1. 1 WebdamExchange and WebdamLog: some models for web data management Alban Galland INRIA Saclay & ENS Cachan Grenoble, 10/12/2010

2. 2 Organization Introduction Representing all Web information as logical sentences Representing all Web data management as logical rules Some clues about implementation Conclusion

3. Introduction

4. 4 Context of the work presented here ERC Grant Webdam on Web Data Management of Serge Abiteboul with two INRIA teams, Leo-Iasi (ex Gemo, INRIA Saclay) and Dahu (LSV, ENS Cachan) Joint work with many people: Émilien Antoine, Serge Abiteboul, Meghyn Bienvenu, David Gross-Amblard, Amélie Marian, Bruno Marnette, Neoklis Polyzotis, Philippe Rigaux, Marie-Christine Rousset…

5. 5 Context: Web data management Scale: lots of users, servers, large volume of data… Distribution heterogeneity: Cloud (social networks), P2P (DHT, gossiping)… Security heterogeneity: login, https, crypto, hidden URL… Terminology heterogeneity: annotation, semantic Web, ontologies… Incomplete information: inconsistencies, belief, trust… The heterogeneity keeps increasing with new systems and new applications arriving Consequence 1: difficulty to perform data integration/management Consequence 2: impossibility to keep control over its own data

6. 6 Thesis: Web data = distributed knowledge Work plan 1. Represent all Web information as logical sentences 2. Represent all Web data management as logical rules 3. Develop a system to validate these ideas Motivation for the approach Facilitate the design/implementation of complex systems Facilitate the control/surveillance of complex systems Use reasoning to optimize query evaluation Use reasoning for semantics/ontologies Use reasoning to manage access control and protect data Use reasoning to analyze properties of systems

7. 7 Motivating example Alice : get me the pictures of my friends where I am with Bob? What is going on: Find the friends of Alice (The iPhone of Alice may remember it) For each answer, say Sue, find where Sue keeps her pictures (She may keep her pictures on Picasa) Find the means to access Sue’s pictures (Alice may ask the private url to a common friend) Find the photos with Bob and Alice (e.g. by querying the meta-data)

8. 8 Motivating example Alice : get me the pictures of my friends where I am with Bob? Issues: heterogeneity of friends Heterogeneity of hosting: Some keep their pictures on trusted servers such as Picasa, some put in on untrusted DHT, some have them on their smartphones… Heterogeneity of access-control: Some are public, some use login- password, some use private url, some use cryptography… Heterogeneity of data description: they may use different models of meta-data (taxonomies, ontologies…)

9. Representing all Web information as logical sentences

10. 10 The information belongs to someone Each information belongs to a principal A principal has an identity (URI) which can be authenticated Two kinds of principal: peer and virtual principal A peer: alice-laptop, alice-iPhone, picasa, facebook, dht-peer- 124, … Storage and processing capabilities A peer typically has a URL and can be sent query/update requests A virtual principal: alice, alice-friends, roc14 A virtual principal relies on peers for storage and processing

11. 11 The kind of information we are talking about Data: pictures, movies, music, emails, ebooks, reports Localization: bookmarks, knowledge such as Alice has an account in Facebook, Sue puts her pictures in Picasa Access: login/password, access rights on servers Annotations /Ontologies: semantic tags in Picasa,RDFS, OWL Services: search engines, yellow pages, dictionaries… Incomplete information: beliefs, probabilistic information… And more…

12. 12 Logical statements to represent information Data: Document: picture34@alice-iPhone(picture34.jpg,09/12/2009,…) Collection: pictures@alice(picture34@alice-iPhone) Localization: where@alice(picture37, picasa/alice) Access right: isOwner@picasa/alice(alice) Access secret : ownSecret@picasa/alice(“alice”, “HG-FT23”) Ontologies: isA@yago.com(“alice”, human-being) Services: addresse@pagesjaunes.fr($Person, $City, $Y) Belief: picture34@alice-iPhone(picture34.jpg,09/12/2009,…,75%) Etc.

13. 13 WebdamExchange focus: authenticated knowledge Base statement: someone states picture37@alice (….) It is annotated with a proof that “someone” can write data of alice In the cryptographic setting, it is a signature of the whole statement using the write secret key of alice Keeping trace of provenance: alice-laptop states picture37@alice (….) requester bob at 12:30, 10/08/2009 alice-Laptop is the performer (the peer who did the update of the data of Alice) bob is the requester (the peer or the user who requested the update) The content is possibly encrypted: alice-laptop states picture37@alice (….) protected for reader@alice requester bob at 12:30, 10/08/2009

14. 14 WebdamExchange focus: authenticated knowledge Communication: external knowledge is knowledge about other principals: alice-laptop says (alice-laptop states picture37@Alice (….) requester bob at 12:30, 10/08/2009) to sue-iphone at 13:15, 15/10/2009 alice-laptop is the performer of the communication sue-iphone is the receiver of the communication External knowledge is authenticated by the performer and is stored by the receiver. The external knowledge keep a trusted trace of the provenance and communication are pilled-up: sue-iphone says (alice-laptop says (alice-laptop states picture37@Alice (….) requester bob at 12:30, 10/08/2009) to sue-iphone at 13:15, 15/10/2009) to bob-iphone at 13:10, 15/10/2009 The time is the time of the performer, there is no global clock

15. 15 The model covers a wide range of data The model does not prescribe any particular architecture for distribution Gossiping, DHT, centralized server Combination of these Based on an abstract notion of localization The model does not prescribe how access control is enforced, e.g.: Documents in Web servers with access protected by login/password Documents protected by cryptographic keys in public sites Based on an abstract notion of secret and hint

16. 16 Summary of WebdamExchange All the information forms a trusted knowledge base Each peer manages some portion of the knowledge base Now, we have to use this distributed knowledge base … for the management of the distributed knowledge base!

