1 WebdamExchange and WebdamLog: some models for web data management Emilien Antoine, Meghyn Bienvenu, Alban Galland Webdam WS, 04/03/2011

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

Introduction

4 Context of the work presented here Joint work with many people: Émilien Antoine, Serge Abiteboul, Meghyn Bienvenu, David Gross-Amblard, Marilena Oita, Amélie Marian, Bruno Marnette, Neoklis Polyzotis, Philippe Rigaux, Marie- Christine Rousset…

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 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 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 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 Complicated application organization… Example of our SocialRock demo:

Representing all Web information as logical sentences

11 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

12 The kind of information we are talking about Data: pictures, movies, music, s, 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…

13 Logical statements to represent information Data: Document: Collection: Localization: picasa/alice) Access right: Access secret : “HG-FT23”) Ontologies: human-being) Services: $City, $Y) Belief: Etc.

14 WebdamExchange focus: authenticated knowledge Base statement: someone states (….) 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 (….) 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 (….) protected for requester bob at 12:30, 10/08/2009

15 WebdamExchange focus: authenticated knowledge Communication: external knowledge is knowledge about other principals: alice-laptop says (alice-laptop states (….) 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 (….) 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

16 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 See presentation of Emilien on WebdamPoor

17 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!

Representing all Web data management as logical rules

19 From WebdamExchange to Webdamlog The logical part of the WebdamExchange statements can easily be translated into datalog facts. Now we want to perform reasoning on these facts in order to locate, exchange, and update information Example: use logical reasoning among peers to locate the pictures of Alice’s friends in which she appears with Bob This motivates Webdamlog, a rule-based language for web data management

20 Why datalog? Datalog: very popular in the 90’s, prehistory by Web time + Natural syntax; reasonably expressive; easy to extend - Recursion not really essential in most applications Datalog extensions Negation and aggregate functionslots of work on these Updates, time, trees, distributionless work on these We use a datalog-like language influenced by Active XMLfor distribution and delegation Hellerstein’s Dedalusfor time and performance

21 Webdamlog Facts (messages) of the form 1,...,a n ) Rules of the form :- (¬) R 1 (U 1 ), …, (¬) R n (U n ) R,R i are relation terms P,P i are peer terms U,U i are tuples of terms Safety condition Intuition: if the body holds for some valuation v, the fact is sent to the peer vP What happens if the body of the rule mentions different peers? Peers need to collaborate to evaluate the rule  rule delegation

22 Webdamlog System: A finite set  of peers Each peer p in  has a local program P(p) and a delegated program D(p), which are both finite sets of rules Each peer p also has a database I(p) consisting of a finite set of facts of the form Semantics: In a state (P,D,I), choose randomly some p Evaluate (P(p)UD(p))(I(p)) This defines the new DB I’(p) Send facts and update delegations of the other peers to define (D’(q),I’(q)) for each peer q≠p The changes to each q are installed instantaneously – we will see how to avoid this if desired Choose another peer and keep going (in a fair way)

23 Features of Webdamlog illustrated Alice: get me the pictures of my friends where I am with Bob $R, $P), $Photo, $Meta), “Alice”), “Bob”) photos, picasa) :- member($X, picasa) -Peers and relations treated as data: they are reified will instantiate with concrete relation and peer is extensional, occurs in data at alice-iphone intensional, derived from data + rules

24 Peer picasa will send the photos as extensional facts to alice-iphone. When Alice terminates her query, she cancels all the delegations. Features of Webdamlog illustrated photos, picasa) :- member($X, picasa) Partial evaluation at alice-iphone ($X  Sue, $R  photos, $P  picasa) Then alice-iphone installs the rest of the rule at picasa: :- “Alice”), “Bob”) $R, $P), $Photo, $Meta), “Alice”), “Bob”) Alice: get me the pictures of my friends where I am with Bob

25 What can we show ? In general, asynchronicity yields non-deterministic systems Identified two types of Webdamlog systems (only positive rules / appropriately stratified negation) for which we have: convergence: all runs eventually reach same state simulation by centralized datalog program Interesting to compare expressivity of different variants of WebdamLog: full / limited / no delegation, presence of time- stamps or ordering of peers… For appropriate notion of simulation, can show that full delegation > limited delegation > no delegation

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

27 Summary of Webdamlog Peer are asynchronously running their own datalog programs They interact by exchanging facts and delegating rules Some things to look at: Evaluation and optimization of queries Acquisition of new rules Reasoning with social information (trust, provenance, etc.)