15. A non-transitory computer readable medium storing instructions for analyzing Uniform Resource Locators (URLs), wherein the instructions when executed cause performance of steps comprising: (a) obtaining a plurality of tokens derived from a plurality of URLs; (b) identifying a set of deep tokens that covers ways in which each deep token, of the set of deep tokens, encodes any separately identifiable key or value, wherein each deep token, of the set of deep tokens, is a part of at least one token of the plurality of tokens; (c) selecting a group of anchors, each anchor being a deep token from the set of deep tokens and selected at least based on a frequency of appearance among the plurality of tokens; (d) forming a set of patterns based on the anchors of the group of anchors, wherein each pattern in the set of patterns comprises (i) one or more anchors from the group of anchors, and (ii) a portion that comprises one or more deep tokens of the set of deep tokens; (e) providing each pattern of the set of patterns as a node in a tree; (f) for one or more iterations, repeating steps (c)-(e) for the portion of each pattern, in the set of patterns, by setting the set of deep tokens to comprise the one or more deep tokens for said portion, and within the tree providing one or more resulting patterns as child nodes of said each pattern and sibling nodes with respect to each other, thereby adding to the tree branches with increasingly specific patterns of anchors and increasingly smaller sets of deep tokens in the portions of the specific patterns; and (g) identifying deep tokens of the set of deep tokens as keys or values by initially identifying frequently appearing deep tokens as keys, and then using information from the tree and the initial identifications as an observed sequence of events for a Viterbi algorithm that outputs, as a hidden state, a probability that a given deep token of the set of deep tokens is a key or a value.
15. A non-transitory computer readable medium storing instructions for analyzing Uniform Resource Locators (URLs), wherein the instructions when executed cause performance of steps comprising: (a) obtaining a plurality of tokens derived from a plurality of URLs; (b) identifying a set of deep tokens that covers ways in which each deep token, of the set of deep tokens, encodes any separately identifiable key or value, wherein each deep token, of the set of deep tokens, is a part of at least one token of the plurality of tokens; (c) selecting a group of anchors, each anchor being a deep token from the set of deep tokens and selected at least based on a frequency of appearance among the plurality of tokens; (d) forming a set of patterns based on the anchors of the group of anchors, wherein each pattern in the set of patterns comprises (i) one or more anchors from the group of anchors, and (ii) a portion that comprises one or more deep tokens of the set of deep tokens; (e) providing each pattern of the set of patterns as a node in a tree; (f) for one or more iterations, repeating steps (c)-(e) for the portion of each pattern, in the set of patterns, by setting the set of deep tokens to comprise the one or more deep tokens for said portion, and within the tree providing one or more resulting patterns as child nodes of said each pattern and sibling nodes with respect to each other, thereby adding to the tree branches with increasingly specific patterns of anchors and increasingly smaller sets of deep tokens in the portions of the specific patterns; and (g) identifying deep tokens of the set of deep tokens as keys or values by initially identifying frequently appearing deep tokens as keys, and then using information from the tree and the initial identifications as an observed sequence of events for a Viterbi algorithm that outputs, as a hidden state, a probability that a given deep token of the set of deep tokens is a key or a value. 16. The non-transitory computer readable medium of claim 15 , wherein the instructions that cause performance of step (c) comprise instructions which, when executed, cause using heuristic rules to group the set of deep tokens appearing in the plurality of tokens.