Describing data sources. Outline Overview Schema mapping languages.
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Transcript of Describing data sources. Outline Overview Schema mapping languages.
Source descriptions
• Which sources are available
• What data exists in each source
• How each source can be accessed
Components of a data integration system
User query is reformulated into a query over the data sources
Working exampleMediated schema:Movie(title, director, year, genre), Actors(title, name)Plays(movie, location, startTime)Reviews(title, rating, description)Data sources:S1:Movie(MID, title)Actor(AID, firstName, lastName, nationality, yearOfBirth)ActorPlays(AID, MID)MovieDetail(MID, director, genre, year)S2: S3:Cinemas(place,movie,start) NYCCinemas(name, title,
startTime)S4: S5:Reviews(title,date,grade,review) MovieGenres(title, genre)S6: S7:MovieDirectors(title, dir) MovieYears(title, year)
Components of source descriptions Schema mappings
What data exists in sources How to map terms used in source schemata with
terms used in the mediated schema Information used to optimize queries
Access pattern limitations Because data sources may differ on the access patterns
supported Source completeness
Components of source descriptions Schema mappings
What data exists in sources How to map terms used in source schemata
with terms used in the mediated schema Information used to optimize queries to the
sources and to avoid illegal access patterns Access pattern limitations
Because data sources may differ on the access patterns supported
Source completeness
Schema mapping Main component of a source description Specification of:
What data exists in the source How the terms used in the source schema relate to the
terms used in the mediated schema Needs to handle semantic heterogeneity:
discrepancies between the source schemata and the mediated schema Relation and attribute names Tabular organization Domain coverage Data-level variations
Query reformulation
Besides schema mappings, source descriptions specify information: To enable the data integration system to optimize
queries posed to the sources Knowing that a data source is known to be complete
saves work by not accessing other data sources that have overlapping data
To avoid illegal access patterns Data sources may differ on which access patterns they
support
Schema mapping languages
Schema mapping: set of expressions that describe a relationship between a set of schemata (typically two). In our case, mediator schema and the schema of the sources Used to reformulate a query formulated in terms of the mediated
schema into appropriate queries on the sources. Result is called logical query plan (query expression that refers
only to the relations in the data sources) It will not be always possible to generate a query plan that produces
all the certain answers Two types of algorithms involved:
To find the best possible logical plan To find all the certain answers
Schema mapping languages based on: query expressions
Semantics of schema mappings A semantic mapping M defines a relation MR over:
I(G) X I(S1) X .... X I(Sn)
Where: I(G) denotes the possible instances of the mediated
schema I(S1), ..., I(Sn) denote the possible instances of the source
relations S1, ..., Sn, respectively
If (g, s1, ..., sn) MR, then g is a possible instance of the mediated schema when the source relation instances are s1, ..., sn
Certain answers
Let M be a schema mapping between a mediated schema G and source schemata S1, ..., Sn that defines the relation MR over I(G) X I(S1) X... X I(Sn).
Let Q be a query over G, and let s1,..., sn be instances of the source relations.
We say that t is a certain answer of Q wrt M and s1, ..., sn
if t Q(g) for every instance g of G s.t.
(g, s1, ..., sn) MR
Properties of schema mapping languages Flexibility: the formalism should be able to express
a wide variety of relationships between schemata. Efficient reformulation: reformulation algorithms
should have well understood properties and be efficient Trade-off: flexibility/expressivness vs efficiency
Easy update: Must be easy to add and remove sources
Schema mapping languages: Global-As-View (GAV) Local-As-View (LAV) Global-Local-As-View.(GLAV)
Two systems (GAV) TSIMMIS [Garcia-Molina+97] – Stanford
Focus: semistructured data (OEM), OQL-based language (Lorel) Creates a mediated schema as a view over the sources Spawned a UCSD project called MIX, which led to a company now
owned by BEA Systems Other important systems of this vein: Kleisli/K2 @ Penn
(LAV) Information Manifold [Levy+96] – AT&T Research Focus: local-as-view mappings, relational model Sources defined as views over mediated schema Led to peer-to-peer integration approaches (Piazza, etc.)
