Abstract:
A system and method of composing a query object for application against a database is provided. The method composes a selection clause for the query. Next, a criteria clause for the query is generated, with the criteria clause comprising input criteria related to the query, additional criteria specified against the query, and generated criteria based on a joint relationship. Next a source clause utilizing elements in the database accessed by the query is generated. A database traversal system and method is provided. The method identifies all tables directly accessible by each table and creates a data structure comprising an entry for each table. The entry comprises an identification field for each table and a link field identifying all tables directly accessible by each table. The data structure is traversed and an optimum path of the traversal paths utilizing data obtained from traversing the data structure is identified.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
   This application claims benefit under 35 USC 119 of Canadian Application 2,327,167 filed on Nov. 30, 2000. 
   FIELD OF THE INVENTION 
   The present invention relates to systems and methods for generating and traversing database query structures, in particular systems and methods for efficient organization and compiling of SQL queries. 
   BACKGROUND OF THE INVENTION 
   A database management system (DBMS) comprises a computer, data storage devices, disk drives and database management software. A relational database management system (RDBMS) is a DBMS which uses relational techniques for storing and retrieving information. The relational database management system comprises computerized information storage and retrieval systems in which data is stored on disk drives. The data is stored in the form of tables which comprise rows and columns. Each row, or tuple, has one or more columns. 
   The RDBMS is designed to accept commands to store, retrieve, and delete data. A well-known set of commands is based on the Structured Query Language or SQL. The term query refers to a set of commands in SQL for retrieving data from the RDBMS. The constructs of SQL allow a RDBMS to provide a response to a particular query with a particular set of data given a specified database content. SQL however does not specify the actual method to find the requested information in the tables on the disk drives. The method in which the query is processed, i.e. query execution plan, affects the overall time for retrieving the data. Data retrieval time may be critical to the operation of the database. Decreasing such retrieval time minimizes the computer and disk access time, and therefore, optimizes the cost of doing the query. 
   Accordingly, there is a need for a dynamic and efficient method and system for generating database queries. 
   SUMMARY OF THE INVENTION 
   In a first aspect, the invention provides a method of composing a dynamic query for application against a database. First, the method composes a selection clause for the query, with the selection clause comprising a results set related to the query. Next, the method composes a criteria clause for the query, with the criteria clause comprising input criteria related to the query and additional criteria specified against the query. Next the method composes a source clause utilizing elements in the database accessed by the query. 
   The method may compose an ordering scheme for results of the query. 
   The method may compose a grouping scheme for results of the query. 
   The method may compose the criteria clause by resolving joint relationships amongst the input criteria and the additional criteria. 
   The method may compose the criteria clause by adding the joint relationships to the criteria clause. Further, the method may compose the source clause by resolving a source after analyzing the selection clause and the criteria clause. The method may compose the query in SQL format. The method may apply the query against the database and results of the query may be provided to an output device. 
   In a second aspect, a query transaction system is provided. The query transaction system comprises a computer, access to a database associated with the computer and a query processing program operating on the computer and generating a query for the database. The query processing program has a selection clause composing module for the query, the selection clause module producing a results set related to the query. The program also has a criteria clause composing module for the query, the criteria clause module processing input criteria related to the query and additional criteria specified against the query. The program also has a source clause composing module utilizing elements in the database identified by the query. 
   The query processing program may further comprise an ordering module for results of the query. 
   The query processing program may further comprise a grouping module for results of the query. 
   For the criteria clause composing module of the query processing program, the module may have a joint relationships resolving module associating the input criteria to the additional criteria. Further, the criteria clause composing module may comprise a module adding the joint relationships to the criteria clause. Also, the source clause composing module may resolve the source after analyzing the selection clause and the criteria clause. 
   In another aspect, an article is provided. The article comprises a computer readable information storage medium and a computer readable program encoded on the medium. The program comprising a method of composing a query for application against a database. The method comprises composing a selection clause for the query, the selection clause comprising a results set related to the query, composing a criteria clause for the query, the criteria clause comprising input criteria related to the query and additional criteria specified against the query, and composing a source clause utilizing elements in the database identified by the query. 
   The method of the computer program may compose an ordering scheme for results of the query. 
   The method of the computer program may compose a grouping scheme for results of the query. 
