Online Analytical Processing (OLAP) Task

An Online Analytical Processing (OLAP) Task is an Interactive Data Analysis Task that is restricted the summarizing past behavior.

• Context:
  • Input: Structured Data.
  • output: one or more Interactive Summary Reports.
  • It can (typically) be supported by an OLAP System.
  • It can involve a Temporal Lag between present the last observation and the current date time.
  • It involves one or more OLAP Operations.
  • It can support a Decision Making Task.
  • …
• Example(s):
  • OLAP Roll-Up, OLAP Drill-Down.
  • OLAP Slicing, OLAP Dicing.
  • Sales OLAP Task.
  • Marketing OLAP Task.
  • …
• Counter-Example(s):
  • an Online Transaction Processing (OLTP).
  • a Data Science Task.
• See: Business Intelligence, OLAP Cube.

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References

2017a

• (OLAP, 2017) ⇒ http://olap.com/olap-definition/ Retrieved: 2017-6-18.
  • OLAP (Online Analytical Processing) is the technology behind many Business Intelligence (BI) applications. OLAP is a powerful technology for data discovery, including capabilities for limitless report viewing, complex analytical calculations, and predictive “what if” scenario (budget, forecast) planning. (...) OLAP is an acronym for Online Analytical Processing. OLAP performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modeling. It is the foundation for many kinds of business applications for Business Performance Management, Planning, Budgeting, Forecasting, Financial Reporting, Analysis, Simulation Models, Knowledge Discovery, and Data Warehouse Reporting. OLAP enables end-users to perform ad hoc analysis of data in multiple dimensions, thereby providing the insight and understanding they need for better decision making.

2017b

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Online_analytical_processing Retrieved:2017-6-18.
  • Online analytical processing, or OLAP ( /ˈoʊlæp/), is an approach to answering multi-dimensional analytical (MDA) queries swiftly in computing. OLAP is part of the broader category of business intelligence, which also encompasses relational database, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications coming up, such as agriculture. The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP). OLAP 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. 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. 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. 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 processes all kinds of queries (read, insert, update and delete).

1999

• (Zaiane, 1999) ⇒ Osmar Zaiane. (1999). “Glossary of Data Mining Terms." University of Alberta, Computing Science CMPUT-690: Principles of Knowledge Discovery in Databases.
  • QUOTE: OLAP: On-Line Analytical Processing. Refers to array-oriented database applications that enable users (analysts, managers and executives) to view, navigate through, manipulate, and analyze multidimensional databases. With OLAP software, users gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user. Exemples of OLAP operations are: Drill-down, Drill-through, Roll-up, Slice, Dice, Pivot, etc.
  • QUOTE: Data Navigation:The process of viewing different dimensions, slices, and levels of detail of a multidimensional database. See OLAP.

1998

• (Kohavi & Provost, 1998) ⇒ Ron Kohavi, and Foster Provost. (1998). “Glossary of Terms.” In: Machine Leanring 30(2-3).
  • OLAP (MOLAP, ROLAP): On-Line Analytical Processing. Usually synonymous with MOLAP (multi-dimensional OLAP). OLAP engines facilitate the exploration of data along several (predetermined) dimensions. OLAP commonly uses intermediate data structures to store pre-calculated results on multidimensional data, allowing fast computations. ROLAP (relational OLAP) refers to performing OLAP using relational databases.

1993

• (Codd et al., 1993) ⇒ Edgar F. Codd, Sharon B. Codd, and Clynch T. Salley. "Providing OLAP (on-line analytical processing) to user-analysts: An IT mandate." Codd and Date 32 (1993).

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