CMPT 354, Simon Fraser University, Fall 2008, Martin Ester 391 Database Systems I Data Warehousing

CMPT 354, Simon Fraser University, Fall 2008, Martin Ester 1

Database Systems I

Data Warehousing


IntroductionIncreasingly, organizations are analyzing current and historical data to identify useful patterns and support business strategies (Decision Support).Emphasis is on complex, interactive, exploratory analysis of very large datasets created by integrating data from across all parts of an enterprise; data is fairly static.Contrast such On-Line Analytic Processing (OLAP) with traditional On-line Transaction Processing (OLTP): mostly long queries, instead of short update transactions.


DBS for Decision SupportData Warehouse: Consolidate data from many sources in one large repository.

Loading, periodic synchronization of replicas.Semantic integration.

OLAP: Complex SQL queries and views. Queries based on “multidimensional” view of data and spreadsheet-style operations.Interactive and “online” (manual) analysis.

Data Mining: Automatic discovery of interesting trends and other patterns.


Data Warehousing

A Data Warehouse is a subject oriented, integrated, time variant, non volatile collection of data for the purpose of decision support. Integrates data from several operational (OLTP) databases.Keeps (relevant part of the) history of the data.Views data at a more abstract level than OLTP systems (aggregate over many detail records).


Data Warehouse Architecture

EXTERNAL DATA SOURCES EXTRACT

INTEGRATETRANSFORM LOAD / REFRESH

DATAWAREHOUSE

MetadataRepository

SUPPORTS

OLAPDATAMINING


Data Warehousing

Integrated data spanning long time periods, often augmented with summary information.Data warehouse keeps the history. Therefore, several gigabytes to terabytes common.Interactive response times expected for complex queries. On the other hand, ad-hoc updates uncommon.


Data Warehousing IssuesSemantic integration: When getting data from multiple sources, must eliminate mismatches, e.g., different currencies, DB schemas.Heterogeneous sources: Must access data from a variety of source formats and repositories.

Replication capabilities can be exploited here.

Load, refresh, purge: Must load data, periodically refresh it, and purge too-old data.Metadata management: Must keep track of source, loading time, and other information for all data in the warehouse.


Multidimensional Data ModelConsists of a collection of dimensions (independent variables) and (numeric) measures (dependent variables).Each entry (cell) aggregates the value(s) of the measure(s) for all records that fall into that cell, i.e. for all records that in each dimension have attribute values corresponding to the value of the cell in this dimension.Example: dimensions Product (pid), Location (locid), and Time (timeid) and measure Sales.


Multidimensional Data Model

11 1 1 25

11 2 1 8

11 3 1 15

12 1 1 30

12 2 1 20

12 3 1 50

13 1 1 8

13 2 1 10

13 3 1 10

11 1 2 35

pid

tim

eid

loci

dsa

les

8 10 10

30 20 50

25 8 15

1 2 3 timeid

p

id11

12

13

locid

Slice locid=1is shown

Tabular representation

Multidimensional representation


Multidimensional Data Model

For each dimension, the set of values can be organized in a concept hierarchy (subset relationship), e.g.

PRODUCT TIME LOCATION

category week month state

pname date city

year

quarter country


Multidimensional Data ModelMultidimensional data can be stored physically in a (disk-resident, persistent) array; called MOLAP (multi-dimensional OLAP) systems. Alternatively, can store as a relation; called ROLAP (relational OLAP) systems. The main relation, which relates dimensions to a measure, is called the fact table. Each dimension can have additional attributes and an associated dimension table.E.g., fact table Transactions(pid, locid, timeid, sales) and (one of the) dimension table Products(pid, pname, category, price)Fact tables are much larger than dimensional tables.


OLAP QueriesInfluenced by SQL and by spreadsheets.A common operation is to aggregate a measure over one or more dimensions.

Find total sales.Find total sales for each city, or for each state.Find top five products ranked by total sales.

We can aggregate at different levels of a dimension hierarchy. A roll-up operation aggregates along the next higher level of the dimension hierarchy.

E.g., given total sales by city, we can roll-up to get sales by state.


OLAP QueriesDrill-down: The inverse of roll-up.

E.g., given total sales by state, can drill-down to get total sales by city.E.g., can also drill-down on different dimension to get total sales by product for each state.

Pivoting: Aggregation on selected dimensions.

E.g., pivoting on Location and Time yields this cross-tabulation:

Slicing and Dicing: Equality and range selections on one or more dimensions.

63 81 144

38 107 145

75 35 110

WI CA Total

1995

1996

1997

176 223 339Total


Comparison with SQL QueriesThe cross-tabulation obtained by pivoting can also be computed using a collection of SQL queries, e.g.

