the summarized data from the operational database, filters them for analysis and decision making processes (2:64). For instance, the manager of the admission and registration department may ask for the number of students at KFUPM taking ENGL-214 last summer. The data warehouse answers this query for him. Then, he would take decision whether to increase number of sections of this particular course or not.Operational Data Vs. Warehoused DataTransactional data and DSS data are different in the summarization level, transaction type, query activities and dimensionality.Summarization levelThe degree to which DSS data are summarized is very high when contrasted with the operational data (5:39). For example, rather than storing thousands of sales transactions for a given store on a given day, the data warehouse might simply store the total number of units sold and the total price during that day. Then, the store manager may decide whether to continue or discontinue selling or producing such products.Transaction typeThe operational database and the data warehouse are different in terms of transaction type. Whereas production data are characterized by update transactions, DSS data are mainly characterized by query (read only) transactions (2:67). The DSS data also require periodic update (2:67) to load new data that are summarized from the transactional database as well as other external sources. Therefore, the warehoused data are historic (2:64) while the operational data represent transactions as they happen.Query activityIt is difficult, if not impossible, to optimize a single database for both processing purposes as well as for decision-making needs. For that reason, the data warehouse is optimized for ad hoc (on demand or as needed) complex queries needed by decision-makers (2:67).The production database, on the other hand, is optimized to allow more processing for the repetitive update transactions (2:67). So, it is difficult to get ad hoc ...