   The method of the computer program may compose the criteria clause by resolving joint relationships amongst the input criteria and the additional criteria. The method may further compose the criteria clause by adding the joint relationships to the criteria clause. The method may also compose the source clause by resolving a source related to the database after analyzing the selection clause and the criteria clause. The method may also apply the query against the database and provided results of the query to an output device. 
   In another aspect, an article is provided. The article comprises a computer readable modulated carrier signal and a computer readable program encoded on the carrier signal. The program comprises a method of composing a query for application against a database. The method comprises composing a selection clause for the query, the selection clause comprising a results set related to the query, composing a criteria clause for the query, the criteria clause comprising input criteria related to the query and additional criteria specified against the query and composing a source clause utilizing elements in the database identified by the query. 
   For the article, the program encoded on the signal may compose an ordering scheme for results of the query. 
   For the article, the program encoded on the signal may compose a grouping scheme for results of the query. 
   For the article, the program encoded on the signal may compose the criteria clause by resolving joint relationships amongst the input criteria and the additional criteria. 
   For the article, the program encoded on the signal may compose the criteria clause by adding the joint relationships to the criteria clause. The program may compose the source clause by resolving a source related to the database after analyzing the selection clause and the criteria clause. 
   In yet another aspect, a method for evaluating traversal paths amongst tables in a database is provided. The database has at least a first and a second table. The method comprises, first, for each table, identifying all tables directly accessible by each table and creating a data structure having an entry for each table. The entry comprises an identification field for each table and a link field identifying the all tables directly accessible by each table. Next, for each entry in the data structure, the method traverses the data structure to visit all other entries in the data structure, if possible, using contents of the link field of each entry. Next, the method identifies an optimum path of the traversal paths utilizing data obtained from traversing entries in the data structure. 
   The method may track the number of hops taken to visit the all other entries for all possible traversal route to the all other entries. The method may have the data structure as a linked list. The method may traverse the data structure in a breadth first manner. Alternatively, the method may traverse the data structure in a depth first manner. The method may identify the optimum path utilizing the number of hops taken to visit the all other entries. The method may have the data structure further comprising a second link field identifying tables which directly access each table. The method may provide the optimum path to an output device. 
   In yet another aspect, a database analysis system is provided. The system comprises a computer, access to a database associated with the computer, the database comprising at least a first table and a second table, and a database traversal program associated with the computer. The traversal program evaluates traversal paths between the first table and the second table. The traversal program has a method which, first, for each table of the plurality of tables, identifies all tables directly accessible by each table and creates a data structure comprising an entry for each table. The entry comprises an identification field for each table and a link field identifying the all tables directly accessible by each table. Next, for each entry in the data structure, the method traverses the data structure to visit all other entries in the data structure, if possible, using contents of the link field of each entry. Next, the method identifies an optimum path of the traversal paths utilizing data obtained from traversing entries in the data structure. 
   In yet another aspect, an article is provided comprising a computer readable instruction storage medium, a database traversal program encoded on the medium. The program evaluates traversal paths in a database. The database comprises at least a first table and a second table. The database traversal program has a method embodied therein. The method comprises, first, for each table of the plurality of tables identifying all tables directly accessible by each table and creating a data structure comprising an entry for each table, the entry comprising an identification field for each table and a link field identifying the all tables directly accessible by each table. Next, for each entry in the data structure, the method traverses the data structure to visit all other entries in the data structure, if possible, using contents of the link field of each entry. Next the method identifies an optimum path of the traversal paths utilizing data obtained from traversing entries in the data structure. 
