Abstract: Indexing objects in a datastore, where the objects are characterized by a set of object properties. For objects from a datastore: a Bloom filter for the object properties, based on a predetermined Bloom filter configuration, is created; the Hamming value of the Bloom filter is determined; a binary logarithm of the Bloom filter is calculated; and the Bloom filter, the Hamming value, the binary logarithm, and a representation of the object is stored in the datastore.

Abstract: Identifying objects in a datastore with specified object properties, where each object is characterized by a Bloom filter, a Hamming value of the Bloom filter, and a binary logarithm of the Bloom filter. A set of object properties is received. A search Bloom filter is created from the object properties. A Hamming value of the search Bloom filter is determined. A binary logarithm of the search Bloom filter is calculated. Objects in the datastore which have respective Hamming values greater than or equal to the Hamming value of the search Bloom filter and respective binary logarithms that are greater than or equal to the binary logarithm of the search Bloom filter are identified.

Abstract: Indexing objects in a datastore, where the objects are characterized by a set of object properties. For objects from a datastore: a Bloom filter for the object properties, based on a predetermined Bloom filter configuration, is created; the Hamming value of the Bloom filter is determined; a binary logarithm of the Bloom filter is calculated; and the Bloom filter, the Hamming value, the binary logarithm, and a representation of the object is stored in the datastore.

Abstract: Indexing objects in a datastore, where the objects are characterized by a set of object properties. For objects from a datastore: a Bloom filter for the object properties, based on a predetermined Bloom filter configuration, is created; the Hamming value of the Bloom filter is determined; a binary logarithm of the Bloom filter is calculated; and the Bloom filter, the Hamming value, the binary logarithm, and a representation of the object is stored in the datastore.

Abstract: Implementing a Bloom filter index as a hierarchical data structure. Bloom filters are received and their bit vectors are decomposed into successive bit sequences. For each bit sequence except the last one, memory for at least storing a pointer to a memory location on the next level is allocated on the level corresponding to the bit sequence. The pointer storage is labeled by the value of the next bit sequence. A pointer to the allocated memory is stored in the pointer storage on the previous level that was labeled by the binary value of the current bit sequence. For the last bit sequence, memory for storing Bloom filters is allocated on the last level. A pointer to the allocated memory is stored in the pointer storage on the second-to-last level that was labeled by the value of the last bit sequence. The Bloom filter is stored in the allocated memory.

Abstract: Indexing objects in a datastore, where the objects are characterized by a set of object properties. For objects from a datastore: a Bloom filter for the object properties, based on a predetermined Bloom filter configuration, is created; the Hamming value of the Bloom filter is determined; a binary logarithm of the Bloom filter is calculated; and the Bloom filter, the Hamming value, the binary logarithm, and a representation of the object is stored in the datastore.

Abstract: Identifying objects in a datastore with specified object properties, where each object is characterized by a Bloom filter, a Hamming value of the Bloom filter, and a binary logarithm of the Bloom filter. A set of object properties is received. A search Bloom filter is created from the object properties. A Hamming value of the search Bloom filter is determined. A binary logarithm of the search Bloom filter is calculated. Objects in the datastore which have respective Hamming values greater than or equal to the Hamming value of the search Bloom filter and respective binary logarithms that are greater than or equal to the binary logarithm of the search Bloom filter are identified.

Abstract: Identifying network devices having specified traits using a multi-level hierarchical data structure. Bloom filters representing traits of network devices are received and their bit vectors are decomposed into successive bytes. For each byte except the last one, memory for storing a pointer to memory on the next level is allocated on the level corresponding to the byte. The pointer storage is labeled by the value of the next byte. A pointer to the allocated memory is stored in the pointer storage on the previous level that was labeled by the value of the current byte. For the last byte, memory for storing references to network devices is allocated on the last level. A pointer to the allocated memory is stored in the pointer storage on the second-to-last level that was labeled by the value of the last byte. A reference to the network device is stored in the allocated memory.

Abstract: Identifying network devices having specified traits using a multi-level hierarchical data structure. Bloom filters representing traits of network devices are received and their bit vectors are decomposed into successive bytes. For each byte except the last one, memory for storing a pointer to memory on the next level is allocated on the level corresponding to the byte. The pointer storage is labeled by the value of the next byte. A pointer to the allocated memory is stored in the pointer storage on the previous level that was labeled by the value of the current byte. For the last byte, memory for storing references to network devices is allocated on the last level. A pointer to the allocated memory is stored in the pointer storage on the second-to-last level that was labeled by the value of the last byte. A reference to the network device is stored in the allocated memory.

Abstract: Identifying network devices having specified traits using a multi-level hierarchical data structure. Bloom filters representing traits of network devices are received and their bit vectors are decomposed into successive bytes. For each byte except the last one, memory for storing a pointer to memory on the next level is allocated on the level corresponding to the byte. The pointer storage is labeled by the value of the next byte. A pointer to the allocated memory is stored in the pointer storage on the previous level that was labeled by the value of the current byte. For the last byte, memory for storing references to network devices is allocated on the last level. A pointer to the allocated memory is stored in the pointer storage on the second-to-last level that was labeled by the value of the last byte. A reference to the network device is stored in the allocated memory.

Abstract: Implementing a Bloom filter index as a hierarchical data structure. Bloom filters are received and their bit vectors are decomposed into successive bit sequences. For each bit sequence except the last one, memory for at least storing a pointer to a memory location on the next level is allocated on the level corresponding to the bit sequence. The pointer storage is labeled by the value of the next bit sequence. A pointer to the allocated memory is stored in the pointer storage on the previous level that was labeled by the binary value of the current bit sequence. For the last bit sequence, memory for storing Bloom filters is allocated on the last level. A pointer to the allocated memory is stored in the pointer storage on the second-to-last level that was labeled by the value of the last bit sequence. The Bloom filter is stored in the allocated memory.

Abstract: Identifying network devices having specified traits using a multi-level hierarchical data structure. Bloom filters representing traits of network devices are received and their bit vectors are decomposed into successive bytes. For each byte except the last one, memory for storing a pointer to memory on the next level is allocated on the level corresponding to the byte. The pointer storage is labeled by the value of the next byte. A pointer to the allocated memory is stored in the pointer storage on the previous level that was labeled by the value of the current byte. For the last byte, memory for storing references to network devices is allocated on the last level. A pointer to the allocated memory is stored in the pointer storage on the second-to-last level that was labeled by the value of the last byte. A reference to the network device is stored in the allocated memory.

Abstract: Implementing a Bloom filter index as a hierarchical data structure. Bloom filters are received and their bit vectors are decomposed into successive bit sequences. For each bit sequence except the last one, memory for at least storing a pointer to a memory location on the next level is allocated on the level corresponding to the bit sequence. The pointer storage is labeled by the value of the next bit sequence. A pointer to the allocated memory is stored in the pointer storage on the previous level that was labeled by the binary value of the current bit sequence. For the last bit sequence, memory for storing Bloom filters is allocated on the last level. A pointer to the allocated memory is stored in the pointer storage on the second-to-last level that was labeled by the value of the last bit sequence. The Bloom filter is stored in the allocated memory.