SELECT SUM(S.sales)FROM Sales S, Times T, Locations LWHERE S.timeid=T.timeid AND S.timeid=L.timeidGROUP BY T.year, L.state

SELECT SUM(S.sales)FROM Sales S, Times TWHERE S.timeid=T.timeidGROUP BY T.year

SELECT SUM(S.sales)FROM Sales S, Location LWHERE S.timeid=L.timeidGROUP BY L.state

SELECT SUM(S.sales) FROM Sales S


The Cube OperatorGeneralizing the previous example, if there are d dimensions, we have 2d possible SQL GROUP BY queries that can be generated through pivoting on a subset of dimensions (without considering selections of specific values for certain dimensions).A Data Cube is a multi-dimensional model of a datawarehouse where the domain of each dimension is extended by the special value „ALL“ with the semantics of aggregating over all values of the corresponding dimension.


The Cube OperatorAn entry of a data cube is called a cell. The number of cells of a datacube with d dimensions is

Each SQL group corresponds to a datacube cell.A single of the 2d different SQL GROUP BY queries can compute the measures for multiple datacube cells.

)1|(|1

d

iiDomain


The Cube OperatorThe Cube Operator computes the measures for all cells (evaluates all possible GROUP BY queries) at the same time.It can be much more efficiently processed than the set of all corresponding (independent) SQL GROUP BY queries.Observation: The results of more generalized queries (with fewer GROUP BY attributes) can be derived from more specialized queries (with more GROUP BY attributes) by aggregating over the irrelevant GROUP BY attributes.


The Cube OperatorProcess more specialised queries first and, based on their results, determine the outcome of more generalised queries. Significant reduction of I/O cost, since intermediate results are much smaller than original (fact) table.


The Cube OperatorLattice of GROUP-BY queries of a CUBE query w.r.t. derivability of the resultsExample

{pid, locid, timeid}

{pid, locid} {pid, timeid} {locid, timeid}

{pid} {locid} {timeid}

{}{A,B,. . .}: set of GROUP BY attributes, X Y: Y derivable from X


Implementation IssuesIn the following, adopting a ROLAP implementation.Fact table normalized (redundancy free).Dimension tables un-normalized.Dimension tables are small; updates/inserts/deletes are rare. So, anomalies less important than query performance.This kind of schema is very common in OLAP applications, and is called a star schema; computing the join of all these relations is called a star join.


Implementation IssuesExample star schema

price

category

pname

pid country

statecitylocid

sales

locidtimeid

pid

holiday_flag

weekdate

timeid month

quarter

year

SALES

TIMES

PRODUCTS LOCATIONS

Fact table: SalesDimension tables: Times, Products, Locations


Bitmap IndexesNew indexing techniques: Bitmap indexes, Join indexes, array representations, compression, precomputation of aggregations, etc.Example Bitmap index:

10100110

112 Joe M 3115 Ram M 5

119 Sue F 5

112 Woo M 4

00100000010000100010

sex custid name sex rating ratingM

FBit-vector:1 bit for eachpossible value.

One row per record.


Bitmap IndexesSelections can be processed using (efficient!) bit-vector operations.Example 1: Find all male customers

Example 2: Find all male customer with a rating of 3 AND the relevant bit-vectors from the bitmap indexes for sex and rating

10100110

112 Joe M 3115 Ram M 5

119 Sue F 5

112 Woo M 4

00100000010000100010

sex custid name sex rating rating


Join IndexesConsider the join of Sales, Products, Times, and Locations, possibly with additional selection conditions (e.g., country=“USA”).A join index can be constructed to speed up such joins (in a relatively static data warehouse). It basically materializes the result of a join.The index contains [s,p,t,l] if there are tuples with sid s in Sales, pid p in Products, timeid t in Times and locid l in Locations that satisfy the join (and selection) conditions.


Join IndexesProblem: Number of join indexes can grow rapidly.In order to efficiently support all possible selections in a data cube, you need one join index for each subset of the set of dimensions.E.g, one join index each for[s,p,t,l], [s,p,t], [s,p,l], [s,t,l], [s,p], [s,t], [s,l]


Bitmapped Join IndexesA variation of join indexes addresses this problem, using the concept of Bitmap indexes.For each attribute of each dimension table with an additional selection (e.g., country), build a Bitmap index.Index contains, e.g., entry [c,s] if a dimension table tuple with value c in the selection column joins with a Sales tuple with sid s. Note that s denotes the compound key of the fact table, e.g. [pid, timeid, locid].The Bitmap index version is especially efficient (Bitmapped Join Index).


