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Chapter: Data Warehousing and Data Mining

DBMS Schemas for Decision Support

All industries have developed considerable expertise in implementing efficient operational systems such as payroll, inventory tracking, and purchasing. the original objectives in developing an abstract model known as the relational model

DBMS Schemas for Decision Support


1.Data layout for business access


All industries have developed considerable expertise in implementing efficient operational systems such as payroll, inventory tracking, and purchasing. the original objectives in developing an abstract model known as the relational model


The relational model is based on mathematical principals. The existing relational database management ( RDBMSs) offer power full solution for a wide variety of commercial and scientific applications


The data warehouse RDBMS typically needs to process queries that are large complex, ad hoc, and data intensive. so data warehouse RDBMS are very different ,it use a database schema for maximizing concurrency and optimizing insert, update, and delete performance


For solving modern business problems such as market analysis and financial forecasting requires query-centric database schemas that are array oriented and multidimensional in nature.


2. Multidimensional data model

Note –need write about Multidimensional data model


3. Star schema

Note –need write about star schema


3.1 DBA view point


A star schema is a relational schema organized around a central table joined to few smaller tables (dimension tables)using foreign key references


The fact table contains raw numeric items that represent relevant business facts (price,dicont values, number of units sold,dollar sold,dollar vaue,etc


The fact are accessed via dimensions since fact tables are pre summarized and aggregated along business dimensions


The dimension table defines business dimension in terms already familiar to user.the dimension table contain a non compound primary key and are heavily indexed and these table are grouped and joined with fact table using foreign key references.


A star schema created for every industry


3.2 Potential performance problem with star schemas



It improve the performance in the star schema design


The table in star schema design contain the entire hierarchy of attributes(PERIOD dimension this hierarchy could be day->week->month->quarter->year),one approach is to create multi part key of day, week, month ,quarter ,year .it presents some problems in the star schema model because it should be in normalized



it require multiple metadata definitions


Since the fact table must carry all key components as part of its primary key, addition or deletion of levels in the physical modification of the affected table.


Carrying all the segments of the compound dimensional key in the fact table increases the size of the index, thus impacting both performance and scalability.




One alternative to the compound key is to concatenate the key into a single key for the attributes (day, week, month, quarter, year) this is used to solve the first above two problems.


The index is remains problem the best approach is to drop the use of meaningful keys in favor of using an artificial, generated key which is the smallest possible key that will ensure the uniqueness of each record


Level indicator



Another potential problem with the star schema design is that in order to navigate the dimensions successfully.

The dimensional table design includes a level of hierarchy indicator for every record.


Every query that is retrieving detail records from a table that stores details & aggregates must use this indicator as an additional constraint to obtain a correct result.



The best alternative to using the level indicator is the snowflake schema


The snowflake schema contains separate fact tables for each level of aggregation. So it is Impossible to make a mistake of selecting product detail. The snowflake schema is even more complicated than a star schema.


Other problems with the star schema design:

The next set of problems is related to the relational DBMS engine & optimization technology.


Pairwise problem:


The traditional OLAP RDBMS engines are not designed for the set of complex queries that can be issued against a star schema.


We need to retrieve related information from several query in a single query has several limitation


Many OLTP RDBMS can join only two tables, so the complex query must be in the RDBMS needs to break the query into series of pairwise joins. And also provide the intermediate result. this process will be do up to end of the result


Thus intermediate results can be large & very costly to create. It affects the query performance. The number of ways to pairwise join a set of N tables is N!( N factorial) for example.


The query has five table 5!=(6x5x4x3x2x1)=120 combinations.


Star schema join problem:


Because the number of pairwise join combinations is often too large to fully evaluate many RDBMS optimizers limit the selection on the basis of a particular criterion. In data warehousing environment this strategy is very inefficient for star schemas.


In a star schema the only table directly related to most other tables in the fact table this means that the fact table is natural candidate for the first pairwise join.


Unfortunately the fact table is typically the very largest table in the query. A pairwise join order generates very large intermediate result set. So it affects query performance.


3.3 Solution to performance problems:


A common optimization provides some relief for the star schema join problem.


The basic idea of this optimization is to get around the pairwise join strategy of selecting only related tables.


When two tables are joined and no columns ―link‖ the tables every combination of two tables’ rows are produced. In terms of relational algebra this is called a Cartesian product.


Normally the RDBMS optimizer logic would never consider Cartesian product but star schema considering these Cartesian products. Can sometimes improve query.

Alternatively it is a Cartesian product of all dimension tables is first generated.


The key cost is the need to generate the Cartesian product of the dimension tables as long as the cost of generating the Cartesian product is less than the cost of generating intermediate results with

the fact table.



4.STARjoin and STARindex


A  STARjoin  is  a  high-speed,  single-pass,  parallelizable  multitable  joins,  and  Brick’s

RDBMS can join more than two tables in a single operation.


