Structure for and method of arranging recursively derived data in a database

Abstract: A structure and method of arranging recursively derived data items in a database. A set of hierarchical iteration levels, one for each iteration of the recursive relation from which the data items are derived, is provided and all data items derived during a given iteration are associated with the corresponding iteration level. Also provided is an index structure including an index set of non-leaf nodes, a sequence set of leaf nodes, and an iteration level index. The leaf nodes include a record of the iteration level of each data item. The data are globally linked according to iteration level or are clustered on pages which are linked according to iteration level. Highly efficient scan and search are implemented by utilizing the iteration level index and the record of iteration level in the leaf nodes to direct the scanning and searching to data generated during a single iteration. The least fixpoint of a set of mutually recursive relations is efficiently calculated by these methods.

Description: BACKGROUND OF THE INVENTION The present invention relates generally to database systems and more particularly to a database structure for recursively derived data items and a method of arranging such data items in a database. Database systems are being used to store and manage more and more different kinds of data. As the use of database systems has expanded and the quantities of data stored in a database have increased, much effort has been devoted to improving existing database systems and developing new systems with new and better capabilities. To "query" a database means to request information from it. The requested information may be obtained directly by retrieving data items stored in the database or may be derived from data items stored in the database. A kind of database query which has grown more important in recent years is a query which derives information recursively. Such a query can be described as a query which queries itself repeatedly. Deriving information recursively requires evaluating a recursive relation among the data items in the database. A general discussion of the mathematical concept of recursion can be found in J. Bradley, Introduction to Discrete Mathematics , ch. 6, Addison-Wesley 1988; see also E. Roberts, Thinking Recursively , John Wiley 1986. For example, assume that a certain database contains a set of data items each expressing a parent-child relation. A few of such data items might be: Bill is father of Richard; Abigail is mother of Richard; Richard is father of Mary; Jane is mother of Mary; John is father of Andrew; Mary is mother of Andrew; and so on. Also assume that the names of the parents of a person are returned by a query of the form "FIND PARENTS OF [X]" where X is the name of the person whose parents are to be found. The name of a parent of a person is an example of the kind of information which is obtainable directly by retrieving a data item in this particular database. Continuing with the above example, it will be noted that the database contains no information about grandparents. However, it will be apparent that such information can be recursively derived from the information in the database, for example by a query of the form "FIND PARENTS OF [FIND PARENTS OF [X]]". Providing the information requested by such a query involves evaluating the recursive relation of grandparent and grandchild. An example of a more complex request is a request to find any common grandparents of X and Y. Two recursive relations would have to be evaluated and the results of these two evaluations would then have to be compared to find any matches. These two recursive relations are referred to as mutually recursive relations. Relations expressed by data items in modern databases are often far more complex than the above examples. For instance, a relation between an airline flight and a pair of cities would be expected to also include departure and arrival times, meal service, number of seats available, continuing service to other cities, fares, and so forth. Generating a trip itinerary to meet the needs of a passenger might require evaluating to a number of mutually related recursive relations. If the number of iterations required to evaluate a recursive relation is known in advance, then evaluating the relation is relatively straight-forward. For example, the request to find the grandparents of X requires exactly two iterations -- one to find the parents of X and one to find the parents of the parents of X. However, if the number of iterations is not known, then the evaluation becomes far more difficult; an example of a request in which the number of iterations is not known is a request to find all ancestors of X. As the volume of data in a database grows larger and the nature of the relations expressed by the data grows more complex, the time required for even a very powerful computer to respond to a complex query can become unacceptably long, especially when the number of iterations required to derive the response is not known in advance. Accordingly, the efficient evaluation of recursive relations, especially sets of mutually recursive relations, has become a matter of critical importance in the design of modern database systems. A comprehensive survey of this problem is presented by F. Bancilhon and R. Ramakrishnan in "An Amateur's Introduction to Recursive Query Processing Strategies" in the Proceedings of the ACM-SIGMOD Conference, Washington, D.C., May 1986. In Computing Surveys, Vol. 11, No. 2, June 1979, pages 121-137, "The Ubiquitous B-Tree", D. Comer discusses the widely-used B-tree standard for file organization. An important advantage to the B-tree is that the tree remains balanced when records are inserted or removed. A popular variation of the B-tree is the B + -tree. In