Patent Application: US-32455805-A

Abstract:
this document discloses a software method , data structure , apparatus , and article of manufacture that dynamically and symmetrically clusters multidimensional data onto linear storage mediums . the disclosed embodiments provide methods to store , maintain , and retrieve multidimensional space based on dimensions and possibly dimension groups of two or more dimensions . these methods dynamically access , add , update , or remove dimension key values from any dimension or any group of dimensions while simultaneously enforcing the symmetry from all combinations of all dimensions and dimension groups . further , this document discloses data structures herein that map the multidimensional data to one - dimensional storage mediums .

Description:
detailed descriptions , example embodiments , and drawing figures below do not limit the scope of the present invention in any way . the claims alone should determine the scope of the present invention . mddc uses uniform bit distribution with dynamic bit interleaving to efficiently and dynamically control symmetry . first mddc uses uniform bit distribution for all dimensions and dimension groups in conjunction with dynamic bit interleaving in lieu of z - ordering , hilbert orderings , gray coding , or other bit interleaving techniques . fig1 depicts a uniform bit distribution key using the lsb to msb method . fig2 demonstrates a simple but effective method to store variable length lsb to msb keys . in fig2 a variable length key occupies one or more bytes . all bytes except the last one in a dimension key have a 1 as the first bit . the last byte has a 0 as its first bit indicating no more bytes to follow . mddc could also use the last bit in each byte in lieu of the first for this purpose . since the lsb to msb format places all trailing zeros up front , all keys end with a 1 bit except a key value of 0 . mddc handles 0 with this technique by simply storing the 0 in a byte where all 8 bits are 0 . this allows mddc to determine the exact length of a variable length bit string even though the storage format in fig2 might contain extra bits . with the storage method in fig2 , the lsb to msb key is able to use 7 bits of each byte and the technique does not require any length fields . this technique is more effective than length fields for any variable length key or key component less than 8 bytes . eight bytes covers the vast majority of all dimension key values . should a majority of dimension key values contain more than 8 bytes , then mddc could alternatively employ key length fields . mddc assigns sequential integers for each dimension key value whether it is a standalone dimension or part of dimension group . mddc does this in the dimensions themselves and not within multidimensional data structures . mddc uses indexes to maintain the next key or list of unused key values in each dimension . when assigning keys in a dimension group , the index includes all parent level dimensions from the dimension group as well as the current dimension . next mddc uses dynamic bit interleaving to integrate all participating dimensions . uniform bit distribution attempts to minimize the number of bits that each dimension key requires to uniquely identify itself amongst other dimension keys from the same dimension or dimension group . uniform bit distribution also attempts to evenly distribute all dimension keys across a set of minimum length bit dimension keys . ideally , 5 bits would represent 32 dimension keys and 10 bits would represent 1024 dimension keys . dynamic bit interleaving then interleaves or splices these bits to form one virtual key . for example , if a dimension only contains two unique keys , this method attempts to assign one key to bit value 0 and the other key to bit value 1 . this method then is able to cluster or partition all the multidimensional data into two partitions with only 1 bit , one partition representing each dimension key and bit value . according to the convention of bit interleaving , mddc alternates between bits from each dimension or dimension group to completely cluster or partition the multidimensional data . ideally , each dimension divides the multidimensional data exactly in half with each bit . consequently , under optimal circumstances , mddc requires a minimum and equal number of bits from each dimension or dimension group to fully cluster or partition the multidimensional data . fig4 and fig5 depict the interleaving process and result . note that mddc need not store the data in interleaved format but preferably generates the interleaving dynamically as it compares keys . when mddc compares keys in this dynamic way it also uses an imaginary bit to terminate variable length bit strings . therefore if two dimension key values have different lengths and have the same bit values through the last bit of the shorter key , mddc uses the imaginary bit to collate the shorter key before the longer key . the imaginary bit does not count against the shorter key in the dynamic bit interleaving process as depicted in the bit interleaving result in fig5 . mddc can use all bits allocated for each dimension key or can limit itself to a predetermined number of bits for each dimension . this allows mddc to dynamically maintain key values in dimensions while improving symmetry in multidimensional data structures . finally , mddc simply appends nonparticipating dimensions after the bits from the dynamic bit interleaving process . mddc also appends bits beyond upper dimension limits in a similar manner . therefore in summary , mddc uses uniform bit distribution and dynamical bit interleaving to dynamically and efficiently maintain symmetry amongst all combinations of dimensions and dimension groups . in this embodiment , mddc merely substitutes uniform bit distribution for dimensions and dimension groups in data structures such as but not limited to b - trees in fig8 and then proceeds to utilize data structures with few other changes . these changes only affect key values and key comparison operations . therefore , mddc does not alter the structure or operation of host data structures in a significant manner . for queries , mddc either searches the data structure with full or partial multidimensional keys . for full multidimensional keys , mddc searches for multidimensional keys in the data structure just as it would for one - dimension data structures . for partial multidimensional keys , mddc replaces missing bits in the bit interleaving with all combinations of 0s and 1s as appropriate and in effect searches for all possible combinations of full multidimensional keys . mddc is able to order these multiple key searches the same way as the order or the data structure thereby increasing efficiency by eliminating the need to search for some keys or at least eliminating the need for i / o operations for some keys . this is especially advantageous if dimension source tables maintain themselves in uniform bit distribution key order since no sorting is required prior to searching the multidimensional data . finally , mddc is able to efficiently search for multiple partial keys from several dimension groups and dimensions in the same way . mddc is able to perform a star - join without a pre - computed cartesian product of the dimensions and dimension groups . mddc is therefore