Nec Laboratories America, Inc.

Nec Corporation

Square, Inc.

Block, Inc.

Yun Chi

Vahit Hakan Hacigumus

Shenghuo Zhu

Hyun Jin Moon

Yihong Gong

Rong Yan

Nathan P McCauley

Junichi Tatemura

V Hakan Hacigumus

Wang-Pin Hsiung

Pengcheng Xiong

Wentao Wu

Koji Hino

Belle L Tseng

Jeffrey F Naughton

Kai Hung Yu

Xiaodan Song

Zhen Guo

Naeem Ishaq

Jenny Duggan

Sean Barker

Dingding Wang

Shenoda Guirguis

Tianbao Yang

Hatem Abdelghani Mahmoud

Technology is disclosed for transferring money anonymously between a sender and a recipient by use of a one-time use token. The method includes generating a one-time use token account for association with a one-time use token. The method includes generating the token and providing the token to the sender device in a machine-readable and transferable format. The method includes receiving a request to charge the one-time use token account after the token has been provided to the recipient device as a form of payment for a transaction. The method includes determining that an amount of the transaction is less than an amount of funds associated with the token and that the time of the transaction is within a time period for the use of the token. The method includes facilitating a transfer to the recipient account and deducting the amount of the transaction from a sender account.

Anonymous payment transactions

In an embodiment, a method performed by a payment service system (PSS) includes receiving from a first payment application associated with the PSS, a request to generate a token. The method includes identifying account information associated with the recipient. The method includes generating an anonymizing token associated with the recipient. The method includes storing an association between the account information of the recipient and the anonymizing token. The method includes providing for display of the anonymizing token within the first payment application. The anonymizing token anonymously embeds recipient information and the account information of the recipient. The method includes, upon receiving an indication of an intent to transfer payment through interaction with the anonymizing token via a second payment application, identifying a recipient account associated with the recipient based on an identification of the stored association and automatically facilitating a transfer of funds to the recipient account.

Technology is disclosed for transferring money anonymously between a sender and a recipient by use of a token. In some embodiments, the disclosed technology involves communication between a mobile payment application ('App') and a payment service system (PSS). Upon the recipient's request, the App, installed on the recipient's device, generates the token, and provides the token to both the recipient and the PSS. The PSS receives both the token and the recipient's identification information from the App, associates the token with the identification information, and stores such association. The recipient, on the other hand, transfers the token to the sender, who forwards it to the PSS to request a money transfer. The PSS receives the token and identifies the recipient's identification information based on the previously stored association with the token. With the identification information, the PSS determines the recipient's financial account to execute the money transfer.

Techniques and arrangements for managing risk of fraudulent transactions made by a point-of-sale (POS) device operating in an online and an offline mode. In some instances, a payment service may maintain a merchant profile with a list of merchant specific criteria. The payment service may encrypt and send the merchant specific criteria to the POS device associated with the merchant. The encrypted data may include a list of payment instruments used in previous successful transactions, customer transaction histories, and other customer information. The POS device may determine, upon receiving a payment instrument identifier in a transaction, whether the payment instrument has been used in a previous successful transaction. If the POS device determines the payment instrument has been used, it may process the transaction. If not, the POS device may warn the merchant that the transaction is a high-risk transaction.

Risk management in online and offline transactions

Systems and methods for predicting query execution time for concurrent and dynamic database workloads include decomposing each query into a sequence of query pipelines based on the query plan from a query optimizer, and predicting an execution time of each pipeline with a progress predictor for a progress chart of query pipelines.

System and methods for predicting query execution time for concurrent and dynamic database workloads

Systems and methods for consolidating workload on one or more computing devices, comprising: generating tenant performance profiles for at least one of a buffer memory size and a buffer warmness; assigning one or more tenants to a database management system (DBMS) instance; configuring the one or more tenants to minimize a total server count needed to host the one or more tenants; and reducing the total server count by adjusting of buffer sizes while satisfying Service Level Agreement (SLA) requirements.