a B + -tree all keys reside in the leaves. The upper levels are organized as an index of a conventional B-tree. The leaf nodes in the lower level are usually linked left-to-right and when so linked are referred to as a "sequence set" which is separate from the index. The B-tree and B + -tree support relatively low-cost find, insert and delete operations but have not solved the problem of efficiently evaluating recursive relations. A set of mathematical operators collectively referred to as "relational algebra" has been developed for the manipulation of data items in a database (see generally C. J. Date, An Introduction to Database Systems (4th Ed.) Vol. I, ch. 13, Addison-Wesley 1986). The relational algebra affords many benefits and is widely used in modern data system design; however, the relational algebra does not have the capability of evaluating recursive relations. New operators such as transitive closure operators have been proposed to give the relational algebra the ability to evaluate recursive relations (R. Agrawal, "Alpha: An Extension of Relational Algebra to Express a Class of Recursive Queries", Proceedings of the Third International Conference on Data Engineering , Los Angeles, California, February 3-5, 1987; S. Ceri et al., "Translation and Optimization of Logic Queries: the Algebraic Approach", Proceedings of the Eleventh International Conference on Very Large Data Bases , Kyoto, Japan, August 1986). Use of such an operator to evaluate a recursive relation r requires finding a "least fixpoint" of a recursive equation of the form r = f(r) where f is a function which will be defined in a succeeding paragraph. The least fixpoint of the recursive relation r is defined as a relation r* which satisfies the following criteria: r* = f(r*) and r* ⊂ p for any p satisfying the equation p = f(p). See A. Aho et al., "Universality of Data Retrieval Languages", Proceedings of the Sixth POPL , 1979. Although not all recursive equations have least fixpoints, if the function f is monotone the equation is guaranteed to have a least fixpoint. A function f consisting only of relational algebra operations is monotone and therefore a recursive equation in which the function f consists only of relational algebra operations has a least fixpoint. See generally A. Tarski, "A Lattice-Theoretical Fixpoint Theorem and its Applications", Pacific Journal of Mathematics , vol. 5, no. 2, pages 285-309, June 1955. To evaluate a pair of mutually recursive relations r 1 and r 2 requires finding the least fixpoints of the following recursive relational algebra equations: r 1 = f 1 (r 1 ,r 2 ) and r 2 = f 2 (r 1 ,r 2 ) More generally, evaluating a set of n mutually recursive relations r 1 through r n requires finding the least fixpoints of a set of recursive equations of the form r i = f i (r 1 , ... , r n ) The functions f i are defined in terms of the definitions of the recursive relations themselves. For example, two mutually recursive relations r 1 and r 2 are defined by the following Horn clauses: r 1 (X,Y)←b 1 (X,Z),r 2 (Z,Y) r 2 (X,Y)←r 1 (X,Z),b 2 (Z,Y) r 1 (X, Y)←b 3 (X,Y) where the b's are base relations and X, Y and Z are columns in the base relations. The functions f 1 and f 2 are then given by: f 1 (r 1 , r 2 ) = b 3 ∪b 1 .r 2 f 2 (r 1 ,r 2 ) = r 1 .b 2 where 0 is a "composition operator" which is a join followed by a projection on the attributes in the target list. More generally, a set of n functions f i are defined in terms of n mutually recursive relations r i as follows: r i = b o i ∪b i .r 1 .r 2 . ... . r n where some of the b 0 may be empty but at least one must not be empty to guarantee that the computation will terminate. Some of the b i may be intermediate relations obtained from one or more other b i . There are various methods of computing the least fixpoint of a recursive equation in a database environment. One of these, the semi-naive method, provides good performance when used with appropriate optimizing techniques. F. Bancilhon et al., cited above; F. Bancilhon, "Naive Evaluation of Recursively Defined Relations" in Brodie and Mylopoulos (eds.), On Knowledge Based Management Systems -- Integrating Database and AI Systems , pub. Springer-Verlag, 1985. The semi-naive method of computing the least fixpoint is an iterative method. For a recursive relation r and a corresponding recursive equation r = f(r), the semi-naive method iteratively computes new values of r satisfying the equation and inserts them into r during successive iterations until no new values of r can be found, as follows: r 0 := ; δr 0 := f(); j := 0; while (δr j ≠ ) do begin r j+1 := r j ∪δr j ; δr j+1 := f(r j+1 ) - f(r j ); j := j + 1; end; where δr is a new value of r,  is the empty set, and j is the variable of iteration. To find the least fixpoints of a set of n mutually recursive relations r i , a set of mutually recursive equations of the form of equation (6) must be evaluated together, as follows: for each r i do r 0 i := ; for each r i do δr 0 i := f i (); j := 0; while (some δr i ≠ ) do begin r j+1 i := r j i ∪ δ r j i ; δr j+1 i := f i (r j+1 1 , ..., r j+1 n ) - f i (r j 1 , ..., r j n ); j := j + 1; end; A differential approach to finding the least fixpoints of a set of n mutually recursive relations has also been proposed (I. Balbin et al., "A Differential Approach to Query Optimization in a Recursive Deductive Database", Technical Report 86/7, Department of Computer Science, University of Mebourne, Australia). This approach is described as follows: (*Initialization*) for each recursive relation r i do begin r 0 i := ; δr 0 i := b 0 i ; end; (*Iteration*) j := 0; repeat for each recursive relation r i do begin j := j + 1; r j i := r j-1 i + δr j-1 i ; Δr j i := b i . [δr j-1 1 .r j 2 .....r j n ∪r j-1 1 .δr j-1 2 .r j 3 .....r j n ∪ ... ∪r j+1 1 .r j-1 2 .r j-1 3 . ....