Oshry Ben-Harush

Shiri Gaber

Alon Grubshtein

Haim Halbfinger

Maor Sade

Or Herman Saffar

Raul Shnier

Noga Gershon

Ohad Arnon

Gilad Braunschvig

Alon Shitrit

Jeroen Zonneveld

Danny Croitoru

Ivan Mlynek

Arthur Wensing

Dhev George Kollannur

Jorge Luis Perez

Ronen Halsadi

Michael Rupert James Thatcher

Marcel Bernard Körner

A search engine responding to a user query to find relevant data assets in a federation business data lake (FBDL) system by monitoring and recording all of the interactions of users interacting with data assets in the FBDL system, providing all of the user interactions to a recommendation engine, calculating relevance of information in the FBDL system to each user, and recommending one or more new data assets to a target user based on the relevance of the information. The relevance comprises the target user's past interactions with the data assets based and the cumulative interactions of other users with the data assets, such that if one or more of the other users has similar interaction behavior to the target user, then knowledge of the one or more other users can impact the relevance of the information with regard to the one or more new data assets suggested to the target user.

Recommendation system for data assets in federation business data lake environments

Techniques are provided for customer support ticket aggregation. One method comprises obtaining a customer support ticket; extracting a topic of the customer support ticket using a topic model based on natural language processing techniques; converting the customer support ticket to a topic vector representation that identifies the extracted topic and comprises a list of words describing the topic based on a collection of processed customer support tickets; extracting features from the customer support ticket; generating a fingerprint for the customer support ticket that comprises the topic vector representation and the extracted features; applying the fingerprint to a machine learning similarity model that compares the fingerprint to fingerprints of processed customer support tickets from the collection of processed customer support tickets; and identifying a processed customer support ticket from the collection of processed customer support tickets that is related to the customer support ticket.

Customer support ticket aggregation using topic modeling and machine learning techniques

Artificial intelligence (AI)-based techniques are provided that predict a quality score for a product-related data structure associated with one or more products. One method comprises obtaining data for a given product-related data structure; evaluating a plurality of first features related to a customer account associated with the given product-related data structure using the obtained data; evaluating a plurality of second features related to the given product-related data structure using the obtained data; processing at least some of the first features and the second features using at least one model that provides a predicted quality score for the given product-related data structure; and applying one or more thresholds to the predicted quality score to determine an acceptance status related to the given product-related data structure. A weighting of the first features and the second features can be learned during a training phase.

Evaluation of product-related data structures using machine-learning techniques

Techniques are provided for extracting anomaly related rules from organizational data. One method comprises obtaining anomaly analysis data integrated from multiple data sources of an organization, wherein the multiple data sources comprise at least one set of labeled anomaly data related to anomalous transactions; extracting features from the integrated anomaly analysis data that correlate with an indication of an anomaly; training multiple machine learning models using the extracted features, where the machine learning models are trained using different combinations of the extracted features; evaluating a performance of the trained machine learning models; and extracting rules from the trained machine learning models based on the performance, wherein the extracted rules are used to classify transactions as anomalous. The trained machine learning models comprise a decision tree comprising paths to an anomaly classification. The extracted rules are optionally in a human-readable format.

Extraction of anomaly related rules using data mining and machine learning

Techniques are provided for facial recognition using a high probability group database. One method comprises maintaining (i) a first database of facial images of individuals, and (ii) a second database of facial images comprising a subset of the individuals from the first database based on a probability of individuals appearing in sequences of image frames at a given time; applying a face detection algorithm to sequences of image frames to identify one or more faces in the sequences of images; and applying a facial recognition to at least one sequence of image frames using at least the second database to identify one or more individuals in the at least one sequence of image frames. The second database is comprised of facial images of: (i) individuals from multiple angles; (ii) individuals that appeared in prior image frames; and/or (iii) individuals that appeared in an image frame generated by a plurality of cameras.

Facial recognition for multi-stream video using high probability group

A machine learning-based recommendation system is provided for root cause analysis of service issues. An illustrative method of a machine learning system comprises extracting features from service issue investigation log sets corresponding to previously considered service issues; generating representations for the service issue investigation log sets based on the corresponding extracted features; and storing the representations in a knowledge base. In conjunction with obtaining an additional service issue investigation log set, the illustrative method generates a representation of the additional service issue investigation log set; identifies representations previously stored in the knowledge base as candidate service issues related to the additional service issue based on pairwise probabilities indicating whether the additional service issue is related to at least a subset of the previously considered service issues; and presents information characterizing the service issue investigation log sets corresponding to respective ones of the identified representations.

