Query Planning with Limited Source Capabilities

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Query Planning with Limited Source Capabilities

• Chen Li
• Stanford University
• Edward Y. Chang
• University of California, Santa Barbara

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Motivation

• Heterogeneous information sources on the WWW
• Information-integration systems
• Limited query capabilities
• Music stores amazon.com, cdnow.com.
• Must specify a value of Artist or Title.
• The sources do not answer queries such as Give
  me all your information about CDs.

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Example
Query Find the prices of CDs containing a song
titled Friends.
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Source tuples
Not all the tuples could be retrieved from
the sources due to the restrictions.
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Traditional approach consider each join at a
time.
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Our approach retrieve as many tuples as possible.
X
X
X
X
This approach could save the user 15 - 10 5!
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Observations

• Access views not in a join to retrieve bindings
• Recursive process
• Some tuples in the answer cannot be retrieved.

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Questions

• How to compute the maximal answer?
• When should we access sources not in a query?
• What sources should be accessed?

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Source views

• A set of source views V with binding patterns
• b a value must be specified for the attribute
• f free
• Each view schema uses a set of global attributes

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Queries

• A query Q includes
• Input attributes I
• Output attributes O.

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Connections

• Connection a set of views that connect I and O
  in Q.
• Meaning natural join of the views.
• Universal-relation-like assumptions, but
  connections can be generated in various ways.

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Question 1 Computing the maximal answer

• Translate a query and source views into a Datalog
  program.
• Borrowed the idea from Duschka and Levy
  IJCAI-97.
• We eliminate useless source accesses.
• Why Datalog programs? Recursion.

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Constructing program ?(Q,V)
Connection rules ans(P) -
V1(s1, C) V2 (C, A, P) ans(P)
- V1(s1, C) V3 (C, A, P)
Fact rule song(s1) -
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• Binding assumptions
• A binding for an attribute is from the
  attributes domain
• Do not allow the strategy of trying all the
  possible strings to test the source (may not
  terminate)
• Any binding is either obtained from the query, or
  from a tuple returned by a source query.
• The program ?(Q,V) computes the maximal answer.

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Question 2 when to access off-query sources?
Query Input A a1 Output D ? Connections
T1 v1,v3, T2 v2,v3 Not all the views need
to accessed.
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Independent connections

• A connection T is independent if all the views in
  T can be queried starting from the input
  attributes as the initial bindings and using only
  the views in T.

• Theorem off-connection source accesses are only
  necessary for nonindependent connections.

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Question 3 what sources should be accessed?

• A view v is relevant to connection T if we may
  miss some answers to T when v is not used.

• How to find all the relevant views of a
  nonindependent
• connection?

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Kernel

• A kernel of a connection is a minimal set of
  attributes that need to be initially bound in
  addition to the input attributes to query the
  full connection.

• A connection may have multiple kernels.

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Algorithm FIND_REL Finding relevant views of a
connection
Find all the relevant views of connection T2
v2,v3
(1) Compute queryable views v1,v2 ,v3,v4,v5
(2) Find a kernel K of T2 K C
(3) Compute all the views that can help produce
bindings for the attributes in K R v1,v2 ,v4

(4) Return R ? T2 v1,v2 ,v3 ,v4.
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Constructing an efficient program

• Compute the relevant views for each connection
• Take the union of all these relevant source
  views
• Use these views to construct a new program
• Remove useless rules.

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Conclusions

• A query-planning framework to compute the maximal
  answer to a query (Duschka and Levy IJCAI-97).
• Techniques for telling when to access off-query
  views
• Algorithms
• finding all the relevant sources for a query
• constructing an efficient program.

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Other related work

• Rajaraman, Sagiv, and Ullman PODS-95
• Shows how to find an equivalent query rewriting
  using views with binding restrictions
• We give the maximal rewriting of a query.
• Optimizing conjunctive queries with binding
  restrictions
• Yerneni, Li, Garcia-Molina, and Ullman ICDT-99
• Florescu et al. SIGMOD-99.
• Testing connection containment
• Li Stanford-CS-TR 2000, using results of
  monadic programs to prove the problem is
  decidable.

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Predicates
EDB predicates IDB predicates v1(S, C) V1 (S,
C) v2(C, A,P) V2 (C, A, P) v3(C, A, P) V3 (C,
A, P) cd(C) song(S) artist(A) price(P
) ans(P)
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Evaluating program ?(Q,V)

• Assume the right side of an ?-rule or a domain
  rule is
• domA1(A1), , domAp(Ap), vi(A1,, Am)
• Once we have bindings for domA1(A1), ,
  domAp(Ap), evaluate the rule and populate the
  domain predicates and ?-predicate.
• Repeat until no more facts can be derived.
• Compute the maximal answer to the query.

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Forward-closure
Given views W ? V, and attributes X, the
forward-closure of X given W, denoted
f-closure(X,W), is the the set of views in W that
can be eventually queried by using the views in
W, starting from the initial bindings X.
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Backward-closure

• Backward-closure of a set of attributes X
  b-closure(X), is the set of views that can help
  retrieve bindings for X.

b-closure(C) v1,v2,v4

• Lemma All backward-closures of a connection
  are
• the same.

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BF-chain, backward-closure

• BF-chain
• Backward-closure

b-closure(C) v1,v2,v4
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Other possibilities of obtaining bindings

• Cached data For a cached tuple ti(a1,a2) for
  view vi(A1,A2), add the following rules to the
  program ?(Q, V)
• vi(a1,a2) -
• domA1(a1) -
• domA2(a2) -
• Domain knowledge
• student(name, dept, GPA).
• dept CS, Physics, Chemistry, etc.

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Computing a partial answer

• Independent connections complete answers are
  computable.
• Nonindependent connections access some relevant
  views. May terminate evaluating the program after
  some results are computed.