=Paper=
{{Paper
|id=None
|storemode=property
|title=A Framework for Event Processing on the Semantic Web
|pdfUrl=https://ceur-ws.org/Vol-623/paper04.pdf
|volume=Vol-623
}}
==A Framework for Event Processing on the Semantic Web==
https://ceur-ws.org/Vol-623/paper04.pdf
A Framework for Event Processing on the
Semantic Web
Srdjan Komazec
Supervised by Dieter Fensel
Phase II Submission
Semantic Technology Institute (STI) - Innsbruck
ICT Technologiepark, Technikerstrasse 21a, 6020 Innsbruck, Austria
srdjan.komazec@sti2.at
Abstract. The ubiquity of the Web has reached the point at which
people and devices are producing numerous and overwhelming streams
of information in the form of events. Early and intelligent detection of
event pattern occurrences complemented with appropriate reactions can
enable just-in-time responses to the situations of interest. This thesis is
exploring the problems and possibilities stemming from the integration of
event-driven reactivity techniques applied to the Semantic Web domain.
1 Problem Statement
Event processing stands for an established computing paradigm (best recognized
by the seminal work of David Luckham [11]) which provides approaches and tech-
niques to process event1 streams and provide responses in a timely fashion. The
main concerns of event processing comprise issues of exploring causal, temporal,
and aggregative relationships between events with a strong emphasis on the real-
time processing aspect. Established event processing application areas include
manufacturing monitoring and control systems, location based services, financial
data systems, defense intelligence, web analytics, and medical systems.
The introduction of the Web and its recent trends towards the Internet of
Services and the Internet of Devices call for an application shift where event
processing approaches are moving from closed and strictly controlled enterprise
and military spaces towards the open, loosely governed and heterogeneous Web
environment. In particular, the Web enables a dramatic increase in the number
of observable events. Seen as a common platform to easily and cheaply exchange
information, the Web of today is in fact already overwhelmed with events such
as dissemination of new multimedia content, readings from the Sensor Web, and
social networking activity notifications. Those events can be observed through
various channels such as emails, RSS feeds and RESTful Web services and are
1
According to [5] an event represents anything that happens. We are adopting this
rather relaxed definition of event because it goes inline with the assumption-free
characteristics of the Web.
2 A Framework for Event Processing on the Semantic Web
targeted primarily for human consumption. The impact of a potential solution
to tame the Web of Events can be substantial since there are already too many
events on the Web and too less meaning of them.
Event processing at the Web scale is suffering from the genuine Web issues
of heterogeneity and scalability, and stresses even more problems peculiar to the
event processing itself such as correlation of events coming from different sources,
identification and discovery of event sources. The Semantic Web, as an exten-
sion of the Web, provides a promising foundation to solve some of the issues of
event processing over the Web. In particular, the problem of event heterogeneity
reconciliation can be solved by the inclusion of machine processable data in the
event description and the application of data mediation techniques. On top of
it, powerful reasoning algorithms can be applied by the event processing agents
in order to foster automation of the common event processing tasks, including
event pattern and constraint matching, rule evaluation, aggregation computing,
and proper time management.
In order to bring the vision of event processing on the Semantic Web to the
reality, two fundamental challenges need to be met. First, event producers should
enrich their event descriptions with machine processable semantics compliant to
the Semantic Web languages. Second, event processing solutions need to inte-
grate the Semantic Web technologies and languages.
The first challenge is not in focus of the thesis, since there are already
first signs that semantically annotated events are finding their way towards the
(Semantic) Web. With recent Twitter announcement of support to annotated
tweets2 , adoption of the Open Graph Protocol3 by Facebook, and wider usage
of semantic technologies in various forms of sensor networks, the semantically
annotated events are starting to pop-up.
The primary focus of this thesis is on the second challenge, where event
processing techniques meet the Semantic Web. Both Semantic Web and Event
Processing communities have recently shown an interest in integrating their re-
sults at various levels. The most prominent approaches stemming from the Se-
mantic Web community are ETALIS [1, 2], ECA-LP and ECA-RuleML [15, 16],
MARS [12], and recently C-SPARQL [3], while as for the Event Processing com-
munity the main stream of work is represented by XChange [7] and RDFTL [14].
Survey of the existing results has shown that presented solutions are par-
tially solving some of the problems of event processing on the Semantic Web
but that there exist no complete and sound framework to bring the power of
event processing on the Semantic Web to its full potential. In particular, the so-
lutions are rarely discussing the effects of underlying model theories and effect of
2
http://apiwiki.twitter.com/Annotations-Overview
3
http://opengraphprotocol.org/
A Framework for Event Processing on the Semantic Web 3
materialization of implicit knowledge onto the mechanisms employed to exhibit
event processing behavior. Furthermore, the solutions are almost exclusively ap-
proaching to the problem of event pattern detection in a backward-chaining
fashion which includes serious performance penalties. It is also visible that cur-
rent solutions are not tackling some phenomena peculiar to the event processing
such as parameter context 4 .