Machine learning-based recommendation system for root cause analysis of service issues

Techniques are provided for system operational analytics using additional features over time-series counters for health score computation. An exemplary method comprises: obtaining log data from data sources of a monitored system; applying a counting function to the log data to obtain time-series counters for a plurality of distinct features within the log data; applying an additional function to the time-series counters for the plurality of distinct features; and processing an output of the additional function using a machine learning model to obtain a health score for the monitored system based on the output of the additional function. The additional function comprises, for example, an entropy function representing a load balancing of a plurality of devices in the monitored system; one or more clustered counts for a plurality of entities in the monitored system; a number of unique values; and/or one or more modeled operations based on correlations between a plurality of different operations in the monitored system.

System operational analytics using additional features for health score computation

Techniques are provided for data-driven scheduling of automated software program test suites. An exemplary method comprises: obtaining a plurality of test cases that test software programs; obtaining a failure likelihood for each of the plurality of test cases; and scheduling the test cases in an order based on the failure likelihoods. Generally, test cases that are more likely to fail are scheduled before test cases that are less likely to fail. Dependencies and/or priorities among the plurality of test cases are also optionally obtained and the scheduling of the test cases is based on the dependencies and/or priorities. The dependencies among the plurality of test cases comprise, for example, an indication of whether a given test case constrains one or more additional test cases.

Data-driven scheduling of automated software program test suites

Techniques are provided for identifying anomalies in an Internet of Things (IoT) activity profile of a user using an analytic engine. An exemplary method comprises obtaining data from a plurality of IoT devices of a user, wherein at least one IoT device comprises an agent device that performs an action on behalf of the user; applying the obtained data to a feature engineering module to convert the obtained data into time-series features that capture behavior and/or characteristics of an IoT environment of the user; and applying the time-series features to an analytic engine comprising a multi-variate anomaly detection method that learns one or more patterns in the IoT activity profile of the user for a normal state and identifies an anomaly with respect to an action performed by the agent device based on a health score indicating a deviation from the learned patterns.

Identifying anomalies in user internet of things activity profile using analytic engine

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

A DNA storage system for binary digital data. The digital data is deduplicated is encoded into a format for representation in DNA, and DNA representing the data is synthesized and stored in pools in a primary library, a hash library and a data library. The primary library stores DNA for accessing a hash object in the hash library corresponding to a hash of the data, and the hash library stores DNA for accessing a data object in the data library that contains the data. The information in the libraries includes information identifying objects, including keys, unique identifiers (UIDs), pool identifiers, and primers.

DNA-based data center with deduplication capability

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 facial recognition using a high probability group database and a facial network of related persons. One method comprises maintaining a probability-based database of facial images comprising a subset of individuals from a first database of facial images of a plurality of individuals based on a probability of individuals appearing in sequences of image frames at a given time; applying a face detection algorithm to at least one sequence of image frames to identify one or more faces in the sequences of image frames; maintaining a facial network of related persons associated with the probability-based database by obtaining facial images of one or more additional individuals from the first database that satisfy a predefined related person criteria with respect to individuals identified in at least one sequence of image frames; and applying a facial recognition to at least sequence of image frames using at least the probability-based database and the facial network of related persons to identify individuals in the sequence of image frames.

Facial recognition for multi-stream video using high probability group and facial network of related persons

Embodiments include facilitating DNA storage of digital data including a plurality of data assets in a network by building a causal graph of the network and the relationship of the data assets; computing a value of each data asset; computing, using the causal graph and data values, a radius of recovery for each data asset; classifying each data asset as appropriate DNA stored by assigning a numerical ranking of each data asset; defining manual constraints and a DNA storage configuration; and generating a ranked list of recommended data assets for storing in the DNA storage using the classification, manual constraints and DNA storage configuration.

Automatic identification, definition and management of data for DNA storage systems

Techniques are provided for data-driven reduction of log message data. An exemplary method comprises: obtaining log files and user-specified configuration parameters, wherein the log files each comprise one or more log messages; generating an event count matrix indicating a number of times each of a plurality of unique messages appeared in a given log file of the log files; generating a correlation graph by inserting similar messages with a mutual undirected edge, wherein similar messages are identified based on a predefined similarity measure; extracting redundant messages from the correlation graph by selecting log messages for inclusion in an uninformative log message filter from sub-graphs of the correlation graph in which any two nodes are connected together, except those log messages satisfying a predefined message frequency criteria; and identifying one or more redundant messages using the uninformative log message filter. The uninformative log message filter is optionally applied to real-time log messages and/or existing file systems.

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Mazkeret Batya, Israel

Omer Sagi