Abstract: One example method includes monitoring performance of an element of a runtime environment, where the monitoring includes collecting performance information concerning the element, analyzing the collected information, detecting, based on the analysis of the collected information, an anomaly in the performance of the element and, in response to detection of the anomaly, automatically marking a snapshot of the runtime environment element, and the marking of the snapshot overrides a retention policy applicable to the snapshot.

Abstract: Techniques are provided for customer service ticket prioritization using multiple time-based machine learning models. One method comprises obtaining a customer service ticket; collecting, in response to the obtaining, features related to the customer service ticket comprising dynamic information regarding activities performed to address the customer service ticket and textual information describing the customer service ticket; applying the collected features to one of multiple machine learning models to obtain a distress score indicating a likelihood that the customer service ticket will become a distressed customer service ticket, wherein the multiple machine learning models each correspond to different time periods and are trained on historical data and the collected features are applied to the one machine learning model based on an age of the customer service ticket; and processing multiple customer support tickets based on the distress score.

Abstract: Techniques are used for balancing load on a storage system according to multiple variables. The techniques may be used to provide, among other things, defining, across at least two variables, a balance constraint for a load on a storage system. Among a set of transfers of volumes from one node to another node in the storage system, a transfer of a volume that minimizes the distance between the load and an ideal balanced state of the storage system is identified. The identified transfer of a volume is added to a combination of transfers of volumes. Whether the combination of transfers of volumes meets the balance constraint is determined. If the combination meets the balance constraint, the combination is selected as a solution to balance the load.

Abstract: Performance testing a data storage system includes recording operating parameters and performance data as the data storage system executes performance tests over a test period, the performance data including one or more measures of a performance characteristic (e.g., latency) across a range of I/O operation rates or I/O data rates for each of the performance tests. Subsets of recorded operating parameters and performance data are selected and applied to a machine learning model to train and use the model, and the model provides a model output indicative for each performance test of a level of validity of the corresponding performance data. Based on the model output indicating at least a predetermined level of validity for a given performance test, the performance data for the performance test are incorporated into a record of validated performance data for the data storage system, usable for benchmarking, regression analysis, hardware qualification, etc.

Abstract: A system can identify a group of computer services that correspond to respective computer hardware. The system can extract features that respectively identify characteristics of the computer services, a feature of the features comprising ratio information representative of a ratio of a first price of a computer service of the group of computer services to a second price of a corresponding computer hardware of the respective computer hardware that corresponds to the computer service. The system can convert the features into a numerical representation. The system can cluster the computer services into multiple clusters based on the numerical representation of the features. The system can identify, from a cluster of the multiple clusters and based on a machine learning model, an anomalous price for at least one computer service of the group of computer services that belongs to the cluster. The system can store an indication of the anomalous price.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving a historical customer dataset, the historical customer dataset reflective of pre-purchase, purchase, and post-purchase stages of a consumption process of a plurality of customers and identifying a plurality of first features, the plurality of first features derived from the historical customer dataset. The method also includes generating a first training dataset from the plurality of first features, training a first machine learning (ML) model using the first training dataset, and determining, using the first ML model, a plurality of second features. The method further includes generating a second training dataset from the plurality of second features and training a second ML model using the second training dataset, wherein the second ML model is trained to output propensity predictions for the plurality of customers.

Abstract: 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.

Abstract: One example method includes generating a searchable video library. Video files are processed to extract text corresponding to the speech and to the images. The extracted text is semantically searched such that specific portions or locations of video files can be identified and returned in response to a query.

Abstract: One example method includes generating personalized content for online sales. Recorded calls and other sources are converted to text. The text is semantically processed and correlated with customer data including customer order data to identify content that impacted the purchase of a product. The content may be incorporated into an online product page to facilitate online sales.

Abstract: A method is provided comprising: obtaining a counterparty contract, the counterparty contract including a contract that is being proposed by a counterparty to a user; performing a segmentation of the counterparty contract to identify a plurality of sentence clusters, each of the sentence clusters corresponding to a different provision in the counterparty contract; generating a plurality of counterparty provision vectors based on the counterparty contract, each of the counterparty provision vectors being generated based on a different one of the plurality of sentence clusters; retrieving a user provision vector, the user provision vector corresponding to a user provision; calculating a plurality of similarity scores for the user provision vector; detecting whether the plurality of similarity scores satisfies a condition that is associated with the user provision; and outputting a notification associated with the user provision when the condition is satisfied.

