Abstract: A location engine is disclosed that estimates the location of a wireless terminal using (i) cell ID, (ii) triangulation, (iii) GPS, (iv) RF pattern-matching, or (v) any combination of them. The location engine is adept at discounting the contribution of apparently reasonable but erroneous data. The location engine receives data that are evidence of the location of a wireless terminal at each of a plurality of different times. The location engine then generates an initial hypothesis for the location of the wireless terminal at each time assuming that all of the data is correct and equally probative. Next, the location engine generates one alternative hypothesis for each initial hypothesis and each datum assuming that the datum is erroneous. Finally, the location engine generates the estimate for the location of the wireless terminal at each time by determining which combination of initial hypotheses and alternative hypothesis is the most self-consistent.

Abstract: A location engine is disclosed that estimates the location of a wireless terminal using (i) cell ID, (ii) triangulation, (iii) GPS, (iv) RF pattern-matching, or (v) any combination of them. The location engine is adept at discounting the contribution of apparently reasonable but erroneous data. The location engine receives data that are evidence of the location of a wireless terminal at each of a plurality of different times. The location engine then generates an initial hypothesis for the location of the wireless terminal at each time assuming that all of the data is correct and equally probative. Next, the location engine generates one alternative hypothesis for each initial hypothesis and each datum assuming that the datum is erroneous. Finally, the location engine generates the estimate for the location of the wireless terminal at each time by determining which combination of initial hypotheses and alternative hypothesis is the most self-consistent.

Abstract: Techniques for cross platform user joining are disclosed. In some embodiments, cross platform user joining includes associating a first user identification (UID) and a second UID with one or more Internet Protocol addresses (IPs); associating the first UID and the second UID with one or more monitored behaviors; and joining the first UID and the second UID based on the one or more IPs and the one or more monitored behaviors.

Abstract: A location engine is disclosed that estimates the location of a wireless terminal using (i) cell ID, (ii) triangulation, (iii) GPS, (iv) RF pattern-matching, or (v) any combination of them. The location engine is adept at discounting the contribution of apparently reasonable but erroneous data. The location engine receives data that are evidence of the location of a wireless terminal at each of a plurality of different times. The location engine then generates an initial hypothesis for the location of the wireless terminal at each time assuming that all of the data is correct and equally probative. Next, the location engine generates one alternative hypothesis for each initial hypothesis and each datum assuming that the datum is erroneous. Finally, the location engine generates the estimate for the location of the wireless terminal at each time by determining which combination of initial hypotheses and alternative hypothesis is the most self-consistent.

Abstract: A location engine is disclosed that estimates the location of a wireless terminal using (i) cell ID, (ii) triangulation, (iii) GPS, (iv) RF pattern-matching, or (v) any combination of them. The location engine is adept at discounting the contribution of apparently reasonable but erroneous data. The location engine receives data that are evidence of the location of a wireless terminal at each of a plurality of different times. The location engine then generates an initial hypothesis for the location of the wireless terminal at each time assuming that all of the data is correct and equally probative. Next, the location engine generates one alternative hypothesis for each initial hypothesis and each datum assuming that the datum is erroneous. Finally, the location engine generates the estimate for the location of the wireless terminal at each time by determining which combination of initial hypotheses and alternative hypothesis is the most self-consistent.

Abstract: Some embodiments provide systems and techniques to facilitate construction of a canonical representation (CR) which represents a logical combination of a set of logical functions. During operation, the system can receive a CR-size limit. Next, the system can construct a set of CRs based on the set of logical functions, wherein each CR in the set of CRs represents a logical function in the set of logical functions. The system can then combine a subset of the set of CRs to obtain a combined CR. Next, the system can identify a problematic CR which when combined with the combined CR causes the CR-size limit to be exceeded. The system can then report the problematic CR and/or a logical function associated with the problematic CR to a user, thereby helping the user to identify an error in the set of logical functions.

Abstract: Methods and apparatuses are described for assigning random values to a set of random variables so that the assigned random values satisfy a set of constraints. A constraint solver can receive a set of constraints that is expected to cause performance problems when the system assigns random values to the set of random variables in a manner that satisfies the set of constraints. For example, modulo constraints and bit-slice constraints can cause the system to perform excessive backtracking when the system attempts to assign random values to the set of random variables in a manner that satisfies the set of constraints. The system can rewrite the set of constraints to obtain a new set of constraints that is expected to reduce and/or avoid the performance problems. The system can then assign random values to the set of random variables based on the new set of constraints.

Abstract: Some embodiments provide a system for adaptively performing state-to-symbolic transformation in a canonical representation which is used for generating random stimulus for a constrained-random simulation. The system can construct a canonical representation for a set of constraints using the set of random variables and the subset of the state variables in the constraints. Next, the system can use the canonical representation to generate random stimulus for the constrained-random simulation, and monitor parameters associated with the constrained-random simulation. Next, the system can add state variables to or remove state variables from the canonical representation based at least on the monitored parameters. The system can then use the modified canonical representation which has a different set of state variables to generate random stimulus for the constrained-random simulation.

Abstract: Some embodiments provide a system for adaptively performing state-to-symbolic transformation in a canonical representation which is used for generating random stimulus for a constrained-random simulation. The system can construct a canonical representation for a set of constraints using the set of random variables and the subset of the state variables in the constraints. Next, the system can use the canonical representation to generate random stimulus for the constrained-random simulation, and monitor parameters associated with the constrained-random simulation. Next, the system can add state variables to or remove state variables from the canonical representation based at least on the monitored parameters. The system can then use the modified canonical representation which has a different set of state variables to generate random stimulus for the constrained-random simulation.

Abstract: Some embodiments provide systems and techniques to facilitate construction of a canonical representation (CR) which represents a logical combination of a set of logical functions. During operation, the system can receive a CR-size limit. Next, the system can construct a set of CRs based on the set of logical functions, wherein each CR in the set of CRs represents a logical function in the set of logical functions. The system can then combine a subset of the set of CRs to obtain a combined CR. Next, the system can identify a problematic CR which when combined with the combined CR causes the CR-size limit to be exceeded. The system can then report the problematic CR and/or a logical function associated with the problematic CR to a user, thereby helping the user to identify an error in the set of logical functions.

Abstract: An approach is provided for managing network device configuration data by functional area. Functional areas supported by a network device are determined. A determination is also made whether a client and the network device support compatible configuration application program interfaces (APIs) for each functional area supported by the network device. User interface objects are generated and displayed on a graphical user interface (GUI). Each user interface object corresponds to one of the functional areas that is supported by the network device and for which the client and network device have compatible configuration APIs. A set of bulk editing functions allows changes to be readily made to multiple configuration data objects and to facilitate creating new configuration data objects.