Knowledge out there on the Web Serge Abiteboul 2014Abiteboul - EDBT keynote, Athenes1

Abiteboul - EDBT keynote, Athenes 1

Knowledge out there on the Web

Serge Abiteboul

2014


• Knowledge out there on the Web:

Video of the talk at the Royal Society

2014

Knowledge out there on the Web

Personal knowledge out there

http://royalsociety.org/events/2013/web-of-tomorrow/




Organization

1. The context2. The personal information management system

1. The concept of Pims2. Pims are coming3. Advantages

3. From information to knowledge4. The Webdamlog language

1. The language in brief2. Probabilities3. Access control

5. Conclusion: some research issues2014


1. The context

2014


Data explosion• data: pictures, music, movies, reports, email, tweets, contacts, schedules…• social interactions: opinions, annotations, recommendation…• metadata: on photos, documents, music…• ontologies: Alice’s ontology and mapping with other ontologies• web localizations: friends account on FB, twitter, lists of blogs… • security: credentials on various systems• data in various organizations

– jobs, schools, insurances, banks, taxes, medical, retirement…• data in various vendors

– amazon, retailers, netflix, applestore… • data that software or hardware sensors capture

– with or without our knowledge– web navigation, phone use, geolocation, "quantified self" measurements, contactless card

readings, surveillance camera pictures, •

…2014


Data dispersion

• Laptop, desktop, smartphone, tablet, car computer• Residential boxes (tvbox), NAS, electronic vaults…• Mail, address book, agenda, todo-lists• Facebook, LinkedIn, Picasa, YouTube, Tweeter• Svn, Google docs, Dropbox• Government services• Business services• Also machine and systems from

– family, friends, associations, work

• Systems even unknown to the user – third party cookies

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Data heterogeneity

Type: text, relational, HTML, XML, pdf…Terminology/structure/ontologySystems: MS, Linux, IOS, AndroidDistributionSecurity protocolsQuality: incomplete / inconsistent information

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Bad news

• Limited functionalities because of the silos– Difficult to do global search, synchronization, task

sequencing over distinct systems…

• Loss of control over the data– Difficult to control privacy– Leaks of private information

• Loss of freedom– Vendor lock-in

2014


Growing resentment

• Against companies– Intrusive marketing, cryptic personalization and business

decisions (e.g., on pricing), and automated customer service with no real channel for customers' voices

– Creepy "big data" inferences• Against governments – NSA and its European counterparts

• Dissymmetry between what these systems know about a person, and what the person actually knows

2014


Future alternatives (for normal people)

1. Continue with this increasing mess– Use a shrink to overcome frustration

2. Regroup all your data on the same platform– Google, Apple, Facebook, …, a new comer– Use a shrink to overcome resentment

3. Study 2 years to become a geek– Geeks know how to manage their information – Use a shrink to survive the experience

4. And, of course, there is the Pims’ way

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2. The personal information management system

2.1 Introduction

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The Pims

• Personal information management system• What is a successful Web service today– Some great software– Some machines on which it runs(and a business model)

• Separate the two facets– Some company provides the software– It runs on your machinewith another business model

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The Pims (1)• The Pims runs software

– The user chooses the code to deploy on the server.– The software is open source, a requirement for security.

• With the user's data– All the user’s personal information

• 0n the user’s server(s) – The user owns it or pays for a hosted server– The server may be a physical or a virtual machine– It may be physically located at the user’s home (e.g., a tvbox) or not– It may run on a single machine or be distributed among several

machines– The server is in the cloud, i.e., it can be reached from everywhere -

personal cloud

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The Pims: the 2 main issues

• Security– Enforced by the Pims: guaranteed by the contract the user

has with the Pims• Reasonably small piece of code; possible to verify it

– Enforced by the services running on it: open source so that we don’t need to trust the providers of these systems

– A higher level of security than now • The management

– Should be epsilon-work– Should require little competence– A company can be paid to do it (in the cloud)

2014



2.2 This is arriving

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It is becoming possible

• System administration is easier– Abstraction technologies for servers– Virtualization and configuration management tools.

• Open source is very active– Open source technology more and more available

• Price of machines is going down– A hosted-low cost server is as cheap as 5€/month– Paying is no longer a barrier for a majority of people

Indeed I am sure you have friends already doing it2014


Many people are working on it

• Many systems & projects– Lifestreams, Stuff-I’ve-Seen, Haystack, MyLifeBits,

Connections, Seetrieve, Personal Dataspaces, or deskWeb.

– YounoHost, Amahi, ArkOS, OwnCloud or Cozy Cloud• Some on particular aspects– Mailpile for mail– Lima for a Dropbox-like service, but at home.– Personal NAS (network-connected storage) e.g. Synologie– Personal data store SAMI of Samsung...

• Many more2014


Data disclosure movement

• Smart Disclosure in the US• MiData in the UK• MesInfos in France

Several large companies (network operators, banks, retailers, insurers…) have agreed to share with a panel of customers the personal data that they have about them

2014


Big companies are interested (1) Pre-digital companies

• E.g., hotels or banks • Disintermediated from their customers by pure

Internet players such as Google, Amazon, Booking.com, Mint.