Focus: Web-based queriable sources
Global-As-View (GAV) Defines the mediated schema as a set of
views over the data sources Mediated schema also referred as global schema
Let G be a mediated schema, and let S = {S1, ..., Sn} be schemata of n data sources,A Global-As-View schema mapping M is a set of expressions of the form:
Gi(X) Q(S) or Gi(X) = Q(S), where Gi is a relation in G, and appears in at most one
expression in M, and Q(S) is a query over the relations in S
Working exampleMediated schema:Movie(title, director, year, genre), Actors(title, name)Plays(movie, location, startTime)Reviews(title, rating, description)Data sources:S1:Movie(MID, title)Actor(AID, firstName, lastName, nationality, yearOfBirth)ActorPlays(AID, MID)MovieDetail(MID, director, genre, year)S2: S3:Cinemas(place,movie,start) NYCCinemas(name, title,
startTime)S4: S5:Reviews(title,date,grade,review) MovieGenres(title, genre)S6: S7:MovieDirectors(title, dir) MovieYears(title, year)
Example of a GAV schema mappingMovie(title, director, year, genre) S1.Movie(MID, title), S1.MovieDetail(MID, director, genre, year)
Movie(title, director, year, genre) S5.MovieGenres(title, genre),S6.MovieDirectors(title, director),S7.MovieYears(title, year)
Plays(movie, location, startTime) S2.Cinemas(location, movie, startTime)
Plays(movie, location, startTime) S3.NYCCinemas(location, movie, startTime)
GAV semantics Let M = M1, ..., Ml be a GAV schema
mapping between G and S = {S1, ..., Sn}, where Mi is of the form Gi(X) Qi(S), or Gi(X) = Qi(S).
Let g be an instance of the mediated schema G, and let s = s1, ..., sn be instances of S1, ...Sn, respectively. The tuple of instances (g, s1, ..., sn) is in MR if for every 1<=i<=l, the following holds: If Mi is a = expression, then the extension of Gi in
g is equal to the result of evaluating Qi on s, If Mi is a expression, then the extension of Gi
in g is a superset of the result of evaluating Qi on s
Reformulation in GAV
To reformulate a query posed over the mediated schema, simply unfold the query with the view definitions
The reformulation resulting from the unfolding is guaranteed to find all the certain answers
Example The query Q, over the mediated schema, asks for comedies starting after 8pm:
Q(title,location,startTime) :- Movie(title,director,year,“comedy”),Plays(title, location, st), st >= 8pm
Reformulating Q with the source descriptions would yield thefollowing four logical query plans:
Q’(title, location, startTime) :- S1.Movie(MID, title),S1.MovieDetail(MID, director, “comedy”,
year),S2.Cinemas(location, movie, st), st >= 8pm
Q’(title, location, startTime) :- S1.Movie(MID, title),MovieDetail(MID, director, “comedy”, year),S3.NYCCinemas(location, title, st), st >= 8pm
Q’(title, location, startTime) :- S5.MovieGenres(title, “comedy”), S6.MovieDirectors(title, director),S7.MovieYears(title, year), S2.Cinemas(location, title, st), st >= 8pm
Q’(title, location, startTime) :- S5.MovieGenres(title, “comedy”), S6.MovieDirectors(title, director),S7.MovieYears(title, year), S3.NYCCinemas(location, title, st), st >= 8pm
Limitations The reformulation may not be the most efficient
method to answer the query Some subgoals may be redundant
In the last two reformulations, the subgoals: S6.MovieDirectors and S7.MovieYears are not needed, since what is really needed for the Movies relations is the genre of the movie.
But there is no way of concluding this in GAV descriptions Adding and removing sources involves considerable
work and knowledge of the sources -> potentially not scalable Ex: if we discover another source that includes only movie
directors To update the source descriptions we need to specify
exactly which sources it needs to be joined with in order to produce tuples of Movie
TSIMMIS [Garcia-Molina+97]
One of the first systems to support semi-structured data according to the OEM data model, which predated XML by several years.
