Slice OLAP Function

A Slice OLAP Function is an OLAP Function that produces a sliced OLAP Cube by allowing the user to select a single dimension from the original OLAP Cube.

• AKA: Slice OLAP Operation, OLAP Slice Query.
• Example(s):
  • From the OLAP Cube representing car sales data in North America, one can slice the cube for cities “Toronto” (i.e. Location dimension).
  • From the OLAP Cube representing car sales data in North America, one can slice the cube for year “2014” (i.e. Time dimension).
  • …
• Counter-Example(s):
  • Dice OLAP Function,
  • Drill-Down OLAP Function,
  • Drill-Through OLAP Function,
  • Drill-Up OLAP Function,
  • Pivot OLAP Function,
  • Roll-Up OLAP Function.
• See: Slice and Dice, OLAP, OLAP User-Initiated Process, Data Cube, ROLAP, OLTP.

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References

2019a

• (GeeksForGeeks, 2019) ⇒ https://www.geeksforgeeks.org/olap-operations-in-dbms/ Retrieved:2019-12-21.
  • QUOTE: Slice: It selects a single dimension from the OLAP cube which results in a new sub-cube creation. In the cube given in the overview section, Slice is performed on the dimension Time = “Q1”.
    

2019b

• (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Online_analytical_processing Retrieved:2019-12-21.
  • Online analytical processing, or OLAP (/ˈoʊlæp/), is an approach to answer multi-dimensional analytical (MDA) queries swiftly in computing. OLAP is part of the broader category of business intelligence, which also encompasses relational databases, 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 emerging, 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 process all kinds of queries (read, insert, update and delete).

2013

• (Van Der Aalst, 2013) ⇒ Wil M.P. van der Aalst(2013, August). "Process cubes: Slicing, dicing, rolling up and drilling down event data for process mining". In Asia-Pacific Conference on Business Process Management (pp. 1-22). Springer, Cham.
  • QUOTE: The slice operation produces a sliced OLAP cube by allowing the analyst to pick specific value for one of the dimensions. For example, for sales data one can slice the cube for location “Eindhoven”, i.e., the location dimension is removed from the cube and only sales of the stores in Eindhoven are considered. Slicing the cube for the year “2012” implies removing the time dimension and only considering sales in 2012 (...)

    Definition 7 (Slice). Let $PCS = (D, type, hier )$ be a process cube structure and $PCV = (D_{sel}, sel)$ a view of $PCS$ . For any $d \in D_{sel}$ and $V \in sel(d): slice_{d,V} (PCV) = (D'_{sel}, sel' )$ with $D'_{sel} = D_{sel} \ {d}$, $sel' (d) = {V}$, and $sel' (d') = sel(d')$ for $d' \in D \ {d}$.

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: Slice: An OLAP function. It is an operation whereby a subset of a multi-dimensional array (or cube) corresponding to a single value for one or more members of the dimensions not in the subset is selected at a given concept level. A slice of a cube is also the result of a slice operation. For example, if the member United States is selected from the Location dimension, then the sub-cube of all the remaining dimensions is the slice that is specified. The data omitted from this slice would be any data associated with the non-selected members of the Location dimension, for example Canada, Mexico, etc. From an end user perspective, the term slice most often refers to a two-dimensional page selected from the cube.

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