Abstract: A method, an Internet of Things system, and a medium for smart gas maintenance management order dispatch warning are provided. The method comprises: assigning a gas work order to an order receiver in response to an assignment module, and determining an warning characteristic of the gas work order; wherein, determining the warning characteristic of the gas work order comprise: based on the fault location characteristic of the gas work order, determining the traffic warning value through the traffic warning value determination module; determining the fault warning value based on the gas fault characteristic of the gas work order through the fault warning value determination module; in response to the warning monitoring module monitoring that the warning characteristic meet the preset condition, providing a warning to the order receiver by the warning module.

Abstract: Embodiments relate to monitoring an information technology (IT) environment having a plurality of domains through key performance indicator (KPI) data. In response to detection of a technical health problem, a first KPI related to the problem is identified. A root cause analysis is performed on the identified KPI generating a knowledge graph. A second KPI related to the first KPI is identified through the discovery of a correlation between the two identified KPIs. A diagnosis is generated for the technical health problem within the IT environment based on the discovered hidden correlation between the first KPI and second KPI. The generated diagnosis includes the root cause of the technical health issue.

Abstract: In accordance with an embodiment, described herein are systems and methods for use with an analytic applications environment, for determination of recommendations and alerts in such environments. A data pipeline or process can operate in accordance with an analytic applications schema adapted to address particular analytics use cases or best practices, to receive data from a customer's (tenant's) enterprise software application or data environment, for loading into a data warehouse instance. When provided as part of a software-as-a-service (SaaS) or cloud environment, the data sourced from a plurality of organizations can be aggregated, to leverage information gleaned from the collective or shared data. The system can be used to generate semantic alerts, including obtaining permission from; and analyzing the collective data of; the plurality of organizations, to determine operational advantages indicated by the data, and providing alerts associated with those operational advantages.

Abstract: A device may receive employee data associated with an employee, and may determine, from the employee data, skill data identifying skills of the employee. The device may process the employee data and the skill data, with a similarity model, to determine similarity scores between the skills of the skill data, and may add or remove one or more skills to or from the skill data for predefined target skill profile categories to generate modified skill data. The device may compare the similarity scores and a predefined threshold to determine anchor skill data from the modified skill data, and may group the anchor skill data for corresponding pluralities of the predefined target skill profile categories to generate groups of the anchor skill data. The device may process the groups and the similarity scores, with a clustering model, to generate clustered anchor skill data, and may perform actions based on the clustered anchor skill data.

Abstract: Systems and methods disclosed herein provide automatic expense categorization of transactions or expenditures based on a machine learning (ML) model trained using anonymized transactional data for expenditures that are stored in a public blockchain. The anonymized transactional data for the expenditure and the expense category, may be distributed throughout the blockchain network and recorded in the blockchain. In some implementations, an expenditure may be submitted to the blockchain network for expense categorization. The transactional data for the expenditure may be anonymized to remove confidential and personal identifying information (PII) before it is distributed throughout the blockchain network. Each participating node of the blockchain network may utilize the ML model to identify an expense category for the expenditure. The participating nodes may provide a consensus mechanism in order to arrive at a shared understanding of how to categorize the expenditure.

Abstract: Geo-fences may be dynamically reconfigured to identify employees that may be of particular interest to users. In some instances, a geo-fence may be defined for a particular user based on a variety of information, in order to identify employees that may be of interest to the user and that are located within proximity to the user. The system may identify employees based on a confidence score that a particular employee or set of employees may have assisted the user during the shopping process. Information regarding the identified employees may be displayed or otherwise output to notify the user of nearby employees that may be of interest, for example in the form of an interactive digital receipt.

Abstract: Strategies for an objective associated with an offering set are obtained. A strategy assigns respective selection probabilities of receiving content associated with the offering set to users of a user population. Strategy optimization iterations are performed with respect to a sub-sample of the population and a subset of the strategies. In a given iteration, weights assigned to the strategies are used to determine aggregated selection probabilities for users, content pertaining to the offering set is presented to users selected based on the aggregated probabilities, and the weights are adjusted based on feedback metrics and an exploration-exploitation tradeoff parameter. Based on weights updated in the iterations, content associated with the offering set is presented to users which were not in the sub-sample.

Abstract: A system and method are disclosed including a supply chain network including one or more entities, one or more items and a plan explainer. The plan explainer generates an optimum inventory level based on a root cause of a goal violation by modeling the supply chain network and one or more business objectives as a hierarchy of objective functions. The plan explainer also solves the hierarchy of objective functions, stores plan explanation data and retrieves the plan explanation data. The plan explainer further generates the root cause of the goal violation by parsing the retrieved plan explanation data and calculates the optimum inventory level based on the root cause of the goal violation. In response to the calculated optimum inventory level, at least one of the one or more entities adjusts inventory levels of the one or more items according to the optimum inventory level. Other embodiments are also disclosed.

Abstract: A system and method for identifying a probability of disease affecting a crop based on data received over a network is described herein, and may be implemented using computers for providing improvements in plant pathology, plant pest control, agriculture, or agricultural management. In an embodiment, a server computer receives environmental risk data, crop data, and crop management data relating to one or more crops on a field. Agricultural intelligence computer system 130 computes one or more crop risk factors based, at least in part, the crop data, one or more environmental risk factors based, at least in part, the environmental data, and one or more crop management risk factors based, at least in part, on the crop management data.