The gap between the Semantic Web and Event Processing can be filled with a
comprehensive framework for event processing on the Semantic Web which builds
upon best of breed from both areas and provides their deeper integration. The
framework should cover the whole event processing life-cycle starting from the
event pattern detection phase, checking for additional constraints and responding
with proper activities. Such a framework would be useful in various application
scenarios, ranging from filtering and processing social networking event streams,
over integrating and aggregating events across the (Semantic) Web, towards
managing semantically enriched enterprise SOA solutions.
2 Main Questions
A possible synthesis of the Semantic Web and Event Processing solutions should
yield an approach which acknowledges the peculiarities of the Semantic Web
in terms of existence of implicit knowledge and usage of reasoning techniques
while providing a performant and scalable detection of event pattern occurrences
and successive processing of those occurrences. In that sense, a possible solution
represents a tradeoff between the expressivity of adopted Semantic Web language
and retained performance and scalability. A general research question this thesis
aims at answering is the following:
How are the underlying model theories of the Semantic Web languages coupled
with the peculiarities of the event processing paradigm affecting the event
pattern detection and event-driven reactivity mechanisms?
The effect of the presence of implicit knowledge on the complex event de-
tection techniques has not been discussed thoroughly in the previous work. The
process of knowledge base closure materialization can be time consuming (thus
not amenable for real-time appliances) and can interfere with the event detec-
tion mechanisms. Solutions such as ETALIS [1, 2] and ECA-LP [15] are basically
promoting the backward-chaining approach in which reactive rules are homog-
enized with deductive rules (i.e. rules capable of materializing new facts). In
such a setting, the materialization of derived knowledge comes alongside with
the detection of an event. However, the used backward-chaining approaches have
proved not to be particularly suitable for event-driven applications, since rule
4
Parameter context stands for a set of approaches to reduce consumed space and
computational overhead by providing a mechanism to choose a meaningful subset of
event occurrences.
4 A Framework for Event Processing on the Semantic Web
evaluation is a time consuming and repetitive process (in particular, it depends
on rule set interdependencies) and as such does not promote incremental eval-
uation of event patterns. Proper performance and scalability can be achieved
only in terms of forward-chaining detection of event patterns where suitable
techniques should be employed to inject support for incremental maintenance of
materializations.
A set of more precise questions this thesis is targeting to answer can be
derived from the general one:
1. How to maintain truth inside of event knowledge base in the context of pa-
rameter contexts? The usage of parameter context is enabling filtering of
received events according to the particular need governed by an application
domain. Since a detection of an event pattern can have multiple interpreta-
tions, the event processing engine must be fine-tuned to react only to recent
occurrences of events, chronicle occurrences, cumulative occurrences, etc. It
comes with surprise that none of the existing approaches is taking into ac-
count the issue of parameter contexts. Coupled with the possible existence of
derived facts materialized through closure computation, the problem is get-
ting even more evident as the inferred knowledge may influence the proper
management of parameter context.
2. How to maintain truth inside of event knowledge base in the context of tim-
ing windows? The notion of timing windows provides a way to focus only on
a time-bounded subset of recently harvested events. As such, proper man-
agement of materialized knowledge in course of timing window changes rep-
resents a sensitive issue, since expiration of a (possibly derived) statement
truth in a knowledge base should be continuously monitored. Solutions such
as C-SPARQL [3] are providing an approach to compute incremental changes
in a secondary RDF store and then use this store for query evaluation. The
problem with this approach is in the coarse grained time-frames during which
the truth is maintained. The rapid arrivals of new events and short expira-
tion times can impose a significant overhead in course of a proper query
evaluation. This thesis aims to attack the problem directly on the primary
structure used to incrementally evaluate event patterns, since this could im-
prove the overall performance and maintain the truth more precisely than
the previous approach.
3. How to optimize event pattern detection structures in terms of multiple
queries and derived knowledge? The surveyed solutions are not in general
discussing the opportunities stemming from the fact that interdependencies
between multiple event patterns provide an opportunity to save computa-
tional resources when evaluating the intermediate results used by more than
one pattern. When it comes to the forward-chaining handling of event pat-
terns and network-based detection of event occurrences (like in RETE ap-
proach [6]) we can run into the same issues as in the case of question 1,
i.e., computing the deductive closure can potentially extend the knowledge
A Framework for Event Processing on the Semantic Web 5
base coverage and introduce a new range of difficulties in terms of sharing
of intermediate event-detection results.
