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In computing, online analytical processing (OLAP) (/ˈoʊlæp/), is an approach to quickly answer multi-dimensional analytical (MDA) queries. The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP). OLAP is part of the broader category of business intelligence, which also encompasses…
The analysis highlights Products and Art as prominent areas in the source structure around Online analytical processing.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
The extracted context around Online analytical processing shows recurring relationship patterns in the source. For example, Online analytical processing → Arbor, Arbor Software, As, Codd, Codd's, Computerworld, Edgar, Essbase, Express, However, Hyperion Solutions, In, Information Resources, Microsoft, Microsoft Analysis Services, OLAP, OLAP Server, Oracle, The, The OLAP Another extracted example is Online analytical processing → As, MOLAP, OLAP, On, Pre-computation, Some MOLAP, Such MOLAP, The. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
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TTTA extracted 57 structured relationships around Online analytical processing. Examples in this analysis include row-level security → instance of → it is possible to successfully model data that would not otherwise fit into a strict dimensional model.The ROLAP approach can leverage database authorization controls and CUBE → instance of → modern ROLAP tools take advantage of latest improvements in SQL language. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| row-level security | instance of | it is possible to successfully model data that would not otherwise fit into a strict dimensional model.The ROLAP approach can leverage database authorization controls | 0.80 | text |
| whereby the query results are filtered depending on preset criteria applied | instance of | it is possible to successfully model data that would not otherwise fit into a strict dimensional model.The ROLAP approach can leverage database authorization controls | 0.80 | text |
| for example | instance of | it is possible to successfully model data that would not otherwise fit into a strict dimensional model.The ROLAP approach can leverage database authorization controls | 0.80 | text |
| to a given user or group of users | instance of | it is possible to successfully model data that would not otherwise fit into a strict dimensional model.The ROLAP approach can leverage database authorization controls | 0.80 | text |
| CUBE | instance of | modern ROLAP tools take advantage of latest improvements in SQL language | 0.80 | text |
| ROLLUP operators | instance of | modern ROLAP tools take advantage of latest improvements in SQL language | 0.80 | text |
| DB2 Cube Views | instance of | modern ROLAP tools take advantage of latest improvements in SQL language | 0.80 | text |
| as well as other SQL OLAP extensions | instance of | modern ROLAP tools take advantage of latest improvements in SQL language | 0.80 | text |
| Microsoft Analysis Services | instance of | but the technology also became available in other commercial products | 0.80 | text |
| Oracle Database OLAP Option | instance of | but the technology also became available in other commercial products | 0.80 | text |
| MicroStrategy | instance of | but the technology also became available in other commercial products | 0.80 | text |
| SAP AG BI Accelerator | instance of | but the technology also became available in other commercial products | 0.80 | text |
The concept neighborhoods around Online analytical processing bring nearby vocabulary together. In this analysis, examples include Processing, Online and Oltp. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Online analytical processing, one of the stronger structural bridges in this analysis connects Online analytical processing with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Online analytical processing to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Products & Art, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Online analytical processing · EN edition · Analysis: TopicsToTalkAbout