17. Representing all Web data management as logical rules

18. 18 From WebdamExchange to WebdamLog The logical part of the WebdamExchange statements can easily be translated into datalog facts. Most of the reasoning of the system can be done using the logical form and datalog-like rules It motivates WebdamLog, a rule-based language for web data management

19. 19 Why datalog? Datalog: very popular in the 90’s, prehistory by Web time + Nicer/more compact syntax; easy to extend - Recursion not really essential Datalog extensions Negation and aggregate functionstons of works on that Updates, time, trees, distributionfewer works on it We use a datalog-like language influenced by Active XMLfor distribution and intensional data Hellerstein’s Dedalusfor time and performance

20. 20 Webdamlog Facts are of the form: m@p(a1,...,an)(sorted) Rules are of the form: R@P(U) :- (not) R1@P1(U1), …, (not) Rn@Pn(Un) R,Ri are message terms P,Pi are peer terms U,Ui are tuples of terms Safety condition Intuition: if the body holds for some valuation v, the message vR@vP(vU) is sent to the peer vP Issue: what happen if the body of the rules mentions different peers?

21. 21 Webdamlog System: A finite set  of peers Each peer p in  has a local program P(p) and some delegated program D(p) consisting of finite sets of rules Each peer p in  has a database I(p), consisting of a finite set of facts of the form m@p(u) Semantics: in a state (P,D,I), choose randomly some p Evaluate (P(p)UD(p))(I(p)) This defines the new database I’(p) This adds facts and update rules of the other peers to define (D’(q),I’(q)) for each q The changes to each q are installed synchronously – we will see how to avoid it if desired Choose another peer and keep going (in a fair way) Peer1 Peer2 Peer3 Peer4

22. 22 Features of WebdamLog illustrated Alice: get me the pictures of my friends where I am with Bob? result@alice-iphone($photo,$X) :- friends@alice-iphone($X), findPhotos@alice-iphone($X,$R,$P), $R@$P($Photo,$Meta), contains@$P($Meta, “Alice”), contains@$P($Meta, “Bob”) Peers and messages as data: they are reified friends@alice-iphone is extensional, in I(alice-iphone) findPhotos@alice-iphone is intensional, in P(alice-iphone)UD(alice- iphone) $R@$P is bounded to a relation of (possibly) another peer contains@$P is a service of that peer

23. 23 Features of WebdamLog illustrated Delegation of rules Alice: get me the pictures of my friends where I am with Bob? result@alice-iphone($Photo,$X) :- friends@alice-iphone($X), findPhotos@alice-iphone($X,$R,$P), $R@$P($Photo,$Meta), contains@$P($Meta, “Alice”), contains@$P($Meta, “Bob”) friends@alice-iphone(Sue); findPhotos@alice-iphone(Sue,photos,picasa/sue) :- Then alice-iphone installs the following rule at picasa/sue: result@alice-iphone($Photo,Sue) :- photos@picasa/sue($Photo,$Meta), contains@picasa/sue($Meta, “Alice”), contains@picasa/sue($Meta, “Bob”) picasa/sue will send the photos as extensional facts to alice-iphone. When Alice terminates her query, it cancels all the delegations.

24. 24 Managing rules at other peers This is complex Regarding implementation, one manages instantiations of rules, i.e., rules and valuation The content of valuations may be constantly changing There could be some negations in the rules This is a security risk Someone else is installing data (facts) or code (rules) in a peer Need to control that carefully

25. 25 Does it means something? Some not-so trivial theorems about positive case or stratified negation case insuring Church-rosser properties (convergence) Natural simulation by centralized systems Some even-less-trivial theorems about comparing expressivity of different variations of WebdamLog: without exchanging rules, without exchanging intensional data, with time-stamp…

26. 26 More refined asynchronicity To model message from peer p to peer q, we may use a “peer” net pq that captures the network Replace a call m@q(u) at p by m@net pq (u) net pq should just relay messages: $M@q($U) :- $M@net pq ($U) Problem: all messages from p to q in the net arrive at the same time Better with time m@net pq (u,t) where t is the time of the send at p $M@q(U) :- $M@net pq (U,T), min( T, $M@net pq (U,T)), using min aggregate function

27. 27 Summary of WebdamLog Peer are asynchronically running their datalog programs They exchange facts and delegations of rules

28. Some clues about implementation

29. 29 Implementation We are implementing two kinds of peers WEP (Webdam Exchange Peer) – all functionalities IWEP (iPad Webdam Exchange Peer) – limited functionalities; rely on proxies We are implementing a social network on top of the system

30. Conclusion

31. 31 Some cool results and still a lot of works WebdamExchange and WebdamLog models capture some nice problems of web data management: distribution, access control… Their good semantics allow us to prove theorems! We are implementing the corresponding system! Many issues are still open Concurrency, optimization, implementation Defining and verifying protocols (access control is not violated, one gets all the information one has access to) Looking for a killer application