Abstract: A computer implemented method of and apparatus for collecting and managing customer related information within an E-Business customer relationship management system. The customer relationship management system includes a database in which customer-related information, as well as privacy policy and privacy consent information governing the collection and use of customer related information, is stored and organized. The database includes tables containing privacy consent default values for customers of the E-Business retailer high-level privacy consent values selected by the customers; specific privacy consent values selected by the customers for a plurality of privacy categories defined by the Platform for Privacy Preference (P3P); and privacy consent values selected by the customers for different customer addresses.

Abstract: Methods, computer programs, information handling systems, and state machines for performing an atomic write to a data block area are disclosed. The atomic write is an in-place write> The method includes receiving one or more data blocks to write to the data block area; and for each data block received: writing the data block to the depot slot; and writing the data block to the data block area after the data block write to the depot slot is completed.

Abstract: A method and apparatus performs a partitioned joins of spatial objects in a database system. In one example arrangement, each spatial object is represented by z-cells in a z-ordered space. The z-cells of certain spatial objects are either duplicated or redistributed across multiple partitions in the database system to enable parallel joins of spatial objects.

Abstract: A database includes a storage to store a data set and a controller to use one or more SQL (Structured Query Language) statements to perform computations associated with a clustering algorithm e.g., a K-means clustering algorithm).

Abstract: A logical data model (LDM) design methodology that utilizes common or shared subject areas, developed for applications across two or more industries, in the design and construction of data warehouse solutions and logical data models for specific customers. The development and use of shared subject areas provides for more effective new LDM development through re-use of common elements and quicker deployment of horizontal applications on all industries.

Abstract: A database system is capable of clustering data in received transactions. Clustering is based on sparse distance computations and/or simplified sufficient statistics. Each of the received transactions contain attributes or dimensions that are binary data. In some implementations, a summary table is also output to enable convenient viewing of the results of clustering.

Abstract: A financial processing system that accesses account, event and organization attributes from a relational database management database, wherein: (1) the account attributes comprise data about accounts being measured, (2) the event attributes comprise data about account-related transactions, and (3) the organization attributes comprise data about the organization's financial status. Profitability calculations are performed using the account, event and organization attributes accessed from the relational database management system, as well as one or more profit factors and one or more rules. The profitability calculations include the following: Profit = Net Interest Revenue (NIR) + Other Revenue (OR) ? Direct Expense (DE) ? Indirect Expense (IE) ? Risk Provision (RP) In this calculation, the Net Interest Revenue (NIR) comprises: NIR = Interest Revenue ? Cost of Funds + Value of Funds ? Interest Expense.

Abstract: A financial processing system that accesses account, event and organization attributes from a relational database management database, wherein: (1) the account attributes comprise data about accounts being measured, (2) the event attributes comprise data about account-related transactions, and (3) the organization attributes comprise data about the organization's financial status. Profitability calculations are performed using the account, event and organization attributes accessed from the relational database management system, as well as one or more profit factors and one or more rules. The profitability calculations include the following: Profit = Net Interest Revenue (NIR) + Other Revenue (OR) ? Direct Expense (DE) ? Indirect Expense (IE) ? Risk Provision (RP) In this calculation, the Net Income Revenue (NIR) comprises: NIR = Interest Revenue ? Cost of Funds + Value of Funds ? Interest Expense.

Abstract: Methods, computer programs, and database systems for analyzing one or more queries are disclosed. Queries may include one or more conditions and one or more sub-queries, with each sub-queries introduced by connecting condition. The method determines the satisfiability of the query, including the satisfiability of the connecting conditions and conditions in the sub-queries. Queries may include one or more conditions of the form (X+Y OP C). The method determines the satisfiability of the query, including the satisfiability of the conditions of the form (X+Y OP C).

Abstract: A financial processing system that accesses account, event and organization attributes from a relational database management database, wherein: (1) the account attributes comprise data about accounts being measured, (2) the event attributes comprise data about account-related transactions, and (3) the organization attributes comprise data about the organization's financial status. Profitability calculations are performed using the account, event and organization attributes accessed from the relational database management system, as well as one or more profit factors and one or more rules.

Abstract: A database system receives a transaction that causes a tuple to be integrated into a join view. In response to receiving the transaction, a predefined first lock is placed on at least a portion of the join view, the predefined first lock conflicting with either a shared lock or an exclusive lock placed on the join view, but not conflicting with another predefined first lock placed on the join view. Also, a latch or semaphore associated with a value of a predetermined attribute of the tuple is obtained before integrating the tuple into the join view.