   In other aspects of the invention, various combinations and subsets of the aspects described above are provided. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The foregoing and other aspects of the invention will become more apparent from the following description of specific embodiments thereof and the accompanying drawings which illustrate, by way of example only, the principles of the invention. In the drawings, where like elements feature like reference numerals (and wherein individual elements bear unique alphabetical suffixes): 
       FIG. 1  is the block diagram of a computer accessing a database system utilizing an embodiment of the invention; 
       FIG. 2  is an exemplary screen shot of software operating on a computer of a search field accessing a database of  FIG. 1 ; 
       FIG. 3  is a block diagram of a query structure used to access the database system of  FIG. 1 ; 
       FIG. 4  is an exemplary set of tables representing data stored in the database system of  FIG. 1 ; 
       FIG. 5  is a flow diagram of an algorithm of the embodiment of the database system of  FIG. 1 ; 
       FIG. 6  is a block diagram of relationship aspects of table elements in the database system of  FIG. 1 ; 
       FIG. 7  is a block diagram of a data structure generated by the embodiment of  FIG. 1 ; 
       FIG. 8  is a listing code implementing the algorithm of  FIG. 5 ; 
     FIG.  9 A(i) is a listing of pseudocode associated with a portion of a query building module associated with the algorithm of  FIG. 5 ; 
     FIG.  9 A(ii) is a continuation of a listing of pseudocode associated with a portion of the query building module of FIG.  9 A(i); 
     FIG.  9 A(iii) is a continuation of a listing of pseudocode associated with a portion of the query building module of FIG.  9 A(ii); 
     FIG.  9 A(iv) is a continuation of a listing of pseudocode associated with a portion of the query building module of FIG.  9 A(iii); 
       FIG. 9B  is a listing of more pseudocode associated with a smart query associated with the algorithm of  FIG. 5 ; 
       FIG. 10  is a block diagram of an exemplary association of tables in a database for an embodiment of  FIG. 1 ; 
       FIG. 11  is a block diagram of a data structure representing the table associations of  FIG. 10 ; and 
       FIG. 12  is a block diagram of a distributed computer network utilizing aspects of the embodiment of  FIG. 1 . 
   

   DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
   The description which follows, and the embodiments described therein, are provided by way of illustrating an example, or examples, of particular embodiments of principles of the present invention. These examples are provided for the purpose of explanation, and not limitation, of those principles and of the invention. In the description which follows, like elements are marked throughout the specification and the drawings with the same respective reference numerals. 
   Referring to  FIG. 1 , computer  100  has software  102  operating thereon allowing queries to be made to database  104  which is accessible by computer  100 . Database  104  is accessible either internally or externally via computer  100 . Database  104  may be a relational database. Queries to database  104  may be in Structured Query Language (SQL). Display  106  provides a visual interface for the user of computer  100  when accessing software  102 . Software  102  causes user prompts and search results of queries to database  104  to be shown on display  106 . Data and queries may be entered to software  102  via keyboard  107  on computer  100 . 
   Software  102  may be encoded on disk  108 . Disk  108  may be inserted into computer  100  via disk drive  110  to allow computer  100  to load software  102  into its memory. Alternatively, software  102  may be embodied onto CD-ROM  112  in an appropriate computer readable code, which may load its contents into computer  110  via CD-ROM drive  114 . It will be appreciated that other medium and mechanisms may be used to load software  102  on to computer  100  including remote downloads wherein the software  102  is transmitted to computer  100  from a remote computer utilizing a modulated carrier signal. 
   Referring to  FIG. 2 , screen shot  200  shows a typical query screen generated by software  102  and shown on display  106  for accessing database  104 . The query screen  200  has fields into which a user enters values to compose a query which will be executed against database  104 . For example, in the preferred embodiment, the fields include description field  202 , manufacturer field  204  and a drop-down menu for price field  206 . Using computer  100 , the user enters values for the fields of, e.g. “stove” in field  202 , “Sears” in field  204 , then selects a price from field  206 , e.g. “$500”. The user then activates the “Search Now” button  208  which causes software  102  to generate an appropriate SQL query for “stoves” from “Sears” which cost “$500” and apply it against database  104 . The results of the query are then provided to the user or the system. The results may be provided on display  106 , to a printer (not shown), to a disk in a written format, to another database, to another computer or to any output device known in the art. 
   Referring to  FIG. 3 , an SQL query generated by software  102  from the criteria entered by the user is shown in query syntax  300 . Query syntax  300  comprises “select” clause  302 , “from” clause  304  and “where” clause  306 . “Select” clause  302  identifies the table columns of database  104  from which the response to the query is generated. “From” clause  304  identifies the tables from which the query will generate its response. “Where” clause  306  identifies specific items  312  from the tables for which the user provided specific search criteria. Relating the contents of clauses  302 ,  304  and  306  to query screen  200 , parameters in field  308  identify the description field  202  and manufacturer field  204 . Parameter  310  identifies the tables in which description field and manufacturer field information are stored in database  104 ; parameters in field  312  identify queries relating to description field  202 , manufacturers field  204  and price field  206 . 
   Referring to  FIG. 4 , a representative series of tables  400  relating to database  104  are shown. In particular, table  402  contains a row listing items contained in the database  102 . Table  404  contains a row listing manufacturers to products and table  406  is a table of products to manufacturers. 
