Abstract: Methods, systems, and computer-readable storage media for processing images captured in a retail store and automatically identifying a product shortage are provided. In one implementation, a system may access information describing a desired placement of products on at least one store shelf. The system may analyze a first image captured at a first time to determine that a first quantity of products exceeds a quantity threshold. The system may analyze a second image captured at a second time later than the first time to determine that a second quantity of products is below the quantity threshold. The system may avoid initiating an unscheduled action to cause restocking until a third time later than the second time. Based on a determination that a third quantity of products at the third time are below the quantity threshold, the system may provide information indicative of a product shortage.

Abstract: A system for reducing product shortage durations in retail stores based on analysis of image data is provided. The system may comprise: a communication interface configured to receive image data from retail stores indicative of a product shortage of a product type relative to information describing a placement of products of a product type on a store shelf; and at least one processor configured to: analyze the image data to detect occurrences of product shortages of the product type in the retail stores and determine durations associated with the occurrences; identify a common factor contributing to the duration of part of the occurrences of the product shortages; determine an action, associated with the at least one common factor, for potentially reducing product shortage durations of future shortages of the product type in the retail stores; and provide information associated with the identified action to an entity.

Abstract: Systems and methods for coordinating actions across two or more retail stores are provided. In one implementation, at least one processor is configured to receive first image data captured from a first retail store; analyze the first image data to identify a first planogram incompliance event for a shelf of the first retail store; generate an instruction for an employee to perform a remedial action; acquire and store feedback from the employee; receive second image data captured from a second retail store; analyze the second image data to identify a second planogram incompliance event for a shelf of the second retail store; determine that the shelves of the first and second retail stores are related: access the feedback from the employee; use the feedback in determining what action to take in response to the second planogram incompliance event; and initiate an action based on the determination.

Abstract: A system for automatically identifying situations to withhold planogram incompliance notifications in a retail store is disclosed. The system has a processor that receives image data depicting products displayed on a store shelf from image sensors mounted in the retail store. The processor identifies a product type associated with the displayed products and determines a placement of products of that product type on the shelf. The processor accesses a planogram describing a desired placement of products and identifies, based on the planogram, a discrepancy between the determined placement of products and the desired placement of product. The processor determines whether a notice-override condition exists. If a notice-override condition exists, the processor, withholds issuance of a user-notification associated with the identified discrepancy. If a notice-override condition does not exist, the processor issues the user-notification associated with the identified discrepancy.

Abstract: A system for processing images captured in a retail store and automatically generating low-stock alerts includes at least one processor configured to: receive image data associated with images captured during a first period of time by one or more image capturing devices mounted in the retail store; analyze the image data to identify a restocking event, at least one facing event, and a low-stock event; determine a demand pattern for products from the specific product type based on the identified restocking event, the at least one facing event, and the low-stock event; receive additional image data associated with images captured during a second period of time, subsequent to the low-stock event; based on the additional image data and the determined demand pattern, predict when a next low-stock event will occur; and generate an alert associated with the predicted next low-stock event.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Business Data Lake searching techniques are provided.

Abstract: A system for reducing product shortage durations in retail stores based on analysis of image data is provided. The system may comprise: a communication interface configured to receive image data from retail stores indicative of a product shortage of a product type relative to information describing a placement of products of a product type on a store shelf; and at least one processor configured to: analyze the image data to detect occurrences of product shortages of the product type in the retail stores and determine durations associated with the occurrences; identify a common factor contributing to the duration of part of the occurrences of the product shortages; determine an action, associated with the at least one common factor, for potentially reducing product shortage durations of future shortages of the product type in the retail stores; and provide information associated with the identified action to an entity.

Abstract: A system for processing images captured in a retail store and automatically identifying a product shortage is provided. The system may comprise at least one processor configured to: access information describing a placement of products; receive image data from an image sensor; analyze a first image captured at a first time to determine that a first quantity of products exceeds a quantity threshold; analyze a second image captured at a second time to determine that a second quantity is below the quantity threshold; based on the determination that the first quantity exceeds the quantity threshold, avoid initiating an unscheduled action to cause restocking the store shelf with products until a third time; analyze a third image captured at the third time to determine that a third quantity is below the quantity threshold; and based on the determination, provide information indicative of a product shortage at the store shelf.

Abstract: Embodiments for finding a root cause of an anomaly in a network environment by representing assets in the network environment as respective nodes in a causal graph, wherein the nodes have a measurable quality that can be tracked and arcs between pairs of nodes represent causal relationships between nodes of the node pairs designating source nodes as processes at the top of a hierarchy of tracked processes, and sink nodes as processes at the bottom of the hierarchy and having characteristics of interest in the environment; detecting anomalies in the tracked processes embodied in the sink nodes; traversing the causal graph in a reverse order from a node in which an outlier is detected; and analyzing nodes along the traversal path to identify a node of the highest hierarchy that shows unusual behavior as the root cause.

Abstract: An automated agent for the causal mapping of complex environments. Specifically, a disclosed method and system entails the application of statistical tools, or causality tests, to measure the strength and direction of causal relations between two or more environment components. Further, the execution of the causality tests may be an offline process that may be triggered periodically to account for changes or updates to an environment over time.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments for finding a root cause of an anomaly in a network environment by representing assets in the network environment as respective nodes in a causal graph, wherein the nodes have a measurable quality that can be tracked and arcs between pairs of nodes represent causal relationships between nodes of the node pairs designating source nodes as processes at the top of a hierarchy of tracked processes, and sink nodes as processes at the bottom of the hierarchy and having characteristics of interest in the environment; detecting anomalies in the tracked processes embodied in the sink nodes; traversing the causal graph in a reverse order from a node in which an outlier is detected; and analyzing nodes along the traversal path to identify a node of the highest hierarchy that shows unusual behavior as the root cause.

Abstract: Embodiments include detecting anomalies in an IT environment using model competition and business patterns by collecting time series data for events for the network including devices and interfaces. An analytics module uses competing time series models with customizable business patterns to find the best fit model. It analyzes the residuals of the best fitting model to find the outliers relative to normal zone data points. A user may classify a detected outlier as normal, in which case, the tracking and investigation mechanism suggests alternate business patterns to be matched against this outlier. A user interface displays a dashboard to present the user with anomalies in the chosen time series, such as in interactive graphical format.

Abstract: Embodiments for identifying significant events for finding a root cause of an anomaly collecting time series data for events for each network device by detecting an anomaly in the time series data comprising an outlier on an edge of the time series data by comparing a predicted value of the event to an actual value of the event using a selected forecasting model; declaring the event to be an anomaly at a particular time if a difference between the predicted value and actual value exceed a defined threshold based on residual values for other devices; analyzing in a combined RNN/LSTM process all events for all devices of the network within a time proximity of the particular time of the anomaly to filter usual events and rank each event relative to the anomaly; and displaying a labeled chart of the time series data showing the anomaly in a graph relative to all the events.