iot-intelligence and web stream reasoning · 2015-11-12 · • streaming new learning examples are...
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IoT-Intelligence and Web Stream Reasoning
Towards Complex Reasoning over Big Data Streams with Answer Set Programming
Alessandra Mileo Senior Research Fellow Insight Centre for Data Analytics, NUIG alessandra.mileo@insight-centre-org
Ali Intizar Research Fellow Insight Centre for Data Analytics, NUIG ali.intizar@insight-centre-org
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IoT- Intelligence at Unit for Reasoning & Querying (URQ)
1. Representation and linking
2. Finding what we need
3. Dynamic Problem Solving (Stream Reasoning)
2 Stream Reasoning Workshop, Vienna, 9th November 2015
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IoT- Intelligence at Unit for Reasoning & Querying (URQ)
1. Representation and linking
2. Finding what we need
3. Dynamic Problem Solving (Stream Reasoning)
Smart Ci=es Enterprise
Communica=on
3 Stream Reasoning Workshop, Vienna, 9th November 2015
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What streams do I need and how “good” are they? (Stream Discovery and
Federa;on)
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Quality and context-aware stream discovery
• What information do I need? – Data interoperability: Semantic descriptions – Interface interoperability: streams as event
services • How good is it?
– ADAPT to quality requirements and preferences for data source selection
– Efficient processing of event logic
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Quality and context-aware stream discovery
• What information do I need? – Data interoperability: Semantic descriptions – Interface interoperability: streams as event
services • How good is it?
– ADAPT to quality requirements and preferences for data source selection
– Efficient processing of event logic
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Seman=c Web
Stream Reasoning Workshop, Vienna, 9th November 2015
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Quality and context-aware stream discovery
• What information do I need? – Data interoperability: Semantic descriptions – Interface interoperability: streams as event
services • How good is it?
– ADAPT to quality requirements and preferences for data source selection
– Efficient processing of event logic
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Seman=c Web
Service Oriented Architectures
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Quality and context-aware stream discovery
• What information do I need? – Data interoperability: Semantic descriptions – Interface interoperability: streams as event
services • How good is it?
– ADAPT to quality requirements and preferences for data source selection
– Efficient processing of event logic
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Seman=c Web
Service Oriented Architectures
Con=nuous constraint checking
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Quality and context-aware stream discovery
• What information do I need? – Data interoperability: Semantic descriptions – Interface interoperability: streams as event
services • How good is it?
– ADAPT to quality requirements and preferences for data source selection
– Efficient processing of event logic
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Seman=c Web
Service Oriented Architectures
DSMS and CEP
Con=nuous constraint checking
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Summary of the Approach
• How to describe complex event services? – Create an Event Service Ontology with Event Patterns.
• How to determine if two event patterns are functionally equivalent? – Create and compare canonical event patterns to find substitutes.
• How to create event compositions and choose the optimal? – Top-down traverse to find functionally-equivalent canonical
patterns. • How to derive event service compositions efficiently?
– Construct and utilize an Event Reusability Hierarchy for event service composition.
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Gao, F., Ali, M.I., Curry, E., Mileo, A.: On Discovery and Integra2on of Urban Data Streams for Real2me Smart City Applica2ons. J. Data Seman=cs (2015) to appear
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Automated Complex Event Implementation System
Semantic Annotation
ACEIS Core
ResourceManagement
Application Interface
Knowledge Base
QoI/QoS
Stream Description
Data Mgmt, Indexing, Caching
User Input
Event Request
Data Federation
Resource Discovery
Event Service Composer
Composition Plan
Subscription Manager
Query Transformer
Query Engine
Query
Results
Constraint Validation
Constraint Violation
Adaptation Manager
Data Store IoT Data Stream
Social Data Stream
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IoT- Intelligence at Unit for Reasoning & Querying (URQ)
1. Representation and linking
2. Finding what we need
3. Dynamic Problem Solving (Stream Reasoning)
Smart Ci=es Enterprise
Communica=on
12 Stream Reasoning Workshop, Vienna, 9th November 2015
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How to leverage the IoT and Seman;c Web infrastructure for (efficient)
Web Stream Reasoning?