able to eliminate much of the expensive i / o and processing resources associated with a full cartesian join on sparsely populated data structures . this is significant since it allows the database optimizer to always choose a star - join for mddc data structures without performance penalties . with the exceptions of how mddc determines key values and how it does comparisons , mddc inserts , updates , and deletes data in the usual way for host data structures . inserts , deletes , and updates are almost identical to their single dimension counterparts . for inserts , it uses the full multidimensional key of the records that it will insert into the multidimensional data to find the correct position in the data structure and then updates the data structure to reflect the new insertions . for deletes , mddc uses the query criteria to search for the deletion candidates and if it finds them updates the data structure to reflect the removals . similarly for updates , mddc uses the query criteria to find the update candidates , updates them , and updates the data structure to reflect the changes if necessary . a practitioner in the art will appreciate the fact that mddc can use a non - dense index and therefore is able to better retain the higher level index portions of its data structure in the ram and cache in computer medium hierarchies such as those in fig7 and fig8 during insert , delete , and update operations for better efficiency . as the descriptions that follow illustrate , mddc provides a more efficient , dynamic , and symmetric software method , data structure , apparatus , and article of manufacture for clustering multidimensional data . mddc is a very flexible and robust technique . but these embodiments only present examples and do not in any way limit the scope of the present invention . the claims alone should be used to determine the scope of the present invention . in general mddc uses variable length bit strings . mddc can assign new integer key values for each new dimension key sequentially . mddc can use indexes for each dimension to determine the next sequential key for each dimension . additionally , such indexes will work for dimensions within dimension groups . with this technique , mddc can dynamically assign dimension keys within dimension groups in the context of parent keys to decrease the overall length of dimension key groups and improve symmetry . these indexes are only used for dimensions . mddc does not require them for the much larger multidimensional data structures . mddc reverses the significant order of dimension keys from msb to lsb to keys in lsb to msb and only uses the number of bits that it needs to uniquely identify each key . this results in improved symmetry and unlimited growth for each dimension . this technique also allows the dimension primary keys to be in the same order as the multidimensional data cluster with regard to each dimension and thereby increases query efficiency . variable length lsb to msb keys can also use key recycling or reuse as internal record identifiers in lieu of external primary keys to decrease average bit string lengths . as the previous paragraph suggests , mddc can use ordered search algorithms such as the “ tetris - algorithm ” or other sorted searches to improve the efficiency of queries . mddc is capable to pre - sorting dimension key values and storing the primary keys for the dimensions in this optimal order so that mddc can further improve query efficiency for the “ tetris - algorithm ” and other sorted search algorithms . mddc can also use a variety of other search algorithms including but not limited to single point searches , star - joins without cartesian products , and independent dimension searches that mddc combines with dynamic bit maps . all these examples and combinations of examples represent distinct embodiments that work in combination with other embodiments . one of the most important results of mddc is that it clusters multidimensional data onto data blocks suitable for storage on linear storage mediums and standard data structures . b - trees as fig8 depicts represent a preferred embodiment since b - trees are robust , efficient , predictable , and ubiquitous in database management systems . mddc , however , can also use isam , binary trees , avl trees , x - trees as well as many other data structures . depending on the underlying data structure , mddc can preload data 100 percent packed or load data dynamically and allow the data structure to dictate packing as mddc inserts records . in this case b - trees would ensure a minimum packing factor of 50 percent . all these examples and combinations of examples represent distinct embodiments that work in combination with other embodiments . mddc can store these data blocks on a variety of hardware platforms but is not limited to the examples that these embodiments enumerate . mddc can store data blocks or records on a single computer with one or more processors , one or more processor caches , ram , and one or more auxiliary memory devices as fig7 depicts . mddc is capable of capitalizing on all available parallelism in such an environment . examples are disk striping and symmetric multiprocessing . mddc can distribute data blocks on multiple computers each with one or more processors , caches , ram devices , and auxiliary memory devices as fig8 depicts . mddc can capitalize on all the advantages from environments such as massively parallel processing , grid computing , and fault tolerance configurations . all these examples and combinations of examples represent distinct embodiments of mddc that work in combination with other embodiments . mddc can store the multidimensional data in compressed format to reduce the overall size of the data structure . in addition , mddc can initialize all data blocks to be within one record of 100 percent capacity . these embodiments work in combination with all other embodiments . in another example embodiment , mddc clusters the multidimensional data and the database defines additional indexes on other dimensions or attributes in the multidimensional data that are not participating in the cluster . the indexes either reference primary keys for the multidimensional data or block identifiers . when the indexes use block identifiers , the database uses dynamic bit maps to combine index restrictions with mddc restrictions since mddc is non - dense and also uses block identifiers to address data . these embodiments work in combination with all other embodiments . in summary , the above embodiments for the present invention describe example software implementations for many specific situations and demonstrate the wide applicability of the present invention but do not limit the present invention in any way . the claims of the present invention alone should determine its scope . 5359724 october , 1994 earle 707 / 205 . 5864857 january , 1999 ohata et al . 707 / 100 . 5940818 august , 1999 malloy et al . 707 / 2 . 5943668 august , 1999 malloy et al . 707 / 3 . 6003036 december , 1999 martin 707 / 102 . 6134541 october , 2000 castelli et al . 707 / 2 . 6182060 january , 2001 hedgcock et al . 707 / 1 . 6460026 october , 2002 pasumansky 707 / 1 . markl , v ., et al ., “ improving olap performance by multidimensional hierarchical clustering ”, 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