A method for a preemptive and graceful shutdown of utilities, facilities or systems in smart cities includes detecting a hazard at external entities based on collected information, determining whether a predefined shutdown plan for the detected hazard exists, generating a new shutdown plan, based on predefined criteria, for the detected hazard, and shutting down the at least one component of the external entities based on the new shutdown plan or the predefined shutdown plan.

The present invention relates generally to a novel and improved method of loading clickstream data into a web analytics platform, and, a commercial off the shelf (COTS) web analytics platform. The process of loading traffic into COTS Web Analytics platform from clickstream data relates to: (1) the process of discovering ecommerce sales by looking for specific tracking beacons in clickstream or resource stream and loading them into a COTS web analytics platform; (2) a process of discovering website “goals” by looking for clusters of tracking “beacon” activity in clickstream or resource stream data; and (3) a process of unlocking the search term that a website visitor typed into a search engine immediately prior to visiting that website when web analytics platforms can't see that data for many searches (99%+) having all of the features described above.

A parking lot data repair method and apparatus, a device, and a storage medium. The method comprises: calculating spatial similarity between two parking lots according to the spatial features of the parking lots and a spatial similarity measurement model; clustering the parking lots into different parking lot clusters according to the spatial similarity, and calculating a spatial similarity probability corresponding to each parking lot cluster; calculating data similarity between two sample parking lots in the same parking lot cluster, and according to the data similarity and the spatial similarity probability corresponding to the parking lot cluster, calculating the data similarity probability of the parking lot cluster under a similar spatial condition; and when the data similarity probability under the similar spatial condition exceeds a probability threshold value, performing data repair on the parking lot to be repaired in the parking lot cluster by means of a cyclic generation-type confrontation network.

The embodiments of the present disclosure provide systems and methods for managing inventory placement, comprising a memory storing instructions and at least one processor configured to execute the instructions. The processor may be configured to receive an identifier of a product for inventory placement, and determine, based on historical shipment data stored in a database, a region with the highest customer demand for the product. The processor may predict, using a machine learning algorithm, a product tag associated with the product based on at least a temperature associated with the region with the highest customer demand for the product. The processor may further modify the database to assign the product tag to the product identifier, and assign the product for placement in a fulfillment center. The fulfillment center may be associated with a fulfillment center tag corresponding to the product tag assigned to the product.

A method is disclosed. A data set including: (a) identifiers of a set of incidents occurring within a defined geographic region to which at least one service provider responded during a first time period and (b) address data identifying a location within the geographic region of each said incident of the set is retrieved over a network. An instruction to generate a heat map of the incidents occurring within the geographic region during the first time period is received from a user via a user interface generated to a display device. In response to the instruction to generate the heat map, the address data is converted to GPS data. A heat map of an aerial view of the geographic region based on the GPS data is generated. The heat map is displayed to the display device in a user interface.

Embodiments disclosed herein generally related to a system and method for rideshare vehicle routing. A computing system receives, from one or more facilities, one or more transaction requests associated with one or more accounts of an organization associated with the computing system. The computing system maps one or more customers to a respective transaction request. For each facility of the one or more facilities, the computing device identifies a geographic location thereof. The computing system categorizes each of the one or more facilities into one or more boundaries. For each boundary, the computing system determines an estimated number of rideshare vehicles to deploy, based at least on a transaction history of each customer of the one or more customers. The computing system transmits the estimated number of rideshare vehicles to be deployed to each boundary to a rideshare computing system.

Embodiments provide computer apparatuses, computer systems and computer-executable methods for recommending a commercial item or entity to a consumer based on geographic zone data. The method includes receiving a first predetermined geographic zone, a first importance score associated with a consumer for the first predetermined geographic zone, and a second importance score associated with a commercial item or entity for the first predetermined geographic zone. The method also includes programmatically generating an overlap score based on the first and second importance scores, and programmatically generating a relevancy score based on the overlap score, the relevancy score indicating a probability that the commercial item or entity is of relevance to the consumer. The method further includes, based on the relevancy score, transmitting instructions to a computing device associated with the consumer to cause the computing device to render a representation of the commercial item or entity.

Methods and systems to predict a supply chain performance are described. A system receives supply chain data for delivery of a product. The supply chain data includes input signals comprising operational plans and observed supply chain operational metrics. The input signals include a delivery date of the product. The system automatically generating predicted supply chain operational metrics across including a value at risk that is predicted for the product. The system automatically infers causal factors that impact the predicted supply chain operational metrics including impacting the value at risk that is predicted for the product. The system automatically generates action recommendations for the supply chain. An action recommendation includes a first predicted value impact and a sequence of actions impacting the product the delivery date of the product and the value at risk that is predicted for the product.

Methods, systems, and devices for venue rating systems and matching consumers with venues that track the consumer's preferences for venue vibes and other criteria are described. The systems and methods include multiple components, include use of real-time video inputs, which allow consumers and businesses to analyze a venue in real-time and help measure and/or determine an experience or other criteria associated with that venue, use of venue level experience metrics, which enable the business associated with the venue to describe an intended experience, vibe and/or other criteria associated with the venue, and use of product and/or service level experience metrics, which allow similar discussion as the venue level experience metrics, but typically on a smaller scale.

A server for machine learning model based recommendations for vehicle remote application is provided. The server includes circuitry configured to retrieve customer subscription data associated with a first set of customers related to a set of vehicles. The set of vehicles are controlled with one or more remote applications associated with the server. The circuitry extracts the first set of features from the customer subscription data and trains a machine learning model based on the first set of features and a first feature of the first set of features. The first feature corresponds to a paid subscription of a remote application. The circuitry determines an importance score for each of the first set of features based on the trained machine learning model. The circuitry generates recommendation information related to the remote application, based on the determined importance score and transmits the recommendation information to electronic devices associated with the server.