Page 58 - Demo

%u062c%u0645%u064a%u0639 %u0627%u0644%u062d%u0642%u0648%u0642 %u0645%u062d%u0641%u0648%u0638%u0629 %u0640 %u0627%u0625%u0644%u0639%u062a%u062f%u0627%u0621 %u0639%u0649%u0644 %u062d%u0642 %u0627%u0645%u0644%u0624%u0644%u0641 %u0628%u0627%u0644%u0646%u0633%u062e %u0623%u0648 %u0627%u0644%u0637%u0628%u0627%u0639%u0629 %u064a%u0639%u0631%u0636 %u0641%u0627%u0639%u0644%u0647 %u0644%u0644%u0645%u0633%u0627%u0626%u0644%u0629 %u0627%u0644%u0642%u0627%u0646%u0648%u0646%u064a%u062958metric transaction throughput query throughput, response A major reason for the separation of data in a Datawarehouse is to help promote the high performance of both systems. An operational database is designed and tuned from known tasks and workloads like indexing and hashing using primary keys, searching for particular records, and optimizing %u201ccanned%u201d queries. On the other hand, data warehouse queries are often complex. They involve the computation of large data groups at summarized levels, and may require the use of special data organization, access, and implementation methods based on multidimensional views. Processing OLAP queries in operational databases would substantially degrade the performance of operational tasks. Moreover, an operational database supports the concurrent processing of multiple transactions. Concurrency control and recovery mechanisms are required to ensure the consistency and robustness of transactions. An OLAP query often needs read-only access of data records for summarization and aggregation Different functions and different data: Missing data: Decision support requires historical data which operational DBs do not typically maintain. Data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources. Data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled. A Multitiered Architecture: 1-The bottom tier is a warehouse database server that is almost always a relational database system. Back-end tools and utilities are used to feed data into the bottom tier from operational databases or other external sources these tools and utilities perform data extraction, cleaning, and transformation as well as load and refresh functions to update the data warehouse 2-The middle tier is an OLAP server that is typically implemented using either (1) a relational OLAP (ROLAP) model or (2) a multi-dimensional OLAP (MOLAP) model