Abstract: A file is received that includes locus information. The received file is preprocessed, including by selecting a size for a Hilbert curve. File contents are transformed using the Hilbert curve. The transformed file contents are down-sampled. A hashing operation is performed on the down-sampled result.

Abstract: A plurality of clustered files is received. A first tile included in the plurality is selected and loaded into a suffix array. A chunk is located in a second file that is also present in the first file. A determination is made that the located chunk is present in a threshold number of additional files included in the plurality of clustered files. A signature is generated for the plurality of clustered files at least in part by using the chunk.

Abstract: A file is received. File contents are transformed using a space-filling curve. The results are down-sampled to generate a sample locus. A cluster identifier is assigned to the file. In response to a determination that the cluster identifier is not present in a data store, a set of candidate nearest neighbors is determined for the cluster identifier. For each candidate nearest neighbor, a set of existing cluster identifiers present in the data store is determined. For each existing cluster identifier, a set of member loci is determined. An edit distance between the sample locus and each of the member loci is determined. Finally, in response to a determination that a first locus in the set of member loci is within a threshold edit distance of the sample locus, one or more properties associated with the first locus is assigned to the file.

Abstract: A file is received. Contents of the file are transformed using a space-filling curve. One example of a space-filling curve that can be used for the transformation is a Hilbert curve. The result of the transformation is down-sampled. One example way of down-sampling is using a blur operation with anti-aliasing. A hashing operation is performed on the down-sampled result. One example of a hashing operation is locality sensitive hashing.

Abstract: A file is received. File contents are transformed using a space-filling curve. The results are down-sampled to generate a sample locus. A cluster identifier is assigned to the file. In response to a determination that the cluster identifier is not present in a data store, a set of candidate nearest neighbors is determined for the cluster identifier. For each candidate nearest neighbor, a set of existing cluster identifiers present in the data store is determined. For each existing cluster identifier, a set of member loci is determined. An edit distance between the sample locus and each of the member loci is determined. Finally, in response to a determination that a first locus in the set of member loci is within a threshold edit distance of the sample locus, one or more properties associated with the first locus is assigned to the file.

Abstract: A file is received. Contents of the file are transformed using a space-filling curve. One example of a space-filling curve that can be used for the transformation is a Hilbert curve. The result of the transformation is down-sampled. One example way of down-sampling is using a blur operation with anti-aliasing. A hashing operation is performed on the down-sampled result. One example of a hashing operation is locality sensitive hashing.

Abstract: A file is received. Contents of the file are transformed using a space-filling curve. One example of a space-filling curve that can be used for the transformation is a Hilbert curve. The result of the transformation is down-sampled. One example way of down-sampling is using a blur operation with anti-aliasing. A hashing operation is performed on the down-sampled result. One example of a hashing operation is locality sensitive hashing.

Abstract: A file is received. Contents of the file are transformed using a space-filling curve. One example of a space-filling curve that can be used for the transformation is a Hilbert curve. The result of the transformation is down-sampled. One example way of down-sampling is using a blur operation with anti-aliasing. A hashing operation is performed on the down-sampled result. One example of a hashing operation is locality sensitive hashing.