3 General Approach
In the heart of any event processing solution lay a language capable of declara-
tively describing arbitrary event patterns and an engine capable of interpreting
such declarations and detecting occurrences of events fulfilling the patterns. As a
matter of completeness and practical applicability, an event processing solution
is usually an integral part of a more general event-driven reactivity framework
which often follows the Event-Condition-Action5 (ECA) paradigm. The frame-
work plays the role of an interface point through which a particular system is
monitored, observations about its behavior are collected in the form of events
and appropriate activities are enacted in order to change the system state or
influence its behavior.
Without major deviations from the aforementioned approach, the artifacts
developed in course of this thesis are following the very same path. The central
part of the thesis is related to the event pattern language and the engine capa-
ble of interpreting event pattern descriptions in order to actively search for the
event occurrences fulfilling the defined pattern. The language will be built on
top of the proven Semantic Web languages RDF(S) and SPARQL, and where
applicable recent results in the area of RDF data stream querying will be used
(such as C-SPARQL, which provides a mean to handle transient streams of RDF
triples through the notions of timing windows, aggregate functions, etc). How-
ever, the detection engine will not rely on common approaches to query RDF
repository but rather on the forward-chaining production rule-like mechanisms
such as RETE [6], and its successors THREAT [13] or LEAPS [4]. The engine
will integrate all the necessary features to properly address the research ques-
tions presented in Section 2. In order to address them, the thesis will follow the
path established by [17] of incremental maintenance of ontology materializations
computed as a result of deductive closures upon the event arrivals and try to
apply them over the event detection network-structures.
In order to enable useful application of the complex event processing solution,
an appropriate ECA rule engine is developed. While the Event part reuses the
developed event pattern language and supporting detection engine, the Con-
dition part is devoted to the traditional query evaluation over the persistent
and volatile data detected in the previous step. The Action part is enabling en-
actment of different types of activities such as updates over data repositories
or execution of management procedures over the observed system. The overall
approach is presented in Figure 1.
5
http://en.wikipedia.org/wiki/Event_condition_action
6 A Framework for Event Processing on the Semantic Web
Event Condition Action
Complex Event
Pattern
Language
Query Action
Evaluation Execution
Complex Event
Detection Engine
Persistent
Data
Observed System
Event Event Event Event Event Event Event Event
Event Stream (Volatile Data)
Fig. 1. The artifacts covered in course of the thesis.
4 Proposed Solution
The solution so far has focused on building a general ECA framework which
provides a testbed for further extensions, quick implementation of the use cases,
but above all the possibility to integrate and test the innovative solutions pre-
sented in Section 2.
The work related to this thesis has been conducted so far in course of two
projects: Service Bundler6 , and COIN7 .
In Service Bundler project the ECA engine is used to process the informa-
tion collected from a set of probes dedicated to observing behavior of single Web
Services and enact appropriate activities upon detection of particular situations
(e.g., updating aggregated statistics and recovering from Web service faults). In
Service Bundler deliverable 3.3 [9] the syntax of ECA language has been defined
where the Event and Condition part are represented as SPARQL SELECT and
ASK queries, while the Action part provides for a possibility to create, update
and delete data in an RDF storage, invoke external Web Services and gener-
ate and disseminate new events over an existing channel. Alongside with the
syntax, the semantics of the langauge has been described, and an appropriate
RDFS ontology governing the concepts related to the Web service monitoring
had been developed. In addition, the API supporting object-oriented represen-
tation of ECA rules has been presented.
6
http://seekda.com/en/research/service-bundler
7
http://www.coin-ip.eu
A Framework for Event Processing on the Semantic Web 7
In COIN project the very same ECA framework is used to monitor and re-
act over the events emitted by the Semantic Execution Environment alongside
with the execution of Semantic Web Services. The implemented ECA rules are
again computing aggregated statistical values (like average/max/min invocation
time, and overall/per user invocation counting) but also communicating to the
external systems through Web service interfaces (i.e., sending notifications to a
service reputation manager).
The realization of the core research issues related to semantically enhanced
event pattern detection is still pending and will be conducted in the near future.
5 Evaluation
Evaluation of the thesis results targets to study and demonstrate usefulness of
the solution by applying it to two application domains. In addition, the complete-
ness and expressivity of the solution are evaluated through an implementation of
the reference use-case in the event processing domain. At the end a performance
study will be conducted.
