Abstract: Queries that reference materialized views may be rewritten to compensate for stale materialized views. A query may be received that references a materialized view. A determination may be made as to whether the materialized view is stale. For a stale materialized view, the query may be rewritten to generate a query plan that obtains changes made to base tables for the materialized view not included in the stale materialized view and considers the change(s) when generating a result for the query from the materialized view. The rewritten query may then be performed to provide a result as if the materialized view were up-to-date.

Abstract: In response to determining that a particular sequence of natural language input has been generated by a first entity participating in a multi-interaction dialog, a first representation of accumulated dialog state associated with the sequence is obtained from a machine learning model at an artificial intelligence service. Based on the first representation, a state response entry is selected from a collection of state response entries. The state response entry indicates a mapping between a second representation of accumulated dialog state, and a response recorded in a training example of the model. The recorded response is implemented.

Abstract: Scaling of query processing resources for efficient utilization and performance is implemented for a database service. A query is received via a network endpoint associated with a database managed by a database service. Respective response times predicted for the query using different query processing configurations available to perform the query are determined. Those query processing configurations with response times that exceed a variability threshold determined for the query may be excluded. A remaining query processing configuration may then be selected to perform the query.

Abstract: Scaling of query processing resources for efficient utilization and performance is implemented for a database service. A query is received via a network endpoint associated with a database managed by a database service. Respective response times predicted for the query using different query processing configurations available to perform the query are determined. Those query processing configurations with response times that exceed a variability threshold determined for the query may be excluded. A remaining query processing configuration may then be selected to perform the query.

Abstract: A database system may use a machine learning model creation system to create a machine learning model from data stored in the database system responsive to a request from a client. The database system may obtain an executable version of the machine learning model, based on an uncompiled hardware agnostic version of the machine learning model, according to the hardware configuration of one or more computing resources selected by the database system to perform requests to the database system that invoke the machine learning model to generate predictions.

Abstract: A database system may include a machine learning model which may be used to perform various data analytics for data stored in the database system. In response to a request to invoke the machine learning model to generate a prediction from data stored in the database system, the database system may perform one or more optimization operations, as part of a query plan, to prepare the data to make it suitable for use by the machine learning model.

Abstract: Materialized views for a database system may be automatically refreshed according to performance benefits. Materialized views may be ordered according to determined performance benefits for the materialized views indicating the performance benefit obtained when a materialized view is used to perform a query at the database system. Materialized views may be selected for refresh operations according to the ordering based on a capacity of the database system to perform refresh operations.

Abstract: Match rules for rewriting queries to use materialized views may be dynamically generated by a database system. A database system may generate rules that indicate whether a given query can use a materialized view and how to rewrite the given query to use the materialized view. A query may be received and the rules may be applied to the query to determine that the query can use the materialized view and to rewrite the query to use the materialized view. The rewritten query can then be executed.

Abstract: A goal-oriented dialog system interacts with a user over one or more turns of dialog to determine a goal expressed by the user; the dialog system may then act to fulfill the goal by, for example, calling an application-programming interface. The user may supply dialog via text, speech, or other communication. The dialog system includes a first trained model, such as a translation model, to encode the dialog from the user into a context vector; a second trained model, such as another translation model, determines a plurality of candidate probabilities of items in a vocabulary. A language model determines responses to the user based on the input from the user, the context vector, and the plurality of candidate probabilities.

Abstract: Techniques for intelligent task-oriented multi-turn dialog system automation are described. A seq2seq ML model can be trained using a corpus of training data and a loss function that is based at least in part on a distance to a goal. The seq2seq ML model can be provided a user utterance as an input, and a vector of a plurality of values output by a plurality of hidden units of a decoder of the seq2seq ML model can be used to select one or more candidate responses to the user utterance via a nearest neighbor algorithm. In some embodiments, the specially adapted seq2seq ML model can be trained using unsupervised learning, and can be adapted to select intelligent, coherent agent responses that move a task-oriented dialog toward its completion.

Abstract: Techniques described herein include a method and system for item demand forecasting that utilizes machine learning techniques to generate a set of quantiles. In some embodiments, several item features may be identified as being relevant to an item forecast and may be provided as inputs to a regression module, which may calculate a set of quantiles for each item. A set of quantiles may comprise a number of confidence levels or probabilities associated with calculated demand values for an item. In some embodiments, costs associated with the item may be used to select an appropriate quantile associated (e.g., based on a corresponding confidence level). In some embodiments, an item demand forecast may be generated based on the calculated demand value associated with the selected quantile. In some embodiments, one or more of the item may be automatically ordered based on that item demand forecast.

