=Paper=
{{Paper
|id=Vol-1080/owled2013_20
|storemode=property
|title=The Optique Project: Towards OBDA Systems for Industry (Short Paper)
|pdfUrl=https://ceur-ws.org/Vol-1080/owled2013_20.pdf
|volume=Vol-1080
|dblpUrl=https://dblp.org/rec/conf/owled/CalvaneseGHHHIJKKKLMNORRRSSSWZ13
}}
==The Optique Project: Towards OBDA Systems for Industry (Short Paper)==
https://ceur-ws.org/Vol-1080/owled2013_20.pdf
The Optique Project:
Towards OBDA Systems for Industry
(Short Paper)
D. Calvanese3 , M. Giese10 , P. Haase2 , I. Horrocks5 , T. Hubauer7 , Y. Ioannidis9 ,
E. Jiménez-Ruiz5 , E. Kharlamov5 , H. Kllapi9 , J. Klüwer1 , M. Koubarakis9 ,
S. Lamparter7 , R. Möller4 , C. Neuenstadt4 , T. Nordtveit8 , Ö. Özcep4 ,
M. Rodriguez-Muro3 , M. Roshchin7 , Marco Ruzzi6 , F. Savo6 ,
M. Schmidt2 , A. Soylu10 , A. Waaler10 , D. Zheleznyakov5
1
Det Norske Veritas, 2 fluid Operations AG, 3 Free University of Bozen-Bolzano, 4 Hamburg
University of Technology, 5 Oxford University, 6 Sapienza University of Rome, 7 Siemens
Corporate Technology, 8 Statoil ASA, 9 University of Athens, 10 University of Oslo
Abstract. In this paper we present the EU Optique project that aims at develop-
ing an end-to-end OBDA system for managing Big Data in industries. We discuss
limitations of state of the art OBDA systems and present the general architecture
of the Optique’s OBDA system that aims at overcoming these limitations.
Keywords: OBDA, ontologies, OWL 2, Big Data, System Architecture
1 Introduction
Accessing the relevant data in Big Data scenarios is increasingly difficult both for end-
user and IT-experts, due to the volume, variety, velocity, and complexity dimensions of
Big Data [1]. This brings a high cost overhead in data access for large enterprises. For
instance, in the oil and gas industry, IT-experts spend 30–70% of their time gathering
and assessing the quality of data [3]. The Optique project1 [5] advocates for a next gen-
eration of the well known Ontology-Based Data Access (OBDA) approach to address
the Big Data dimensions and in particular the data access problem. The project aims at
solutions that reduce the cost of data access dramatically.
OBDA systems address the data access problem by presenting a general ontology-
based and end-user oriented query interface over heterogeneous data sources (see Fig-
ure 1). The core elements in a classical OBDA systems are an ontology, describing
the application domain, and a set of mappings, relating the ontological terms with the
schemata of the underlying data sources. End-users formulate queries using the ontolog-
ical terms and thus they are not required to understand the structure of the data sources.
These queries are then automatically translated using the ontology and mappings into
an executable code over the data sources.
State of the art OBDA systems based on the classical architecture, however, have
shown among others the following limitations:
1
http://www.optique-project.eu/
End users
Query
Ontology
Mappings Mappings
...
Heterogeneous data sources
Fig. 1. A general view on Ontology Based Data Access
– The usability of OBDA systems is hampered by the need to use a formal query lan-
guage which is difficult for end-users even if they know the ontological vocabulary.
– The prerequisites of OBDA, i.e., ontology and mappings, are in practice expensive
to obtain. Additionally, they are not static artefacts and should evolve according
to the new end-users’ information requirements. In current OBDA systems, boot-
strapping and maintenance of ontologies and mappings are in a premature.
– The scope of existing systems is too narrow. The chosen expressiveness of the on-
tology and mapping language are focused on very concrete solutions. Management
of streaming data is essentially ignored despite their importance for industry.
– The efficiency of the translation process and the execution of the queries is too low.
2 Architecture
Figure 2 shows an overview of the Optique OBDA architecture which aims at overcom-
ing the limitations above. The architecture is developed using the three-tier approach
and has three layers:
– The presentation layer consists of three main user interfaces: (i) to compose queries,
(ii) to visualise answers to queries, and (iii) to maintain the system by managing
ontologies and mappings. The first two interfaces are for both end-users and IT-
experts, while the third one is meant for IT-experts only.
– The application layer consists of several main components of the Optique’s system,
supports its machinery, and provides the following functionality: (i) query formu-
lation [4], (ii) ontology and mapping management [6], (iii) query answering [2, 9],
and (iv) processing and analytics of streaming data [8].
– The data and resource layer consists of the data sources that the system provides
access to, that is, relational, semistructured, temporal databases and data streams.
It also includes a cloud that provides a virtual resource pool.
