Tables time zone adjuster

A method comprising:

• receiving a query of datetime data;
• identifying a user that submitted the query;
• updating a normalized score of the user based on the query and a query history of the user;
• identifying the data in a set of datetime columns in a datastore;
• identifying a set of nodes of a directed graph, wherein each node represents one of the datetime columns or a context for the query;
• identifying an edge having a largest weight between the set of nodes;
• extracting data from the datetime columns according to an expression for the edge;
• and
• updating the weighting of the edge in the directed graph according to the normalized score.

A system comprising:

• a computer processor;
• memory;
• and
• instructions stored in the memory and executable by the computer processor to cause the computer processor to perform operations, the operations comprising:
• processing a set of query texts to identify a set of expressions, wherein each expression references a set of columns of datetime data in a datastore;
• training a statistical model to determine a distribution of the datetime data for each column that was identified, wherein each data in the distribution of the datetime data comprises a timestamp, and wherein the statistical model is a heuristic model that assumes the distribution of the datetime data to be biased to a local daytime;
• processing the set of expressions to generate a directed graph comprising a plurality of nodes and a plurality of edges, wherein each node represents one of the columns or a transformation applied by one of the expressions to one of the columns;
• and
• generating a weight for edges of the directed graph according to a distribution of the datetime data in the columns and a usage index of a corresponding expression, wherein generating the weight for each edge of the directed graph further comprises:
• for each edge, determining a data shift for the distribution of the datetime data based on an expression of the set of expressions,
• wherein weighting each edge according to the usage index further comprises:
• for each edge, counting a number of unique users that submitted a query including the expression;
• assigning normalized scores to the unique users based on respective query histories of the unique users, and
• weighting the edge of the directed graph according to the normalized scores.