Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-7, 9-12, 14-17 and 19 are rejected under 35 U.S.C 103(a) as being unpatentable over Milousheff et al. (U.S. Pub. 2014/0279977A1) in view of Liehl et al. (U.S. Pub. 2005/0267868 A1)
With respect to claims 1,17, Milousheff et al. discloses a non-transitory computer-readable medium storing computer-executable instructions that, when executed by at least one computer processor, cause the at least one computer processor to carry out operations comprising:
identifying a join formed of a fact table and a plurality of dimension tables stored at one or more data sources (i.e., “ The star join query schema includes three joins 525-1 to 525-3. Join 525-1 joins column DATE_ID (Foreign Key) in fact data table 520 with DATE_ID (Primary Key) in dimension data table 510. Join 525-2 joins column DIM1_ID (Foreign Key) in fact data table 520 with DIM1_UID in dimension data table 505. Join 525-3 joins column DIM2_ID (Foreign Key) in fact data table 520 with DIM2_UID in dimension data table 515.”(0048) and “generate a view of persisted dimension attribute data as dual values utilizing a star join” (abstract) and star join is identifying a join format as claimed invention);
determining that the join is a record-preserving join when it has only one record for each record of the fact table (i.e., “ The star join query schema includes three joins 525-1 to 525-3. Join 525-1 joins column DATE_ID (Foreign Key) in fact data table 520 with DATE_ID (Primary Key) in dimension data table 510. Join 525-2 joins column DIM1_ID (Foreign Key) in fact data table 520 with DIM1_UID in dimension data table 505. Join 525-3 joins column DIM2_ID (Foreign Key) in fact data table 520 with DIM2_UID in dimension data table 515.”(0048) and record preserving join is star join as claimed invention since one record, for example, join Join 525-1 joins column DATE_ID (Foreign Key) in fact data table 520 with DATE_ID (Primary Key) in dimension data table 510);
generating an optimized data structure including the record-preserving join (i.e., “queries of fact tables joined with dimension tables are optimized using a star join. . ”(0024));
storing the optimized data structure in a cache memory storing a plurality of optimized data structures; and
modifying a query plan to obtain query results that satisfy a query by reading the optimized data structure in lieu of reading any of the fact table or the plurality of dimension tables stored at the one or more data sources (i.e., “queries of fact tables joined with dimension tables are optimized using a star join. . ”(0024) and “queries of fact tables joined with dimension tables are optimized using a star join. ”(0028));.
However, Milousheff et al. does not discloses storing the optimized data structure in a cache memory storing a plurality of optimized data structures. But, Liebl et al. discloses storing the optimized data structure in a cache memory storing a plurality of optimized data structures (i.e.,. “The plan is stored either in the database (for static compilation [see reference CAK+8 1]) or in an in-memory cache (for dynamic queries). Most modern query optimizers determine the best plan for executing a given query by mathematically modeling the execution cost for each of many alternative QEPs and choosing the one with the cheapest estimated cost)”(0005)). It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include Liebl et al.’s features in order to fast, dynamic query for the stated purpose has been well known in the art as evidenced by teaching of Liebl et al. Both reference teaches the same field such query plan with optimization.
With respect to claims 3, 9, 14 and 19, Milousheff et al. discloses wherein the operations further comprise autonomously deciding to generate the optimized data structure (i.e., “Star join query optimization may automatically implemented by the data warehouse database (e.g., SQL Server.TM., Oracle.TM.). Star join query optimization may not require a special database or application configuration.”(0019)).
With respect to claim 4, 10, and 15, Milousheff et al. discloses the non-transitory computer-readable medium of claim 3, wherein the decision to generate the optimized data structure is based on a determination that reading the optimized data structure in lieu of reading at least some dataset from a plurality of data sources improves processing of an expected workload (i.e., “queries of fact tables joined with dimension tables are optimized using a star join. . ”(0024) and “queries of fact tables joined with dimension tables are optimized using a star join. ”(0028) and “A star join may use bitmap filtering for improving the performance of some types of queries by the effective retrieval of rows from fact tables.”(0018) and “Example embodiments provide a dimensional persistence structure, metadata layer, and uniform SQL access layer to reduce data inflation and increase load performance. In addition, example embodiments efficiently access data utilizing a star join of the fact table data to the dimension table data.”(0022));.
With respect to claims 5, Milousheff et al. discloses wherein each of the plurality of optimized data structures includes a record-preserving join (i.e., “ The star join query schema includes three joins 525-1 to 525-3. Join 525-1 joins column DATE_ID (Foreign Key) in fact data
table 520 with DATE_ID (Primary Key) in dimension data table 510. Join 525-2 joins column DIM1_ID (Foreign Key) in fact data table 520 with DIM1_UID in dimension data table 505. Join 525-3 joins
column DIM2_ID (Foreign Key) in fact data table 520 with DIM2_UID in dimension data table 515.”
(0048) and record preserving join is star join as claimed invention since one record, for example, join Join 525-1 joins column DATE_ID (Foreign Key) in fact data table 520 with DATE_ID (Primary Key) in dimension data table 510);.
