Data Analysis and OLAP Dr. Ms. Pratibha S. Yalagi Topic Title Name & Affiliation with mail-id Data Analysis and OLAP Institute Logo Institute name & website Dr. Ms. Pratibha S. Yalagi pratibhayalagi@gmail.com Assistant Professor, Department of Information Technology. Walchand Institute of Technology, Solapur. www.witsolapur.org

Learning Outcomes At the end of this lecture, students will be able to Specify the Learning Objectives & Outcomes At the end of this lecture, students will be able to Design the data cube for a given data set. Can apply the OLAP operations for a data cube. Add footer with Institute name, logo and page number Do not put date in the footer Walchand Institute of Technology, Solapur

Data Analysis and OLAP Be specific to the contents Online Analytical Processing (OLAP) Interactive analysis of data, allowing data to be summarized and viewed in different ways in an online fashion (with negligible delay) Data that can be modeled as dimension attributes and measure attributes are called multidimensional data. Highlight & Differentiate the points wherever necessary Dimension attributes define the dimensions on which measure attributes (or aggregates thereof) are viewed e.g. the attributes item_name, color, and size of the sales relation Measure attributes measure some value can be aggregated upon e.g. the attribute number of the sales relation Walchand Institute of Technology, Solapur

Cross Tabulation of sales by item-name and color Specify the reference of the data or contents used cross-tabulation (cross-tab), also called as pivot-table source: Database Systems Concepts Sixth Edition The table above is an example of a Values for one of the dimension attributes form the row headers Values for another dimension attribute form the column headers Other dimension attributes are listed on top Values in individual cells are (aggregates of) the values of the dimension attributes that specify the cell Walchand Institute of Technology, Solapur

Relational Representation of Cross-tabs Cross-tabs can be represented as relations We use the value all is used to represent aggregates The SQL:1999 standard actually uses null values in place of all despite confusion with regular null values Provide the data in readable & visible format Walchand Institute of Technology, Solapur

Data Cube Use the proper & standard data for the example A data cube is a multidimensional generalization of a cross-tab Can have n dimensions; we show 3 below Cross-tabs can be used as views on a data cube source: Database Systems Concepts Sixth Edition Walchand Institute of Technology, Solapur

Use the pointer & mouse click effects for stepwise elaboration OLAP Operations Pivoting: changing the dimensions used in a cross-tab Slicing: creating a cross-tab for fixed values only Sometimes called dicing, particularly when values for multiple dimensions are fixed. Dicing: Creating a sub cube for all dimensions Rollup: moving from finer-granularity data to a coarser granularity Drill down: The opposite operation-that of moving from coarser-granularity data to finer-granularity data Walchand Institute of Technology, Solapur

Pivoting Pivot allows an analyst to rotate the cube in space to see its various faces. For example, cities could be arranged vertically and products horizontally while viewing data for a particular quarter. Pivoting could replace products with time periods to see data across time for a single product. Provide the image source (reference) if taken from some other resource Image source: https://en.wikipedia.org/wiki/OLAP_cube Walchand Institute of Technology, Solapur

Image source: https://en.wikipedia.org/wiki/OLAP_cube Slicing Slice is the act of picking a rectangular subset of a cube by choosing a single value for one of its dimensions, creating a new cube with one fewer dimension. The picture shows a slicing operation: The sales figures of all sales regions and all product categories of the company in the year 2004 are "sliced" out of the data cube. Image source: https://en.wikipedia.org/wiki/OLAP_cube Walchand Institute of Technology, Solapur

Image source: https://en.wikipedia.org/wiki/OLAP_cube Dicing Dice operation produces a sub cube by allowing the analyst to pick specific values of multiple dimensions. The picture shows a dicing operation: The new cube shows the sales figures of a limited number of product categories, the time and region dimensions cover the same range as before. Image source: https://en.wikipedia.org/wiki/OLAP_cube Walchand Institute of Technology, Solapur

Rollup & Drilldown Drill Down/Up allows the user to navigate among levels of data ranging from the most summarized (up) to the most detailed (down).The picture shows a drill-down operation: The analyst moves from the summary category "Outdoor-Schutzausrüstung" to see the sales figures for the individual products. Roll-up involves summarizing the data along a dimension. The summarization rule might be computing totals along a hierarchy or applying a set of formulas such as "profit = sales - expenses" Walchand Institute of Technology, Solapur

Use the same font & color for similar points & vary the fontsize if necessary OLAP Implementation Early OLAP systems are precomputing all possible aggregates in order to provide online response Space and time requirements for doing so can be very high 2n combinations of group by It suffices to precompute some aggregates, and compute others on demand from one of the precomputed aggregates Can compute aggregate on (item-name, color) from an aggregate on (item-name, color, size) For all but a few “non-decomposable” aggregates such as median is cheaper than computing it from scratch Walchand Institute of Technology, Solapur

OLAP Implementation (Cont.) Can use maximum three colors for effective representation OLAP Implementation (Cont.) Several optimizations available for computing multiple aggregates Can compute aggregate on (item-name, color) from an aggregate on (item-name, color, size) Can compute aggregates on (item-name, color, size), (item-name, color) and (item-name) using a single sorting of the base data Walchand Institute of Technology, Solapur

Extended Aggregation in SQL:1999 Highlight the areas wherever necessary The cube operation computes union of group by’s on every subset of the specified attributes E.g. consider the query select item-name, color, size, sum(number) from sales group by cube(item-name, color, size) This computes the union of eight different groupings of the sales relation: { (item-name, color, size), (item-name, color), (item-name, size), (color, size), (item-name), (color), (size), ( ) } where ( ) denotes an empty group by list For each grouping, the result contains the null value for attributes not present in the grouping Walchand Institute of Technology, Solapur

Provide the reflection spot or takeaways at the end Exercise assignment Provide the reflection spot or takeaways at the end Apply the rollup and drill down operations on the above menswear and womenswear data separately and find out the results. Walchand Institute of Technology, Solapur

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