efficient discovery of xml data redundancies
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Efficient Discovery of XML Data Redundancies. Cong Yu and H. V. Jagadish University of Michigan, Ann Arbor - VLDB 2006, Seoul, Korea September 12 th , 2006. Talk Outline. Motivating Example A Comprehensive Notion of XML FD XML Redundancy Discovery Algorithms Experimental Evaluation - PowerPoint PPT PresentationTRANSCRIPT
Efficient Discovery of XML Data Redundancies
Cong Yu and H. V. JagadishUniversity of Michigan, Ann Arbor
-VLDB 2006, Seoul, Korea September 12th, 2006
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Talk Outline•Motivating Example
•A Comprehensive Notion of XML FD
•XML Redundancy Discovery Algorithms
•Experimental Evaluation
•Conclusion
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An Example XML Document
warehouse
state state
store
bookname
book
store
name
book
state
ISBN title au au
“Borders”“Borders”
“… 269”“DB” “R.R.”“J.G.”
store
name“Amazon”
ISBN title“… 269” “DB”
ISBN title au au“… 269” “DB” “R.R.”“J.G.”
price“$59.9” price
“$51.1”
price“$59.9”
… …
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• An example constraint:For any two books, if they have the same ISBN, then they have the same title.
• Similar to Equality Generating Dependencies (EGDs) [BV84] and Nested EGDs [YP04]
Constraints on XML Data
TargetCondition
Element(s)Implication Element(s)
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Data Redundancies•E.g., title is redundantly stored•Result of “non-optimal” design of the
database schema in the presence of constraints
•Lead to: Update anomalies Increased cost for data transfer and
manipulation
•Constraints are the properties of data May not be known at the design phase
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GoalEfficiently Discover
Redundancies From the XML Database By Discovering
Satisfied Constraints
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Main Contributions•A comprehensive notion of XML FD
Capturing a semantically richer set of XML constraints
Definition of XML data redundancy in terms of XML FDs and XML Keys
•Efficient algorithms for discovering FDs and data redundancies from an XML database
•Experimental Evaluation
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Talk Outline•Motivating Example
•A Comprehensive Notion of XML FD
•XML Redundancy Discovery Algorithms
•Experimental Evaluation
•Conclusion
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Example XML Constraints• Hierarchical: condition and/or implication
elements can come from multiple hierarchies
state
store
bookname
book
store
name
book
state
ISBN title au au
“Borders”“Borders”
“… 269”“DB” “R.R.”“J.G.”
store
name“Amazon”
ISBN title“… 269” “DB”
ISBN title au au“… 269” “DB” “R.R.”“J.G.”
price“$59.9” price
“$51.1”
price“$59.9”
… …
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• Set elements: condition and/or implication elements can involve set elements
Example XML Constraints, Cont’d
store
bookname
book
store
name
book
ISBN title au au
“Borders”“Borders”
“… 269”“DB” “R.R.”“J.G.”
store
name“Amazon”
ISBN title“… 269” “DB”
ISBN title au au“… 269” “DB” “R.R.”“J.G.”
price“$59.9” price
“$51.1”
price“$59.9”
… …
state state
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Functional Dependencies (FDs)
•FDs are used to describe constraints in relational databases
•A similar notion of FD is needed for XML
•Challenges: Target is difficult to specify due to the
hierarchical structure Set elements introduce new semantics
XML FD needs richer semantics !