Integrated via
Information Workbench
Presentation Query Formulation Interface Answer visualisation
Ontology and Mapping
Layer Management Interface
Visualisation Stream analytics Ontology and Mapping Manager's
Query Formulation engines Processing Components
mining, log analyses, etc
Processing Components
Bootstrapper Ontology
Query by Navigation QDriven ont Analyser and
Context Sens. Ed construction - ontology Evolution Engine Mapping
Direct Editing Ontology - mappings Transformator Revision
Faceted Search Export funct. processing: - configuration Approximator control &
1-time Q
Stream Q
reasoners, Shared - queries Editing
SPARQL Feedback funct. module extractors, triple - answers ontology mapping
etc. store - history
- etc.
Query Answering Component
Query transformation Distributed Query Execution
Q Planner
Query Rewriting Setup module Optimisation
Semantic QOpt Semantic Index Data Federation Shared
Syntactic QOpt Materialisation database
1-time Q SQL Stream Q
1-time Q
Query Execution
Stream Q Data Federation
SPARQL
Application
Layer
Data,
Cloud (virtual
Resource ... RDBs, triple stores,
... data streams resource pool)
temporal DBs, etc.
Layer
Components Colouring Convention Types of Users
Group of Front end: Optique External Expert End
Component solution
components mainly Web-based solution users users
Fig. 2. The general architecture of the Optique OBDA system
The entire Optique system will be integrated via the Information Workbench plat-
form [7].2 We now briefly describe the four application layer components and the In-
formation Workbench platform.
The query formulation component aims at providing a friendly interface for non-techni-
cal users combining multiple representation paradigms (query by navigation, faceted
search, context sensitive editing, etc.). Furthermore, this component will also integrate
a query-driven ontology extension subcomponent to insert new end-users’ information
requirements in the ontology.
The ontology and mapping management component will provide tools to (i) semi-auto-
matically bootstrap an initial ontology and mappings and (ii) maintain the consistency
between the evolving mappings and the evolving ontology.
The query answering component is compound of two large subcomponents: (i) query
transformation subcomponent [2], and (ii) distributed query optimisation and process-
ing component [9]. The transformation subcomponent is responsible for translating,
usually referred to as rewriting, of queries received from the Query Formulation com-
ponent, e.g., SPARQL queries, into an optimised executable code that should be eval-
uated over the data sources and streams in the data layer, e.g., into a set of SQL or
2
www.fluidops.com/information-workbench/
sliding-window queries. In the nutshell, the transformation compiles the ontology in
the input SPARQL query and then translates the result into an SQL query by means of
mappings. Besides the compilation of ontology, the transformation component applies
different query optimisation techniques, including syntactic and semantic query optimi-
sation which may require ontology reasoning. Moreover, the transformation subcompo-
nent creates and maintains a so-called semantic index that supports query optimisation.
The Quest system [12] will be the core part of the Optique’s query transformation,
while we plan to develop novel rewriting and optimisation techniques to deal, e.g., with
streaming data, and to employ other systems, such as P EGASUS [11].
Distributed query optimisation subcomponent provides query planning and execu-
tion. It distributes queries to individual servers and use massively parallelised (cloud)
computing. The ADP [10, 13] system for complex dataflow processing in the cloud is
going to be the core part of the Optique’s distributed query processing. Its distinguished
features that will guarantee efficiency of Optique’s query processing are massive par-
allelism, i.e., running queries with the maximum amount of parallelism at each stage
of execution, and elasticity, i.e., by allowing a flexibility to execute the same query
with the use of resources that depends on the the resource availability for this particular
query, and the execution time goals.
Processing and analytics for streaming data. This component is primarily motivated
by the need of large industries. For example, Siemens3 encompasses several terabytes
of temporal data coming from sensors, with an increase rate of about 30 gigabytes per
day. Addressing this challenge requires a number of techniques and tools which should
be integrated in several modules of the Optique solution. For example, the query for-
mulation module should support window queries and the query transformation module
should support rewriting of such queries. It is also necessary to develop appropriate
formalisms to support ontological modelling of streaming data. Besides that, analytical
tools are required for stream processing.
The Information Workbench is a generic platform for semantic data management, which
provides a central triple store for managing the OBDA system assets (such as ontolo-
gies, mappings, etc.), generic interfaces and APIs for semantic data management, and a
flexible user interface that will be used for implementing the query formulation compo-
nents. The user interface follows a semantic wiki approach, based on a rich, extensible
pool of widgets for visualization, interaction, mashup, and collaboration, which can be
flexibly integrated into semantic wiki pages, allowing developers to compose compre-
hensive, actionable user interfaces without any programming efforts.
3 Conclusions
The Optique system will provide an end-to-end OBDA solution for Big Data access
which will address a number of important industry requirements. The technology and
3
http://www.siemens.com
system will be developed in a close cooperation of six universities, two industrial part-
ners, and two use cases: Statoil and Siemens. The system will be deployed and evalu-
ated in our use cases. It will provide valuable insights for the application of semantic
technologies to Big Data integration problems in industry.
Acknowledgements. The research presented in this paper was financed by the Sev-
enth Framework Program (FP7) of the European Commission under Grant Agreement
318338, the Optique project.
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