Bitmapped Join Indexes

Consider a query with conditions price=10 and country=“USA”. Suppose tuple (with sid) s in Sales joins with a tuple p with price=10 and a tuple l with country =“USA”. There are two (Bitmap) join indexes; one containing [10,s] and the other [USA,s].Intersecting these indexes tells us which tuples in Sales are in the join and satisfy the given selection.

price

category

pname

pid country

statecitylocid

sales

locidtimeid

pid

holiday_flag

weekdate

timeid month

quarter year

SALES

TIMES

PRODUCTS LOCATIONS


Sequences in SQLSQL-92 supports only (unordered) sets of tuples.Trend analysis is difficult to do in SQL-92, e.g.:

Find the % change in monthly salesFind the top 5 product by total salesFind the trailing n-day moving average of sales

The first two queries can be expressed with difficulty, but the third cannot even be expressed in SQL-92 if n is a parameter of the query.The WINDOW clause in SQL:1999 allows us to formulate such queries over a table viewed as a sequence of tuples (implicitly, based on user-specified sort keys).


The WINDOW ClauseA window is an ordered group of tuples around each (reference) tuple of a table.The order within a window is determined based on an attribute specified by the SQL statement.The width of the window is also specified by the SQL statement.The tuples of the window can be aggregated using the standard (set-oriented) SQL aggregate functions (SUM, AVG, COUNT, . . .).SQL:1999 also introduces some new (sequence-oriented) aggregate functions, in particular

RANK, DENSE_RANK, PERCENT_RANK.


The WINDOW Clause

Let the result of the FROM and WHERE clauses be “Temp”.Conceptually, Temp is partitioned according to the PARTITION BY clause. Similar to GROUP BY, but the answer has one tuple for each tuple in a partition, not one tuple per partition!Each partition is sorted according to the ORDER BY clause.

SELECT L.state, T.month, AVG(S.sales) OVER W AS movavgFROM Sales S, Times T, Locations LWHERE S.timeid=T.timeid AND S.locid=L.locidWINDOW W AS (PARTITION BY L.state

ORDER BY T.monthRANGE BETWEEN INTERVAL `1’ MONTH PRECEDINGAND INTERVAL `1’ MONTH FOLLOWING);


The WINDOW Clause

For each tuple in a partition, the WINDOW clause creates a “window” of nearby (preceding or succeeding) tuples.

Definition of window width can be value-based, as in example, using RANGE.Can also be based on number of tuples to include in the window, using ROWS clause.

The aggregate function is evaluated for each tuple in the partition based on the corresponding window.

SELECT L.state, T.month, AVG(S.sales) OVER W AS movavgFROM Sales S, Times T, Locations LWHERE S.timeid=T.timeid AND S.locid=L.locidWINDOW W AS (PARTITION BY L.state

ORDER BY T.monthRANGE BETWEEN INTERVAL `1’ MONTH PRECEDINGAND INTERVAL `1’ MONTH FOLLOWING);


Top N QueriesSometimes, want to find only the „best“ answers (e.g., web search engines).If you want to find only the 10 (or so) cheapest cars, the DBMS should avoid computing the costs of all cars before sorting to determine the 10 cheapest.Idea: Guess a cost c such that

the 10 cheapest cars all cost less than c, and thatnot too many other cars cost less than c.

Then add the selection cost<c and evaluate the query.If the guess is right: we avoid computation for cars that cost more than c.If the guess is wrong: need to reset the selection and recompute the original query.


Top N Queries

„Cut-off value“ c is chosen by query optimizer

SELECT TOP 10 P.pid, P.pname, S.salesFROM Sales S, Products PWHERE S.pid=P.pid AND S.locid=1 AND S.timeid=3ORDER BY S.sales DESC

SELECT P.pid, P.pname, S.salesFROM Sales S, Products PWHERE S.pid=P.pid AND S.locid=1 AND S.timeid=3

AND S.sales > cORDER BY S.sales DESC


Online AggregationConsider an aggregate query, e.g., finding the average sales by state. If we do not have a corresponding (materialized) data cube, processing this query from scratch can be very expensive.In general, we have to scan the entire fact table.But the user expects interactive response time.An approximate result may be acceptable to the user.


Online AggregationCan we provide the user with some approximate results before the exact average is computed for all states?Can show the current “running average” for each state as the computation proceeds.Even better, we can use statistical techniques and sample tuples to aggregate instead of simply scanning the aggregated table.E.g., we can provide bounds such as “the average for Wisconsin is 2000 102 with 95% probability“.


SummaryDecision support is an emerging, rapidly growing subarea of database systems.Involves the creation of large, consolidated data repositories called data warehouses.Warehouses exploited using sophisticated analysis techniques: complex SQL queries and OLAP “multidimensional” queries (or automatic data mining methods).New techniques for database design, indexing, view maintenance, and interactive (online) querying need to be developed.

Documents

CMPT 354, Simon Fraser University, Fall 2008, Martin Ester 391 Database Systems I Data Warehousing