Abstract: A framework for intelligent memory replacement of loaded data blocks by requested data blocks is provided. For example, various factors are taken into account to optimize the selection of loaded data blocks to be discarded from the memory, in favor of the requested data blocks to be loaded into the memory. In some implementations, correlations between the requested data blocks and the loaded data blocks are used to determine which of the loaded data blocks may become candidates to be discarded from memory.

Abstract: Disclosed herein are technologies for quantitative evaluation and optimization of user interface and software design. In one implementation, a design step in a software process is chosen. The software process may be evaluated with different design elements, and time penalties for the different design elements may be determined during the evaluation. A design element of the design step may then be changed to one of the different design elements with the lowest time penalty.

Abstract: The method includes determining an assignment completion time distribution based on a task set defining a project, determining a project completion time distribution based on the assignment completion time distribution and the task set, determining a project cost based on assignments of the task set, and generating a list of project task assignments based on the project cost and the project completion time distribution.

Abstract: Disclosed herein are technologies for quantitative evaluation and optimization of user interface and software design. In one implementation, a design step in a software process is chosen. The software process may be evaluated with different design elements, and time penalties for the different design elements may be determined during the evaluation. A design element of the design step may then be changed to one of the different design elements with the lowest time penalty.

Abstract: A framework for intelligent memory replacement of loaded data blocks by requested data blocks is provided. For example, various factors are taken into account to optimize the selection of loaded data blocks to be discarded from the memory, in favor of the requested data blocks to be loaded into the memory. In some implementations, correlations between the requested data blocks and the loaded data blocks are used to determine which of the loaded data blocks may become candidates to be discarded from memory.

Abstract: According to an aspect, a system includes transportation module configured to generate a transportation plan for packages scheduled to be routed through a regional logistics network such that the transportation plan minimizes transportation costs. The transportation module may include a pickup plan module, a depot-to-depot plan module, and a delivery plan module. The pickup plan module may be configured to compute a pickup transportation plan for packages to be picked-up from the customers in the customer area of an origin depot. The depot-to-depot plan module may be configured to compute a depot-to-depot transportation plan for packages transferred between the origin depot and a destination depot. The delivery plan module may be configured to compute a delivery transportation plan for delivering packages from the destination depot to the customers within the customer area of the destination depot.

Abstract: An association rule accelerator may be used to access a transaction database storing a plurality of transactions, each transaction including one or more items. The association rule accelerator also may select a sampling rate based on an item frequency of frequent items within the transaction database, relative to a sampled item frequency of sampled items within a corresponding sampled transaction database. An an association rule selector may determine, using the selected sampling rate and corresponding sampled transaction database, frequent item sets within the sampled transactions, and may further determine an association rule relating at least two items of the sampled transactions, based on the frequent item sets.

Abstract: Methods, systems, and computer program products for logistics management are described. A root node in a tree representing a home base may be established, the root node comprising a capacity of a corresponding delivery vehicle. One or more unassigned delivery points may be added as a child node of the root node if a path to the corresponding unassigned delivery point is feasible. A next level delivery point may be added as a child node of a node in a tree levelindex if the delivery point is unassigned and a path to the next level delivery point is feasible. The adding step may be repeated for each combination of unassigned delivery point and node(s) in the tree levelindex.

Abstract: The method includes determining an assignment completion time distribution based on a task set defining a project, determining a project completion time distribution based on the assignment completion time distribution and the task set, determining a project cost based on assignments of the task set, and generating a list of project task assignments based on the project cost and the project completion time distribution.

Abstract: The method includes determining at least one business objective on which to base a recommendation list for a first item, associating a configurable target with the business objective, the configurable target being based on a goal for a second item, determining at least one business constraint relating the first item with the second item, the at least one business constraint being based on the business objective and the associated configurable target and generating the recommendation list for the first item based on a list of candidate items and the business constraint.

Abstract: An association rule accelerator may be used to access a transaction database storing a plurality of transactions, each transaction including one or more items. The association rule accelerator also may select a sampling rate based on an item frequency of frequent items within the transaction database, relative to a sampled item frequency of sampled items within a corresponding sampled transaction database. An an association rule selector may determine, using the selected sampling rate and corresponding sampled transaction database, frequent item sets within the sampled transactions, and may further determine an association rule relating at least two items of the sampled transactions, based on the frequent item sets.