Events are encapsulated pieces of information that flow from one event agent to another. In order to process an event, additional information that is external to the event is often needed. This is achieved using a process called event enrichment. Current approaches to event enrichment are external to event processing engines and are handled by specialized agents. Within large-scale environments with high heterogeneity among events, the enrichment process may become difficult to maintain. This paper examines event enrichment in terms of information completeness and presents a unified model for event enrichment that takes place natively within the event processing engine. The paper describes the requirements of event enrichment and highlights its challenges such as finding enrichment sources, retrieval of information items, finding complementary information and its fusion with events. It then details an instantiation of the model using Semantic Web and Linked Data technologies. Enrichment is realised by dynamically guiding a spreading activation algorithm in a Linked Data graph. Multiple spreading activation strategies have been evaluated on a set of Wikipedia events and experimentation shows the viability of the approach.

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Digital Enterprise Research Institute www.deri.ie
Towards Unified and Native Enrichment in
Event Processing Systems
Souleiman Hasan, Sean O’Riain, Edward Curry
Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland
In proceedings of The he 7th ACM International Conference on Distributed Event-Based Systems
June 29 - July 3, 2013, Arlington, Texas, USA
Stefan.Decker@deri.org
http://www.StefanDecker.org/

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Talk Overview
n Introduction
¨ IoT, Cyber-Physical Systems
¨ Event Incompleteness
n Current Approaches
n Proposed Approach
¨ Challenges for Enrichment
¨ Proposed Model
¨ Implications
n Linked Data Instantiation
¨ Evaluation
n Summary and Future Directions
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Big Data & IoT

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Cyber-Physical Systems
Smart
City
Smart
Grid
Smart
Building
Smart
Enterprise

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Event Processing Systems
n Three dimensions of decoupling
n Removal of explicit dependencies between event
producers and consumers
ð Scalable deployment
n Information exchange only by Events
n (Eugster et al., 2003)
Space
Time
SynchProducer Consumer
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Problem – Event Incompleteness
n Event producers and consumers are decoupled
ð Event producers may have very little knowledge about
consumers information needs
Environmental Sensors
(Event Observers/Producers)
{(type= "energy consumption") and
(floor= “first floor") and
(consumption="high")}
(type, "energy consumption”)
(device, "heater x”)
(consumption, "high”) Event Processing
Engine
Business User
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Dimensions of Incompleteness
n Event Format: lacks syntactical structure
¨ E.g. plain text against conjunctive subscription
n Event Semantics: events lack a reference
scheme
¨ E.g. schema-less tuples
n Lack of Background Knowledge
¨ E.g. complementary information exists in external DB
n Incompleteness Addressable by Transformation
¨ E.g. transforming amounts of multiple measurement units
n Temporal Segmentation
¨ E.g. Complementary information exists in past or future
events
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Current Approaches
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Event Enrichment
(type, "energy consumption”)
(device, "heater x”)
(consumption, "high”)
(type, "energy consumption”)
(device, "heater x”)
(consumption, "high”)
(room, “202e”)
(floor, “second floor”)
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DEVICE ROOM FLOOR Color
heater x 202e second floor white
heater y 313 third floor blue
Meta
Data
IoT
Heater
Event
Enriched
Event

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n Dedicated agents to complement events
Agent-based Event Enrichment
Producer
Producer
Rule1
Event Processing
Agent
Enricher
Enricher
Consumer
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n Pros
¨ Events complete with respect to consumer’s need
¨ Low false positives/negatives rate
n Cons
¨ Ad-hoc and external to event processing engines
¨ Difficult to develop and maintain enrichment logic
¨ Difficult to optimise enrichment process
Agent-based Event Enrichment
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Proposed Approach
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Proposed Approach
n We need
¨ Event enrichment to be integrated into the event
processing paradigm as a core task of event
processing engines
n Proposal
¨ Unified declarative language for event processing
and enrichment
¨ Enrichment element as a declarative specification
for engine to enrich events with complementary
information items
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Unified and Native Enrichment