   Referring to  FIG. 5 , algorithm  500  provides a flow chart for software  102  of the main functional aspects of the embodiment which processes database requests, such as a request generated from screen shot  200  ( FIG. 2 ), to generate SQL queries, such as SQL query  300  ( FIG. 3 ). First, software  102  is initialized at step  502 . Initialization may include aspects such as turning on the computer  100 , initializing the access to appropriate databases and loading appropriate software to and from appropriate computers. Next, step  504  initializes query. In this step, a new query is defined, appropriate resources are allocated to it and a “select” clause is built. 
   In step  506  the “where” clause  306  ( FIG. 3 ) is built by creating a predicate based on the user specified search criteria. Next, additional predicates are added to the predicate. For example, if there are any common search criteria, they may be provided as an additional predicate and appended to the query as hard-coded operands. Utilizing additional set predicates enables a query to be built without having to identify repeatedly common query elements for each query. Accordingly, such hard-coded operands enable the query to be executed faster without parsing additional elements of the query. 
   Also in step  506 , joint predicates are resolved from the user inputs and any common predicates. In the embodiment all table relationships, either direct or indirect, are stored in a predetermined file, built from a predetermined XML file. The file contains a relationship dictionary which is searched by software  102  to ascertain relationships existing amongst tables. The file is parsed and a dictionary of table links is generated. For example, for Tables A, B and C, Table A and Table B may be linked through Table C via the relationship TableA.col1=TableC.col2 and TableC.col3=TableB.col4. The dictionary entry will have a key of “TableA &amp; TableB” and its associated element would be “TableA.col1=TableC.col2 and TableC.col3=TableB.col4.” It can be appreciated that such table relationships may be provided through a separate database catalogue. Next, joint predicates are added to the predicates to create the “where” clause  306 . 
   Next, “from” clause  304  is created in step  508 . Therein, source tables are resolved in the “from” clause  304  using explicit instructions from the user and implicit information from the source tables in the “select” clause  302  and “where” clause  306 . 
   Grouping and ordering of the clauses are performed in steps  510  and  512  and the query statement is executed in step  514 . 
   Referring to  FIG. 6 , Rose diagram  600  illustrates aspects of SQL statements modelled by objects used by the embodiment. Rose diagram  600  comprises a series of objects showing interrelationships amongst objects by arrows. Each arrow relates child object (source) to a parent object (destination). A number associated with the head of the arrow indicates the number of parents associated with each child. Using an object oriented design for modelling a SQL statement, components for the SQL statement can be dynamically created and manipulated as objects. 
   In particular, query object  602  is the central query object interface for the embodiment. It contains one or more Attribute Info Objects  604 . Result object  606  contains data retrieved by executing the query. Predicate object  608  may be related in a zero-to-one relationship to query object  602 . Predicate object  608  models the complex conditions for the related SQL statement. Operator object  610  has a one-to-one association with predicate object  608 . Operator object  610  assigns an attribute value to an attribute  612 . Attribute object  612  models a searchable attribute. It is created from AttributeInfo object  604 . AttributeInfo  604  is an object containing the metadata of each column in the database table. 
   For each SQL statement, attribute object  612  contains an operator object  610  and an attribute value object  614 . Table object  616  is associated with query object  602 , in a one-to-many relationship. Smart query object  618  is associated with query  602  as a child. Catalogue query object  620  is associated with smart query object  618  as a child. Both smart query object  618  and catalogue query  620  are appended to query  602  using elements of the embodiment in order to streamline operation and execution of query  602 . Further detail on the operation of Smart Query object is provided later. 
   Referring to  FIG. 7 , relationships associated with predicate object  608  may be used to dynamically compose a query tree  700  for the following SQL query in Example 1: 
   EXAMPLE 1 
   
       
       
         
           Select T1.referenceNumber, T2.colour 
           from CatalogueEntry T1, AttributeValue T2 
           where (T1.refId=“123” and T2.colour=“red”) and (T1.Name=“Sears”) and (T1.refId=T2.refId) 
         
       
     
  
   There are two predicates with the query, namely predicate  702  and predicate  704  which both comprise an AND operator. Predicate  704  operates on attribute  706  and attribute  708 . Attribute  708  associates the catalogue reference ID field of Table 1 (“T1”) with the value of “123”. Attribute  708  associates the Colour Attribute Info of Table 2 (“T2”) with colour “red”. These tables and values are represented by elements  710 ,  712 ,  714 , and  716 , respectively. Attribute  718  equates the nName field of T1, represented by attribute  720 , with a value of “Sears”, represented by the value  722 . Attribute  724  equates the T1.refId field  710  with T2.refId field  726 . Predicate  702  operates on predicate  704 , attribute  718  and attribute  724 . 