δr j-1 n ]; δr j i := Δr j i - r j i ; end; until all δr j i =  The above methods of finding a least fixpoint have been implemented in database environments by means of separate application programs. From the foregoing, it will be appreciated that there is a need for a data structure and method for arranging recursively derived data items in a database in a way which provides efficient evaluation of recursive relations, especially if the relations are mutually recursive relations and if the number of iterations is not known in advance, by such means as the direct computation of least fixpoints without any need of separate application programs. SUMMARY OF THE INVENTION The present invention provides a data base structure for recursively derived data items and a method of arranging such data items in a database according to claims 1 and 4. Advantageous embodiments can be found in the subclaims. In a preferred embodiment of the invention, a database structure for recursively derived data items includes a plurality of hierarchical iteration levels, one for each iteration of a recursive relation from which the data items are derived; an index structure for indexing the data items; and means for linking together the various data items associated with each iteration level. The index structure preferably includes a sequence set having a plurality of leaf nodes, an index set having a plurality of non-leaf nodes, and an iteration level index. The nodes of the sequence set are arranged sequentially in a bottom index level. Each node provides a reference between a pointer to one of the data items, a key value which uniquely identifies that data item, and the iteration level associated with that data item. The nodes of the index set are arranged hierarchically to define a plurality of index levels above the bottom level, each level having fewer nodes than the level beneath it. Each node encompasses a range of the key values of the sequence set and all of the nodes on any level together encompass all of said key values. Each node provides an access path for each key value within its range to a node on the next lower level having said key value within its range. The iteration level index provides a reference between each iteration level and a data item associated with that level. In one embodiment each data item associated with a given iteration level is assigned to a page associated with that level and the pages of that level are chained together. In another embodiment the individual data items associated with a given iteration level are globally chained to each other. A new data item is inserted by finding the existing data having the same iteration level and the closest key value, inserting the new item adjacent thereto, linking the new data item to the others having the same iteration level if the data items are globally linked, and adjusting the index and sequence sets to include a search path to the new item. If the new data item has the same key value as an existing item, the new item is preferably discarded. Or, if the key values are the same the two items are compared and the new one is discarded only if they are identical. Either of these tests can be performed with minimal overhead during evaluation of the recursive relation, and if all data items derived during a given iteration are discarded then the iterations are stopped. Thus, an efficient test of when to stop iterations is provided if the number of iterations required to evaluate the recursive relation is not known in advance. A set of data items arranged according to the invention can be searched very efficiently for a data item having an iteration level within a specified range by following an access path through the index set to the leaf node corresponding with the item being searched for, checking the iteration level of that item, and returning the item only if its iteration level is within the specified range. The data may be scanned with high efficiency to find any data items satisfying a specified condition and having a specified iteration level by utilizing the iteration level index to locate a first data item having an iteration level within the specified range and then following the linking of the data items having that level until all such items have been scanned. Performing a scan in this manner provides the requested data items automatically arranged in order on the key value. A nested loop join is easily performed by scanning a first set of data items at a specified iteration level; for each data item found during the scan, searching a second set of data items at a specified iteration level for a match, and joining each item found in the scan with any corresponding one found in the search. Similarly, a merge scan join is accomplished by simultaneously scanning a first set of data items at a first iteration level and a second set at a second iteration level and joining any data item found in the first scan with any matching item found in the second scan. Other aspects and advantages of the present invention will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, illustrating by way of example the principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS FIGURE 1 is an illustration of a data structure according to the invention wherein the data are clustered by iteration level; FIG. 2 is an illustration of a data structure like that of FIG. 1 but wherein the data are not clustered; and FIG. 3 is an illustration of an insertion of a new data item into the data structure shown in FIG. 2. DESCRIPTION OF THE PREFERRED EMBODIMENT As shown in the drawings for purposes of illustration, the invention is embodied in a novel structure and method of arranging recursively derived data in a database. There has been a need for a way to directly evaluate mutually recursive relations in a database especially if the number of iterations is not known in advance. The