Abstract: Systems and methods are disclosed for assisting users in assessing costs of transactions. For instance, method may include: determining a cost value of at least one transaction associated with a user; classifying the user into a category, based on consumer interest characteristics of the user and consumer interest characteristics associated with the category; determining a relatable product based on the consumer interest characteristics associated with the category, the relatable product being a product purchased by a plurality of persons classified in the category; determining a relatable cost value, the relatable cost value being a representation of the cost value of the at least one transaction using a quantity of the respective relatable product; and presenting the relatable cost value to the user.

Abstract: Data from a knowledge graph associated with an enterprise may be obtained. The knowledge graph may include a plurality of node descriptors that indicate locations, assets, and sensor data feeds of the enterprise, and a plurality of relationship descriptors that indicate relationships amongst the locations, the assets, and the sensor data feeds of the enterprise. An anomaly detection or prediction model associated with a selected one of the plurality of node descriptors may be auto-created based on the data from the knowledge graph. In the auto-creation, one of a plurality of model types for the anomaly detection or prediction model may be selected based on an identified node type of the selected node descriptor.

Abstract: A customer risk trigger associated with a customer may be identified. A response to the customer risk trigger may be detected. First risk analysis data related to the customer risk trigger may be gathered, based on the response, from a first datastore. Second risk analysis data related to the customer risk trigger may be gathered, based on the response, from a second datastore. A customer risk profile to model risk attribute(s) of the customer may be gathered. The risk attributes may represent a risk correlation between the customer and a prohibited act. Customer risk visualization tool(s) configured to facilitate visual user interaction with the customer risk profile may be gathered. The customer risk visualization tools may be rendered in a display of the computing system. The customer risk visualization tools provide a customer-centric view of risk for various applications, including anti-money laundering applications.

Abstract: A system and method are disclosed for solving a hierarchy of objective functions using one or more hierarchical optimizations using explanation data stored in a database according to the one or more hierarchical optimizations, retrieving the explanation data, generating a response to a query for a goal violation by parsing the retrieved explanation data, and displaying the response.

Abstract: In some implementations, a device may receive release data identifying new releases associated with cloud providers. The device may receive customer data associated with customers of the cloud providers. The device may receive interest data identifying interests of the customers. The device may filter the release data and may process the filtered release data and taxonomy data identifying historical release data, with a model, to generate classification data identifying classifications of the release data. The device may process the filtered release data, the classification data, and the customer data, with a model, to identify a set of release data that is relevant for each customer, and to generate sets of release data for the customers. The device may identify additional data of the filtered release data that is relevant for each of the customers. The device may supplement the sets of release data with the additional data.

Abstract: A retail environment is modeled and simulated based on aggregated operational data. Real-time aggregated data for operations of the retail environment is detected and predictive adjustments to the retail environment are provided for making one or more changes to the retail environment based on the modeled and simulated environment.

Abstract: A computer-implemented position-centric personnel modeling apparatus, system, and method may be provided. A nodal hierarchy may be created to correlate with the personnel structure of a business enterprise, wherein each node may be a cyberspace representation of an individual within the personnel structure of the business enterprise. Attributes correlating to individual personnel in the business enterprise may be mapped to a respective node. Objectives may subsequently be tasked within the model and the nodal hierarchy may be rearranged based on the results. Results from the model may optionally be reflected by a business enterprise.

Abstract: Methods, apparatus, systems, and articles of manufacture to estimate cardinality of users represented in arbitrarily distributed bloom filter arrays are disclosed. An apparatus includes a communications interface to obtain a first Bloom filter array generated by a first computer of a first database proprietor and obtain a second Bloom filter array generated by a second computer of a second database proprietor. The apparatus includes a cardinality analyzer to estimate a total number of unique users included in a union of corresponding ones of the first and second users represented in the first and second Bloom filter arrays regardless of whether the first and second users are mapped, based on a first distribution or a second distribution different than the first distribution, to the corresponding first and second Bloom filter arrays.

Abstract: At least some aspects of the present disclosure are directed to systems and methods of an agronomic condition prediction and alerts engine. In some cases, the engine receives a set of field data and the set of field data includes one or more of geospatial data, crop data, user data, agronomic data, and weather data. In some cases, the engine retrieves a set of agronomic profile data from an agronomic data repository, where each of the set of agronomic profile data representing an agronomic condition and comprising one or more of geospatial profile, crop profile, pest profile, agronomic profile, user data profile, and weather profile corresponding to the agronomic condition. In some embodiments, the engine applies a multivariable processing to the set of field data using the set of agronomic profile data and generates an agronomic condition indicator indicative of one or more agronomic conditions.

Abstract: A method including retrieving a product information from a database is provided. The method includes associating the product information with multiple classification values, forming a vector associated with a consumer product. The classification values form coordinates of the vector in a vector space that comprises multiple vectors associated with multiple consumer products. The method includes determining a cluster in the vector space, including at least one vector selected according to a relative distance within a cluster boundary. The method includes selecting a discriminator vector from a vector difference between a first vector in a first cluster in the vector space and a second vector in a second cluster in the vector space and identifying a new consumer product associated with a new vector that is formed by adding the discriminator vector to a third vector from the vector space, the third vector associated with a known consumer product.

Abstract: A system for autonomous workflow management and execution is disclosed. The system includes a workflow builder module to create a workflow based on directed acyclic graphs, conversational submodules and a defined process. The system includes a data stream module to enable the workflow from the workflow builder module to achieve objectives based on a workflow specification, constraints and security considerations. The system includes a stream processor module including a data ingestion and processing submodule to fetch data from the data stream module and process the data based on operations to define and evaluate rules, heuristics and models with the workflow. The system includes a learning module to continuously recompute potential objectives and outcomes based on analysis of the optimized data. The processing subsystem includes a workflow orchestrator module to make decisions for automation of the workflow execution by triangulating data from a user, the data sources and the desired outcomes.