Abstract: A system and method of metadata refinement using behavioral patterns is disclosed. In some embodiments, user behavioral data for results of a search query is received. The results can include an untagged item and a plurality of tagged items. A determination can then be made that the tagged items have been assigned a plurality of types of metadata. The untagged item can then be identified as a candidate to be tagged with at least one of the plurality of types of metadata assigned to the tagged items. In some embodiments, the user behavioral data comprises clickstream data indicating that a user selected the untagged item and at least one of the tagged items during a single search event.

Abstract: A system and method of metadata refinement using behavioral patterns is disclosed. In some embodiments, user behavioral data for results of a search query is received. The results can include an untagged item and a plurality of tagged items. A determination can then be made that the tagged items have been assigned a plurality of types of metadata. The untagged item can then be identified as a candidate to be tagged with at least one of the plurality of types of metadata assigned to the tagged items. In some embodiments, the user behavioral data comprises clickstream data indicating that a user selected the untagged item and at least one of the tagged items during a single search event.

Abstract: A system and method of metadata refinement using behavioral patterns is disclosed. In some embodiments, user behavioral data for results of a search query is received. The results can include an untagged item and a plurality of tagged items. A determination can then be made that the tagged items have been assigned a first type of metadata not assigned to the untagged item. The untagged item can then be identified as a candidate to be tagged with the first type of metadata assigned to the tagged items based on the user behavioral data. In some embodiments, the user behavioral data comprises clickstream data indicating that a user selected the untagged item and the tagged items during a single search event.

Abstract: A system and method of metadata refinement using behavioral patterns is disclosed. In some embodiments, user behavioral data for results of a search query is received. The results can include an untagged item and a plurality of tagged items. A determination can then be made that the tagged items have been assigned a first type of metadata not assigned to the untagged item. The untagged item can then be identified as a candidate to be tagged with the first type of metadata assigned to the tagged items based on the user behavioral data. In some embodiments, the user behavioral data comprises clickstream data indicating that a user selected the untagged item and the tagged items during a single search event.

Abstract: A system and method of metadata refinement using behavioral patterns is disclosed. In some embodiments, user behavioral data for results of a search query is received. The results can include an untagged item and a plurality of tagged items. A determination can then be made that the tagged items have been assigned a first type of metadata not assigned to the untagged item. The untagged item can then be identified as a candidate to be tagged with the first type of metadata assigned to the tagged items based on the user behavioral data. In some embodiments, the user behavioral data comprises clickstream data indicating that a user selected the untagged item and the tagged items during a single search event.

Abstract: A system and method of metadata refinement using behavioral patterns is disclosed. In some embodiments, user behavioral data for results of a search query is received. The results can include an untagged item and a plurality of tagged items. A determination can then be made that the tagged items have been assigned a first type of metadata not assigned to the untagged item. The untagged item can then be identified as a candidate to be tagged with the first type of metadata assigned to the tagged items based on the user behavioral data. In some embodiments, the user behavioral data comprises clickstream data indicating that a user selected the untagged item and the tagged items during a single search event.

Abstract: A parasitics estimation method for an integrated circuit estimates a geometry graph of an interconnect with reference to cells of an imaginary grid system that overlays the area of the integrated circuit. Cells are identified as including a path of the interconnect. A geometry graph of the interconnect is estimated as crossing the centers of cell boundaries along the interconnected path. Parasitic estimation is performed taking the interconnect path to be the geometry graph.

Abstract: A global router uses an algorithm for controlling routing based upon rough topologies. In one embodiment a set of regions and layers is specified over which the routing must occur. Tunnels can be defined outside of which routing cannot occur. Topologies of previous iterations are considered. Restrictions on the amount of deviation between successive iterations can be specified. As a result, successive iterations of the global router do not cause large changes in the timing delays of circuits. In addition, partial tunnels can be stretched to accommodate nearby nets. Also described are an integrated circuit that is produced using the layout-constrained global routing algorithm and a computer-readable medium storing computer-executable instructions and at least one data structure for implementing the algorithm.