Abstract: Embodiments of the present invention provide a system, method, and computer program product to detect and localize non-technical losses in an energy distribution system. A computing device determines the energy consumption of a link and/or node that is associated with an energy grid. The computing device determines the mismatch between the determined energy consumption of the link and/or node and the predicted energy consumption of the link and/or node. The computing device also determines the location of unauthorized energy consumption within the energy that is associated with the mismatch.

Abstract: A method for improving power factor includes collecting consumption data of at least one appliance, building a consumption profile for each of the at least one appliance based on the consumption data collected, reconstructing reactive power consumption of each of the at least one appliance based on the consumption profile for each of the at least one appliance, and computing a schedule for each of the at least one appliance in accordance the reactive power consumption of each of the at least one appliance to improve power factor while respecting at least one constraint.

Abstract: Methods and arrangements for managing implementation of a power transformer. There is determined at least one parameter currently relating to operation of a transformer, as well as a revenue value currently assigned to the transformer. There is determined a depreciation value currently assigned to the transformer. There is determined a power output for the transformer for a future time slot that increases total revenue with respect to a life cycle of the transformer, the power output being determined based on the at least one parameter, the revenue value, and the depreciation value.

Abstract: Methods and arrangements for enforcing demand management in electrical grids. Electrical power is provided from an electrical network to at least one substation, the electrical network including an electrical generation grid in communication with the at least one substation, the at least one substation comprising a power limiter. Electrical power is provided from the at least one substation to a plurality of locations, each location comprising a smart power demand manager which controls electrical power delivery to at least power-drawing item. There is detected, with respect to the electrical network, at least one discrepancy comprising at least one of: an unbalanced network load; an unscheduled network load; a change in network supply; and a change in network condition. In response to such detection, electrical power delivery is controlled via employing the power limiter of the at least one substation in collaboration with at least one smart power demand manager.

Abstract: Methods, systems, and articles of manufacture for generating an ensemble of forecasting models are provided herein. A method includes identifying a given environmental event from multiple items of input data; estimating an accuracy value for each of multiple forecasting models applied to an environmental event related to the given environmental event based on historical data; computing a cost and one or more resource requirements for each of the multiple forecasting models; and determining an ensemble of one or more of the multiple forecasting models to apply to the given environmental event based on (i) said estimated accuracy value for each of the multiple forecasting models, and (ii) said cost and said one or more resource requirements for each of the multiple forecasting models.

Abstract: An apparatus and article of manufacture for estimating loss rates of links in a smart grid include identifying end-to-end passage rates from a source to each of multiple receiver and/or load meters in a smart grid, and estimating a loss in at least one link of the grid by identifying the sparsest solution of link loss rates that matches the end-to-end passage rates. Also, a system includes a memory, a processor coupled thereto, and software modules executing on the processor, including a monitoring module for monitoring power flow of receiver and/or load meters in a smart grid, a measurement collector module for collecting measurements to compute end-to-end passage rates from a source to each receiver and/or load meter, and a processing module for estimating a loss in a link of the grid by identifying the sparsest solution of link loss rates matching the end-to-end passage rates.

Abstract: Embodiments of the present invention provide a system, method, and computer program product to detect and localize non-technical losses in an energy distribution system. A computing device determines the energy consumption of a link and/or node that is associated with an energy grid. The computing device determines the mismatch between the determined energy consumption of the link and/or node and the predicted energy consumption of the link and/or node. The computing device also determines the location of unauthorized energy consumption within the energy that is associated with the mismatch.

Abstract: A method for estimating loss rates of links in a smart grid includes identifying end-to-end passage rates from a source to each of multiple receiver and/or load meters in a smart grid, and estimating a loss in at least one link of the grid by identifying the sparsest solution of link loss rates that matches the end-to-end passage rates. Also, a system includes a memory, a processor coupled thereto, and software modules executing on the processor, including a monitoring module for monitoring power flow of receiver and/or load meters in a smart grid, a measurement collector module for collecting measurements to compute end-to-end passage rates from a source to each receiver and/or load meter, and a processing module for estimating a loss in a link of the grid by identifying the sparsest solution of link loss rates matching the end-to-end passage rates.

Abstract: An apparatus and an article of manufacture for improving power factor include collecting consumption data of at least one appliance, building a consumption profile for each of the at least one appliance based on the consumption data collected, reconstructing reactive power consumption of each of the at least one appliance based on the consumption profile for each of the at least one appliance, and computing a schedule for each of the at least one appliance in accordance the reactive power consumption of each of the at least one appliance to improve power factor while respecting at least one constraint.