Abstract: In one embodiment, a computer-implemented method includes receiving a query. A target confidence level is established for the query, the target confidence level representing a requested level of accuracy for a result of the query. At least one individual confidence level is assigned to each of a plurality of analytics engines. One or more analytics engines are queried based on the query. A group of the analytics engines are selected, by a computer processor, where the analytics engines in the selected group have query responses to the query that are deemed to be similar to one another, and where the selection of the selected group is at least partially based on the target confidence level. The query responses from the selected group of analytics engines are summarized into a final result, where the final result is an answer to the query.

Abstract: Embodiments include method, systems and computer program products for real-time data clustering using multiple representatives associated with the data cluster. In some embodiments, a data point may be received from a data source. A data cluster may be identified. A set of representatives associated with the data cluster may be obtained. The data point may be compared to the set of representatives associated with the data cluster. A determination may be made to add the data point to the data cluster. The data point may be associated with the data cluster. The set of representatives associated with the data cluster may be updated, where one of the set of representatives associated with the data cluster is replaced with the data point.

Abstract: Embodiments include method, systems and computer program products for real-time data clustering using multiple representatives associated with the data cluster. In some embodiments, a data point may be received from a data source. A data cluster may be identified. A set of representatives associated with the data cluster may be obtained. The data point may be compared to the set of representatives associated with the data cluster. A determination may be made to add the data point to the data cluster. The data point may be associated with the data cluster. The set of representatives associated with the data cluster may be updated, where one of the set of representatives associated with the data cluster is replaced with the data point.

Abstract: A method, system and computer product for identifying, coordinating and managing mail includes: identifying mail packaging according to a unique identifier, the unique identifier linking the mail to a payment account, a sender and a recipient; identifying a recipient; processing deposited mail by the carrier, wherein said processing includes recognizing the unique identifier, calculation of postage of the mail and charging the postage to the payment account; and routing the mail to the recipient.

Abstract: In one embodiment, a computer-implemented method includes receiving a query. A target confidence level is established for the query, the target confidence level representing a requested level of accuracy for a result of the query. At least one individual confidence level is assigned to each of a plurality of analytics engines. One or more analytics engines are queried based on the query. A group of the analytics engines are selected, by a computer processor, where the analytics engines in the selected group have query responses to the query that are deemed to be similar to one another, and where the selection of the selected group is at least partially based on the target confidence level. The query responses from the selected group of analytics engines are summarized into a final result, where the final result is an answer to the query.

Abstract: In one embodiment, a computer-implemented method includes receiving a query. A target confidence level is established for the query, the target confidence level representing a requested level of accuracy for a result of the query. At least one individual confidence level is assigned to each of a plurality of analytics engines. One or more analytics engines are queried based on the query. A group of the analytics engines are selected, by a computer processor, where the analytics engines in the selected group have query responses to the query that are deemed to be similar to one another, and where the selection of the selected group is at least partially based on the target confidence level. The query responses from the selected group of analytics engines are summarized into a final result, where the final result is an answer to the query.

Abstract: In one embodiment, a computer-implemented method includes receiving a query. A target confidence level is established for the query, the target confidence level representing a requested level of accuracy for a result of the query. At least one individual confidence level is assigned to each of a plurality of analytics engines. One or more analytics engines are queried based on the query. A group of the analytics engines are selected, by a computer processor, where the analytics engines in the selected group have query responses to the query that are deemed to be similar to one another, and where the selection of the selected group is at least partially based on the target confidence level. The query responses from the selected group of analytics engines are summarized into a final result, where the final result is an answer to the query.