Abstract: Methods, computer programs, and database systems for performing a database query that includes a join on an equality condition are disclosed. The method includes defining two or more first-table partitions and two or more corresponding second-table partitions. The method includes performing the join on the first-table partition and the second-table partition, storing the result, and merging the results.

Abstract: A method, system and apparatus for performing an inclusion hash join and an exclusion hash join using a hash join are provided. The inner and outer tables are transposed to left and right tables, respectively. A hash table is created from the left table, the right table is scanned, and the hash table is probed. If the right table hash value matches a hash table value, the actual values are compared. If they match, the current outer table row is included in the result. If an exclusion condition is present, the hash table is probed with the right-table-row's hash value. If no hash values match, the outer table row is added to the result and the next row is addressed. If one or more hash values match, the actual values are compared.

Abstract: A financial processing system that accesses account, event and organization attributes from a relational database management database, wherein: (1) the account attributes comprise data about accounts being measured, (2) the event attributes comprise data about account-related transactions, and (3) the organization attributes comprise data about the organization's financial status. Profitability calculations are performed in the computer using the account, event and organization attributes accessed from the relational database management system, as well as one or more profit factors and one or more rules. The profitability calculations comprise the following: Profit (ai) = Net Interest Revenue (NIR) (ai) + Other Revenue (OR) (ai) ? Direct Expense (DE) (ai) ? Indirect Expense (IE) (ai) ? Risk Provision (RP) (ai) for an account ai.

Abstract: Report specification generators, interfaces, and methods are provided. Existing specification objects are selected from a data store. New specification objects are created as shell specification objects. Moreover, computations are selected from the data store. The specification objects, the shell specification objects, and the computations are organized within a grid. In one embodiment, the grid is used to generate a template report schema. In another embodiment, a preview tool populates the grid with report data in order to present a preview.

Abstract: A method and system for identifying manufacturing anomalies in a manufacturing system comprising a plurality of products which are manufactured with a plurality of manufacturing parameters is disclosed. The system comprises a data mining program applied to the data warehouse for analyzing the stored manufacturing parameters to define a normal manufacturing parameter subset. The data mining program is further for detecting that at least one of the plurality of manufacturing parameters is excluded from the normal subset. The system further comprises a reporting means for reporting the at least one detected manufacturing parameter. The data mining program may further detect that a plurality of the manufacturing parameters are excluded from the first subset. The data mining program then analyzes the detected plurality of manufacturing parameters to define a second normal subset of the detected plurality of manufacturing parameters.

Abstract: A system, method and computer program for integrating a database and a data warehouse are disclosed. A request intended to be run on the database is received. If the request would benefit from a service provided by the data warehouse, data necessary to fulfill the request is transferred from the database to the data warehouse and the request is submitted to the data warehouse. Otherwise, the request is submitted to the database.

Abstract: A method, computer program, database system and data structure are disclosed for compressing a decimal type field in a database including one or more tables. Each of the one or more tables includes one or more rows. Each of the one or more rows includes an instance of the decimal type field. Space is allocated in one of P amounts to an instance of the decimal type field in a row, the amount being chosen based on the size of the value being stored in the instance of the decimal type field. One or more presence bits are set in the row to identify the amount of space allocated.

Abstract: A method, computer program and system for optimizing similarity string filtering are disclosed. A first data string comprising one or more data characters and selecting a second data string comprising one or more data characters are selected. At least one of a defined set of shapes is applied to the first data string to generate one or more patterns associated with the first data string. At least one of the defined set of shapes is applied to the second data string to generate one or more patterns associated with the second data string. The one or more patterns associated with the first data string are compared with the one or more patterns associated with the second data string to determine if one or more matching patterns exist. The first data string and the second data string are linked if one or more matching patterns exist.

Abstract: A Customer Relationship Management (CRM) system includes a CRM Client, CRM Engine, and Relational Database Management System (RDBMS). The CRM Client, CRM Engine, and RDBMS integrate a suite of services that allow users to plan, manage, and execute promotional or marketing campaigns, build customer segments, score customers, and analyze customer behavior, product purchases, and response to promotional campaigns. The services include an automated Analytic Data Set Creation service, which simplifies and automates the process of creating analytic data sets useful for modeling and analysis out of operational data stored in the relational database, and a Response Modeling service, which automatically creates promotion response models to score individual customers based on that model in order to predict which customers are most likely to respond to a future promotional campaign.