Abstract: A system for bidder support in combinatorial auctions includes a machine readable storage medium storing instructions and a processor to execute the instructions throughout a duration of an auction. The processor executes the instructions to receive bids for a multi-item multi-unit (MIMU) combinatorial auction. The processor executes the instructions to track the status of each sub-auction of the MIMU auction. The status for each sub-auction includes a value of the respective sub-auction and a last winning bid of the respective sub-auction. The processor executes the instructions to determine bidder support information including winning levels, deadness levels, winning bids, and live bids based on the status of each sub-auction.

Abstract: A mobile device includes a positioning module sampling at least a position of the mobile device at a sampling rate when active and a processor capable of determining whether a mobile device is travelling or dwelling based on at least the sampled position of the mobile device. The processor further identifies a travel mode for a trip segment for the mobile device based on at least the sampled position of the mobile device when the mobile device is travelling and identifies an activity when then the mobile device is dwelling.

Abstract: A mobile device includes a positioning module sampling at least a position of the mobile device at a sampling rate when active and a processor capable of determining whether a mobile device is travelling or dwelling based on at least the sampled position of the mobile device. The processor further identifies a travel mode for a trip segment for the mobile device based on at least the sampled position of the mobile device when the mobile device is travelling and identifies an activity when then the mobile device is dwelling.

Abstract: An example method for generating a user profile for a user based on a static profile and a dynamic profile of the user includes compressing dynamic rules into aggregated rules for validation. The dynamic rules associated with a particular user are matched to one or more validated aggregated rules to generate the dynamic profile for that user. The static profile for the user is generated for the user based on factual user information and historical transactional data for the user. The static and dynamic profile are then combined to form the user profile.

Abstract: A mobile device includes a positioning module sampling at least a position of the mobile device at a sampling rate when active and a processor capable of determining whether a mobile device is travelling or dwelling based on at least the sampled position of the mobile device. The processor further identifies a travel mode for a trip segment for the mobile device based on at least the sampled position of the mobile device when the mobile device is travelling and identifies an activity when then the mobile device is dwelling.

Abstract: In one embodiment, a method includes accessing, by a computing device, at least a portion of a user profile that is based at least in part on historical data associated with a user and receiving, by a computing device, data indicative of a current state of the user from a remote system. The current state of the user is the user's current activity. The method also includes selecting, by a computing device, content by matching one or more attributes of the content to the portion of the user profile and the current state of the user and providing, by a computing device, the selected content for display to the user.

Abstract: A process, system and computer software are provided to facilitate at least one recommendation to one or more users and/or one or more applications using multidimensional data. In particular, a multidimensional space is defined, wherein each dimension is capable of providing variable information. At least one dimension corresponds to user profiles. The multidimensional space is filled with multidimensional data corresponding to dimensions of the multidimensional space. Then, information from the multidimensional space is retrieved. Further, the recommendation is generated based on the retrieved information. In addition, it is possible to generate a recommendation-specific query. This can be performed by generating the recommendation-specific query to access a multidimensional cube and retrieve data stored therein. Then, the recommendation can be issued based on the data retrieved from the multidimensional cube.

Abstract: In one embodiment, a method includes accessing multidimensional data corresponding to a multidimensional space having at least three dimensions. Each dimension has a number of attributes characterizing the dimension, and each attribute is capable of providing variable information. The method includes retrieving data corresponding to each of at least three dimensions of the multidimensional space and generating for a user a recommendation based on the data retrieved from each dimension.

Abstract: A process, system and computer software are provided to facilitate at least one recommendation to one or more users and/or one or more applications using multidimensional data. In particular, a multidimensional space is defined, wherein each dimension is capable of providing variable information. At least one dimension corresponds to user profiles. The multidimensional space is filled with multidimensional data corresponding to dimensions of the multidimensional space. Then, information from the multidimensional space is retrieved. Further, the recommendation is generated based on the retrieved information. In addition, it is possible to generate a recommendation-specific query. This can be performed by generating the recommendation-specific query to access a multidimensional cube and retrieve data stored therein. Then, the recommendation can be issued based on the data retrieved from the multidimensional cube.

Abstract: A process, system and computer software are provided to facilitate at least one recommendation to one or more users and/or one or more applications using multidimensional data. In particular, a multidimensional space is defined, wherein each dimension is capable of providing variable information. At least one dimension corresponds to user profiles. The multidimensional space is filled with multidimensional data corresponding to dimensions of the multidimensional space. Then, information from the multidimensional space is retrieved. Further, the recommendation is generated based on the retrieved information. In addition, it is possible to generate a recommendation-specific query. This can be performed by generating the recommendation-specific query to access a multidimensional cube and retrieve data stored therein. Then, the recommendation can be issued based on the data retrieved from the multidimensional cube.

Abstract: A process, system and computer software are provided to facilitate at least one recommendation to one or more users and/or one or more applications using multidimensional data. In particular, a multidimensional space is defined, wherein each dimension is capable of providing variable information. At least one dimension corresponds to user profiles. The multidimensional space is filled with multidimensional data corresponding to dimensions of the multidimensional space. Then, information from the multidimensional space is retrieved. Further, the recommendation is generated based on the retrieved information. In addition, it is possible to generate a recommendation-specific query. This can be performed by generating the recommendation-specific query to access a multidimensional cube and retrieve data stored therein. Then, the recommendation can be issued based on the data retrieved from the multidimensional cube.