Mediator Specification Language (MSL): logic-based OO language used as a view definition language targeted to the OEM data model and to the integration of heterogeneous data sources Based on Datalog, among others
Wrappers accept queries expressed in MSL and compare them with the patterns (MSL templates) given in the wrapper specification file
An instance of a GAV mediation system We define our global schema as views over the sources
XML vs. Object Exchange Model<book> <author>Bernstein</author> <author>Newcomer</author> <title>Principles of TP</title></book>
<book> <author>Chamberlin</author> <title>DB2 UDB</title></book>
O1: book { O2: author { Bernstein } O3: author { Newcomer } O4: title { Principles of TP }
}
O5: book { O6: author { Chamberlin } O7: title { DB2 UDB }}
User queries in TSIMMISSpecified in OQL-style language called Lorel
OQL was an object-oriented query language that looks like SQL Lorel is, in many ways, a predecessor to XQuery
Based on path expressions over OEM structures: select book where book.title = “DB2 UDB” and book.author =
“Chamberlin”
This is basically like XQuery, which we’ll use in place of Lorel and the MSL template language. Previous query restated:
for $b in AllData()/bookwhere $b/title/text() = “DB2 UDB” and $b/author/text() = “Chamberlin”return $b
Query Answering in TSIMMIS
Basically, it’s view unfolding, i.e., composing a query with a view
The query is the one being asked The views are the MSL templates for the wrappers Some of the views may actually require
parameters, e.g., an author name, before they’ll return answers Common for web forms (see Amazon, Google, …) XQuery functions (XQuery’s version of views) support
parameters as well, so we’ll see these in action
Recall SQL View Unfolding/Expansion A view consisting of branches and their customers
Find all customers of the Perryridge branch
create view all_customer as (select branch_name, customer_name from depositor, account where depositor.account_number =
account.account_number ) union (select branch_name, customer_name from borrower, loan where borrower.loan_number = loan.loan_number )
select customer_namefrom all_customerwhere branch_name = 'Perryridge'
A Wrapper Definition in MSLWrappers have templates and binding patterns ($X) in MSL:
B :- B: <book {<author $X>}> // $$ = “select * from book where author=“ $X //
If the template is matched by the query issued to the mediator, an SQL query is issued over Book(author, year, title), which is the relation stored in the data source
In XQuery, this might look like:define function GetBook($x AS xsd:string) as book {
for $b in sql(“Amazon.DB”, “select * from book where author=‘” + $x
+”’”)return <book>{$b/title}<author>$x</author></book>
}
…
The GetBook’s results is unioned with others to form the view Mediator()
How to Answer the Query
Given our query:for $b in Mediator()/bookwhere $b/title/text() = “DB2 UDB” and
$b/author/text() = “Chamberlin”return $b
Find all wrapper definitions that: Contain enough “structure” to match the
conditions of the query Or have already tested the conditions for us!
Query Composition with Views
We find all views that define book with author and titleas output, and we compose the query with each:
define function GetBook($x AS xsd:string) as book {for $b in
sql(“Amazon.DB”, “select * from book where author=‘” + $x +
“’”)return <book> {$b/title} <author>{$x}</author></book>
}
for $b in Mediator()/bookwhere $b/title/text() = “DB2 UDB” and $b/author/text() = “Chamberlin”return $b
book
title author
…
…
Matching View Output to Our Query’s Conditions
Determine that $b/author/text() $x by matching the pattern on the function’s output:define function GetBook($x AS xsd:string) as book {
for $b in sql(“Amazon.DB”, “select * from book where author=‘” + $x +
“’”)return <book>{ $b/title } <author>{$x}</author> </book>
}
let $x := “Chamberlin”for $b in GetBook($x)/bookwhere $b/title/text() = “DB2 UDB” return $b
book
title author
… …
The Final Step: Unfoldinglet $x := “Chamberlin”
for $b in ( for $b’ in sql(“Amazon.com”,
“select * from book where author=‘” + $x + “’”) return <book>{ $b/title }<author>{$x}</author></book> )/bookwhere $b/title/text() = “DB2 UDB” return $b
This can be simplified into:
for $b in sql(“Amazon.com”,“select * from book where author=‘Chamberlin’”)
where $b/title/text() = “DB2 UDB” return $b
Virtues of TSIMMIS
Early adopter of semistructured data, greatly predating XML Can support data from many different kinds of
sources Obviously, doesn’t fully solve heterogeneity
problem Presents a mediated schema that is the union
of multiple views Query answering based on view unfolding
Easily composed in a hierarchy of mediators
Big limitation of TSIMMIS
Mediated schema is basically the union of the various MSL templates – as they change, so may the mediated schema
Local-As-View
Opposite approach to GAV Focus on describing each data source as
precisely as possible and independently of any other sources Instead of specifying how to compute tuples of the
mediated system LAV expressions describe data sources as
queries over the mediated schema
The Local-as-View Model
The basic model is the following: Local sources are views over the mediated schema Sources have the data – mediated schema is virtual Sources may not have all the data from the domain –
“open-world assumption”
The system must use the sources (views) to answer queries over the mediated schema
LAV schema mappings
Let G be a mediated schema and
let S = {S1, ..., Sn} be schemata of n data sources.
