Query Processing in Data Integration (Gathering and Using Source Statistics)

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Query Processing in Data Integration(Gathering
and Using Source Statistics)
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Query Optimization Challenges
-- Deciding what to optimize --Getting the
statistics on sources --Doing the optimization
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Information Integration
Text/Data Integration
Service integration
Data Integration
Collection Selection
Data aggregation (vertical integration)
Data Linking (horizontal integration)
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Extremes of automation in Information Integration

• Fully automated II (blue sky for now)
• Get a query from the user on the mediator schema
• Go discover relevant data sources
• Figure out their schemas
• Map the schemas on to the mediator schema
• Reformulate the user query into data source
  queries
• Optimize and execute the queries
• Return the answers

• Fully hand-coded II
• Decide on the only query you want to support
• Write a (java)script that supports the query by
  accessing specific (pre-determined) sources,
  piping results (through known APIs) to specific
  other sources
• Examples include Google Map Mashups

(most interesting action is in between)
E.g. We may start with known sources and their
known schemas, do hand-mapping and support
automated reformulation and optimization
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What to Optimize

• Traditional DB optimizers compare candidate plans
  purely in terms of the time they take to produce
  all answers to a query.
• In Integration scenarios, the optimization is
  multi-objective
• Total time of execution
• Cost to first few tuples
• Often, the users are happier with plans that give
  first tuples faster
• Coverage of the plan
• Full coverage is no longer an iron-clad
  requirement
• Too many relevant sources, Uncontrolled overlap
  between the sources
• Cant call them all!
• (Robustness,
• Access premiums)

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Roadmap

• We will first focus on optimization issues in
  vertical integration (data aggregation )
  scenarios
• Learning source statistics
• Using them to do source selection
• Then move to optimization issues in horizontal
  integration (data linking) scenarios.
• Join optimization issues in data integration
  scenarios

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Query Processing Issues in Data Aggregation

• Recall that in DA, all sources are exporting
  fragments of the same relation R
• E.g. Employment opps bibliography records
  item/price records etc
• The fragment of R exported by a source may have
  fewer columns and/or fewer rows
• The main issue in DA is Source Selection
• Given a query q, which source(s) should be
  selected and in what order
• Objective Call the least number of sources that
  will give most number of high-quality tuples in
  the least amount of time
• Decision version Call k sources that .
• Quality of tuples may be domain specific (e.g.
  give lowest price records) or domain independent
  (e.g. give tuples with fewest null values)

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Issues affecting Source Selection in DA

• Source Overlap
• In most cases you want to avoid calling
  overlapping sources
• but in some cases you want to call overlapping
  sources
• E.g. to get as much information about a tuple as
  possible to get the lowest priced tuple etc.
• Source latency
• You want to call sources that are likely to
  respond fast
• Source quality
• You want to call sources that have high quality
  data
• Domain independent E.g. High density (fewer null
  values)
• Domain specific E.g. sources having lower cost
  books
• Source consistency?
• Exports data that is error free

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Learning Source Statistics

• Coverage, overlap, latency, density and quality
  statistics about sources are not likely to be
  exported by sources!
• Need to learn them
• Most of the statistics are source and query
  specific
• Coverage and Overlap of a source may depend on
  the query
• Latency may depend on the query
• Density may depend on the query
• Statistics can be learned in a qualitative or
  quantitative way
• LCW vs. coverage/overlap statistics
• Feasible access patterns vs. binding pattern
  specific latency statistics
• Quantitative is more general and amenable to
  learning
• Too costly to learn statistics w.r.t. each
  specific query
• Challenge Find right type of query classes with
  respect to which statistics are learned
• Query class definition may depend on the type of
  statistics
• Since sources, user population and network are
  all changing, statistics need to be maintained
  (through incremental changes)

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Source Limitations

• Sources are not really fully-relational databases
• Legacy systems
• Limited access patters
• (Cans ask a white-pages source for the list of
  all numbers)
• Limited local processing power
• Typically only selections (on certain attributes)
  are supported
• Access limitations modeled in terms of allowed
  (feasible) binding patterns with which the
  source can be accessed
• E.g. S(X,Y,Z) with feasible patterns f,f,b or
  b,b,f

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Access Restrictions Recursive Reformulations

• Create Source S1 as
• select
• from Cites
• given paper1
• Create Source S2 as
• select paper
• from ASU-Papers
• Create Source S3 as
• select paper
• from AwardPapers
• given paper
• Query select from AwardPapers

S1bf(p1,p2) - cites(p1,p2) S2(p) -
Asp(p) S3b(p) - Awp(p) Q(p) - Awp(p) Awp(p)
- S3b(p) Asp(p) -
S2(p) Cites(p1,p2) - S1bf(p)
Dom(p) - S2(p) Dom(p) - Dom(p1), S1(p1,p)
KwokWeld, 96 Duschka Levy, 97
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Learning Using Source Statistics
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Managing Source Overlap

• Often, sources on the Internet have overlapping
  contents
• The overlap is not centrally managed (unlike
  DDBMSdata replication etc.)
• Reasoning about overlap is important for plan
  optimality
• We cannot possibly call all potentially relevant
  sources!
• Qns How do we characterize, get and exploit
  source overlap?
• Qualitative approaches (LCW statements)
• Quantitative approaches (Coverage/Overlap
  statistics)

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Local Completeness Information

• If sources are incomplete, we need to look at
  each one of them.
• Often, sources are locally complete.
• Movie(title, director, year) complete for years
  after 1960, or for American directors.
• Question given a set of local completeness
  statements, is a query Q a complete answer to Q?