• In Pims, they can rebuild direct interaction • The playing field is neutral – Unlike on the Internet where they have less data

• They can offer new services without compromising privacy

2014


Big companies are interested (2) Home appliances companies

• Many boxes deployed at home or in datacenters– Internet access provider "boxes”, NAS servers,

"smart" meters provided by energy vendors, home automation systems, "digital lockers”…

• Personal data spaces dedicated to specific usage

• Could evolve to become more generic• Control of private Internet of objects2014



2.3 Advantages

2014


Advantages

• User control over their data– Who has access to what, under what rules, to do what

• User empowerment– They choose freely services & they can leave a service

• Participation to a more “neutral” Web– With the "network effects", the main platforms are

accumulating data/customers and distorting competition– The Pims bring back fairness on the Web– Good practices are encouraged, e.g., interoperability,

portability

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Advantages – New functionalities

• Single identity/login• Semantic global search with (personal) ontology• Synchronization/backups across services• Access control management across services• Task sequencing across services• Exchange of information between “friends” • Connected objects control, a hub for the IoT• Personal big data analysis

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3. From information to knowledge

2014

(aka let’s move a tad more technical)


Machines prefer knowledge

• Integration of data & information sources– It is easier to integrate knowledge than information

• Collaboration between services & devices– It is easier for services to collaborate using

knowledge than with information• Problem solving based on knowledge inference

2014


Humans as well

• The users of the system are human beings– They want support for managing information– But they are not geeks– They don’t want to program

• To facilitate the interactions between humans and machines,

We should use declarative languages !

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It all started with datalog

• Popular in the 90’s• Some followers in 00’s

– A., Afrati, Atzeni, Cali, Greco, Gotloeb, Milo, Sacca, Ullman…• Recent revival

– 2010 Oege de Moor’s workshop @oxford• Datalog 2.0

– 2010 Joe Hellerstein’s keynote @pods• Datalog Redux: Experience and Conjecture

– 2014 Frank Neven’s keynote @icdt• Remaining CALM in declarative networking

• Now featuring: Webdamlog2014


Requirement 1: Distribution

• Different machines• Different users

• We use the notion of principal here– family@alice(Bob)– agenda@Alice-iPhone(…)– friends@Alice-FaceBook(…)

• A principal comes with identity and privileges

2014


Requirement 2: Privacy

• Control of who sees what in a distributed environment

• Access control• Should be clear from the first part of the talk

this is a most important issueTutorial on privacy by Nicolas Anciaux, Benjamin Nguyen, Iulian Sandu Popa – Today at 2:00

2014

31

Requirement 3: Probabilities

• We have to deal with negation– Elvis was not French

• With negations, come contradictions– Elvis Presley died in 1977; The King is alive

• There are different points of view– Elvis’s music is the best; it stinks

• Measure uncertainty with probabilities

2014 Abiteboul - EDBT keynote, Athenes

The more I see, the less I know for sure. John Lennon


So, what is the goal

• A datalog-style language with distributionaccess controlprobabilities

We are lucky, there is such a language:Webdamlog

2014


4. The Webdamlog language

(aka let’s be serious)4.1 Webdamlog in brief

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Facts and rules

Facts are of the form R@p(a1,…,an)– p is a principal, i.e., Serge, Serge’s-iPhone, Facebook/Serge, [email protected]

Rules are of the form

$R@$P($U) :- $R1@$P1($U1), ..., $Rn@$Pn($Un)– $R, $Ri are relation terms– $P, $Pi are peer terms – $U, $Ui are tuples of terms– Safety condition– Also negations: ignored here

2014

mailto:[email protected]


The semantics of rules

Classification based on locality and nature of head predicates (intentional or extensional)

• Local rule at my-laptop: all predicates in the body of the rules are from my-laptop

Local with local intentional head datalogLocal with local extensional head database updateLocal with non-local extensional head messaging between peersLocal with non-local intentional head view definitionNon-localgeneral delegation

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Local rules with local head

Intensional local head – datalog [at my-iphone]

fof@my-iphone($x, $y) :- friend@my-iphone($x,$y)fof@my-iphone($x,$y) :- friend@my-iphone($x,$z),

fof@my-iphone($z,$y)

Extensional local head – database updates[at my-iphone]

believe@my-iphone(“Alice”, $loc) :-tell@my-iphone($p,”Alice”, $loc), friend@my-iphone($p)

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Local rules & non-local extensional head

Messaging between peers$message@$peer($name, “Happy birthday!”) :-

today@my-iphone($date),birthday@my-iphone($name, $message, $peer, $date)

Example

– today@my-iphone(3/25)

– birthday@my-iphone("Manon”, “sendmail”, “gmail.com”, 3/25)

– [email protected]("Manon”, “Happy birthday”)

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Local rules & non-local intentional head