System and method for SLA-aware database consolidation using per-tenant memory size configuration

Systems and methods for prioritizing queries in a cloud based database by determining a priority score in real time under an imprecise query execution time; and incrementally maintaining query priority scores.

System and methods for prioritizing queries under imprecise query execution time

Systems and methods are disclosed to perform performance prediction for cloud-based databases by building on a computer a cloud database performance model using a set of training workloads; and using a learned model on the computer to predict database performance in the cloud for a new workload, wherein for each reference workload r and hardware configuration h, system throughput t, average throughput of &#x3b1;and standard deviation &#x3c3;, comprising normalizing each throughput as: 

Portable workload performance prediction for the cloud

A method for balancing database workloads among a plurality of database servers includes when a new server is available, selecting a set of master and slave databases to be migrated to the new server; and migrating the selected databases to result in a balanced new workload among all servers while minimizing migration cost; and during operation, if workload unbalance is detected in real time from a workload change in a certain database, iteratively selecting one database at a time to migrate to a different server to balance the workload.

Balancing database workloads through migration

A computer implemented method of workload scheduling under two different types of Service Level Agreements (SLAs), namely soft and hard SLA which employ both a deadline- and cost-aware scheduler called iCBS-DH. The scheduler is evaluated against deadline and cost performance of various scheduling policies under a large range of SLA cost function and deadline types.

Scheduling methods using soft and hard service level considerations

Methods and systems for database migration from a multitenant database include taking a snapshot of an original database to be migrated with a hot backup process, such that the database is still capable of answering queries during the hot backup process; maintaining a query log of all queries to the tenant database after the hot backup process begins; initializing a new database at a target server using the snapshot; replaying the query log synchronize the new database with the original database; and answering new queries with the new database and not the original database.

Latency-aware live migration for multitenant database platforms

There are provided a system and method for predicting query execution time in a database system. A cost model determination device determines a cost model of a database query optimizer for the database system. The cost model models costs of queries applied to the database system. A profiling device determines profiling queries for profiling input/output cost units and processor cost units relating to the database system, and profiles the cost units using the profiling queries to output profiled cost units. A calibrating device calibrates cost units in the cost model responsive to the profiled cost units to output calibrated cost units. A sampling re-estimator samples and re-estimates a cardinality estimation of a final query plan to output an updated cardinality estimation. A predictor applies the calibrated cost units and the updated cardinality estimation in the cost model to generate a prediction of an execution time of a given query.

Predicting query execution time

An efficient cost-based scheduling method called incremental cost-based scheduling, iCBS, maps each job, based on its arrival time and SLA function, to a fixed point in the dual space of linear functions. Due to this mapping, in the dual space, the job will not change their locations over time. Instead, at the time of selecting the next job with the highest priority to execute, a line with appropriate angle in the query space is used to locate the current job with the highest CBS score in logarithmic time. Because only those points that are located on the convex hull in the dual space can be chosen, a dynamic convex hull maintaining method incrementally maintains the job with the highest CBS score over time.

Highly scalable cost based SLA-aware scheduling for cloud services

An admission control system for a cloud database includes a machine learning prediction module to estimate a predicted probability for a newly arrived query with a deadline, if admitted into the cloud database, to finish its execution before said deadline, wherein the prediction considers query characteristics and current system conditions. The system also includes a decision module applying the predicted probability to admit a query into the cloud database with a target of profit maximization with an expected profit determined using one or more service level agreements (SLAs).

Admission control in cloud databases under service level agreements

Systems and methods are disclosed to schedule jobs in a cloud computing infrastructure by receiving in a first queue jobs with deadlines or constraints specified in a hard service level agreement (SLA); receiving in a second queue jobs with a penalty cost metric specified in a soft SLA; and minimizing both constraint violation count and total penalty cost in the cloud computing infrastructure by identifying jobs with deadlines in the first queue and delaying jobs in the first queue within a predetermined slack range in favor of jobs in the second queue to improve the penalty cost metric.

Growing community of inventors

Monte Sereno, CA, United States of America

Yun Chi