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Data
Stream
s
Stream Query Processing
Seman=c Complex Event Processing
relevant events
complex events
Stream Reasoning
solu=on sets
Data
Stream
s Da
ta
Stream
s Applica=ons
Scalability
Expressivity
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The StreamRule idea
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• 2-tier approach: not all data streams are relevant for complex reasoning • Enrich the ability of complex reasoning over data streams • Keep the solution scalable • Leverage existing engines from both stream processing and non-
monotonic reasoning research areas
Mileo, A., Abdelrahman, A., Policarpio, S., Hauswirth, M.: Streamrule: A nonmonotonic stream reasoning system for the Seman2c Web. In: RR 2013, 247–252
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Limitations
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• The more expressive the inference task, the longer it takes to perform reasoning
• Bottleneck when results are returned not as fast as the next input arrives
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Limitations
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• The more expressive the inference task, the longer it takes to perform reasoning
• Bottleneck when results are returned not as fast as the next input arrives
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Adaptation Heuristics: ongoing work
• More than an engineering problem – How to model interactions between RSP and ASP components,
including different semantics, input split, window-size tuning,…
• Design and runtime features – E.g. operational semantics (design) and throughput (runtime)
• Streaming rate and window size: where’s the tradeoff?
• Reasoning Complexity: how far can we go? How can we parametrise the complexity to estimate the execution time
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Goal
Given a fixed streaming size S with fixed complexity C and unit of time U, find a window size W such that the time required to process S events using windows of
size W is less than or equal to one unit of time
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Tω S,W( )≤U
ClingoTranslator Translatorfacts answer setevents answers
Asp Rules
Reasoner
Germano, S., Pham, T.L., Mileo, A.: Web stream reasoning in prac2ce: on the expressivity vs. scalability tradeoff. In: Web Reasoning and Rule Systems -‐ 9th Interna=onal Conference, RR 2015, Berlin, Germany, August 4-‐5, 2015, Proceedings. (2015) 105–112
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Experimental setup
• Dataset – Simulated randomly generated events of the type
event(type, name, value, latitude, longitude) E.g. event(weather, strong-wind, 2014-11-26T13:00:00, 38.011736, 12.186724)
• Reasoning tasks – Ranking event criticality – Contextualizing events based on user status – Default rule to detect changes in event criticality
• Run – Streaming size up to 30000 – Reasoner triggered 20 times for each S
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Empirical Results
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Empirical Results
T 20000( ) =1232ms
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Empirical Results
T 20000( ) =1232msT 5000( ) = 216ms
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Empirical Results
Tω 20000, 5000( ) = 200005000
⎡⎢⎢
⎤⎥⎥×T(5000)
= 4×216ms = 864ms
T 20000( ) =1232msT 5000( ) = 216ms
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Empirical Results
Tω 20000, 5000( ) = 200005000
⎡⎢⎢
⎤⎥⎥×T(5000)
= 4×216ms = 864ms
T 20000( ) =1232msT 5000( ) = 216ms
Tω 20000, 2000( ) = 200002000
⎡⎢⎢
⎤⎥⎥×T(2000)
=10×72ms = 720ms
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Ongoing work
• Relaxing the independence assumption – Extended notion of dependency graph – Possibly use duplication – Smaller input sets given to the same ASP program – Demonstrate correctness of results
• Going parallel – Explore parallelism of SPARK for higher scalability – Requires to map an ASP program to SPARK jobs
• Correlation between reasoning complexity and execution time
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Reasoning Complexity
Streaming rate Window size
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Dealing with Uncertainty and learning rela;onal
structures
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IoT data are messy: deal with uncertainty
• Expressive inference – non-monotonicity, noisy, partial and inconsistent data
• “ease of” declarative logic-based reasoning to model a problem/domain. Still we need to manage uncertainty and non-monotonicity
• Probabilistic rules for uncertain knowledge and learning by example – represent, use, infer and learn probabilistic knowledge (PrASP)
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Can we (learn the) answer to questions about uncertain knowledge using qualitative (declarative) inference in dynamic environments?