Abstract: Particular embodiments provide functionality for predicting purchasing needs according to multidimensional data. The multidimensional data may define a multidimensional space. The multidimensional space may have at least three dimensions, and each of the dimensions may be capable of providing variable information, and/or having a type that is different from a type of another one of the dimensions. Particular embodiments may retrieve information from data associated with the multidimensional space. Predictions regarding purchasing needs may be generated based on the retrieved information. Purchasing recommendations may then be provided based on the predictions.

Abstract: A process, system and computer software are provided to facilitate at least one recommendation to one or more users and/or one or more applications using multidimensional data. In particular, a multidimensional space is defined, wherein each dimension is capable of providing variable information. At least one dimension corresponds to user profiles. The multidimensional space is filled with multidimensional data corresponding to dimensions of the multidimensional space. Then, information from the multidimensional space is retrieved. Further, the recommendation is generated based on the retrieved information. In addition, it is possible to generate a recommendation-specific query. This can be performed by generating the recommendation-specific query to access a multidimensional cube and retrieve data stored therein. Then, the recommendation can be issued based on the data retrieved from the multidimensional cube.

Abstract: A process, system and computer software are provided to facilitate at least one recommendation to one or more users and/or one or more applications using multidimensional data. In particular, a multidimensional space is defined, wherein each dimension is capable of providing variable information. At least one dimension corresponds to user profiles. The multidimensional space is filled with multidimensional data corresponding to dimensions of the multidimensional space. Then, information from the multidimensional space is retrieved. Further, the recommendation is generated based on the retrieved information. In addition, it is possible to generate a recommendation-specific query. This can be performed by generating the recommendation-specific query to access a multidimensional cube and retrieve data stored therein. Then, the recommendation can be issued based on the data retrieved from the multidimensional cube.

Abstract: A process, system and computer software are provided to facilitate at least one recommendation to one or more users and/or one or more applications using multidimensional data. In particular, a multidimensional space is defined, wherein each dimension is capable of providing variable information. At least one dimension corresponds to user profiles. The multidimensional space is filled with multidimensional data corresponding to dimensions of the multidimensional space. Then, information from the multidimensional space is retrieved. Further, the recommendation is generated based on the retrieved information. In addition, it is possible to generate a recommendation-specific query. This can be performed by generating the recommendation-specific query to access a multidimensional cube and retrieve data stored therein. Then, the recommendation can be issued based on the data retrieved from the multidimensional cube.

Abstract: A process, system and computer software are provided to facilitate at least one recommendation to one or more users and/or one or more applications using multidimensional data. In particular, a multidimensional space is defined, wherein each dimension is capable of providing variable information. At least one dimension corresponds to user profiles. The multidimensional space is filled with multidimensional data corresponding to dimensions of the multidimensional space. Then, information from the multidimensional space is retrieved. Further, the recommendation is generated based on the retrieved information. In addition, it is possible to generate a recommendation-specific query. This can be performed by generating the recommendation-specific query to access a multidimensional cube and retrieve data stored therein. Then, the recommendation can be issued based on the data retrieved from the multidimensional cube.

Abstract: Exemplary non-transitory computer-readable medium, method and system for providing at least one recommendation to users and applications using multidimensional data. The multidimensional data can define a multidimensional space defined by a Cartesian product of the dimensions. The multidimensional space can have at least three dimensions, and each of the dimensions can be capable of (i) providing variable information, and (ii) having a type that is different from a type of another one of the dimensions. The exemplary non-transitory computer-readable medium, method and system can retrieve information from data associated with the multidimensional space. Further, the exemplary non-transitory computer-readable medium, method and system can generate the at least one recommendation based on the retrieved information. Further, at least one of the dimensions can include profiles.

Abstract: A process, system and computer software are rovided to facilitate at least one recommendation to one or more users (420) and/or one or more applications (430) using multidimensional data (410). In particular, the multidimensional data (410) which define a multidimensional space (400) is accessed. The multidimensional space (400) can have more than two dimensions, each capable of providing variable information. Then, information from the multidimensional space is retrieved. Further, the recommendation is generated based on the retrieved information. In addition, it is possible to generate a recommendation-specific query (440). This can be performed by generating the recommendation-specific query (440) to access a multidimensional cube and retrieve data stored therein. Then, the recommendation can be issued based on the data retrieved from the multidimensional cube.

Abstract: Exemplary non-transitory computer-readable medium, method and system for providing at least one recommendation to users and applications using multidimensional data. The multidimensional data can define a multidimensional space defined by a Cartesian product of the dimensions. The multidimensional space can have at least three dimensions, and each of the dimensions can be capable of (i) providing variable information, and (ii) having a type that is different from a type of another one of the dimensions. The exemplary non-transitory computer-readable medium, method and system can retrieve information from data associated with the multidimensional space. Further, the exemplary non-transitory computer-readable medium, method and system can generate the at least one recommendation based on the retrieved information. Further, at least one of the dimensions can include profiles.

Abstract: A system and method for generating and validating a user profile (25) for a user based on a static profile (10) and a dynamic profile, (15) of the user. The method and system compresses the dynamic rules (15) into aggregated rules so that the user can view a comparatively small number of the aggregated rules and select the desired rules from the aggregated rules based on user-desired criteria. The method and system validates user rules (60) using a processing device, which are retrieved from a storage device. The user rules are separated into at least one subset of a user set. Then, it is determined if a particular rule of the at least one subset is one of acceptable, unacceptable and undecided based on a defined criteria (415). If the particular rules of the at least one subset are acceptable, the particular rules of the at least one subset are provided (e.g. assigned) to a corresponding user (435).