Problems 1. Sources may not be
interested in giving these! ?Need to learn
?hard to learn! 2. Even if sources are
willing to give, there may not be any
big enough LCWs Saying I
definitely have the car with
vehicle ID XXX is useless
Advertised description
True source contents
Guarantees (LCW Inter-source comparisons)
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Quantitative ways of modeling inter-source overlap

• Coverage Overlap statistics Koller et. al.,
  97
• S1 has 80 of the movies made after 1960 while
  S2 has 60 of the movies
• S1 has 98 of the movies stored in S2
• Computing cardinalities of unions given
  intersections

Who gives these statistics? -Third party
-Probing
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BibFinder Case Study

• See the bibfinder slides

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Case Study BibFinder

• BibFinder A popular CS bibliographic mediator
• Integrating 8 online sources DBLP, ACM DL, ACM
  Guide, IEEE Xplore, ScienceDirect, Network
  Bibliography, CSB, CiteSeer
• More than 58000 real user queries collected
• Mediated schema relation in BibFinder
• paper(title, author,
  conference/journal, year)
• Primary key titleauthoryear
• Focus on Selection queries
• Q(title, author, year) - paper(title, author,
  conference/journal, year),
• 
  conferenceSIGMOD

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Selecting top-K sources for a given query

• Given a query Q, and sources S1.Sn, we need the
  coverage and overlap statistics of sources Si
  w.r.t. Q
• P(SQ) is the coverage (Probability that a random
  tuple belonging to Q is exported by source S)
• P(S1..SjQ) is the overlap between S1..Sj
  w.r.t. query Q (Probability that a random tuple
  belonging to Q is exported by all the sources
  S1..Sj).
• If we have the coverage and overlap statistics,
  then it is possible to pick the top-K sources
  that will give maximal number of tuples for Q.

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Computing Effective Coverage provided by a set of
sources
Suppose we are calling 3 sources S1, S2, S3 to
answer a query Q. The effective coverage we
get is P(S1US2US3Q). In order to compute
this union, we need the intersection (overlap)
statistics (in addition to the coverage
statistics)
Given the above, we can pick the optimal
3-sources for answering Q by considering all
3-sized subsets of source set S1.Sn, and
picking the set with highest coverage
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Selecting top-K sources the greedy way
Selecting optimal K sources is hard in general.
One way to reduce cost is to select sources
greedily, one after other. For example, to
select 3 sources, we select first source Si as
the source with highest P(SiQ) value. To
pick the jth source, we will compute the residual
coverage of each of the remaining sources,
given the 1,2j-1 sources we have already
picked The residual coverage computation
requires overlap statistics). For example
picking a third source in the context of
sources S1 and S2 will require us to calculate
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Response time can depend on the query type
Range queries on year
Effect of binding author field
--Response times can also depend on the time of
the day, and the day of the week.
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Multi-objective Query optimization

• Need to optimize queries jointly for both high
  coverage and low response time
• Staged optimization wont quite work.
• An idea Make the source selection be dependent
  on both (residual)coverage and response time

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Results on BibFinder
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Challenges in gathering overlap statistics

• Sources are incomplete and partially overlapping
• Calling every possible source is inefficient and
  impolite
• Need coverage and overlap statistics to figure
  out what sources are most relevant for every
  possible query!

• We introduce a frequency-based approach for
  mining these statistics

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Outline

• Motivation
• BibFinder/StatMiner Architecture
• StatMiner Approach
• Automatically learning AV Hierarchies
• Discovering frequent query classes
• Learning coverage and overlap Statistics
• Using Coverage and Overlap Statistics
• StatMiner evaluation with BibFinder
• Related Work
• Conclusion

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Motivation

• We introduce StatMiner
• A threshold based hierarchical mining approach
• Store statistics w.r.t. query classes
• Keep more accurate statistics for more frequently
  asked queries
• Handling the efficiency and accuracy tradeoffs by
  adjusting the thresholds

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BibFinder/StatMiner
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Query List
Each query q corresponds to a Vector of
coverage/overlap Statistics. If there are 3
sources S1, s2, s3, we have P(S1q),P(S2q),
P(S3q) P(S1S2q), P(S2S3q) P(S1S3q)
P(S1S2S3q) ?A sparse vector with
exponential dimensions ?By keeping
thresholds on min overlap, we can avoid
remembering small values ?The larger the
thresholds, the sparser the vectors
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Issues in Storing Using Statistics