Red Brick’s RDBMS supports the creation of specialized indexes called STARindexes. it created on one or more foreign key columns of a fact table.


5. Bitmapped indexing


The new approach to increasing performance of a relational DBMS is to use innovative indexing techniques to provide direct access to data. SYBASE IQ uses a bit mapped index structure. The data stored in the SYBASE DBMS.


5.1 SYBASE IQ- it is based on indexing technology; it is a stand alone database.


Overview: It is a separate SQL database. Data is loaded into a SYBASE IQ very much as into any relational DBMS once loaded SYBASE IQ converts all data into a series of bitmaps which are than highly compressed to store on disk.


Data cardinality:

Data cardinality bitmap indexes are used to optimize queries against a low cardinality data.


That is in which-The total no. of potential values in relatively low. Example: state code data cardinality is 50 potential values and general cardinality is only 2 ( male to female) for low cardinality data.


The each distinct value has its own bitmap index consisting of a bit for every row in a table, if the bit for a given index is ―on‖ the value exists in the record bitmap index representation is a 10000 bit long vector which has its bits turned on(value of 1) for every record that satisfies ―gender‖=‖M‖ condition‖


Bit map indexes unsuitable for high cardinality data


Another solution is to use traditional B_tree index structure. B_tree indexes can often grow to large sizes because as the data volumes & the number of indexes grow.


B_tree indexes can significantly improve the performance,


SYBASE IQ was a technique is called bitwise (Sybase trademark) technology to build bit map index for high cardinality data, which are limited to about 250 distinct values for high cardinality data.



Index types:


The first of SYBASE IQ provide five index techniques, Most users apply two indexes to every column.


the default index called projection index and other is either a low or high – cardinality index. For low cardinality data SYBASE IQ provides.


Low fast index: it is optimized for queries involving scalar functions like SUM,AVERAGE,and COUNTS.


Low disk index which is optimized for disk space utilization at the cost of being more CPU-intensive.



SYBAEE IQ technology achieves the very good performance on adhoc quires for several reasons


Bitwise technology: this allows various types of data type in query. And support fast data aggregation and grouping.

Compression: SYBAEE IQ uses sophisticated algorithms to compress data in to bit maps.


Optimized m/y based programming: SYBASE IQ caches data columns in m/y according to the nature of user’s queries, it speed up the processor.


Columnwise processing: SYBASE IQ scans columns not rows, it reduce the amount of data. the engine has to search.

Low overhead: An engine optimized for decision support SYBASE IQ does not carry on


overhead associated with that. Finally OLTP designed RDBMS performance.


Large block I/P: Block size of SYBASE IQ can turned from 512 bytes to 64 Kbytes so system can read much more information as necessary in single I/O.


Operating system-level parallelism: SYBASE IQ breaks low level like the sorts, bitmap manipulation, load, and I/O, into nonblocking operations.


Projection and ad hoc join capabilities: SYBASE IQ allows users to take advantage of known join relation relationships between tables by defining them in advance and building indexes between tables.


Shortcomings of indexing:-

The user should be aware of when choosing to use SYBASE IQ    include


No updates-SYNBASE IQ does not support updates .the users would have to update the source database and then load the update data in SYNBASE IQ on a periodic basis.



Lack of core RDBMS feature:-Not support all the robust features of SYBASE SQL server, such as backup and recovery

Less advantage for planned queries: SYBASE IQ, run on preplanned queries.


High memory usage: Memory access for the expensive i\o operation


5.6 Conclusion:-


SYBASE IQ promise increased performance without deploying expensive parallel hardware system.


6. Column local storage:-


It is an another approach to improve query performance in the data warehousing environment


For example thinking machine operation has developed an innovative data layout solution that improves RDBMS query performance many times. Implemented in its CM_SQL RDBMS product, this approach is based on storing data columnwise as opposed to traditional row wise approach.


In figure-.(Row wise approach) This approach works well for OLTP environment in which a typical transaction accesses a record at a time. However in data warehousing the goal is to retrieve multiple values of several columns.


For example if a problem is to calculate average minimum, maximum salary the columnwise storage of the salary field requires a DBMS to read only one record.(use Column –wise approach)


7. Complex data types:-


The best DBMS architecture for data warehousing has been limited to traditional alphanumeric data types. But data management is the need to support complex data types , Include text, image, full-motion video &sound.


Large data objects called binary large objects what’s required by business is much more than just storage:


The ability to retrieve the complex data type like an image by its content. The ability to compare the content of one image to another in order to make rapid business decision and ability to express all of this in a single SQL statement.


The modern data warehouse DBMS has to be able to efficiently store, access and manipulate complex data. The DBMS has to be able to define not only new data structure but also new function that manipulates them and often new access methods, to provide fast and often new access to the data.


An example of advantage of handling complex data types is a insurance company that wants to predict its financial exposure during a catastrophe such as flood that wants to support complex data.

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