1. A method of a query editor comprising: generating a data profile comprising a set of characteristics captured at various granularities of an initial result set generated from an initial query of a database using a processor and a memory; determining what a user expects in at least one of the initial result set of the initial query and a subsequent result set of a subsequent query based on one of the data profile and a heuristically estimated data profile; enabling the user to evaluate a semantic accuracy of the subsequent query based on a likely expectation of the user as determined through the set of characteristics of the data profile; generating a social data catalog table of information about how users are interacting with at least one of the database and a sample database; populating the social data catalog table with metadata, a logical definition and description of attributes, information about usage, page views between the users, a social data network, and a statistical data profile; extracting information from external data sources and social media profiles to generate the social data catalog table of information; and crowd sourcing information from a ranked list of knowledgeable users to generate a ranked order of priority of information presented in profile pages of a curated answers system, wherein the information about usage includes related tables and join predicates as well as relevant filters associated with each table of the at least one of the database and the sample database, wherein the social data network includes a list of users who are knowledgeable about a particular object related to the another query, and wherein the metadata is at least one of a schema name, a table in a schema, a name of an attribute, a data type of the attribute, a primary key associated with the attribute, a constraint of the attribute, a functional dependency between the attributes, an index, a foreign key, a field name, a column name, a table name, and a query description.
1. A method of a query editor comprising: generating a data profile comprising a set of characteristics captured at various granularities of an initial result set generated from an initial query of a database using a processor and a memory; determining what a user expects in at least one of the initial result set of the initial query and a subsequent result set of a subsequent query based on one of the data profile and a heuristically estimated data profile; enabling the user to evaluate a semantic accuracy of the subsequent query based on a likely expectation of the user as determined through the set of characteristics of the data profile; generating a social data catalog table of information about how users are interacting with at least one of the database and a sample database; populating the social data catalog table with metadata, a logical definition and description of attributes, information about usage, page views between the users, a social data network, and a statistical data profile; extracting information from external data sources and social media profiles to generate the social data catalog table of information; and crowd sourcing information from a ranked list of knowledgeable users to generate a ranked order of priority of information presented in profile pages of a curated answers system, wherein the information about usage includes related tables and join predicates as well as relevant filters associated with each table of the at least one of the database and the sample database, wherein the social data network includes a list of users who are knowledgeable about a particular object related to the another query, and wherein the metadata is at least one of a schema name, a table in a schema, a name of an attribute, a data type of the attribute, a primary key associated with the attribute, a constraint of the attribute, a functional dependency between the attributes, an index, a foreign key, a field name, a column name, a table name, and a query description. 10. The method of claim 1 , further comprising computing the data profile through a result set profiler module through which: a first set of hash tables maps each attribute value of an attribute to a number of times a particular value is observed in the attribute, a second set of hash tables maps patterns exhibited by an attribute value to a count of times a pattern is observed in the attribute, wherein the pattern is constructed based on a regular expression of string-valued attributes.