Physical Design Patterns in Information SystemsKarim Ali & Sarah NadiCS848 – Spring 2010July 14th, 2010

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Outline Stages of Design Elements of Physical Design in Information

Systems Different Physical Designs

▪ Disk Based Relational Database Systems (DRDB)▪ Memory Based Relational Database Systems (MMDB)▪ XML Databases▪ Data Warehouses

Future Work Open Problems Summary & Conclusions

Physical Design Patterns Karim Ali & Sarah Nadi

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Stages of DesignConceptual Design

Logical Design

Physica

l Design

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•Describes the intended behavior

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Elements of Physical Design

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Indexes

Data needs to be organized for quick searching

I/O operations are expensive --> need to minimize

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Materialized Views

Repeated complicated queries should not have to be executed every time

Save execution time, and I/O reads by pre-computing the results & storing them

Materialized views are store on disk

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Paritioning

Divides the data into related partitions▪ Horizontal Partitioning: divides tables into

sets of rows according to a specific attribute (E.g. Date ranges)▪ Vertical Partitioning: divides table into the

sets of attributes that are usually accessed together

Reduces table scan time Improves performance

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Clustering

Records that are accessed together are physically located together

Reduces the number of pages to be queried

Can have multi-dimensional clustering based on more than one criteria

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Data Compression

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Sriping, Mirroring, Denormalization

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Physical Design of Different Information Systems

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Disk Based Relational Database Systems (DRDB)

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DRDB: Indexes

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DRDB: Materialized Views

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DRDB: Paritioning

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DRDB: Clustering

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DRDB: Summary

Summary table/figure

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Main Memory Database Systems (MMDB) Data resides in main memory Cheaper to access main memory

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MMDB: Indexes

Factors to consider:▪ I/O operations are cheaper▪ Should be cache conscious

Types of indexes used:▪ B+trees▪ T Trees▪ Cache Sensitive Search Trees▪ Cache Sensitive B+ Trees▪ Prefetching B+ Trees▪ J+ Trees and pJ+ trees

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MMDB: Materialized Views

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MMDB: Partioning

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MMDB: Clustering

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MMDB: Summary

Summary table/figure

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Data Warehouses

Collection of data and decision support technologies

Used in:▪ Retail: user profiling▪ Finance: claims analysis, risk analysis, credit

card analysis, and fraud detection▪ Healthcare: outcomes analysis

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DW: Challenges

Data is usually▪ Extremely large▪ Multi-dimensional

Priority for aggregated and summarized data

Ad-hoc and complex queries Expensive operations: aggregation,

and joins▪ the fact table participates in every join

Figure ??Physical Design Patterns

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DW: Design

ROLAP▪ Relational implementation of DW▪ Multidimensional view of data is achieved

through star scheme

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DW: Indexes

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DW: Materialized Views

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DW: Partitioning

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DW: Clustering

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DW: Summary

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XML Databases

XML-enabled DBs:▪ Maps XML documents to relational tables

Native XML DBs:▪ Data structures store actual XML

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XML DBs: Indexes

Same index structures can be used Need adjustments

▪ Need a numbering schema for the XML nodes

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XML DBs: Materialized Views

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XML DBs: Paritioning

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XML DBs: Clustering

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XML DBs: Summary

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Future Work & Open Problems

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Future Work

Looking at automating physical design (put some examples of work here and say its time permitting)

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Open Problems in Physical Design

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Summary & Conclusions

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Big summary table(s)

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Conclusions

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Thank you

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References

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