View definitionboyMeetsGirl@gossip-site($girl, $boy) :-

girls@my-iphone($girl, $event),boys@my-iphone($boy, $event)

• Semantics of boyMeetGirl@gossip-site is a join of relations girls and boys from my-iphone

• Defines a view at some other peer

2014


Non-local rules

General delegation(at my-iphone): boyMeetsGirl@gossip-site($girl, $boy) :-

girls@my-iphone($girl, $event),

boys@alice-iphone($boy, $event)

Example: girls@my-iphone(“Alice”, “Julia's birthday”) – my-iphone installs the following rule at alice-iphoneboyMeetsGirl@gossip-site(“Alice”, $boy) :-

boys@alice-iphone($boy, “Julia's birthday”)

Useful to distribute work and exchange knowledge2014


The thesis

The Web should turn into a distributed knowledge base where peers share facts and rules, and collaborateThe language Webdamlog is a first step towards that goal Missing– Probabilities– Access control

2014



4.2 Probabilities

2014


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Deduction with Contradictions in DatalogS.A., Daniel Deutch and Victor Vianu– Tomorrow, 11:00

2014



4.3 Access control

2014


Requirements

Data access Users would like to control who can read and modify their information

Data dissemination Users would like to control how their data are transferred from one participant to another

Application control Users would like to control which applications can run on their behalf, and what information these applications can access.

2014


The general picture

• Coarse grain for extensional relations– read access to the relation

• Fine grain for intensional relations– read access to tuple t requires read access to the

tuples that lead to deriving t• Delegation controlled in a sandbox• Focus on read privilege here

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Read: default

• Extensional relations– if you have read privilege to the relation

• Intensional relations– if you have read privilege to the relation &– if you can read all the tuples that have been used

to create this fact – provenance of the fact

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Fine grain access control

[at Bob] album@Alice($p,$f) :- photo@Bob($p,$f)[at Sue] album@Alice($p,$f) :- photo@Sue($p,$f)

– album@Alice is intensional– Both Bob and Sue contribute to it– Peter who has read privilege to album@Alice and

photo@Bob only does not see the photos of Sue

2014


Paranoiac access control

[at Bob] album@Alice($p,$f) :- photo@Bob($p,$f), friends@Bob($f)

– Issue: you can read Bob’s photos only if you have read privilege on friends@Bob that Bob wants to keep private

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Declassification

[at Bob] photo@Alice($p,$f) :- photo@Bob($p,$f), [ hide friends@Bob($f) ]

– Hide: blocks the provenance from friends@Bob– Bob declassify this data just for the evaluation of

this rule– You can declassify only tuples you own grant ↦

privilege2014


Issues with non local rules

[at Bob] message@Sue(“I hate you”) :- date@Alice(d)

aliceSecret@Bob(x) :- date@Alice(d), secret@Alice(x)Ignoring access rights, by delegation, this results in running[at Alice]

message@Sue(“I hate you”) :- date@Alice(d)aliceSecret@Bob(x) :- date@Alice(d),

secret@Alice(x)

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Default solution: sand box

We run the rule at Alice in a Sandbox• We use the access rights of Bob

So the second rule does not succeed in sending secrets

• The message specifies that this is done at Bob’s requestSo requires authentication/signatures

Alternative: delegation without sandbox2014


5. Conclusion: some research issues

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Explaining

• Users want to understand the informationthey see, the answers they are given

– In their professional/social life • Difficulties– Reasoning with large number of facts – Information is often probabilistic and not public– Requires knowing how the information was

obtained (its provenance)

2014


Serendipity

• You may hear by chance a song that is going to totally obsess you

• A librarian may suggest your reading an article that will transform your research

This is serendipity

• A perfect search engine • A perfect recommendation

system• A perfect computer assistantSuch systems are boring

They lack serendipity

2014

Design programs that would introduce serendipity in our lives


Hypermnesia

Exceptionally exact or vivid memory, especially as associated with certain mental illnesses

For a user: We cannot live knowing that any word, any move will leave a trace?

For the ecosystem: We cannot store all the data we produce – lack of storage resources

2014

Forgetting is Key to a Healthy MindScientific AmericanImage: Aaron Goodman

A main issue is to select the information we choose to keep


Babel of human-machine-interaction

• Each time a user interacts with a data source, does he have to use the ontology of that source ?

• No! • Instead of a user adapting to the ontologies of

the N systems he uses each day• We want the N systems to adapt to the user’s

ontology

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Religion…science…machines

• Knowledge used to be determined by religion• Knowledge used to be determined scientifically• Knowledge will now be determined by machines?

• Decisions are increasingly made by machines– Stock market (automatic trading)– Fully automated factory– Fully automated metros– Death penalty (killer drones)…

2014


to the digital world!

• We will soon be living in a world surrounded by machines that – acquire knowledge and decide for us

• What will we do with that technology? • Will we become smarter? • Will we become master or slave of the new

technology?

2014


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Knowledge out there on the Web Serge Abiteboul 2014Abiteboul - EDBT keynote, Athenes1