Business Intelligence: First European Summer School, eBISS by Alejandro Vaisman, Esteban Zimányi (auth.), Marie-Aude

By Alejandro Vaisman, Esteban Zimányi (auth.), Marie-Aude Aufaure, Esteban Zimányi (eds.)

Business Intelligence (BI) delivers a company the potential of gathering and examining inner and exterior info to generate wisdom and cost, delivering determination aid on the strategic, tactical, and operational degrees. company Intelligence is now impacted through the massive facts phenomena and the evolution of society and clients, and desires take into consideration high-level semantics, reasoning approximately unstructured and established information, and to supply a simplified entry and higher figuring out of various BI instruments obtainable trough cellular units. particularly, BI purposes needs to focus on extra heterogeneous (often Web-based) resources, e.g., from social networks, blogs, competitors’, suppliers’, or vendors’ information, governmental or NGO-based research and papers, or from examine publications.

The lectures held on the First eu enterprise Intelligence summer time university (eBISS), that are provided the following in a longer and sophisticated layout, conceal not just validated BI applied sciences like information warehouses, OLAP question processing, or functionality concerns, yet expand into new facets which are very important during this new atmosphere and for novel functions, e.g., semantic applied sciences, social community research and graphs, providers, large-scale administration, or collaborative choice making.

Combining papers through best researchers within the box, this quantity will equip the reader with the cutting-edge history beneficial for inventing the way forward for BI. it's going to additionally give you the reader with a good foundation and plenty of guidelines for additional study during this growing to be field.

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Additional resources for Business Intelligence: First European Summer School, eBISS 2011, Paris, France, July 3-8, 2011, Tutorial Lectures

Example text

The clauses include: (1) maintained by {system | user}, (2) refresh {immediate | deferred}, (3) data initially {immediate | deferred}, and (4) enable query optimization. A MQT can be maintained either by a system or by a user. In the first case, the maintained by system clause is used. This is a default clause. For a system-maintained MQT, one can define either automatic or non-automatic refreshing mode. In the automatic mode, a MQT is refreshed automatically as the result of changes in the content of its base tables.

B 0 represents the 20 bit, B 1 represents the 21 bit, etc. Every value of an indexed attribute is represented on the same number of n bits. As a result, the encoded values in a table form n bitmaps. The bitmaps are called bit-slices. Data retrieval and computation are supported either by the bit-sliced index arithmetic [63] or by means of a dedicated retrieval function [55]. Additionally, a mapping data structure is required for mapping the encoded values into their real values [55]. Compression Techniques The second approach that allows to reduce the size of a bitmap index defined on an attribute of high cardinality is based on compression.

Lines 10-12), creating a temporary bitmap. Next, the bitmap for ’London’ is retrieved (cf. line 8) and it is ANDed with the temporary bitmap (cf. line 7). The result bitmap is then converted to ROWIDs of records fulfilling the criteria (cf. line 6). In this query plan table Time was accessed with the support of a B-tree index defined on its primary key, rather than by means of the bitmap join index. 42 R. Wrembel ------------------------------------------------------------------------------------|Id | Operation |Name |Rows |Bytes |Cost(%CPU)|Time | ------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1| 58 | 13 (8)|00:00:01| | 1 | SORT AGGREGATE | | 1| 58 | | | | 2 | NESTED LOOPS | | 21| 1218 | 13 (8)|00:00:01| | 3 | HASH JOIN | | 22| 1012 | 12 (9)|00:00:01| | 4 | TABLE ACCESS FULL |PRODUCTS | 3| 51 | 3 (0)|00:00:01| | 5 | TABLE ACCESS BY INDEX ROWID |SALES | 1155|33495 | 8 (0)|00:00:01| | 6 | BITMAP CONVERSION TO ROWIDS | | | | | | | 7 | BITMAP AND | | | | | | | 8 | BITMAP INDEX SINGLE VALUE |BI_CU_SALES| | | | | | 9 | BITMAP OR | | | | | | |10 | BITMAP INDEX SINGLE VALUE|BI_PR_SALES| | | | | |11 | BITMAP INDEX SINGLE VALUE|BI_PR_SALES| | | | | |12 | BITMAP INDEX SINGLE VALUE|BI_PR_SALES| | | | | |13 | TABLE ACCESS BY INDEX ROWID |TIME | 1| 12 | 1 (0)|00:00:01| |14 | INDEX UNIQUE SCAN |PK_TIME | 1| | 0 (0)|00:00:01| ------------------------------------------------------------------------------------- Alternatively, it is possible to create one bitmap join index on all of the dimension attributes.

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