A Local-As-View schema mapping M is a set of expressions of the form Si(X) Qi(G) or Si(X) = Qi(G), where: Qi is a query over the mediated schema G, and Si is a source relation and it appears in at most
one expression in M
Recap. exampleMediated schema:Movie(title, director, year, genre), Actors(title, name)Plays(movie, location, startTime)Reviews(title, rating, description)Data sources:S1:Movie(MID, title)Actor(AID, firstName, lastName, nationality, yearOfBirth)ActorPlays(AID, MID)MovieDetail(MID, director, genre, year)S2: S3:Cinemas(place,movie,start) NYCCinemas(name, title,
startTime)S4: S5:Reviews(title,date,grade,review) MovieGenres(title, genre)S6: S7:MovieDirectors(title, dir) MovieYears(title, year)
LAV example In LAV, sources S5-S7 would be described as projection
queries over the Movie relation in the mediated schemaS5.MovieGenres(title, genre) Movie(title, director, year, genre)
S6.MovieDirectors(title, dir) Movie(title, director, year, genre)
S7.MovieYears(title, year) Movie(title, director, year, genre)
In LAV, we can express constraints on the contents of data sources
S9(title, year, “comedy”) Movie(title, director, year, “comedy”), year >= 1970
LAV semantics Let M= M1, ..., Ml be a LAV schema mapping
between G and S ={S1, ..., Sn}, where Mi is of the form Si(X) Qi(G) or Si(X) = Qi(G).
Let g be an instance of the mediated schema G, and let s = s1, ..., sn be instances of S1, ..., Sn, respectively. The tuple of instances (g, s1, ..., sn) is in MR if for every 1<=i<=l, the following holds: If Mi is an expression, then the result of evaluating Qi over
g is equal to si If Mi is a expression, then the result of evaluating Qi over
g is a subset of si
Reformulation in LAVMain advantages: flexibility + enables expressing
incomplete information data sources are described in isolation => the system, and not the
designer, will find ways of combining data from multiple sources Easier for a designer to add/remove sources
Example: Q(title) :- Movie(title, director, year, “comedy”), year >= 1960
Using sources S5-S7, we obtain the reformulation:Q’(title) :- S5.MovieGenres(title, “comedy”), S7.MovieYears(title, year), year >= 1960
Using source S9, we obtain the reformulation:Q’(title) :- S9(title, year, “comedy”)
The Information Manifold [Levy+96] When you integrate something, you have some conceptual model of the integrated domain
Define that as a basic frame of reference, everything else as a view over it
Local as View
May have overlapping/incomplete sources Define each source as the subset of a query over the
mediated schema We can use selection or join predicates to specify that a
source contains a range of values:ComputerBooks(…) Books(Title, …, Subj), Subj = “Computers”
Advantages and Shortcomings of LAV Enables expressing incomplete information More robust way of defining mediated schemas
and sources Mediated schema is clearly defined, less likely to
change Sources can be more accurately described
Computationally more expensive!
References Chapter 4, Draft of the book on “Principles of Data Integration” by
AnHai Doan, Alon Halevy, Zachary Ives (in preparation). Sudarshan Chawathe, Hector Garcia-Molina, Joachim
Hammer, Kelly Ireland, Yannis Papakonstantinou, Jeffrey Ullman, and Jennifer Widom.The TSIMMIS project: Integration of heterogeneous information sources. In proceedings of IPSJ, Tokyo, Japan, October 1994.
Alon Y. Levy, Anand Rajaraman, and Joann J. Ordille. Querying Heterogeneous Information Sources Using Source Descriptions. In Proceedings of the International Conference on Very Large Databases (VLDB), 1996.
Zach Ives, slides of the course: “Database and Information Systems”, Fall 2007, available at: http://www.seas.upenn.edu/~zives/07f/cis550/