With respect to claims 6, 11, and 16, Milousheff et al. discloses wherein generating the optimized data structure comprises: preserving a quantity of all records of the fact table such that the record-preserving join has a corresponding quantity of records despite any of the plurality of dimension tables having a quantity of records different from a quantity of records in the fact table (i.e., “queries of fact tables joined with dimension tables are optimized
using a star join.”(0024) and “Data warehouses may employ dimensionally modeled star or snowflake schemas. These schemas may have one or more associated fact tables that contain transactional data and many dimension tables that define the fact table data. The dimension tables may store information such as product data, customer information, and times and dates. Foreign keys may be utilized for maintaining relationships between rows in the fact tables and between rows in the dimension tables.”(0018) and Examiner asserts quantity of record of table of many dimension tables different from a quality records in the table. ).
With respect to claim 7 and 12, Milousheff et al. discloses a non-transitory computer-readable medium storing computer-executable instructions that, when executed by at least one computer processor, cause the at least one computer processor to carry out operations comprising:
the optimized data structures each including a respective record-preserving join (i.e., “queries of fact tables joined with dimension tables are optimized using a star join. . ”(0024));
wherein each respective record-preserving join is formed of a respective fact table and a respective plurality of dimension tables i.e., “ The star join query schema includes three joins 525-1 to 525-3. Join 525-1 joins column DATE_ID (Foreign Key) in fact data table 520 with DATE_ID (Primary Key) in dimension data table 510. Join 525-2 joins column DIM1_ID (Foreign Key) in fact data table 520 with DIM1_UID in dimension data table 505. Join 525-3 joins column DIM2_ID (Foreign Key) in fact data table 520 with DIM2_UID in dimension data table 515.”(0048) and “generate a view of persisted dimension attribute data as dual values utilizing a star join” (abstract) and star join is identifying a join format as claimed invention), and
wherein the respective record-preserving join includes a record for each record of the respective fact table and “Data warehouses may employ dimensionally modeled star or snowflake schemas. These schemas may have one or more associated fact tables that contain transactional data and many dimension tables that define the fact table data. The dimension tables may store information such as product data, customer information, and times and dates. Foreign keys may be utilized for maintaining relationships between rows in the fact tables and between rows in the dimension tables.”(0018) and ., “ The star join query schema includes three joins 525-1 to 525-3. Join 525-1 joins column DATE_ID (Foreign Key) in fact data table 520 with DATE_ID (Primary Key) in dimension data table 510. Join 525-2 joins column DIM1_ID (Foreign Key) in fact data table 520 with DIM1_UID in dimension data table 505. Join 525-3 joins column DIM2_ID (Foreign Key) in fact data table 520 with DIM2_UID in dimension data table 515.”(0048) and record preserving join is star join as claimed invention since one record, for example, join Join 525-1 joins column DATE_ID (Foreign Key) in fact data table 520 with DATE_ID (Primary Key) in dimension data table 510); and
modifying a query plan to obtain query results by reading at least some of the plurality of optimized data structures in lieu of reading data stored in a plurality of data sources (i.e., “queries of fact tables joined with dimension tables are optimized using a star join. . ”(0024) and “queries of fact tables joined with dimension tables are optimized using a star join. ”(0028));.
However, Milousheff et al. does not discloses storing a plurality of optimized data structures in a cache memory. but, Liebl et al. discloses storing a plurality of optimized data structures in a cache memory (i.e.,. “The plan is stored either in the database (for static compilation [see reference CAK+8 1]) or in an in-memory cache (for dynamic queries). Most modern query optimizers determine the best plan for executing a given query by mathematically modeling the execution cost for each of many alternative QEPs and choosing the one with the cheapest estimated cost)”(0005)). It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include Liebl et al.’s features in order to fast, dynamic query for the stated purpose has been well known in the art as evidenced by teaching of Liebl et al. Both reference teaches the same field such query plan with optimization.
Claims 2, 8 13, and 18 are rejected under 35 U.S.C 103(a) as being unpatentable over Milousheff et al. (U.S. Pub. 2014/0279977A1) and Liehl et al. (U.S. Pub. 2005/0267868 A1) in further view of Cherninch et al.. (U.S. Pub. 2008/0033914 A1) A1)
With respect to claims 2, 8, 13, and 18, Milousheff and Liehl et al. disclose all limited recited in claim 1 except for wherein the operations further comprise pruning any dataset, from the record-preserving join, that is not referred to in the query plan. However, Cherninch et al. discloses wherein the operations further comprise pruning any dataset, from the record-preserving join, that is not referred to in the query plan (i.e., “It is impractical to assess that many plans for all but trivial values of n, so a query optimizer must somehow prune the space of join orderings that are considered. A good query optimizer is therefore one that can so prune this search space as to ensure that the remaining plan set includes good orderings.”(0044)). It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include Cherninch et al.’s features in order to have system cost model that is reasonably accurate, good query plans for the stated purpose has been well known in the art as evidenced by teaching of Cherniack et al. Both reference teaches the same field such query plan with optimization.
Conclusion
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/HUNG T VY/Primary Examiner, Art Unit 2163 January 21, 2026