Abstract: A payment processing system continuously processes transactions. Multiple modeling technologies are used within a fraud detection system to each handle different blocks of segmented transaction data in order to detect fraud. Model data is created from recent segmented transaction data that is currently handled by an existing model; each transaction includes a fraud flag as to whether it was fraudulent or not. The model data is used to train, validate and test any of a number of types of models where the fraud flag is the target. Performance metrics such as Sensitivity, Review Rate and False Positives are calculated for each trained model. An operational objective is used to determine a value for a particular performance metric which then dictates values for other performance metrics for each model. The best model is chosen by optimizing the performance metrics. The best model is configured and deployed to replace the existing model.

Abstract: A payment processing system continuously processes transactions. A group of transactions satisfying criteria within a recent measurement window are extracted from transaction data. A second group of transactions satisfying the criteria within a past baseline window are also extracted. An attribute of transactions is selected for analysis and a risk metric value is calculated for transactions in the measurement window having a particular attribute value. The same risk metric is used to calculate a baseline value for transactions in the baseline window having the same attribute value. The baseline value may be normalized and adjusted. The risk metric value is compared to the baseline value and an alert is generated if the risk metric value is greater. Any of a variety of risk metrics are used. Total dollar amount and total order count from the measurement window compared to similar from the baseline window are also used to inform the alert.

Abstract: A payment processing system continuously processes transactions. Upon arrival, in real-time, a transaction is parsed and each entity is used to retrieve other previous transactions associated with that entity in order to build a link analysis web. Iteratively, each entity in the retrieved transactions is used to retrieve other associated transactions, etc. Before retrieval of associated transactions, each entity is checked whether it appears in a bypass table. If so, that entity is not used to retrieve transactions. Once built, transactions and entities within the link analysis web are analyzed to determine whether the current transaction is fraudulent. Off-line or online, risk metrics such as address mismatch rate, bank decline rate, ticket decline rate and new payment device rate, along with the proportion of a linked entity is used to determine whether that linked entity is erroneous and should be placed into the bypass table and excluded from the web.

Abstract: A payment processing system continuously processes transactions having data elements. A profile table exists for each data element of the transactions and is continuously updated with risk variables in real time. Risk metrics of various types are calculated in real-time if criteria are met. A risk metric percentage is compared to one of two thresholds to determine whether or not to trigger a strike and whether or not to turn on alert for a particular data element of a profile table. If an alert is turned on then the thresholds become more restrictive for that particular data element for future transactions. Fraud model parameters are also adjusted if a strike is triggered or an alert is turned on. An alert is turned off after passage of time and if the risk metric for a future transaction is below a threshold. Rules are created in real time if an alert is on.

Abstract: A payment processing system continuously processes transactions. Multiple modeling technologies are used within a fraud detection system to each handle different blocks of segmented transaction data in order to detect fraud. Model data is created from recent segmented transaction data that is currently handled by an existing model; each transaction includes a fraud flag as to whether it was fraudulent or not. The model data is used to train, validate and test any of a number of types of models where the fraud flag is the target. Performance metrics such as Sensitivity, Review Rate and False Positives are calculated for each trained model. An operational objective is used to determine a value for a particular performance metric which then dictates values for other performance metrics for each model. The best model is chosen by optimizing the performance metrics. The best model is configured and deployed to replace the existing model.

Abstract: A payment processing system continuously processes transactions. Upon arrival, in real-time, a transaction is parsed and each entity is used to retrieve other previous transactions associated with that entity in order to build a link analysis web. Iteratively, each entity in the retrieved transactions is used to retrieve other associated transactions, etc. Before retrieval of associated transactions, each entity is checked whether it appears in a bypass table. If so, that entity is not used to retrieve transactions. Once built, transactions and entities within the link analysis web are analyzed to determine whether the current transaction is fraudulent. Off-line or online, risk metrics such as address mismatch rate, bank decline rate, ticket decline rate and new payment device rate, along with the proportion of a linked entity is used to determine whether that linked entity is erroneous and should be placed into the bypass table and excluded from the web.

Abstract: A payment processing system continuously processes transactions. A group of transactions satisfying criteria within a recent measurement window are extracted from transaction data. A second group of transactions satisfying the criteria within a past baseline window are also extracted. An attribute of transactions is selected for analysis and a risk metric value is calculated for transactions in the measurement window having a particular attribute value. The same risk metric is used to calculate a baseline value for transactions in the baseline window having the same attribute value. The baseline value may be normalized and adjusted. The risk metric value is compared to the baseline value and an alert is generated if the risk metric value is greater. Any of a variety of risk metrics are used. Total dollar amount and total order count from the measurement window compared to similar from the baseline window are also used to inform the alert.