structure and method of the invention provide a highly efficient way to evaluate mutually recursive relations by, for example, computing the least fixpoint of such relations. This permits the use of relational algebra techniques as augmented by newly-proposed operators and makes possible the efficient evaluation of sets of mutually recursive relations even if the number of iterations required for such evaluation is not known in advance. As shown illustratively in the figures, in a preferred embodiment of the invention a database structure for recursively derived data items such as data items A, B, D, F, G, H, M, N, P, R, T, and Z comprises a plurality of hierarchical iteration levels such as levels one through three as depicted in an iteration level index 101, one such level corresponding with each iteration of the recursive relation from which the data items are derived, all data items derived during a given iteration being associated with the corresponding iteration level; an index structure generally designated 103 for indexing the data items; and means such as pointers 105, 107 and 109 for linking the data items associated with each iteration level. The index structure 103 includes a sequence set 111 having a plurality of leaf nodes such as the node 113 arranged sequentially to define a bottom index level of the structure, each such node providing a reference between a pointer such as the pointer 115 to one of the data items such as the data item T to which the pointer 115 points, a key value such as the value "5" which uniquely identifies that data item, and the iteration level such as the level "2" associated with that data item. The structure 103 also includes an index set 117 having a plurality of non-leaf nodes such as the node 119 arranged hierarchically to define a plurality of index levels such as the levels 121, 123 and 125 above the bottom index level of the structure, each level having fewer nodes than the level beneath it, each node encompassing a range of the key values of the sequence set, all of the nodes on any level together encompassing all of said key values, each range encompassed by a node being included in the range encompassed by a node on the next higher level, each node providing for any key value within its range an access path such as the path depicted by an arrow 127 to a node on the next lower level having said key value within its range. The structure 103 also includes the iteration level index 101 providing a reference between each iteration level and a data item associated with that level. In one embodiment the linking means comprises global chain references such as the arrows 129 in FIG. 2 which link data items associated with each iteration level. In another embodiment each data item associated with a given iteration level is assigned to a page such as the page 131 in FIG. 1, each page being associated with that iteration level. The linking means comprises page chain references such as the arrows 105, 107 and 109 which link the pages associated with each iteration level. The invention provides a method of arranging recursively derived data items in a database wherein the data are arranged according to the structure described above. New data items are inserted into the data structure described above as they are derived. Accordingly, if such data are clustered according to iteration level they would ordinarly be assigned to pages sequentially as they are generated. However, this is not required and the data can be assigned to pages in any order so long as only data items derived during a given iteration level are assigned to pages corresponding with that iteration level. Of course, if the data are not clustered by iteration level, then each data item is chained with others of the same level. A new data item is inserted by identifying which of the data items associated with the iteration level of the new item has the closest key value to the key value of the new item, inserting the new data item into the database adjacent the identified data item, linking the new data item to the other data items associated with the iteration level of the new item, creating a new leaf node having a pointer to the new data item, inserting the new leaf node into the sequence set, and adjusting the index set to include a path to the new leaf node. The index set is adjusted in a manner similar to that used to adjust the index set of an ordinary B-tree as described, for example, in C. J. Date, An Introduction to Database Systems (4th Ed.) vol. 1 pages 63 through 67. If the data are clustered by iteration level, no linking is required other than assignment of the data item to a page of the correct iteration level because the pages corresponding with that iteration level are chained together. The process of insertion of a new data item is illustrated in FIG. 3, wherein a new data item K has been added to the global data set 129. A new leaf node 133 has been created and inserted in proper sequence between existing nodes 135 and 137, displacing the node 137 which has been moved one position to the right. The data item K has been chained to the item P of level 2 as indicated by an arrow 139 and the index tree has been adjusted as indicated by a changed value in the non-leaf node 119. A new pointer as indicated by an arrow 141 has been provided between the node 133 and the data item K. Preferably duplicate items of data are discarded. This may be done by discarding the new data item if its key value is the same as the key value of the identified data item next to which it is to be placed. If desired, the two data items may be compared to be sure they are identical before the new item is discarded. An advantage to eliminating duplicate data items is that iteration can be stopped if all the items generated during an iteration are discarded. This provides an efficient test for when to stop