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Previous Notions• Path Based Notion [LLL02,VLL04]
Example: {/warehouse/state/store/book/ISBN} /warehouse/state/store/book/title
Format: LHS RHS Semantics: for any two RHS nodes, same
(associated) LHS indicates same RHS
• Tree Tuple Based Notion [AL04] A tree tuple is a data tree, with exactly one data
node for each schema element Format: LHS RHS Semantics: for any two tree tuples, same LHS
indicates same RHS
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• Both capture hierarchical constraints• Neither can capture set constraints• {/store/book/ISBN} /store/book/au
Violated in previous Satisfied if the two au nodes are a single set
• {/store/book/title,/store/book/au} /store/book/ISBN Undefined in previous Intuitive if au nodes are
a single set
Previous Notions, cont’d
store
bookname
ISBN title au au
“Borders”
“… 269”“DB” “R.R.”“J.G.”price
“$59.9”
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A New Comprehensive Notion•Generalized Tree Tuple
A data tree constructed around a pivot data node (np)
Entire subtree rooted at np is kept
All ancestors of np and their “attributes” are kept
•Tuple Class CP
The set of all generalized tree tuples, whose pivot nodes share the same path P (called pivot path)
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warehouse
state state
store
bookname
book
store
name
book
state
ISBN title au au
“Borders”“Borders”
“… 269”“DB” “R.R.”“J.G.”
store
name“Amazon”
ISBN title“… 269” “DB”
ISBN title au au“… 269” “DB” “R.R.”“J.G.”
price“$59.9” price
“$51.1”
price“$59.9”
… …
Example Generalized Tree Tuple
Pivot
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warehouse
state state
store
bookname
book
store
name
book
state
ISBN title au au
“Borders”“Borders”
“… 269”“DB” “R.R.”“J.G.”
store
name“Amazon”
ISBN title“… 269” “DB”
ISBN title au au“… 269” “DB” “R.R.”“J.G.”
price“$59.9” price
“$51.1”
price“$59.9”
… …
Example Generalized Tree TuplePivot
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XML FD•<CP, LHS, RHS>: LHS RHS w.r.t. CP
•Semantics:
for any two generalized tree tuple t1, t2 in CP, if they share the same LHS, they have the same RHS.
•E.g., {./title, ./au} ./ISBN, w.r.t. C/warehouse/state/store/book
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Repeatable Elements Are Special
warehouse
state state
store
bookname
book
store
name
book
state
ISBN title au au
“Borders”“Borders”
“… 269”“DB” “R.R.”“J.G.”
store
name“Amazon”
ISBN title“… 269” “DB”
ISBN title au au“… 269” “DB” “R.R.”“J.G.”
price“$59.9” price
“$51.1”
price“$59.9”
… …
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Essential Tuple Classes•Definition:
Tuple classes with pivot paths that correspond to repeatable schema elements
C/warehouse/state/store/book is essential
C/warehouse/state/store/name is not
•Express XML FDs that are expressible with non-essential tuple classes
•See paper for detailed proof
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XML Key and Data Redundancy
• Let attribute @key uniquely identify each node in the entire data tree
• <CP, LHS> is an XML Key, when the database satisfies XML FD: LHS ./@key w.r.t. CP
• Similar to the relative key notion proposed in [BDF+01]
• Data redundancy exists if the database: Satisfies the XML FD <CP, LHS, RHS>,
But <CP, LHS> is not an XML key
RHS is redundantly stored.
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Talk Outline•Motivating Example
•A Comprehensive Notion of XML FD
•XML Redundancy Discovery Algorithms
•Experimental Evaluation
•Conclusion
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Strategy•Discover satisfied XML FDs and Keys
•Data redundancies can then be discovered based on the definition
•First, we need an efficient representation of the XML data
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• Each essential tuple class a relation Similar to nested relations [OY87,MNE96] All relations together form a hierarchy Tree tuples can be reconstructed by joining @key
with parent
Hierarchical Representation of XML Data
R_state@key parent 2 root 3 root 18 root. . . . .
R_store@key parent name 4 3 Borders 12 3 Amazon 19 18 Borders
R_book@key parent ISBN title price 6 4 …269 DB $59.9 13 12 …269 DB $51.1 20 19 …269 DB $59.9
R_au@key parent @text 10 6 R.R. 11 6 J.G. 24 20 R.R. 25 20 J.G.