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What is Streaming PrASP
A framework that uses: 1. PrASP as an uncertainty reasoning server to reason over Streaming
Web Data 2. Continuous Query Processing over Linked Data Streams for data
filtering
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Nickles, M., Mileo, A.: Web stream reasoning using probabilis2c answer set programming. In: Web Reasoning and Rule Systems (RR) 2014. (2014) 197–205
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What is Streaming PrASP
A framework that uses: 1. PrASP as an uncertainty reasoning server to reason over Streaming
Web Data 2. Continuous Query Processing over Linked Data Streams for data
filtering
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What is PrASP then?
Nickles, M., Mileo, A.: Web stream reasoning using probabilis2c answer set programming. In: Web Reasoning and Rule Systems (RR) 2014. (2014) 197–205
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PrASP is…
… an experimental Statistical Relational Learning (SRL) reasoner based on Answer Set Programming (ASP) … represent, use infer and learn probabilistic knowledge
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PrASP can…
Ma`hias Nickles, Alessandra Mileo: A System for Probabilis2c Induc2ve Answer Set Programming. SUM 2015: 99-‐105
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Queries
Examples
Hypotheses
PrASP 0.6 inference core
ASP grounder/solver
(default: Clingo 3)
PrASP program (given knowledge base)
Pre-processing(translation of FOL
syntax, ...)
Spanning program(disjunctions from
weighted formulas)
F2LP(optional)
Filteringof formulas
with no or very little influence
on queries
Possible worlds(sampled answer sets)
System of linear equations (or inequalities)
CVC4(SMT solver;
optional)
Probability distribution over possible worlds (maximum entropy
solution of equations)
Query probabilities
Simplificationof rules with
independent events Native solver for linear systems
PrASP learning core
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Streaming PrASP
• Streaming new beliefs are added incrementally to a loaded PrASP program
• Streaming new learning examples are added to the set of learning examples E
• Assert/Retract, time decay and sliding windows supported
• Windows prefixes realized by a caching mechanism (no reactive ASP used) for faster processing
• Preprocessing based on RDF query processing over streams (SPARQL 1.1 + streaming operators)
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Streaming PrASP framework
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PrASPASP grounder/
solver
PrASP CQELS client(RDF filtering client)
RDF data stream
Probabilistic ASP/FOLbeliefs/examples stream
CQELS/SPARQL query
PrASP program (knowledge base)
Probabilistic queries
Static learning examples
CQELS
Incremental results
Hypotheses
trans-formation
pattern
t
t
Query & learningresults
t window
window
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Streaming PrASP framework
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PrASPASP grounder/
solver
PrASP CQELS client(RDF filtering client)
RDF data stream
Probabilistic ASP/FOLbeliefs/examples stream
CQELS/SPARQL query
PrASP program (knowledge base)
Probabilistic queries
Static learning examples
CQELS
Incremental results
Hypotheses
trans-formation
pattern
t
t
Query & learningresults
t window
window
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Ongoing work
• More experiments on Web Stream Reasoning with PrASP
– Looking for Usecase Scenarios and DATA to test PrASP – Building a set of modules that can be downloaded and used for
feedback • Continuously exploring options for optimization, especially in the
learning task
• More ambitious goals – Structure Learning – Relation between Streaming ASP and PrASP (currently not using
Streaming ASP)
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Data
Stream
s
Stream Query Processing
Seman=c Complex Event Processing
relevant events
complex events
Stream Reasoning
solu=on sets
Data
Stream
s Da
ta
Stream
s Applica=ons
Scalability
Expressivity
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IoT-Intelligence and Web Stream Reasoning
Towards Complex Reasoning over Big Data Streams with Answer Set Programming
Alessandra Mileo Senior Research Fellow Insight Centre for Data Analytics, NUIG alessandra.mileo@insight-centre-org
Ali Intizar Research Fellow Insight Centre for Data Analytics, NUIG ali.intizar@insight-centre-org