iteration if the number of iterations required to evaluate a recursive relation is not known. The invention provides a highly efficient method of searching data items which have been arranged as described to find a data item having a specified key value and an iteration level within a specified range. This search method comprises selecting an access path from the paths provided by the non-leaf nodes to locate a leaf node associated with the specified key value; determining whether the iteration level associated with said leaf node is within the specified range; and returning the data item associated with said leaf node only if said determination is affirmative. The invention also provides a very efficient method of scanning a set of data items to find any which satisfy a specified condition and have a specified iteration level. This method comprises utilizing the iteration level index to locate a first data item associated with that iteration level, following the linking of the data items to locate the other data items associated with that iteration level, examining each located data item in sequence to determine whether it satisfies the prescribed condition, and if the examined item satisfies the prescribed condition, retrieving the item, the items retrieved during the scan being arranged in sequential order according to their key values. The above search and scan techniques can be combined to perform join operations such as nested loop joins and merge scan joins. The ability to perform such joins is what gives the invention the ability to efficiently evaluate least fixpoints. A method of performing a nested loop join according to the invention comprises scanning a first set of the data items to find any data items satisfying a specified condition and having a specified iteration level, for each data item found in the preceding step, searching a second set of the data items to find any data items having a key value corresponding with the key value of said data item and a specified iteration level, and joining each data item found in the scan with any corresponding data item found in the search. A method of performing a merge scan join according to the invention comprises simultaneously scanning a first set of data items having a first specified iteration level and a second set of data items having a second iteration level, and joining any data item found in the scan of the first set with any matching data item found in any scan of the second set. In essence, the first scan pauses at each item during its scan and the second scan advances until no matches are found. Because the data in both sets are arranged in order by key value, only a single pass through each set is required to complete the merge scan operation. A mathematical description of the invention is provided in an appendix hereto, which appendix is incorporated herein by this reference. From the foregoing it will be appreciated that the data structure and method of the invention provide a way to efficiently respond to complex database queries. A set of mutually recursive relations is evaluated, for example by computing the least fixpoints of the relations, with a minimum of overhead even if the number of recursions required to perform the evaluation is not known in advance.

Claims: A database structure for recursively derived data items (A ... Z), the database structure comprising: an index structure (103) for indexing the data items, the index structure including a sequence set (111) having a plurality of leaf nodes (113, 135, 137) arranged sequentially to define a bottom index level of the structure; and an index set (117) having a plurality of non-leaf nodes (119) arranged hierarchically to define a plurality of index levels (121, 123, 125) above the bottom index level of the structure, each level having fewer nodes than the level beneath it, each node encompassing a range of the key values of the sequence set, all of the nodes on any level together encompassing all of said key values, each range encompassed by a node being included in the range encompassed by a node on the next higher level, each node providing for any key value within its range an access path (127) to a node on the next lower level having said key value within its range, the database structure characterized by: a plurality of hierarchical iteration levels (1 ... 4), one such level corresponding with each iteration of the recursive relation from which the data items are derived, all data items derived during a given iteration being associated with the corresponding iteration level; a reference provided by each leaf node between a pointer (115) to one of the data items, a key value which uniquely identifies that data item, and the iteration level associated with that data item; an iteration level index (101) providing a reference between each iteration level and a data item associated with that level; and means for linking the data items associated with each iteration level. A database structure according to claim 1 wherein the linking means comprises global chain references (129) which link the data items associated with each iteration level. A database structure according to claim 1 wherein each data item associated with a given iteration level is assigned to a page (131) associated with that iteration level. A database structure according to claim 3 wherein the linking means comprises page chain references (105, 107, 109) which link the pages associated with each iteration level. A method of arranging recursively derived data items (A ... Z) in a database, the method comprising: defining a sequence set (111) having a plurality of leaf nodes (113, 135, 137) arranged sequentially to define a bottom index level of an index structure (103); defining an index set (117) having a plurality of non-leaf nodes (119) arranged hierarchically to define a plurality of index levels (121, 123, 125) above the bottom index level of the index structure, each level having fewer nodes than the level beneath it; and assigning each node a range of the key values of the sequence set such that all of the nodes on any level together encompass all of said key values, each range encompassed by a node is included in the range encompassed by a node on the next higher level, and each node provides for any key value within its range an access path (127) to a node on the next lower level having said key value within its range, the method characterized by the steps of: defining a plurality of hierarchical iteration levels (1 ... 