Incremental Materialization of RDF Graph Closures for Stream Reasoning Alexandre Mello Ferreira (PhD student) 22/11/2010

Alexandre Mello Ferreira DEI Outline 2 Introduction Problem statement RDF in a nutshell Our proposed approach Envisioned scenario Model Time-stamped streams Incremental maintenance of materialization for RDF streams Implementation and first results Jena Deductive rules Results

Alexandre Mello Ferreira DEI Outline 3 Introduction Problem statement RDF in a nutshell Our proposed approach Envisioned scenario Model Time-stamped streams Incremental maintenance of materialization for RDF streams Implementation and first results Jena Deductive rules Results

Alexandre Mello Ferreira DEI Problem statement 4 Large use of sensed data Urban computing Green computing Monitoring systems in large environments lead to complex and cost hungry systems Invisibility to keep ontology representation in memory due to often updates How to make data representation more useful E.g. (from IBGE):

Alexandre Mello Ferreira DEI Problem statement 5 Large use of sensed data Urban computing Green computing Monitoring systems in large environments lead to complex and cost hungry systems Invisibility to keep ontology representation in memory due to often updates How to make data representation more useful E.g. (from IBGE): [1960] [2010] [2050]

Alexandre Mello Ferreira DEI RDF in a nutshell 6 Resource Description Framework (RDF) is a W3C recommendation for resource description Basically composed by: SUBJECT: something identified (resource) PREDICATE: property that describes the subject OBJECT: either the property value or another resource POLIMI DEI “DEI” “Dipartimento di Elettronica e Informazione” dc:title edu:hasDept geo:long geo:lat

Alexandre Mello Ferreira DEI RDF in a nutshell 7 Web semantics vocabularies Serialization syntax: Notion 3 (“N3”) RDF/XML

Alexandre Mello Ferreira DEI Outline 8 Introduction Problem statement RDF in a nutshell Our proposed approach Envisioned scenario Model Time-stamped streams Incremental maintenance of materialization for RDF streams Implementation and first results Jena Deductive rules Results

Alexandre Mello Ferreira DEI Envisioned scenario 9 Typical 5,000 square-foot data center Demand side – IT systems Supply side – Cooling systems and power systems Power consumption (watts) Usage percentage (%)

Alexandre Mello Ferreira DEI Envisioned scenario 10 Volume Mid-range High-end Server ID Rack CPUs (8x) Diks (4x) Mode Virtualization eligible AMD Intel Server ID Usage Consumption Mode Server ID Usage Consumption Mode RESOURCE TYPE STATIC DATA DEDUCTED DATA

Alexandre Mello Ferreira DEI Envisioned scenario 11 RDF/XML sample of the background knowledge

Alexandre Mello Ferreira DEI Outline 12 Introduction Problem statement RDF in a nutshell Our proposed approach Envisioned scenario Model Time-stamped streams Incremental maintenance of materialization for RDF streams Implementation and first results Jena Deductive rules Results

Alexandre Mello Ferreira DEI Time-stamped streams 13 Static data Stream data Derived data Time-stamped data

Alexandre Mello Ferreira DEI Incremental maintenance 14 Based on the work developed by Volz and Prof. Ceri database research group, the following triples definitions are considered: T in  enter in the window  stream T stay  stay in the window  stream and derived T exp  exit the window (expire)  stream and derived d T new  Triples trigged by T in and not in T stay d T renew  Triples trigged by T in and in T stay d T timestamp  Triples trigged by both d T new and d T renew T +  add to the materialization T -  remove fom the materialization T result = (T inicial U T + ) \ T -

Alexandre Mello Ferreira DEI Incremental maintenance 15 The implemented solution (time 6): s1 c c d2 isConsuming hasDisk hasCPU LowPower hasMode 47 3 isUsing

Alexandre Mello Ferreira DEI Incremental maintenance 16 The implemented solution (time 7): s1 c c3 7 2 d2 isConsuming hasDisk hasCPU LowPower hasMode LowPower hasMode 47 3 isUsing

Alexandre Mello Ferreira DEI Incremental maintenance 17 The implemented solution (time 7): s1 c c3 7 2 d2 isConsuming hasDisk hasCPU LowPower hasMode LowPower hasMode 47 3 isUsing

Alexandre Mello Ferreira DEI Incremental maintenance 18 The implemented solution (time 8): s1 c5 54 c3 7 d isConsuming isUsing hasDisk hasCPU LowPower hasMode c isUsing 52 hasCPU isConsuming LowPower hasMode quietMode hasMode True eliVirt

Alexandre Mello Ferreira DEI Outline 19 Introduction Problem statement RDF in a nutshell Our proposed approach Envisioned scenario Model Time-stamped streams Incremental maintenance of materialization for RDF streams Implementation and first results Jena Deductive rules Results

Alexandre Mello Ferreira DEI Jena2 inference subsystem 20 Framework to develop semantic web app It provides: RDF API Reading and writing RDF/XML, N3, and N-Triples In-memory and persistence storage SPARQL query engine 1.// creates an empty RDF model 2.Model myRDFmodel = ModelFactory.createDefaultModel(); 3. 4.// creates a new generic rule reasoner to support user defined rules 5.Reasoner reasoner = new GenericRuleReasoner(Rule.parseRules(ruleSrc)); 6.reasoner.setDerivationLogging(true); 7. 8.// creates a new inference model which performs RDF inference over myRDFmodel 9.// using my previous defined reasoner 10.InfModel inf = ModelFactory.createInfModel(reasoner, myRDFmodel);

Alexandre Mello Ferreira DEI Deductive rules 21

Alexandre Mello Ferreira DEI Deductive rules 22

Alexandre Mello Ferreira DEI Deductive rules 23

Alexandre Mello Ferreira DEI First results 24 Average time to maintain the materialization vs window sliding Axis X represents the number of arrival stream triples It depends on the type of the triple

Alexandre Mello Ferreira DEI First results 25 Average time to maintain the materialization vs incremental number of monitored data (sensors) Axis X represents the number of sensed components It keeps homogeneous regarding to scalability

Alexandre Mello Ferreira DEI Conclusion remarks 26 Next steps Merge our scenario with Urban computing in order to come up with comparable experiments Try alternative inference engines and compare their features Apply the proposed approach to a real data center environment (like in GAMES project)

Alexandre Mello Ferreira DEI 27