1. May 28, 2002 P2P Databases 1 Philip A. Bernstein Microsoft Research Fausto Giunchiglia Univ. of Trento Anastasios Kementsietsidis Univ. of Toronto John Mylopoulos Univ. of Toronto Luciano Serafini Univ. of Trento Ilya Zaihrayeu Univ. of Trento Data Management for Peer-to-Peer Computing: A Vision

2. May 28, 2002 P2P Databases 2 A Peer to Peer (P2P) Research Project Database and AI researchers intermittently connect to exchange research ideas … about P2P DBs Seattle Toronto Trento (Italy)

3. May 28, 2002 P2P Databases 3 Is There a Role for P2P DBs ? Peers come and go, but must still be able to interoperate. To us, the big question is how to cope with DBs that  are incomplete, overlapping, and mutually inconsistent  dynamically appear and disappear  have limited connectivity. Scenario  Databases of medical patients  Complete integration is likely to be infeasible  But dynamic integration of DBs relevant to one patient could have high value.

4. May 28, 2002 P2P Databases 4 Contributions Why P2P databases are different A P2P database scenario A logic for P2P databases Architecture and implementation issues

5. May 28, 2002 P2P Databases 5 A Model for P2P Databases Each peer is a node with a database. It exchanges data and services with acquaintances (i.e. other peers). The set of acquaintances changes often, due to  site availability  changing usage patterns Peers are fully autonomous.  No global control or central server. Hence no global schema  It would be impractical to build one for each peer  And it might be impossible because of inconsistencies

6. May 28, 2002 P2P Databases 6 A Motivating Scenario  A patient may be described in several DBs, which use different patient id formats, disease descriptions, etc.  When a patient is admitted to the hospital, H becomes acquainted with D  The acquaintance is dropped when treatment is over  When the doctor prescribes a drug, D becomes acquainted with P  A patient is injured skiing, so more DBs get involved H: Hospital P: Pharmacist D: Doctor Ski Clinic

7. May 28, 2002 P2P Databases 7 Proposal: Local Relational Model (LRM) A logic for P2P data integration Instead of a global schema, each peer has  coordination formulas – each specifies semantic interdependencies between two acquaintances  binary domain relations – each specifies how symbols in one database translate to symbols in an acquaintance’s database. Each expression in a coordination formula is relative to just one participating database Use coordination formulas and domain relations for query and update processing.

8. May 28, 2002 P2P Databases 8 A Coordination Formula p: pharmacist DB medication(PrescriptionID, Pid, Prod) d: doctor DB treatment(Tid, Pid, Description, Type) where type  {“hospital”, “home”}. (  i:x).A(x) means for all v in the domain of database i, A(v) is true. A coordination formula (  p:y).(  p:z).(p: (  x).medication(x, y, z)  d: (  w).treatment(w, y, z, “home”) ) “There’s a row in treatment in the doctor DB for each row in medication in the pharmacist DB”

9. May 28, 2002 P2P Databases 9 Domain Relation A row in domain relation r ik specifies that value d 1 in DB i corresponds to value d 2 in DB k r ik may be partial r ik,r ki need not be symmetric Example - DB i contains lengths in meters and DB k in kilometers (total but not symmetric)  r ik (x) = roundToClosestK(x) r ik (653)=1, r ik (453)=0  r ki (x) = x*1000 r ki (1)=1000

10. May 28, 2002 P2P Databases 10 Queries A query is a coordination formula of the form A(x)  i: q(x), where  A(x) is a coordination formula  x has n variables  i is the database against which the query is posed  q is a new n-ary predicate symbol A relational space is a pair where db is a set of DBs and r associates an r ik with each pair of DBs ⊨  A relational space satisfies a coordination formula  The answer to a query: {d  dom i | ⊨ ((  i:x).A(x)  i:x=d)} n

11. May 28, 2002 P2P Databases 11 Interpreting a Query A query ((i:P(x)  j:R(y))  k:S(x,y) )  h: q(x,y) Evaluate P,R,S in i,j,k (respectively) Map these results via r ih,r jh,r kh to sets s i,s j,s k And then compute ((s i  s j )  s k )

12. May 28, 2002 P2P Databases 12 View-Based Data Integration The standard model for data integration is based on  Global as view  Local as view How does this relate to LRM? Could use LRM to express view definitions  Either map “standard” view defns to LRM, or  Specify a restricted LRM for view definitions Could customize LAV/GAV query interpretation for such LRM views and queries This is work in progress.

13. May 28, 2002 P2P Databases 13 Implementation Architecture A classic multi-database system, with  A protocol for adding/dropping acquaintances  LRM query processing (domain mapping logic) that can cope with chains of acquaintances  Dynamic approach to materialized view creation Tools to help a user establish an acquaintance (e.g. yellow pages, defining domain relations & coord formulas,...) Local Information Source Wrapper Query Mgr Update Mgr User Interface LRM layer A Node P2P Network

14. May 28, 2002 P2P Databases 14 Summary Why P2P databases are different A P2P database scenario A logic for P2P databases (LRM)  Coordination formulas and domain relations  Query semantics Architecture and implementation issues

15. May 28, 2002 P2P Databases 15 Theoretical Results Provide inference rules for coordination formulas Prove that the rules are sound and complete. Define a generalized relational theory as a theory with domain closure, distinct domain values, and a finite number of possible relation extensions (CWA). Define relational multi-context system as a family of relational languages (one per database) with a generalized relational theory (in T) and a set of coordination formulas (in R) for each one. Prove that for any relational multi-context system, there’s a unique maximal relational space that satisfies it. (Generalizes Reiter’s result on CWA.) Other results on recursive queries and query evaluation.