   Referring to  FIG. 8 , the embodiment traverses the query tree and constructs code  800  which embodies the SQL query of Example 1. First, code  802  constructs simple attribute conditions. Next, composite search conditions, using predicates, are constructed through code at  804 . Finally, the query is executed through code at  806  and results are returned through code at  808 . 
   Details of the pseudo code underlying the creation of appropriate data structures for code  800  are now provided. 
   Referring to FIG.  9 A(i) and  FIG. 5 , aspects of pseudocode for algorithm  500  used to build a results set are shown. First, per step  504 , pseudocode in section  900  defines a results set information object  901  for the query. Code in  902  builds a “select” clause for the query by consecutively adding AttributeInfo objects  904  into the results set information object  901 . 
   Referring to FIG.  9 A(ii),  9 A(iii) and  FIG. 5 , the first part of a “where” clause is built per step  506 . A series of two parts for a predicate set are built in sections  906   a , and  906   b . For each predicate, an operator is defined at  908   a  and  908   b , then a series of operands are added per sections  910   a  and  910   b.    
   Referring to FIG.  9 A(iv) and  FIG. 5 , the remaining part of the “where” clause is built. Code  912  creates an object for the joint predicates associated with the query. Code at  914  adds the joint relationships to the existing predicates. Finally, SetPredicate code  916  adds the smart query and the catalog query predicates to the existing predicates. 
   Next, for step  508 , code  918  resolves the source tables for the query. Ordering and grouping of predicates by clause (steps  510  and  512 ) are performed by code  920 . Finally, the query is executed for step  514  using code  922 . 
   Referring to  FIG. 9B , further detail is provided for the smart query predicate. First, a test is conducted to determine whether any hard coded predicates are to be added, per line  924 . If hard coded predicates exist, they are added to the existing predicate per the code at  926 . 
   If hard coded predicates do not exist, then joint table predicates are resolved through code at  928 , embodied specifically in code  930 . The joint predicates provide information on how tables are related to each other. These relationships are required to conduct a search based on multiple tables on a relational database since some information can expand several tables. 
   Another aspect of the embodiment provides a system and method of evaluating the number of hops between tables when determining links amongst elements in tables when queries are executed. 
   As noted earlier, when executing a query, multiple tables are often associated with it. Accordingly table joint conditions must be specified amongst the tables. There are two types of table joints: (i) a direct foreign key relationship, where a column in table A is a foreign key to table B; and (ii) an indirect foreign key relationship, where the foreign key relationships are described in separate tables and the relationships may involve several indirect tables. 
   Referring to  FIG. 10 , table relationship  1000  is an example of relationships amongst Table A  1002 , Table B  1004 , Table C  1006 , Table D  1008  and Table E  1010 . Tables in  FIG. 10  are related by arrows, such as arrow  1012 . The tail of the arrow indicates the source table in the relationship. The head of the arrow points to the table associated with the source. For Table A  1002 , each of Table B  1004 , Table C  1006 , and Table D  1008  is associated with it, i.e. Table A  1002  can recognize a link to each of those tables. Table C  1006  is associated with Table B  1004 . Tables D  1008  and E  1010  are associated with Table C  1006 . 
   Links amongst tables can be direct or indirect. Table A  1002  recognizes a direct link to Table C  1006 . Table C  1006  recognizes a direct link to Table E  1010 . However, Table A  1002  can recognize a link to Table E  1010  via the link provided by Table C  1006 . In database operations, links amongst tables are frequently calculated. In order to minimize traversal times amongst the tables, any traversal amongst tables should select the shortest path. 
   In order to determine the shortest path, attributes of tables are traversed to determine all tables involved in the query. A table graph is then created at initialization. A query framework then traverses the table graph to determine the joint predicates for these tables. Then, a composite predicate is formed with the user attribute predicates and the table joint predicates. 
   To determine a relationship between two tables, the tables in a database are traversed to generate a list of all direct foreign references. For each table, an inlist and an outlist is produced. This information is provided to a mapping comprising many-linked lists. 
