A computer-implemented method for identifying a level of similarity between a user-provided data item and a data item within a set of data documents, the method comprising: clustering, by a reference map generator executing on a first computing device, in a two-dimensional metric space, a set of data documents selected according to at least one criterion, generating a semantic map; associating, by the semantic map, a coordinate pair with each of the set of data documents; generating, by a parser executing on the first computing device, an enumeration of terms occurring in the set of data documents; determining, by a representation generator executing on the first computing device, for each term in the enumeration, occurrence information including: a number of data documents in which the term occurs, a number of occurrences of the term in each data document, and the coordinate pair associated with each data document in which the term occurs; generating, by the representation generator, for each term in the enumeration, a sparse distributed representation using the occurrence information; storing, in a sparse distributed representation-database, each of the generated sparse distributed representations; receiving, by a filtering module executing on a second computing device, from a third computing device, a filtering criterion including at least one security-related and/or brand-related term; generating, by the representation generator, for the filtering criterion, at least one sparse distributed representation; receiving, by the filtering module, a plurality of streamed documents from a data source; generating, by the representation generator, for a first of the plurality of streamed documents, a compound sparse distributed representation for a first of the plurality of streamed documents, wherein the compound sparse distributed representation is generated by converting each individual data item of the first of the plurality of streamed documents into its single sparse distributed representation by retrieving the previously generated sparse distributed representation and using a binary OR operation to form the single compound sparse distributed representation from the individual sparse distributed representations; determining, by a similarity engine executing on the second computing device, a distance between the filtering criterion sparse distributed representation and the generated compound sparse distributed representation for the first of the plurality of streamed documents; and acting, by the filtering module, on the first streamed document, based upon the determined distance. clustering, by a reference map generator executing on a first computing device, in a two-dimensional metric space, a set of data documents selected according to at least one criterion, generating a semantic map; associating, by the semantic map, a coordinate pair with each of the set of data documents; generating, by a parser executing on the first computing device, an enumeration of terms occurring in the set of data documents; determining, by a representation generator executing on the first computing device, for each term in the enumeration, occurrence information including: a number of data documents in which the term occurs, a number of occurrences of the term in each data document, and the coordinate pair associated with each data document in which the term occurs; generating, by the representation generator, for each term in the enumeration, a sparse distributed representation using the occurrence information; storing, in a sparse distributed representation-database, each of the generated sparse distributed representations; receiving, by a filtering module executing on a second computing device, from a third computing device, a filtering criterion including at least one security-related and/or brand-related term; generating, by the representation generator, for the filtering criterion, at least one sparse distributed representation; receiving, by the filtering module, a plurality of streamed documents from a data source; generating, by the representation generator, for a first of the plurality of streamed documents, a compound sparse distributed representation for a first of the plurality of streamed documents, wherein the compound sparse distributed representation is generated by converting each individual data item of the first of the plurality of streamed documents into its single sparse distributed representation by retrieving the previously generated sparse distributed representation and using a binary OR operation to form the single compound sparse distributed representation from the individual sparse distributed representations; determining, by a similarity engine executing on the second computing device, a distance between the filtering criterion sparse distributed representation and the generated compound sparse distributed representation for the first of the plurality of streamed documents; and acting, by the filtering module, on the first streamed document, based upon the determined distance.