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Intra-Relation FDs
state
store
bookname
book
store
name
book
state
ISBN title au au
“Borders”“Borders”
“… 269”“DB” “R.R.”“J.G.”
store
name“Amazon”
ISBN title“… 269” “DB”
ISBN title au au“… 269” “DB” “R.R.”“J.G.”
price“$59.9” price
“$51.1”
price“$59.9”
… …
• {./ISBN} ./title, w.r.t. C/warehouse/state/store/book
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Present in R_book
Inter-Relation FDs
state
store
bookname
book
store
name
book
state
ISBN title au au
“Borders”“Borders”
“… 269”“DB” “R.R.”“J.G.”
store
name“Amazon”
ISBN title“… 269” “DB”
ISBN title au au“… 269” “DB” “R.R.”“J.G.”
price“$59.9” price
“$51.1”
price“$59.9”
… …
• {../name, ./ISBN} ./price, w.r.t. C/warehouse/state/store/book
Present in R_store
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Overview of the Discovery Process•Only interested in minimal FDs•Bottom-Up•At each relation
Discover intra-relation FDs and Keys Discover inter-relation FDs and Keys
involving descendant relations Generate candidate inter-relation FDs and
Keys for examination at the parent level
•Attribute Partition as the basic data structure
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Attribute Partition•Groups tuples
according to the attribute value
•∏{price} for Cbook = { {t6,t20}, {t13} }
∏{@key} for Cbook = { {t6}, {t20}, {t13} }
∏{price, @key} for Cbook = { {t6}, {t20}, {t13} }
•FD: LHS RHS w.r.t. CP is satisfied iff:
∏LHS∪RHS = ∏LHS
R_book@key parent ISBN title price 6 4 …269 DB $59.9 13 12 …269 DB $51.1 20 19 …269 DB $59.9
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Set Attribute Partition • Generated through
refinement Initialize ∏{au} for R_book to be { {t6, t13, t20} }
∏{@text} for R_au = { {t10, t24}, {t11, t25} }
{ {t6, t20}, {t6, t20} }
∏au for R_book =
{ {t6, t20}, {t13} }
• ∏au can then be used as
a normal partition
R_au@key parent @text 10 6 R.R. 11 6 J.G. 24 20 R.R. 25 20 J.G.
R_book@key parent ISBN title price 6 4 …269 DB $59.9 13 12 …269 DB $51.1 20 19 …269 DB $59.9
Convert to parent
Refine ∏{au} using partitions in ∏{@text}
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Discovery Algorithms•DiscoverFD:
Discover intra-relation FDs and Keys Similar to existing relational algorithms
•DiscoverXFD: Discover inter-relation FDs and Keys Key component:
Candidate inter-relation XML FD generation
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Generating Candidate Inter-Relation FDs
• Let P' be a parent relation of P
• Parent satisfaction property For LHS∪X RHS w.r.t. CP to hold for any
attribute set X in relation P', LHS∪{./parent} RHS w.r.t. CP must hold
• Child implication property For LHS∪X RHS w.r.t. CP to be a non-trivial FD
for any attribute set X in relation P', LHS RHS w.r.t. CP must not hold
• An FD is a candidate inter-relation FD if it satisfies both properties
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Talk Outline•Motivating Example
•A Comprehensive Notion of XML FD
•XML Redundancy Discovery Algorithms
•Experimental Evaluation
•Conclusion
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Real Datasets
• DBLP contains a fair amount of redundancy, as noted earlier in [AL04] as well
• ~ 10% redundancies in PIR (measured as # of redundant elements over total # of elements), schema modification reported to PIR
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Scalability on XMark
• Linear in terms of scale factor (# of elements) – even though exponential in theory
• Orders of magnitude faster than direct application of a state-of-the-art relational discovery algorithm The latter takes over 3 hours to run on XMark scale factor 1
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Related Work•XML Integrity Constraints (FDs and
Keys) [BDF+01], [LLL02], [FS03]
•XML Normal Form [AL04], [VLL04]
•Nested Relation Normal Form [OY87], [MNE96]
•Relational FD discovery FUN, Dep-Miner, TANE, fdep, FastFDs
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Conclusion•A comprehensive notion of XML FDs and
Keys, capturing set semantics
•A system for for detecting XML data redundancies through the discovery of FDs and Keys
•The system is practical for real datasets and out-performs direct application of the best available relational algorithm by orders of magnitude.
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Questions ?