   After the mapping is generated, to determine a relationship between two tables, the outgoing list from the first table is examined. From each element in the outgoing list, the mapping is traversed through its outlist until the destination table or a dead-end is reached. For each pass leading to the destination table, a variable containing the distance of hops required to get to the destination table is stored. Accordingly, the shortest path between the originating and destination tables may be selected from the path having the smallest number stored in its variable. The shortest path may be the optimum path. 
   Referring to  FIG. 11 , data structure  1100  representing elements of the table relationship shown in  FIG. 10  is shown. Data structure  1102  represents an entry for Table A  1002 ; similarly, data structure  1104  represents an entry for Table B  1004 ; data structure  1106  represents Table C  1006 ; data structure  1108  represents Table D  1008 ; and data structure  1110  represents Table E  1010 . Data structure  1102  has an infield  1112  identifying all table elements which call on table A  1002 . Infield  1112  is empty as there is no table which calls on Table A  1002 . Outfield  1114  identifies all tables which Table A  1002  may access. These include Table B  1004 , Table C  1006  and Table D  1008 , as indicated by the direction of the arrows on  FIG. 10 . Similarly, data structure  1102  has infield  1116  containing a reference to Table A  1002  and outfield  1118  containing Table C  1006 . Similarly, Table C  1006  has infield  1120  containing references to Table A  1002  and Table B  1004 . Outfield  1122  of data structure  1106  contains references to Table D  1008  and Table E  1010 . Infield of data structure  1108  contains a reference to Table A  1002  and Table C  1006 . Outfield  1126  of data structure  1108  is empty. Infield  1128  of data structure  1110  contains a reference to Table C  1006 . Outfield  1130  is empty. 
   Accordingly, a linked data structure, such as a linked list, may be generated wherein starting from one data structure and traversing through all outfield data elements, a network of linkages amongst the table elements may be generated. For example, beginning with data element  1102 , a link from Table A is made to Table B. Then traversing from Table B in data structure  1104 , a link is made to Table C through outfield  1118 . Next, a link to data structure  1106  provides a link to Table D through outfield  1122 . Finally, Table D data structure  1108  ends with an outfield at outfield  1126 . Accordingly, traversal reverts back up to Table A to determine if any other linkages can be made. Accordingly, a link to Table C from outfield  1114  is made. This leads to an access to Table D through outfield  1122  of Table C. Following the link through Table D leads to a null field at outfield  1126 . Reverting back to Table A data structure  1102 , Table D entry in outfield  1114  leads directly to the null field  1126  of data structure  1108 . 
   The next unresolved outfield is examined. As Table B has all of its outfields resolved, for Table C data structure  1106  is examined for contents of its outfield  1122 , namely table E. At Table E, data structure  1110  shows that its outfield is null in field  1130 . Accordingly, the entire tree has been traversed with all elements in this manner. Next, each traversal route can be summed for its routing costs. For a system where each traversal is an equivalent cost, it can be shown that by traversing the data structures to go from Table A to Table B may be done in one step. Similarly, the cost to go from Table A to Table C is either one or two hops. The cost to go from Table A to table D is one, two or three hops. The cost to go from Table A to Table E is two hops. By tracking all costing routes, the most efficient route may be selected. It can be appreciated that other algorithms may be used to traverse the tree and other costing mechanisms may be used to weight each traversal path amongst table elements which may be implemented in other embodiments to determine an optimum path. 
   Referring to  FIG. 12 , computer network  1200  is shown. Network  1200  comprises network system  1202 , such as the Internet, which connects computer  100  to server  1204 . It can be appreciated that software  102  may be provided to computer  100  via server  1204 . Databases  104 A and  104 B are distributed along network  1202 . Server  1204  and computer  100  access databases  104 A and  104 B through network  1202 . 
   As far as the user on computer  100  is concerned, he does not have knowledge of the distributed nature of the information coming to computer  100  over network  1202 . In the preferred embodiment, software  102  in computer  100  utilizes electronic java beans (EJB) to provide access to the system. 
   Although the present invention has been described in accordance with the embodiments shown, one of ordinary skill in the art will readily recognize that there could be variations to the embodiments and those variations would be within the spirit and scope of the present invention. Accordingly, many modifications may be made by one of ordinary. skill in the art without departing from the spirit and scope of the appended claims.