• Storing statistics for each query is
  disadvantageous
• Too many queries
• Stored statistics can only be useful if the same
  query comes up again
• Idea1 Focus on only frequently asked queries
• Idea 2 Store statistics w.r.t. query classes
• Generate query classes by clustering..
• When a new query comes, we can map it to some
  existing query classes
• But Clustering directly on queries wont work
• Because we wont know how to map a new query into
  existing query classes
• Idea First do subspace clusteringcluster
  attribute values
• A query class is then defined as a cross product
  of attribute value clusters

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AV Hierarchies and Query Classes
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StatMiner
A query is a vector of overlap statistics
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Learned Conference Hierarchy
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Using Coverage and Overlap Statistics to Rank
Sources
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Outline

• Motivation
• BibFinder/StatMiner Architecture
• StatMiner Approach
• Automatically learning AV Hierarchies
• Discovering frequent query classes
• Learning coverage and overlap Statistics
• Using Coverage and Overlap Statistics
• StatMiner evaluation with BibFinder
• Related Work
• Conclusion

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BibFinder/StatMiner Evaluation

• Experimental setup with BibFinder
• Mediator relation Paper(title,author,conference/j
  ournal,year)
• 25000 real user queries are used. Among them
  4500 queries are randomly chosen as test queries.
  
• AV Hierarchies for all of the four attributes
  are learned automatically.
• 8000 distinct values in author, 1200 frequent
  asked keywords itemsets in title, 600 distinct
  values in conference/journal, and 95 distinct
  values in year.

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Learned Conference Hierarchy
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Space Consumption for Different minfreq and
minoverlap

• We use a threshold on the support of a class,
  called minfreq, to identify frequent classes
• We use a minimum support threshold minoverlap to
  prune overlap statistics for uncorrelated source
  sets.
• As we increase any of the these two thresholds,
  the memory consumption drops, especially in the
  beginning.

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Accuracy of the Learned Statistics

• Absolute Error
• No dramatic increases
• Keeping very detailed overlap statistics would
  not necessarily increase the accuracy while
  requiring much more space. For example
  minfreq0.13 and minoverlap0.1 versus
  minfreq0.33 and minoverlap0

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Plan Precision

• Here we observe the average precision of the
  top-2 source plans
• The plans using our learned statistics have high
  precision compared to random select, and it
  decreases very slowly as we change the minfreq
  and minoverlap threshold.

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Plan Precision on Controlled Sources
We observer the plan precision of top-5 source
plans (totally 25 simulated sources). Using
greedy select do produce better plans. See
Section 3.8 and Section 3.9 for detailed
information
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Number of Distinct Results

• Here we observe the average number of distinct
  results of top-2 source plans.
• Our methods gets on average 50 distinct answers,
  while random search gets only about 30 answers.

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Applications

• Path Selection in Bioinformatics LNRV03
• More and More Bioinformatics sources available on
  Internet
• Thousands of paths existing for answering users
  queries
• Path Coverage and Overlap Statistics are needed
• Text Database Selection in Information Retrieval
• StatMiner can provide a better way of learning
  and storing representatives of the databases
• Main Ideas
• Maintain a query list and discover frequent asked
  keyword-sets
• Learn keyword-set hierarchy based on the
  statistics distance
• Learn and store coverage (document frequency) for
  frequent asked keyword-set classes.
• A new query will be mapped to a set of close
  classes and use their statistics to estimate
  statistics for the query.
• Advantages
• Multiple-word-term Scalability

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Latency statistics(Or what good is coverage
without good response time?)

• Sources vary significantly in terms of their
  response times
• The response time depends both on the source
  itself, as well as the query that is asked of it
• Specifically, what fields are bound in the
  selection query can make a difference
• ..So, learn statistics w.r.t. binding patterns

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Query Binding Patterns

• A binding pattern refers to which arguments of a
  relational query are bound
• Given a relation S(X,Y,Z)
• A query S(Rao, Y, Tom) has binding pattern
  bfb
• A query S(X,Y, TOM) has binding pattern ffb
• Binding patterns can be generalized to take
  types of bindings
• E.g. S(X,Y,1) may be ffn (n being numeric
  binding) and
• S(X,Y, TOM) may be ffs (s being string binding)
• Sources tend to have different latencies based on
  the binding pattern
• In extreme cases, certain binding patterns may
  have infinite latency (i.e., you are not allowed
  to ask that query)
• Called infeasible binding patterns

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(Digression)

• LCWs are the qualitative versions of
  quantitative coverage/overlap statistics
• Feasible binding patterns are qualitative
  versions of quantitative latency statistics

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Binding-specific latency stats are more effective
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Combining coverage and response time

• Qn How do we define an optimal plan in the
  context of both coverage/overlap and response
  time requirements?
• An instance of multi-objective optimization
• General solution involves presenting a set of
  pareto-optimal solutions to the user and let
  her decide
• Pareto-optimal set is a set of solutions where no
  solution is dominated by another one in all
  optimization dimensions (i.e., both better
  coverage and lower response time)
• Another idea is to combine both objectives into a
  single weighted objective

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Combining Coverage Repsonse TimeTwo Models
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It is possible to optimize for first tuples
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Different kinds of plans