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Challenges for Enrichment
1. Determination of Enrichment Source (ES)
¨ E.g. BMS relational database via a connection
string “Server=www.example.com/
rdbms;Database=BMS-DB;”.
2. Retrieval of Information Items from the
Enrichment Source
¨ E.g. SQL query against a query interface
3. Finding the Complementary Information for
an Event in the Enrichment Source
¨ E.g. recursively join on the ID attribute in the
LOCATIONS table and query for n times.
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4. Fusion of Complementary Information with the
Event
¨ E.g.
¨ E.g.
Challenges for Enrichment
(type, "energy consumption”)
(device, "heater x”)
(consumption, "high”)
(type, "energy consumption”)
(device, "heater x”)
(consumption, "high”)
(room, “202e”)
(floor, “second floor”)
(type, "energy consumption”)
(device, "heater x”)
(consumption, "high”)
(type, "energy consumption”)
(device, "heater x”)
(consumption, "high”)
(location, “202e, second floor”)
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Produce or Consumer-side
n Producer side enrichment
¨ Producers can provide enrichment elements with
events
¨ Identify enrichment source and mechanism
n Consumer side enrichment
¨ Have better knowledge of completeness from
their perspective
¨ Proposed here as a unified element with the
subscription matching element
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¨ Enrichment Element
– ENRICH FROM identify the enrichment source(s)
– RETRIEVE BY specifies retrieval mechanism for
atomic information items
– FIND BY dictate retrieval of information items
from the enrichment source(s)
– FUSE BY define fusion approach to integrate
retrieved data
¨ Matching Element
– as in current matching languages
Unified Subscription
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Formal Model
ESe
MVS(U)
U
HVS(U)
A1 A2
B1
A3
AB1
B2 B3
AB2 AB3
W=e ES
∩
The universe U, the event e, the enrichment source ES, the
world W, the enrichment view HVS, and a matching view MVS
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Formal Model
n Successful Enrichment
¨ Completeness
n Minimal Successfully Enriched Event
¨ Precision
n Approximately Minimal Successfully
Enriched Event
¨ Cost to turn an enriched event into a minimal
successfully enriched event
(More details on formal definition in paper)
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Implications
n Sharing & Re-usability of Enrichment Elements
¨ Expert consumers can share enrichment knowledge
with less informed consumers
n Distribution of Enrichment
¨ Distribute enrichment process across nodes to
achieve an optimal overall completeness
n Approximation in Event Processing Engines
¨ Matching over partially complete events would need
to account for still missing information
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Linked Data Instantiation
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Linked Data Instantiation
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Event
Model
Enrichment
Source
Model
Enrichment source is accessible by
dereferencing URIs associated with
it as per Principles of Linked Data

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Linked Data Instantiation
n Unified Subscription Language
ENRICH FROM http://www.myenterprise.org/devices
RETRIEVE BY ‘DEREF’
FIND BY ‘Spreading Activation’
‘UniformWeightsAllAdjacent’
FUSE BY ‘UNION’
{?event rdf:type ont:EnergyConsumption.
?event (?p){3} building:SecondFloor.}
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Evaluation Dataset
n English Dbpedia (1st of August 2012)
n Constructed from instances of class
dbpedia-owl:Event
n Total of 24,000 events
¨ “Football Match”, “Race”, “Music Festival”, “Space Mission”,
“Election”, “10thcentury BC Conflicts”, “Academic
Conferences”, “Aviation Accidents And Incidents In 2001”,
etc
n Each event contains one triple in the form:
¨ <eventURI, rdf:type, dbpedia-owl:Event>.
n Enrichment Source: Whole Dbpedia dataset
¨ Potnetially 250 million triples for enrichment
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Evaluation – FIND BY
n FIND BY - Spreading Activation Strategies
n UniformWeightsAllAdjacent
¨ Activation spreads equally to all adjacent nodes
n UniformWeightsRandomAdjacent
¨ Activation spreads equally to random set of
adjacent nodes
n DifferentWeightsSemRel
¨ Activation spreads based on semantic relatedness
to terms in matching element of subscription
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Evaluation – Matching Elements
n Path-shaped graph used to generate matching elements
Matching Element
1 ?event rdf:type dbpedia-owl:Event.
?event (?p){1}
bpedia:England_national_football_tea
m
2 ?event rdf:type dbpedia-owl:Event.
?event (?p){2}
dbpedia:Queens_Park_Rangers_F.C..
3 ?event rdf:type dbpedia-owl:Event.
?event (?p){3}
dbpedia: Loftus_Road.
4 ?event rdf:type dbpedia-owl:Event.
?event (?p){4}
dbpedia: Fulham_F.C..
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n SemRel
(unified
approach)
performs best
n Less effective
with complex
subscriptions
n Completeness
and precision
not weighted
equally
Enrichment Results
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
1 2 3 4
F5Score
Subscription
UniformWeightsAllAdjacents UniformWeightsRandomAdjacents
DifferentWeightsSemRel
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Summary and Future Directions
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Summary and Future Directions
n Event enrichment can be integrated as a
core task of event processing engines
¨ Unified enrichment logic with event subscription
logic
¨ Native enricher tackles incompleteness before
matching
n Future Work
¨ Formalizing the enrichment language element
¨ Leverage commonalities among multiple
consumers for enrichment optimization
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Hasan, S., O’Riain, S., and Curry, E. 2013. “Towards Unified and Native
Enrichment in Event Processing Systems,” In 7th ACM International
Conference on Distributed Event-Based Systems (DEBS 2013),
Arlington, Texas, USA: ACM, pp. 171–182.
http://www.edwardcurry.org/publications/DEBS2013_Enrichment.pdf
Further Reading