SQL s. k ju l listen ESSkewEL or s i k w l listen SEEkwl, Structured Query Language is a domainspecific language used in. Online analytical processing Wikipedia. Online analytical processing, or OLAP, is an approach to answering multi dimensional analytical MDA queries swiftly in computing. 1 OLAP is part of the broader category of business intelligence, which also encompasses relational database, report writing and data mining. 2 Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management BPM,3budgeting and forecasting, financial reporting and similar areas, with new applications coming up, such as agriculture. 4 The term OLAP was created as a slight modification of the traditional database term online transaction processing OLTP. 5OLAP tools enable users to analyze multidimensional data interactively from multiple perspectives. OLAP consists of three basic analytical operations consolidation roll up, drill down, and slicing and dicing. 6 Consolidation involves the aggregation of data that can be accumulated and computed in one or more dimensions. For example, all sales offices are rolled up to the sales department or sales division to anticipate sales trends. By contrast, the drill down is a technique that allows users to navigate through the details. For instance, users can view the sales by individual products that make up a regions sales. Slicing and dicing is a feature whereby users can take out slicing a specific set of data of the OLAP cube and view dicing the slices from different viewpoints. These viewpoints are sometimes called dimensions such as looking at the same sales by salesperson or by date or by customer or by product or by region, etc. Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad hoc queries with a rapid execution time. 7 They borrow aspects of navigational databases, hierarchical databases and relational databases. OLAP is typically contrasted to OLTP online transaction processing, which is generally characterized by much less complex queries, in a larger volume, to process transactions rather than for the purpose of business intelligence or reporting. Whereas OLAP systems are mostly optimized for read, OLTP has to process all kinds of queries read, insert, update and delete. Overview of OLAP systemseditAt the core of any OLAP system is an OLAP cube also called a multidimensional cube or a hypercube. It consists of numeric facts called measures that are categorized by dimensions. The measures are placed at the intersections of the hypercube, which is spanned by the dimensions as a vector space. The usual interface to manipulate an OLAP cube is a matrix interface, like Pivot tables in a spreadsheet program, which performs projection operations along the dimensions, such as aggregation or averaging. Complex Update Query In Db2 CommandsThe cube metadata is typically created from a star schema or snowflake schema or fact constellation of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables. Each measure can be thought of as having a set of labels, or meta data associated with it. A dimension is what describes these labels it provides information about the measure. A simple example would be a cube that contains a stores sales as a measure, and DateTime as a dimension. Each Sale has a DateTime label that describes more about that sale. For example. Sales Fact Table. Complex Update Query In Db2 ErrorDiscusses the relationship between XML and databases. Describes some of the types of software available to process XML documents with databases. 2 Instance Monitoring All aspects of the DB2 instance need to be monitored Take a look at the big picture Think of DB2 as an ecosystem Do not tune for the. Time Dimension. 2. Multidimensional databaseseditMultidimensional structure is defined as a variation of the relational model that uses multidimensional structures to organize data and express the relationships between data. 8 The structure is broken into cubes and the cubes are able to store and access data within the confines of each cube. Each cell within a multidimensional structure contains aggregated data related to elements along each of its dimensions. 9 Even when data is manipulated it remains easy to access and continues to constitute a compact database format. The data still remains interrelated. Multidimensional structure is quite popular for analytical databases that use online analytical processing OLAP applications. 1. Analytical databases use these databases because of their ability to deliver answers to complex business queries swiftly. Data can be viewed from different angles, which gives a broader perspective of a problem unlike other models. 1. AggregationseditIt has been claimed that for complex queries OLAP cubes can produce an answer in around 0. OLTP relational data. 1. The most important mechanism in OLAP which allows it to achieve such performance is the use of aggregations. Aggregations are built from the fact table by changing the granularity on specific dimensions and aggregating up data along these dimensions. The number of possible aggregations is determined by every possible combination of dimension granularities. The combination of all possible aggregations and the base data contains the answers to every query which can be answered from the data. 1. Because usually there are many aggregations that can be calculated, often only a predetermined number are fully calculated the remainder are solved on demand. The problem of deciding which aggregations views to calculate is known as the view selection problem. View selection can be constrained by the total size of the selected set of aggregations, the time to update them from changes in the base data, or both. The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time. View selection is NP Complete. Many approaches to the problem have been explored, including greedy algorithms, randomized search, genetic algorithms and A search algorithm. OLAP systems have been traditionally categorized using the following taxonomy. 1. Multidimensional OLAP MOLAPeditMOLAP multi dimensional online analytical processing is the classic form of OLAP and is sometimes referred to as just OLAP. MOLAP stores this data in an optimized multi dimensional array storage, rather than in a relational database. Some MOLAP tools require the pre computation and storage of derived data, such as consolidations the operation known as processing. Such MOLAP tools generally utilize a pre calculated data set referred to as a data cube. The data cube contains all the possible answers to a given range of questions. As a result, they have a very fast response to queries. On the other hand, updating can take a long time depending on the degree of pre computation. Pre computation can also lead to what is known as data explosion. Other MOLAP tools, particularly those that implement the functional database model do not pre compute derived data but make all calculations on demand other than those that were previously requested and stored in a cache. Advantages of MOLAPFast query performance due to optimized storage, multidimensional indexing and caching. Smaller on disk size of data compared to data stored in relational database due to compression techniques. Automated computation of higher level aggregates of the data. It is very compact for low dimension data sets. Array models provide natural indexing. Effective data extraction achieved through the pre structuring of aggregated data. Disadvantages of MOLAPWithin some MOLAP systems the processing step data load can be quite lengthy, especially on large data volumes. This is usually remedied by doing only incremental processing, i. Some MOLAP methodologies introduce data redundancy. ProductseditExamples of commercial products that use MOLAP are Cognos Powerplay, Oracle Database OLAP Option, Micro. Strategy, Microsoft Analysis Services, Essbase, TM1, Jedox, and ic. Cube. Relational OLAP ROLAPeditROLAP works directly with relational databases and does not require pre computation. The base data and the dimension tables are stored as relational tables and new tables are created to hold the aggregated information. It depends on a specialized schema design. This methodology relies on manipulating the data stored in the relational database to give the appearance of traditional OLAPs slicing and dicing functionality. In essence, each action of slicing and dicing is equivalent to adding a WHERE clause in the SQL statement. ROLAP tools do not use pre calculated data cubes but instead pose the query to the standard relational database and its tables in order to bring back the data required to answer the question.