Section 6- IRE Marketing Warehouse (TM)

2.3.1 Saved Answer Sets

Mercantile Software Systems, Inc. (MSSI) recognized that one of business' key requirements for a DSS/Marketing Warehouse is the ability to quickly drill down for more detailed information. Another crucial requirement is the need to support high-speed answers to complex queries such as:

Find all customers in Pennsylvania with red cars AND all customers in New Jersey with 2 children.

A robust DSS package needs to support both full Boolean syntax and query nesting in order to effectively process complex queries. Forcing the user to formulate these queries in SQL could result in a departmental uprising (translation: they won't touch it with a 10 foot cyberpole).

Also, given the heuristic nature of the DSS, the user must be able to leverage the answer from previous queries. One of the features that makes the IRE(TM) Marketing Warehouse a leader in DSS technology is that it saves answer sets as compressed lists of pointers to the rows matching the search criteria, even when the query criteria crosses multiple tables. The IRE(TM) Marketing Warehouse is optimized to return answers in seconds -- not hours or days. In benchmark tests, complex queries that take over 48 hours to satisfy when executed on conventional RDBMS technology, take the same RDBMS only minutes with the IRE(TM) Marketing Warehouse.

Another advantage of saved answer sets is that they may be shared by users with appropriate access. This set-based approach empowers users by allowing them to start with simple queries and rapidly build more complex ones. Since the answer sets are saved, building complex queries becomes a simple process of combining previously saved answer sets in any Boolean fashion required. We refer to this as the LEGO® approach -one builds a query based upon previous results. Having the ability to save answer sets is mandatory if a Marketing Warehouse is to be effective at tracking marketing campaigns. This powerful feature is unique to IRE(TM) Marketing Warehouse and is unavailable from comparative DSS vendors.

This logarithmic graph shows a typical query response time comparisons between conventional RDBMS and the IRE(TM) Marketing Warehouse.
See the benchmark that generated this graph.

blue line

2.3.2 MultiUniverse

Most DSSs are built around a single central assumption or "universe". For instance, one may design queries either with respect to customers or their purchases, but not both. So far, we have discussed situations in which the customer fits certain profiles and the users are probably marketing or sales people who query the customer universe. But what if the inventory manager needs to use the system to analyze data important to him or her? Typical DSSs would require you to build a separate DSS that is normalized for the "purchases" universe. This requires more systems, more support, and unnecessary data redundancy. Having the ability to operate in multiple universes would greatly enhance the power and effectiveness of a DSS.

The IRE(TM) Marketing Warehouse offers such a technology. With the IRE (TM) Marketing Warehouse MultiUniverse, users can change their focus from customers to purchases without the need for a separate system or redundant data. The MultiUniverse option allows for dynamic context switching between different universes within any query. No other DSS software package can offer this option.

The Marketing User asks, "How many customers...?"

The Inventory Manager asks, "How many purchases of XYZ occurred in New Jersey and Pennsylvania (where the stock is needed)?"

2.3.3 Full Boolean Set-based Querying

The IRE(TM) Marketing Warehouse allows you to query the database using any number of columns in any number of tables while freely mixing Boolean operations within a query string. This allows the data analyst to perform complex operations without incurring additional computing time from the underlying RDBMS platform. Each query search field is mapped to a table and a column within the underlying database. This mapping ability shields the query builder from the intricacies of complex SQL queries. Functions include:

	  Sum 			  Min
	  Max 			  Mean
	  Mode 			  Median
	  Top bracketing


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