Implementing Database Coordination in P2P Networks *

1
Implementing Database Coordination in P2P
Networks

• Ilya Zaihrayeu

SemPGRID-04, 18 May 2004, New York, USA
work with Fausto Giunchiglia
2
Why P2P Databases

• P2P data sharing files relational data?
• File sharing KaZaa Morpheus more than 460
  million downloads (download.com, May 2004)
• P2P databases academia testbeds so far..
• Promises large-scale fault-tolerant
  multi-database system with low start-up and
  maintenance costs, and high output for an
  individual party
• Difficulties data integration solutions are not
  applicable due to centralized nature
• Challenges new methodologies, theories and
  algorithms, models, mechanisms and tools need to
  be developed

3
Why P2P Databases, contd

• Application non performance critical domains,
  where local autonomy of each party is essential
• Medical care scenario
• John is going for skiing and suffers an accident
• John is taken to local clinic for treatment
  doctors need to know whether John has
  contraindication against some drugs
• John does not know these details, but his
  database layer has a link to family doctors
  databases
• Cooperating real estate agents example
• Agents coordinate their data to push sales
• When on the site of a customer who wants to sell,
  agent updates his database and makes data
  available for other agents
• When on the site of a customer who may want to
  buy, agent shows details from his database, and
  may query other agents databases
• Other examples scientific databases (genomic
  data), tourism, etc

4
Data Coordination Model

• Interest Groups group of peers able to answer
  queries about a certain topic
• e.g., group topic Tourism in Trentino, Real
  Estate in Scotland, etc
• each Interest Group has group manager (GM) which
  helps in maintenance of the group
• Acquaintances known nodes that contribute
  data
• acquaintance query a query over the relations
  of an acquaintance which results satisfy some
  local relation
• Correspondence Rules solve heterogeneity
  problem at instance level
• semantic heterogeneity at structure level is
  solved by acquaintance queries
• Coordination Rules coordinate data (queries and
  updates) with acquaintances

5
Interest Groups

• Help to cope with large number of nodes by
  clustering the network
• Nodes self-organize into interest groups
• A node may form a child interest group
• One node may belong to multiple groups
• Use schema matching to monitor group constitution
• GM is to support group constitution, talk to
  other GMs and provide information about the group
  to newcomers

All topics

Arts
Shopping


Movies
Publications
Computers
Music
Books
Lyrics
6
Acquaintance query

• Acquaintance query is a conjunctive query
• q(X) - r1(X1), , rn(Xn)
• q(X) head, refers to local relation
• r1(X1), , rn(Xn) subgols of the body, refers
  to the relation of an acquaintance and
  comparison predicates
• X, X1,, Xn variables or constants
• E.g., P1 films (title, year, genre) - P2 movie
  (title, year, director) genres (title, genre)
  yeargt1995

7
Correspondence Rules and Coordination Rules

• Correspondence rules define how constants from
  the local domain are translated into constants in
  the domain of an acquaintance (forward
  translation) and vice versa (backward
  translation)
• not necessarily symmetric, e.g. currency
  translation
• Coordination Rules goal is data coordination
  with acquaintances and acquainted nodes
• activated by user (user query) or from the
  network (network query, results, update)

8
Algorithmic notes

• Query answering algorithm
• Use acquaintance queries and correspondence rules
  to translate queries and data
• Propagate to acquaintances if acquaintance
  queries are relevant
• Compute only new tuples, reconcile results
• Process loops in query propagation, define
  termination point (no propagation using
  acquaintance queries that have been already used)
• Getting acquainted protocol
• Retrieve database schemas and then apply a
  matching operator on them
• Based on the matching results, generate (with
  help of user) acquaintance queries,
  correspondence rules, tune up coordination rules
• Updates handling (work with E. Franconi, G.
  Kuper, A. Lopatenko)
• Data may go through a loop more than once, define
  termination point

9
Implementing P2P databases on top of JXTA

• Benefits
• system platform, networking protocol independence
• IP-independence (location independence)
• gives basic blocks for building P2P applications
• We implement Interest Groups and Acquaintances in
  JXTA
• We encode database related functionalities into a
  set of custom JXTA services (DB-related services)

DB-related services
Node-level services
Group-level services


Queries handler
DB operations
Screening service
GM service
10
Architecture
User
A node
Nodes on the network
PDBMS
A P2P database network
User Interface (UI)
User-1
Database Manager (DBM)
User-2
JXTA Layer
Wrapper
User-n
Source Database (SDB)
SS
11
Architecture, contd
12
Demo toy databases and topology
Rendezvous peer

• Relations
• Movie (title, year, genre)
• Credits (name, title, role)
• Movie2 (title, year, director)
• Genre (title, genre)

1,2
1
(2-2)
2
3
(2-2)
(1-1)
(1-3,4)
Q
(2-2)
(4-1)
0
2
3
(3-3)
1,2
4
2,3,4
(4-4)
Mediator peer
5
4
13
Query example 1

• List titles of movies featuring Tom Hanks
• Q(t) - Credits (n,t,r) nTom Hanks

1,2
1
(2-2)
2
3
(2-2)
(1-1)
(1-3,4)
Q
(2-2)
(4-1)
0
2
3
(3-3)
1,2
(2-2)
4
2,3,4
(4-4)
5
4
14
Query example 2

• Titles of drama movies issued after 1995
• Q(t) - Movie (t,y,g) gDrama ygt1995

1,2
1
(2-2)
2
3
(2-2)
(1-1)
(1-3,4)
Q
(2-2)
(4-1)
0
2
3
(3-3)
1,2
4
2,3,4
(4-4)
5
4
15
Query example 3

• Names of actors playing in action movies in
  2003
• Q(n) - Movie (t,y,g) Credits (n,t,r)
  rActor gAction y2003

1,2
1
(2-2)
2
3
(2-2)
(1-1)
(1-3,4)
Q
(2-2)
(4-1)
0
2
3
(3-3)
1,2
4
2,3,4
(4-4)
5
4
16
References

• F. Giunchiglia and I. Zaihrayeu. Making peer
  databases interact - a vision for an architecture
  supporting data coordination. 6th International
  Workshop on Cooperative Information Agents
  (CIA-2002), Madrid, Spain, September 18 -20,
  2002.
• P. Bernstein, F. Giunchiglia, A. Kementsietsidis,
  J. Mylopoulos, L. Serafini, and I. Zaihrayeu,
  Data management for peer-to-peer computing A
  vision, WebDB, 2002.
• A. Halevy, Z. Ives, D. Suciu, and I. Tatarinov,
  Schema mediation in a peer data management
  system, ICDE, 2003.
• V. Kantere, I. Kiringa, J. Mylopoulos, A.
  Kementsietsidis, and M. Arenas, Coordinating
  peer databases using ECA rules, DBISP2P,
  September 2003.
• Enrico Franconi, Gabriel Kuper, Andrei Lopatenko,
  Ilya Zaihrayeu (2004). The coDB Robust
  Peer-to-Peer Database System. Proc. of the 2nd
  Workshop on Semantics in Peer-to-Peer and Grid
  Computing (SemPGrid'04), 2004
• JXTA project, see http//www.jxta.org

17
Announcement

• Submission deadline 30 June, 2004
• www.p2pkm.org

18
Thank you