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    Properties of Relational Decomposition

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    manivannan muruesan
    The document discusses properties of relational decomposition. It states that decomposition breaks down a table into multiple tables to eliminate issues like anomalies. The key properties are: 1) Attribute preservation, where attributes from the original table appear in at least one new table. 2) Dependency preservation, where dependencies in the original table are retained or can be inferred in the new tables. 3) Non-additive join, where joining the new tables does not create any spurious tuples. 4) Lossless join, where joining the new tables reproduces the original table exactly. Decomposition aims to avoid redundancy while maintaining relational properties.

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    Properties of Relational Decomposition

    Uploaded by

    manivannan muruesan
    324 views3 pages
    The document discusses properties of relational decomposition. It states that decomposition breaks down a table into multiple tables to eliminate issues like anomalies. The key properties are: 1) Attribute preservation, where attributes from the original table appear in at least one new table. 2) Dependency preservation, where dependencies in the original table are retained or can be inferred in the new tables. 3) Non-additive join, where joining the new tables does not create any spurious tuples. 4) Lossless join, where joining the new tables reproduces the original table exactly. Decomposition aims to avoid redundancy while maintaining relational properties.

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    decomposistion

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    The document discusses properties of relational decomposition. It states that decomposition breaks down a table into multiple tables to eliminate issues like anomalies. The key properties are: 1) Attribute preservation, where attributes from the original table appear in at least one new table. 2) Dependency preservation, where dependencies in the original table are retained or can be inferred in the new tables. 3) Non-additive join, where joining the new tables does not create any spurious tuples. 4) Lossless join, where joining the new tables reproduces the original table exactly. Decomposition aims to avoid redundancy while maintaining relational properties.

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    324 views3 pages

    Properties of Relational Decomposition

    Uploaded by

    manivannan muruesan
    The document discusses properties of relational decomposition. It states that decomposition breaks down a table into multiple tables to eliminate issues like anomalies. The key properties are: 1) Attribute preservation, where attributes from the original table appear in at least one new table. 2) Dependency preservation, where dependencies in the original table are retained or can be inferred in the new tables. 3) Non-additive join, where joining the new tables does not create any spurious tuples. 4) Lossless join, where joining the new tables reproduces the original table exactly. Decomposition aims to avoid redundancy while maintaining relational properties.

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    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    of 3

    Properties of Relational Decomposition

    When a relation in the relational model is not appropriate normal form then the
    decomposition of a relation is required. In a database, breaking down the table into
    multiple tables termed as decomposition. The properties of a relational decomposition are
    listed below :
    1. Attribute Preservation:
    Using functional dependencies the algorithms decompose the universal relation
    schema R in a set of relation schemas D = { R1, R2, ….. Rn } relational database
    schema, where ‘D’ is called the Decomposition of R.
    The attributes in R will appear in at least one relation schema Ri in the decomposition,
    i.e., no attribute is lost. This is called the Attribute Preservation condition of
    decomposition.
    2. Dependency Preservation:
    If each functional dependency X->Y specified in F appears directly in one of the
    relation schemas Ri in the decomposition D or could be inferred from the
    dependencies that appear in some Ri. This is the Dependency Preservation.
    If a decomposition is not dependency preserving some dependency is lost in
    decomposition. To check this condition, take the JOIN of 2 or more relations in the
    decomposition.
    For example:
    R = (A, B, C)
    F = {A ->B, B->C}
    Key = {A}

    R is not in BCNF.
    Decomposition R1 = (A, B), R2 = (B, C)
    R1 and R2 are in BCNF, Lossless-join decomposition, Dependency preserving.
    Each Functional Dependency specified in F either appears directly in one of the
    relations in the decomposition.
    It is not necessary that all dependencies from the relation R appear in some relation
    Ri.
    It is sufficient that the union of the dependencies on all the relations Ri be equivalent
    to the dependencies on R.
    3. Non Additive Join Property:
    Another property of decomposition is that D should possess is the Non Additive Join
    Property, which ensures that no spurious tuples are generated when a NATURAL
    JOIN operation is applied to the relations resulting from the decomposition.

    4. No redundancy:
    Decomposition is used to eliminate some of the problems of bad design like
    anomalies, inconsistencies, and redundancy.If the relation has no proper
    decomposition, then it may lead to problems like loss of information.

    5. Lossless Join:
    Lossless join property is a feature of decomposition supported by normalization. It is
    the ability to ensure that any instance of the original relation can be identified from
    corresponding instances in the smaller relations.
    For example:
    R : relation, F : set of functional dependencies on R,
    X, Y : decomposition of R,
    A decomposition {R1, R2, …, Rn} of a relation R is called a lossless decomposition
    for R if the natural join of R1, R2, …, Rn produces exactly the relation R.
    A decomposition is lossless if we can recover:
    R(A, B, C) -> Decompose -> R1(A, B) R2(A, C) -> Recover -> R’(A, B, C)
    Thus, R’ = R
    Decomposition is lossless if:
    X intersection Y -> X, that is: all attributes common to both X and Y functionally
    determine ALL the attributes in X.
    X intersection Y -> Y, that is: all attributes common to both X and Y functionally
    determine ALL the attributes in Y
    If X intersection Y forms a superkey of either X or Y, the decomposition of R is a
    lossless decomposition.
    STUDENT DATABASE

    RNO STDNAME STDYEA ADDRESS BRANCH BUILDING STAFFID STAFFSUBJEC STAFF


    R T AGE
    1 A 3 PUTTUR CSE B1 101 DBMS 38
    2 B 2 TIRUPATHI ECE B1 102 COA 40
    . . . . . . . .
    . . . . . . .
    100 AAA 3 NELLORE CSE D1 205 DS 50

    DECOMPOSE TABLE 1

    RNO STDNAME STDYEAR ADDRESS BRANCH BUILDING


    1 A 3 PUTTUR CSE B1
    2 B 2 TIRUPATHI ECE B1
    . . . . .
    . . . .
    100 AAA 3 NELLORE CSE D1

    DECOMPOSE TABLE 2

    RNO STAFFID STAFFSUBJEC STAFF


    T AGE
    1 101 DBMS 38
    2 102 COA 40
    . . .
    . . .
    100 205 DS 50

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