Methods and arrangements for forecasting at least one attribute of a future event based on a repository of historical event data associated with historical events comparable to the future event. A plurality of event data points are obtained from the repository of historical event data. The plurality of event data points are grouped in accordance with at least one category and a plurality of subcategories to create at least one data tree. Certain of the grouped event data points are designated to form a set of candidate data attributes, and the designated set of candidate data attributes are compared to a set of data attributes from the at least one data tree associated with the future event. Based on the comparing, there is identified a data attribute missing from the set of data attributes from the at least one data tree associated with the future event, and a value for the missing data attribute is forecast. Other variants and embodiments are broadly contemplated herein.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of forecasting at least one attribute of a future event based on a repository of historical event data associated with historical events comparable to the future event, said method comprising: utilizing at least one processor to execute computer code that performs the steps of: obtaining a plurality of event data points from the repository of historical event data; grouping the plurality of event data points in accordance with at least one category and a plurality of subcategories to create at least one data tree; designating certain of the grouped event data points to form a set of candidate data attributes; comparing the designated set of candidate data attributes to a set of data attributes from the at least one data tree associated with the future event; based on said comparing, identifying a data attribute missing from the set of data attributes from the at least one data tree associated with the future event; and forecasting a value for the missing data attribute. 2. The method according to claim 1, wherein said grouping comprises developing an ontological structure comprising the at least one category and the plurality of subcategories. 3. The method according to claim 1, wherein said identifying comprises assigning a configurable similarity function with respect to each of the candidate data attributes. 4. The method according to claim 1, wherein said designating comprises assigning a status identifier to each event data point. 5. The method according to claim 4, wherein said assigning of a status identifier comprises appending a designation of one of: mandatory or optional. 6. The method according to claim 1, wherein said forecasting comprises employing an aggregate weighted similarity with respect to the at least one of the data trees and the other data trees. 7. The method according to claim 6, wherein said employing of an aggregate weighted similarity comprises assigning weights to the designated candidate data attributes, wherein the assigned weights comprise one or more of: weights which are learned using a model from the repository of historical event data; and weights provided via user input. 8. The method according to claim 1, wherein said comparing comprises: identifying known attributes of the future event; comparing the known attributes of the future event with corresponding attributes of events in the repository of historical event data, via using a comparison function; based on said comparing, determining an aggregate weighted similarity between the future event and events in the repository of historical event data; and thereupon forecasting the missing attribute of the future event via employing a scope function. 9. The method according to claim 8, wherein said employing of a scope function comprises ranking the events in the repository of historical event data in decreasing order of aggregate weighted similarity. 10. The method according to claim 9, wherein said ranking comprises using customized graph matching. 11. The method according to claim 9, wherein: the scope function is a top-N scope function; and said estimating comprises using solely the top-N ranked events in the repository of historical event data to estimate the missing attribute of the future event. 12. The method according to claim 1, wherein the event candidate data attributes comprise: a plurality of mandatory event fields; and at least one event field comprising a temporal starting point and a temporal ending point. 13. An apparatus for forecasting at least one attribute of a future event based on a repository of historical event data associated with historical events comparable to the future event, said apparatus comprising: at least one processor; and a computer readable storage medium having computer readable program code embodied therewith and executable by the at least one processor, the computer readable program code comprising: computer readable program code configured to obtain a plurality of event data points from the repository of historical event data; computer readable program code configured to group the plurality of event data points in accordance with at least one category and a plurality of subcategories to create at least one data tree; computer readable program code configured to designate certain of the grouped event data points to form a set of candidate data attributes; computer readable program code configured to compare the designated set of candidate data attributes to a set of data attributes from the at least one data tree associated with the future event; computer readable program code configured, based on said comparing, to identify a data attribute missing from the set of data attributes from the at least one data tree associated with the future event; and computer readable program code configured to forecast a value for the missing data attribute. 14. An computer program product for forecasting at least one attribute of a future event based on a repository of historical event data associated with historical events comparable to the future event, said apparatus comprising: at least one processor; and a computer readable storage medium having computer readable program code embodied therewith and executable by the at least one processor, the computer readable program code comprising: computer readable program code configured to obtain a plurality of event data points from the repository of historical event data; computer readable program code configured to group the plurality of event data points in accordance with at least one category and a plurality of subcategories to create at least one data tree; computer readable program code configured to designate certain of the grouped event data points to form a set of candidate data attributes; computer readable program code configured to compare the designated set of candidate data attributes to a set of data attributes from the at least one data tree associated with the future event; computer readable program code configured, based on said comparing, to identify a data attribute missing from the set of data attributes from the at least one data tree associated with the future event; and computer readable program code configured to forecast a value for the missing data attribute. 15. The computer program product according to claim 14, wherein the grouping comprises developing an ontological structure comprising the at least one category and the plurality of subcategories. 16. The computer program product according to claim 14, wherein the identifying comprises assigning a configurable similarity function with respect to each of the candidate data attributes. 17. The computer program product according to claim 14, wherein the designating comprises assigning a status identifier to each event data point. 18. The computer program product according to claim 14, wherein the forecasting comprises employing an aggregate weighted similarity with respect to the at least one of the data trees and the other data trees. 19. The computer program product according to claim 14, wherein the comparing comprises: identifying known attributes of the future event; comparing the known attributes of the future event with corresponding attributes of events in the repository of historical event data, via using a comparison function; based on said comparing, determining an aggregate weighted similarity between the future event and events in the repository of historical event data; and thereupon estimating the missing attribute of the future event via employing a scope function. 20. A method comprising: maintaining a plurality of ontological data trees from a repository of historical event data, each of the ontological data trees relating to previous events; wherein each of the ontological data trees is constructed in accordance with at least one category and a plurality of subcategories; applying an aggregate weighted similarity function to the ontological data tree of the future event with respect to the ontological data trees of the previous events; ranking the ontological data trees of the previous events in decreasing order of aggregate weighted similarity; applying a scope function technique to the ranked ontological data trees of previous events, and thereupon selecting a top-N plurality of ontological data trees of previous events; and estimating the missing attribute of the future event via applying an estimation function based on the top-N plurality of ontological data trees of previous events.