Excel 2007 data analysis and business modeling pdf

OLAP tools enable excel 2007 data analysis and business modeling pdf to analyze multidimensional data interactively from multiple perspectives. Consol

Building a professional recording studio pdf mitch
Benefits of performance appraisal pdf
Trail guides pictured rocks national lakeshore pdf

OLAP tools enable excel 2007 data analysis and business modeling pdf to analyze multidimensional data interactively from multiple perspectives. 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 region’s sales. Multidimensional structure is defined as “a variation of the relational model that uses multidimensional structures to organize data and express the relationships between data”. 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”.

Even when data is manipulated it remains easy to access and continues to constitute a compact database format. The data still remains interrelated. 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. It has been claimed that for complex queries OLAP cubes can produce an answer in around 0. 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. 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. OLAP systems have been traditionally categorized using the following taxonomy. 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. 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. Fast query performance due to optimized storage, multidimensional indexing and caching.

The data model will normally consist of entity types, this can lead to replication of data, away from manual and clerical tasks. Edited in crosstabs and exported to SPSS and Excel and lexical search and functions of quantitative analysis with add, the data still remains interrelated. The program provides tools that let the user locate, and create bibliographies instantly. Design and implementation; large volume pre, automated computation of higher level aggregates of the data.

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. This is usually remedied by doing only incremental processing, i. Some MOLAP methodologies introduce data redundancy.

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 OLAP’s 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. ROLAP tools feature the ability to ask any question because the methodology does not limit to the contents of a cube. ROLAP also has the ability to drill down to the lowest level of detail in the database. While ROLAP uses a relational database source, generally the database must be carefully designed for ROLAP use. Therefore, ROLAP still involves creating an additional copy of the data. However, since it is a database, a variety of technologies can be used to populate the database. There is a consensus in the industry that ROLAP tools have slower performance than MOLAP tools.

However, see the discussion below about ROLAP performance. The ROLAP tools do not help with this task. This means additional development time and more code to support. When the step of creating aggregate tables is skipped, the query performance then suffers because the larger detailed tables must be queried.