5.1 Study of language expressivity in the context of reference
use-case
In [8] Etzion et al. have presented an informal specification of a reference use-case
which challenges various aspects of an event processing solution. The problem
is build on top of the fast flower delivery use case in which various parties
must be orchestrated at run-time by exchanging and reacting to the particular
events in order to ensure high quality of the service. The use-case challenges
expressivity of a complex event processing solution in several ways. First, it
requires rather expressive language to describe event patterns which supports
time windowing, correlations of events, and aggregation support. Second, since
the overall system includes various event prosumers (flower storage, delivery
vehicles, and the management system run by flower store association) certain
support for interoperability and scalability is expected. Third, the system is
also expected to take into account both volatile and persistent data (e.g., when
reevaluating drivers rankings). Some existing solutions are already claiming to
have an implementation of the reference use-case (like ETALIS8 ). A comparison
of the solution provided in this thesis and the existing solutions is also considered.
5.2 Empirical study of implementation effectiveness
The effectiveness of the language, detection engine, and rule engine developed in
the course of this thesis will be empirically studied through the implementation
of two use cases coming from different domains.
8
http://code.google.com/p/etalis
8 A Framework for Event Processing on the Semantic Web
The first domain concerns with filtering and reactivity over semantically an-
notated streams of events produced by a social networking solutions such as
Twitter. The possible application of the even-driven reactivity and event pro-
cessing is in the domain of detecting particular social trends. The setup for the
study is under development.
The second domain of application targets Semantically-enabled Service Ori-
ented Architectures (SESAs) and in particular Semantic Execution Environment
(SEE). So far SESA has neglected the importance of events generated by it. A
SEE implementation could be improved by introducing event-based monitoring
and reactivity in terms of auditing, run-time performance optimization, adapta-
tion, and resilience to failures. As in the case of the previous application domain,
the evaluation will cover study of the solution effectiveness when it faces the
peculiarities of a SEE implementation, such as distributed execution, heteroge-
neous event descriptions, and performant event processing in case of burst event
streams.
A part of the second use case has already been implemented and published
in Komazec et al. [10]. As presented in Figure 2, all the SEE brokers are in-
strumented, thus capable of emitting events. The events are registered in the
RDF repository governed by an appropriate ontology. Decisions regarding the
actions taken upon sensing/deriving knowledge about the system are delegated
to the Monitoring and Complex Event Processing component. The component
detects (complex) situations of interest by consulting the registered events, ana-
lyzing them in some broader context (e.g. consulting additional knowledge) and
selects/executes appropriate actions, which will be enacted over the system.
5.3 Complex event detection engine performance evaluation
An evaluation of the event detection engine performance will be carried out.
Where applicable, the same measurements will be carried out over the match-
ing solutions. A quantitative analysis such as throughput and scalability in the
context of different parameters (allocated memory, frequency of events and com-
plexity of event pattern descriptions) will be performed over the recorded mea-
surements.
6 Conclusions and Future Work
This thesis is targeting to provide a solution for event processing in the con-
text of the Semantic Web. In contrast to the previous approaches the thesis is
aiming to provide a comprehensive event processing framework consisting of an
expressive event pattern language and a novel event detection engine which is
capable of maintaining the truth in the event knowledge base in course of rapidly
changing data. The event pattern detection mechanism is complemented by an
ECA rule-based solution, which enables application of the event processing in
A Framework for Event Processing on the Semantic Web 9
Monitoring and Complex Event Processing
Monitoring RDF ECA
RDF Engine
Storage
RDF RDF RDF RDF RDF RDF
RDF Triple Stream
Web Service Modeling Execution Environment (WSMX)
Monitoring and Complex Event Processing
Network Network
Core Management
User N Execution Semantics
Service
User M Provider X
Back-end
Communication
Data Mediation
Choreography
Business
Orchestration
Ranking and
System Z
Invocation
Grounding
Notication
Discovery
Selection
Manager
Process P Broker
Back-end Service
System O Provider Y
Business
Process Q
Resource Manager
Parsers and
Reasoners
Ontologies Goals Web Services Mediators
Fig. 2. A SEE implementation enriched with the ECA rule engine.
various domains such as social networking and semantically enriched SOA sys-
tem monitoring.
Work on the thesis has reached the point in which the working environment
has been set up and elaboration of answers is in the initial stage. In particular,
the near future will yield the work focusing on the core thesis problems related to
the influence of advanced aspects, such as timing windows, parameter contexts
and multiple query optimizations, on the event pattern detection process in
presence of derived knowledge.
Acknowledgments This work was supported by the COIN project (EU FP7
Project 216256; www.coin-ip.eu) funded by the European Community within
the IST-Programme of the 7th Framework Research Programme.
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