Abstract: A payment processing system continuously processes transactions having data elements. A profile table exists for each data element of the transactions and is continuously updated with risk variables in real time. Risk metrics of various types are calculated in real-time if criteria are met. A risk metric percentage is compared to one of two thresholds to determine whether or not to trigger a strike and whether or not to turn on alert for a particular data element of a profile table. If an alert is turned on then the thresholds become more restrictive for that particular data element for future transactions. Fraud model parameters are also adjusted if a strike is triggered or an alert is turned on. An alert is turned off after passage of time and if the risk metric for a future transaction is below a threshold. Rules are created in real time if an alert is on.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for analyzing historical data in a data warehouse. A data warehouse is provided. The data warehouse includes several database tables. Every database table has a start time column and an end time column. A query is issued to the data warehouse. The query includes customized query predicates against the start time column values and end time column values of a database table. A response is received from the data warehouse. The response includes table records satisfying the query and having valid (START_TIME, END_TIME) ranges that cover a fixed time point specified by the customized query predicates of the query. Methods for updating a database table in a data warehouse, and a data warehouse system are also described.

Abstract: A method, system, and computer program product for optimizing database queries in a data processing system. A query optimization (QO) utility maximizes the query coverage of Materialized Query Tables (MQTs) in order to efficiently process various types of incoming queries to a database engine. In particular, the QO utility enables MQT technology for nullable foreign key columns in a number of table joins involving fact tables and dimension tables, in which the fact tables and dimension tables have column data which are not null. The QO utility also enables MQT query matching for a relational database engine for non-additive measures, and improves the performance of existing query-matching capabilities for a relational database engine for additive measures. The QO utility specifies a number of functionally dependent relationships between the columns of a table in order to enhance the query matching abilities of the database engine.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for analyzing historical data in a data warehouse. A data warehouse is provided. The data warehouse includes several database tables. Every database table has a start time column and an end time column. A query is issued to the data warehouse. The query includes customized query predicates against the start time column values and end time column values of a database table. A response is received from the data warehouse. The response includes table records satisfying the query and having valid (START_TIME, END_TIME) ranges that cover a fixed time point specified by the customized query predicates of the query. Methods for updating a database table in a data warehouse, and a data warehouse system are also described.

Abstract: A method, system, and computer program product for optimizing database queries in a data processing system. A query optimization (QO) utility maximizes the query coverage of Materialized Query Tables (MQTs) in order to efficiently process various types of incoming queries to a database engine. In particular, the QO utility enables MQT technology for nullable foreign key columns in a number of table joins involving fact tables and dimension tables, in which the fact tables and dimension tables have column data which are not null. The QO utility also enables MQT query matching for a relational database engine for non-additive measures, and improves the performance of existing query-matching capabilities for a relational database engine for additive measures. The QO utility specifies a number of functionally dependent relationships between the columns of a table in order to enhance the query matching abilities of the database engine.

Abstract: A tuned low-noise amplifier is disclosed. A device in accordance with the present invention comprises a first current source, a second current source, a comparator, coupled to the first current source and the second current source, for providing a control signal, and a third current source, receiving the control signal and coupled to the tuned low-noise amplifier, wherein a current in the third current source is proportional to a current in the first current source and the second current source, where values of the first current source, the second current source, and the third current source are based on a quasi-Proportional-To-Absolute-Temperature (PTAT) curve.

Abstract: A computer implemented method for generating data for a database. A plurality of slices are identified within the database. The plurality of slices are described using metadata for the database. A set of slices are selected from the plurality of slices based a policy to form a selected set of slices. A recommendation to materialize the selected set of slices is generated.

Abstract: A method, apparatus and program storage device for optimizing a data warehouse model and operation. Incoming queries issued against a data warehouse having a table join optimized by using a new query reroute technique. The reroute technique also enables data warehouse users to use the standard query-reroute technology to optimize SQL queries issued against a data warehouse that involves recursive hierarchies.

Abstract: Systems, methods, and computer products that efficiently update multidimensional data cubes by using hierarchy dependency relationships that are present in the tables of the multidimensional database. An embodiment of the present invention improves the techniques associated with refreshing multidimensional database information when the multidimensional data has been changed.

Abstract: Systems, methods, and computer products that efficiently update multidimensional data cubes by using hierarchy dependency relationships that are present in the tables of the multidimensional database. An embodiment of the present invention improves the techniques associated with refreshing multidimensional database information when the multidimensional data has been changed.

Abstract: A method which is accurate and yet relatively simple, rapid and inexpensive for estimating 3-D coordinates of EEG (electroencephalograph) electrode positions on the head. The electrode positions of any number of scalp electrodes placed according to the standard 10/10 electrode position system are computed by a computer system based on 14 manually measured inter-electrode distances and 9 electrode-to-skull landmark straight-line distances. The measurements are made manually using digital calipers.

Abstract: The spatial resolution of electroencephalograph (EEG) medical images is improved using "Deblurring" based on a digital computer based analysis using volumetric finite elements and a biophysical model of the passive electrical conductivity of a subject's head.

Abstract: A pre-collections system identifies debtors when they contact a company for a new purchase. A customer service representative (or other automated process) takes payment for the prior debt in real time. The pre-collections system is tightly integrated with an order processing system. Order processing system channels (web site, IVR, CSR, etc.) are enabled to redirect the customer debtor into a corresponding pre-collections system channel. Customers are routed back seamlessly in real time to the point in the order processing system where they left off. The customer is re-instated immediately when their payment is cleared. The system keeps track of blacklisted customers. Logic determines whether to resubmit an item, reclaim a product or service or to immediately collect upon the debt.