4), one such level corresponding with each iteration of the recursive relation from which the data items are derived; associating all data items derived during a given iteration with the corresponding iteration level; associating each leaf node with a pointer (115) to one of the data items, a key value which uniquely identifies that data item, and the iteration level associated with that data item; defining an iteration level index (101) to provide a reference between each iteration level and a data item associated with that level; and linking the data items associated with each iteration level. A method according to claim 5 wherein linking the data items comprises globally chaining (129) the data items associated with a given iteration level. A method according to claim 6 and further comprising inserting a new data item into the database by: identifying which of the data items associated with the iteration level of the new item has the closest key value to the key value of the new item; inserting the new data item into the database adjacent the identified data item; linking the new data item to the other data items associated with the iteration level of the new item; creating a new leaf node having a pointer to the new data item; inserting the new leaf node into the sequence set; and adjusting the index set to include a path to the new leaf node. A method according to claim 5 and further comprising assigning each data item associated with a given iteration level to a page (131) associated with that iteration level. A method according to claim 8 wherein linking the data items comprises chaining the pages associated with a given iteration level. A method according to claim 9 and further comprising inserting a new data item by: identifying which of the data items associated with the iteration level of the new item has the closest key value to the key value of the new item; assigning the new data item to a page associated with the iteration level of the new item at a location adjacent the identified data item; creating a new leaf node having a pointer to the new data item; inserting the new leaf node into the sequence set; and adjusting the index set to include a path to the new leaf node. A method according to claim 7 or claim 10 and further comprising discarding the new data item if its key value is the same as the key value of the identified data item. A method according to claim 7 or claim 10 and further comprising discarding the new data item if it is identical to the identified data item. A method according to claim 11 or claim 12 and further comprising stopping iteration of the recursive relation if all the items generated during an iteration are discarded. A method according to any of claims 5, 6, 8 or 9 and further comprising searching a set of data items to find a data item having a specified key value and an iteration level within a specified range, said step of searching comprising: selecting an access path from the paths provided by the non-leaf nodes to locate a leaf node associated with the specified key value; determining whether the iteration level associated with said leaf node is within the specified range; and returning the data item associated with said leaf node only if said determination is affirmative. A method according to any of claims 5, 6, 8 or 9 and further comprising scanning a set of data items to find any data items satisfying a specified condition and having a specified iteration level, said step of scanning comprising: utilizing the iteration level index to locate a first data item associated with that iteration level; following the linking of the data items to locate the other data items associated with that iteration level; examining each located data item in sequence to determine whether it satisfies the prescribed condition; and if the examined item satisfies the prescribed condition, retrieving the item, the items retrieved during the scan being arranged in sequential order according to their key values. A method according to any of claims 5, 6, 8 or 9 and further comprising performing a nested loop join by: scanning a first set of the data items to find any data items satisfying a specified condition and having a specified iteration level; for each data item found in the preceding step, searching a second set of the data items to find any data items having a key value corresponding with the key value of said data item and a specified iteration level; and joining each data item found in the scan with any corresponding data item found in the search. A method according to any of claims 5, 6, 8 or 9 and further comprising performing a merge scan join by: simultaneously scanning a first set of data items having a first specified iteration level and a second set of data items having a second iteration level; and joining any data item found in the scan of the first set with any matching data item found in any scan of the second set. A computer comprising the database structure of any of Claims 1-4.

Inventors: LU HONGJUN, SHAN MING-CHIEN, LU, HONGJUN, SHAN, MING-CHIEN
Applicants: HEWLETT PACKARD CO, LU HONGJUN, HEWLETT-PACKARD COMPANY, LU, HONGJUN
Application year: 1989
Main classification: G06F17
IPC codes: G06F17, G06F12