Abstract: Techniques for securely monitoring an air-gapped machine. Systems, methods, and devices for generating a status message representing a state of an air-gapped machine, converting the status message to a visual code, displaying the visual code to a display monitor connected to the air-gapped machine, capturing image data of the visual code at a camera connected to a monitoring machine, and transmitting the image data to the monitoring machine, thereby causing the visual code to be accessible by a user of the monitoring machine. Techniques for verifying the integrity of the status message, and optionally, encrypting the status message.

Abstract: Techniques described herein relate to a method for identifying breakpoints. The method may include obtaining, by a breakpoint identification device, an entity group data set corresponding to an entity group; generating, using the entity group data set, an enhanced entity group data set comprising the entity group data set and a derived data item; performing a clustering analysis using at least a portion of the enhanced entity group data set to obtain a plurality of entity clusters; training a machine learning (ML) model using the enhanced entity group data set to obtain a trained ML model; using the trained ML model and a set of simulated entities to perform a breakpoint analysis; generating a breakpoint graph based on the breakpoint analysis; and providing recommendations to an interested entity based on a breakpoint identified using the breakpoint graph.

Abstract: Systems and methods for providing data protection operations including cyber-threat protection operations. A sentiment analysis may be performed using language analysis to identify or determine a general or specific sentiment with or without intent to do harm. A score of the sentiment is then determined to assess risk. The data backup policy can be updated based on the assessed risk.

Abstract: Information technology resource forecasting based on time series analysis is described. A system creates multiple forecasts for an information technology resource by applying corresponding multiple time series models to first data associated with the information technology resource. The system selects a model of the multiple time series models by comparing the multiple forecasts for the information technology resource to second data associated with the information technology resource. The system outputs a forecast that is created by applying the selected model to third data associated with the information technology resource.

Abstract: A system, method, and computer-readable medium are disclosed for improved searching of contact information that includes receiving a request for a member's contact information. An initial list of candidates is returned. Based on organizational charts, distances between candidates and a point of reference are calculated, calculation is made as to a number of messages that are exchanged between candidates and the point of reference. Scores are determined based on the calculations. The scores are aggregated, and a refined list of candidates' contact information based on the aggregated scores is returned.

Abstract: An information handling system includes a data store configured to store an account associated with a first alert and a second alert. A processor may receive the first alert and the second alert, map the first alert and the second alert to a first node and a second node of a causality graph, traverse the causality graph starting from the sink node to the source node to determine an association between the first alert and the second alert, and generate the account based on the association between the first alert and the second alert.

Abstract: A search engine responding to a user query to find relevant data assets in a federation business data lake (FBDL) system based on interactions of known users interacting with data assets in the FBDL system. Data assets are optimally placed for minimal latency or maximal load. Data asset recommendations and past data asset access information are input as features to a time-series model for predicting future data access patterns. An expected latency and load risk is then determined and scored by a weighted mean of these values, and placement optimization is simulated using an optimization method (e.g., genetic algorithm). Using the scoring and simulation, a data asset placement engine is then used to move the locations of the data assets to minimize latency and/or to minimize maximal load.

Abstract: 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 interactions of known users interacting with data assets in the FBDL system. Predicted data usage for unknown or new users is derived by training a generative model that uses reconstructive self-supervised learning (SSL) techniques to generate possible values for missing data usage features of the unknown users. The predicted usage is then used to generate similarity scores that are combined for those of the known users to help inform the search engine processing to return relevant results to a target user.

Abstract: A search engine responding to a user query to find relevant data assets in a federation business data lake (FBDL) system. The search engine receives a search query from an unprivileged user or a user not having sufficient privileges to access the FBDL. It returns initial results to the unprivileged user including a first data asset recommendation responsive to the search query. It then determines a causal reason that the first data asset was recommended, and uses a similarity engine conditioned on the causal reason to return a replacement data asset in response to the search query.

Abstract: A search engine responding to a user query to find relevant data assets in a federation business data lake (FBDL) system based on interactions of known users interacting with data assets in the FBDL system. Data assets are optimally placed for minimal latency or maximal load. Data asset recommendations and past data asset access information are input as features to a time-series model for predicting future data access patterns. An expected latency and load risk is then determined and scored by a weighted mean of these values, and placement optimization is simulated using an optimization method (e.g., genetic algorithm). Using the scoring and simulation, a data asset placement engine is then used to move the locations of the data assets to minimize maximal load that comprises a load risk representing how close a current load is to a service level agreement (SLA) requirement set by a system provider.