Abstract: Duplicate storage elimination is performed in units of block data generated by dividing a data stream into arbitrary-sized blocks. Further, sub-block data is generated by further dividing the block data into a plurality of pieces of data, and sub-address data based on the data content of each of the pieces of sub-block data is stored in a predetermined storage device. As such, duplicate storage elimination is also performed in sub-block data units based on the sub-address data.

Abstract: Duplicate storage elimination is performed in units of block data generated by dividing a data stream into arbitrary-sized blocks. Further, sub-block data is generated by further dividing the block data into a plurality of pieces of data, and sub-address data based on the data content of each of the pieces of sub-block data is stored in a predetermined storage device. As such, duplicate storage elimination is also performed in sub-block data units based on the sub-address data.

Abstract: A fixed prefix peer to peer network has a number of physical nodes. The nodes are logically divided into a number of storage slots. Blocks of data are erasure coded into original and redundant data fragments and the resultant fragments of data are stored in slots on separate physical nodes such that no physical node has more than one original and/or redundant fragment. The storage locations of all of the fragments are organized into a logical virtual node (e.g., a supernode). Thus, the supernode and the original block of data can be recovered even if some of the physical nodes are lost.

Abstract: Systems and methods for data management and data processing are provided. Embodiments may include systems and methods relating to fast data selection with reasonably high quality results, and may include a faster data selection function and a slower data selection function. Various embodiments may include systems and methods relating to data hashing and/or data redundancy identification and elimination for a data set or a string of data. Embodiments may include a first selection function is used to pre-select boundary points or data blocks/windows from a data set or data stream and a second selection function is used to refine the boundary points or data blocks/windows. The second selection function may be better at determining the best places for boundary points or data blocks/windows in the data set or data stream. In various embodiments, data may be processed by a first faster hash function and slower more discriminating second hash function.

Abstract: A fixed prefix peer to peer network has a number of physical nodes. The nodes are logically divided into a number of storage slots. Blocks of data are erasure coded into original and redundant data fragments and the resultant fragments of data are stored in slots on separate physical nodes such that no physical node has more than one original and/or redundant fragment. The storage locations of all of the fragments are organized into a logical virtual node (e.g., a supernode). Thus, the supernode and the original block of data can be recovered even if some of the physical nodes are lost.

Abstract: A fixed prefix peer to peer network has a number of physical nodes. The nodes are logically divided into a number of storage slots. Blocks of data are erasure coded into original and redundant data fragments and the resultant fragments of data are stored in slots on separate physical nodes such that no physical node has more than one original and/or redundant fragment. The storage locations of all of the fragments are organized into a logical virtual node (e.g., a supernode). Thus, the supernode and the original block of data can be recovered even if some of the physical nodes are lost.

Abstract: Systems and methods for data management and data processing are provided. Embodiments may include systems and methods relating to fast data selection with reasonably high quality results, and may include a faster data selection function and a slower data selection function. Various embodiments may include systems and methods relating to data hashing and/or data redundancy identification and elimination for a data set or a string of data. Embodiments may include a first selection function is used to pre-select boundary points or data blocks/windows from a data set or data stream and a second selection function is used to refine the boundary points or data blocks/windows. The second selection function may be better at determining the best places for boundary points or data blocks/windows in the data set or data stream. In various embodiments, data may be processed by a first faster hash function and slower more discriminating second hash function.