Indexing OLAP DataIndexing OLAP DataSunita SarawagiSunita Sarawagi

Monowar Hossain

York University

AgendaAgendaRequirements on Indexing methodsExisting indexing methodsOptimization of R-Tree for OLAP dataR-Tree VS Bit-mapped IndicesConclusion

Requirements on Indexing methodsRequirements on Indexing methodsSymmetric partial match queries

– Continuous e.g. “time between Jan to July 94”– Discontinuous e.g. “first month of each year”

Indexing at multiple levels of aggregation– Pre-computation group-bys – Indexing summary data

Handing multiple traversal ordersEfficient batch updateHandling sparse data efficiently

Existing methodsExisting methods Multidimensional array-based methods

– Works efficiently when data is dense– Essbase’s schema

E.G. four dimensional cube : product and store (sparse), time and scenarios ( dense)

– B-tree on Product and Store– Two-dimensional array on time and scenarios

– Evaluation of Essbase’s schema May cause multiple searches.

– E.g. searching store = “something” on product-store index Performance depends on ability to find enough dense

dimensions. Efficient batch update

Existing methods… Cont...Existing methods… Cont... Bit mapped indices

– Pros: Low cardinality data, bit maps are both spaced and retrieval

efficient. Supports bitwise operations Access data is clustered All dimensions handles symmetrically

– Cons Range queries Increased space overhead of storing the bit-maps specially for

high cardinality data Expensive batch update as all bit mapped indices have to be

modified even for a single row insertion

Existing methods... Cont…Existing methods... Cont…Bit-mapped indices variants

– Compression– Hybrid– Dynamic Bit-maps

Existing methods... Cont…Existing methods... Cont… Hierarchical Indices

– Example: Product - Store Index product first also store summaries on product level. For each product value, create index for Store and store

summaries for product-store level

– Pros: Allows faster access to higher levels data Dimensions are symmetrically handled

– Cons: Widely used index storage overhead The average retrieval efficiency can suffer because of large

indexing structure

Existing methods… Cont…Existing methods… Cont…Multidimensional indices

– Use of of the indexed methods designed for spatial data

E.g RTree, GridFiles etc.

Optimized R-Tree of OLAP dataOptimized R-Tree of OLAP data Rectangular dense region (only the boundaries that

contain more than threshold number of points– Contains a pointer to variable length array of (TIDs or

the tuples itself)– Points in sparse regions

Finding dense regions– Ask Expert?– Use of clustering algorithm (similar algorithm: image

analysis)

Need evaluation!!

R-Tree VS Bit-mapped indicesR-Tree VS Bit-mapped indicesR-Tree Pros:

– Allows range queries– Smaller space overhead– Update is more efficient

Bit-mapped Pros:– Faster Bit-wise operation– Efficient for low cardinality, few restricted

dimensions, and sparse data.

ConclusionConclusionHigh level overviewRecommended readings

– MOLAP VS OLAP – R-Tree and variants– R-Tree alternatives – Computational of multidimensional aggregates– And More…..