Abstract: A method for scheduling EV battery swap based on IoV, in which the roadside units regard battery-swap station clusters with a high degree of potential cooperation as a whole, and gather them into a single battery swap area; taking a service rate of a cluster of swap stations as an assessment target, and mainly examining a service capability, a service quality, and location information of each of swap station nodes themselves, and a current state of those electric vehicles that require to swap battery; providing a solution of the best joint actions for the overall electric vehicles to maintain the overall service balance of every swap station and improve a long-term performance of Internet of Vehicles. According to the invention, a battery of the electric vehicles can be swapped as soon as possible, and every battery swap station can maintain business balance.

Abstract: A method for scheduling EV battery swap based on IoV, in which the roadside units regard battery-swap station clusters with a high degree of potential cooperation as a whole, and gather them into a single battery swap area; taking a service rate of a cluster of swap stations as an assessment target, and mainly examining a service capability, a service quality, and location information of each of swap station nodes themselves, and a current state of those electric vehicles that require to swap battery; providing a solution of the best joint actions for the overall electric vehicles to maintain the overall service balance of every swap station and improve a long-term performance of Internet of Vehicles. According to the invention, a battery of the electric vehicles can be swapped as soon as possible, and every battery swap station can maintain business balance.

Abstract: The present teaching relates to request management and data recovery in a data system. In one example, a failure in connection with first data is detected at a first node in a data system. Information associated with a most recent transaction related to the first node is obtained from the persistent storage. Each of other nodes in the data system is requested to transmit one or more transaction requests previously sent to the first node after the most recent transaction. The one or more transaction requests are received from at least one of the other nodes. A sequence of one or more transactions associated with the one or more transaction requests is determined. The one or more transactions are executed according to the sequence in order to recover the first data at the first node.

Abstract: The present teaching relates to scheduling transactions at a node in a data system. In one example, a request is obtained for performing a first transaction at the node. A mixing condition associated with the first transaction is determined. The first transaction is initiated. The first transaction is suspended when the mixing condition is met. A second transaction is carried out at the node. The first transaction and the second transaction are directed to a same portion of data at the node.

Abstract: The present teaching relates to request management and data recovery in a data system. In one example, a request is received for a transaction of data by a first node in the data system. A second node in the data system is determined based on the request. The second node is to carry out the transaction. The request is stored into a first request queue at the first node. The request is sent to the second node. A notice is obtained indicating that information associated with the transaction has been persistently stored. The request is removed from the first request queue in response to the notice.

Abstract: The present teaching relates to managing a request in a data system of a plurality of nodes in a network. In one example, a request is received for a transaction of data by a first node in the data system. A second node in the data system is determined based on the request, where the second node is to carry out the transaction. The request is stored into a first request queue at the first node. The request is sent to the second node. A notice is obtained by the first node indicating that information associated with the transaction has been persistently stored. The request is removed from the first request queue in response to the notice being obtained.

Abstract: The present teaching relates to scheduling transactions at a node in a data system. In one example, a request is obtained for performing a first transaction at the node. A mixing condition associated with the first transaction is determined. The first transaction is initiated. The first transaction is suspended when the mixing condition is met. A second transaction is carried out at the node. The first transaction and the second transaction are directed to a same portion of data at the node.

Abstract: The present teaching relates to scheduling transactions at a node in a data system. In one example, a request is obtained for performing a first transaction at the node. A mixing condition associated with the first transaction is determined. The first transaction is initiated. The first transaction is suspended when the mixing condition is met. A second transaction is carried out at the node. The first transaction and the second transaction are directed to a same portion of data at the node.

Abstract: A method and apparatus for generating a signal processing pipeline based, at least in part, on manipulation of a GUI, the signal processing pipeline comprising one or more signal processing operations.

Abstract: A method and apparatus for generating a signal processing pipeline based, at least in part, on manipulation of a GUI, the signal processing pipeline comprising one or more signal processing operations.

Abstract: The present teaching relates to request management and data recovery in a data system. In one example, a request is received for a transaction of data by a first node in the data system. A second node in the data system is determined based on the request. The second node is to carry out the transaction. The request is stored into a first request queue at the first node. The request is sent to the second node. A notice is obtained indicating that information associated with the transaction has been persistently stored. The request is removed from the first request queue in response to the notice.

Abstract: The present teaching relates to scheduling transactions at a node in a data system. In one example, a request is obtained for performing a first transaction at the node. A mixing condition associated with the first transaction is determined. The first transaction is initiated. The first transaction is suspended when the mixing condition is met. A second transaction is carried out at the node. The first transaction and the second transaction are directed to a same portion of data at the node.

Abstract: The present teaching relates to monitoring data in a plurality of data sources of heterogeneous types. In one example, a request is received for monitoring data in the data sources of heterogeneous types. One or more metrics are determined based on the request. The request is converted into one or more queries based on the one or more metrics. Each of the one or more queries is directed to at least one of the data sources of heterogeneous types. A monitoring task is created for monitoring the data in the data sources based on the one or more queries in response to the request.

Abstract: Latent Dirichlet allocation (LDA) analysis on a dataset is performed on an MPP relational database by distributing subsets of said dataset to a plurality of segments of the MPP database, and performing LDA analysis in parallel on the respective subsets on the plurality of segments using Gibbs sampling. An object library on each segment provides executable objects of user defined functions that can be called by an SQL query when the query requires functionality provided by an object.

Abstract: A method for providing an advertisement is disclosed. The method, to be implemented on a machine having at least one processor, storage, and a communication platform connected to a network may include receiving a request for an advertisement to be inserted into a content page to be presented to a user. The method further includes selecting an advertisement relevant to the content page and the user based on a content-advertisement index and a user-advertisement index, constructed based on user browsing context, and transmitting information indicative of the selected advertisement as a response to the request. Further disclosed is a method for generating multi-dimensional index.

Abstract: A method, article of manufacture, and apparatus for process data. In some embodiments, this includes receiving a desired sample size from a user, increasing the desired sample size based on a policy, using the increased desired sample size to determine a number of samples to take from a database segment, wherein the database segment includes a portion of a dataset, generating a sub-dataset based on the determined number of samples to take from the database segment, wherein the sub-dataset only includes identifiers, joining the generated sub-dataset with the dataset, and storing the joined dataset in a storage device.

Abstract: A method, system, and process for representing a decision tree in a tabular format is discussed. The format may contain all the necessary information to traverse the nodes in parallel on a distributed system while consuming an efficient amount of resources. In some embodiments, the tree may be stored in a relational database as a table.

Abstract: A method, article of manufacture, and apparatus for process data. In some embodiments, this includes determining a dataset, wherein the dataset includes a plurality of data values and a plurality of identifiers, and wherein the plurality of identifiers are in a non-continuous order, and wherein the dataset is stored across a number of segments, determining a number of samples to take from the dataset, identifying a minimum identifier and a maximum identifier, and a total number of identifiers, calculating a density based on the plurality of identifiers, minimum identifier and maximum identifier, calculating a number of samples to take from a segment based on the calculated density, the number of samples to take from the dataset, and the number of segments, generating a sub-dataset based on the calculated number of samples to take from a segment, joining the generated sub-dataset with the dataset, and storing the joined dataset in a storage device.

Abstract: A method, system, and process for generating attribute class statistics for a decision tree is discussed. In an embodiment the ACS's are calculated in parallel on a distributed architecture, and may consume a constant amount of resources.

Abstract: A method, system, and process for classifying a dataset using a decision tree is discussed. The decision tree may be stored in a novel format on a distributed architecture, and the dataset classification may be performed in parallel on the distributed architecture.