=Paper=
{{Paper
|id=None
|storemode=property
|title=Enabling Semantic Integration of Streaming Data Sources
|pdfUrl=https://ceur-ws.org/Vol-623/paper01.pdf
|volume=Vol-623
}}
==Enabling Semantic Integration of Streaming Data Sources==
https://ceur-ws.org/Vol-623/paper01.pdf
Enabling Semantic Integration of Streaming
Data Sources
Jean-Paul Calbimonte
Ontology Engineering Group, Departamento de Inteligencia Artificial, Facultad de
Informática, Universidad Politécnica de Madrid,
Campus de Montegancedo s/n 28660, Boadilla del Monte, Spain
jp.calbimonte@upm.es
Abstract. We propose a distributed ontology-based approach for in-
tegration of streaming live data sources, using extensions of SPARQL
for streams and declarative mappings for query rewriting. The challenge
of exposing live data from streams such as those provided by sensor
networks, using semantically rich models and queries is becoming more
relevant nowadays. The goal of this PhD thesis1 is to investigate, analyse
and propose solutions that bridge the gap between semantic data access,
streaming query evaluation and data integration.
1 Problem Statement
Nowadays in scientific and industrial environments, the amount of data in form
of heterogeneous streams is becoming one of the main sources of information
and knowledge acquisition. Advances in wireless communications and sensor
technologies have enabled the deployment of networks of interconnected devices
capable of ubiquitous data capture, processing and delivery of such streams.
Many solutions for accessing and processing this type of data have been
devised in the last decade[3, 1, 9, 15, 11, 8, 2], including acquisitional and non-
acquisitional streams. However the management and querying techniques ex-
plored so far have not provided consistent solutions for the problem of semantic
heterogeneity of streaming data sources. This problem has become even more ev-
ident given the increasing number of sensor infrastructures in several domains,
each one having completely different schemas, stream rates, quality of service
and delivery mechanisms.
On the other hand the approaches for semantic data provision have largely
focused on stored data[16]. And only very few solutions have been proposed for
querying streams using semantic technologies [5, 7]. A contribution on this area
is becoming an imperative need in the scope of the recent efforts of making
information accessible to the web of data, and in particular from the increasing
number of streaming sources such as sensor networks. Having such a information
space would enable applications to obtain live data published by third-party
providers regardless of formats, schemas, rates and underlying implementation.
1
The author completed the first year of the thesis (Phase I), under the supervision of
Oscar Corcho at Universidad Politcnica de Madrid.
2
2 Main Research Questions
Our work is centred on the question of how to integrate both streaming and
stored information sources using rewriting techniques and how to expose a con-
sistent view of the continuously generated data as RDF for the semantic web.
This will also prompt us to identify the suitable language extensions to existing
approaches that take into account streaming operators in SPARQL queries [5, 7].
In this context we are also interested in finding appropriate means to represent
the mappings and correspondences between ontological elements and stream/re-
lational elements, in a way that can be reused for query rewriting from SPARQL
queries to native streaming queries. In this scope we also ask how we can opti-
mise the query rewriting approach so that it is possible to efficiently query live
streaming data for the original sources, without increasing the time response
beyond acceptable time frames.
By tackling these issues we intend to provide the foundations of an infras-
tructure that is able to provide live data though SPARQL-extended queries with
stream capabilities, not from one but several and heterogeneous sources that are
mapped to ontological views. An integrated and distributed platform of such
characteristics has not been devised yet and we believe that it can have a cer-
tain impact on the community. Nevertheless there is a number of problems that
we will leave for future or complementary research. Namely the ability to au-
tomatically generate mappings or the discovery of a-priori unknown streaming
data sources.
3 General Approach
Our work is organised in the following phases that cover the planned research.
Related work First we have studied the literature and existing approaches
for the main topics that we cover in this thesis: relational-to-ontology data ac-
cess, querying RDF streams, stream management systems and distributed query
processing. Understanding the base approaches will help us coming up with a
solution to the problems we are interested in, and knowing what techniques and
technologies we can reuse or base upon for our approach.
Ontology-based data access Once we have studied the previous works we
have presented a first approach for ontology-based data access to streams [10].
This consists of a simple mechanism of using mapping assertions based on the
R2 O[6] language that relate stream elements to ontology elements. Then an ex-
tended version of SPARQL with extensions for streaming queries (based on the
C-SPARQL language[5]) is used to issue queries over the ontological view and a
translation component transforms them to stream queries in a language that can
be executed by a stream management system (such as SNEE[12], STREAM[3],
etc.). See Section 4 for details.
Stream integration After the data access step we plan to propose an approach
for the integration of heterogeneous streaming data sources. This includes the
mapping relationships from both stream and stored sources to ontological views,
3
and the rewriting of queries to a distributed query processor that is able to
execute and integrate data from the streaming sources. We need to formally
specify the algebra to which our streaming queries will be transformed, and
indicate the optimisation algorithms that we will employ or design, including
classical pushing of operators, optimisations of joins and query planning tasks.
A service implementing this integration & query functionality will be provided
at the end of this stage.
Evaluation & optimisation Once we have the core component of the thesis,
we will evaluate the approach from three main perspectives: the query execu-
tion response time and overhead of the rewriting process; the expressiveness of
queries rewritten from extended-SPARQL to native streaming languages; the
expressive power of mappings from streaming elements to ontology elements.
We plan using known benchmarks for streaming data sources[4] (See Section 5).
We will iteratively use optimisation techniques and progressively evaluate the
results that we obtain during this phase. The resulting prototype will provide
a distributed integration service for heterogeneous streaming and stored data
sources using SPARQL with streaming extensions (named SPARQLSTR in this
paper) as a query language, providing live data for the semantic web.
4 Proposed Solution
Our approach consists in creating an Ontology-based streaming data integra-
tion service (Fig 1) that can receive requests over a global ontological view in
SPARQLSTR . This global ontology can optionally be aligned and represented in
terms of other ontologies that the sources are mapped to. This can be done using
Ontology-to-Ontology mappings that can be partially computed using alignment
technologies. Using these correspondences the original query can be rewritten
(query reconciliation) if necessary. Sources can be exposed in terms of ontologi-
cal views using a set of Ontology-to-Source mappings, which correlate ontology
entities and stream/stored entities. These are provided with a-priori knowledge
of the ontologies and sources schemas, and are based on the R2 O[6] mapping
language, which has been extended to support streaming queries and data.
So when a SPARQLSTR query arrives, it is translated (query translation) to
an internal algebra that is capable of dealing with streaming and stored sources.
This transformation is made using the already mentioned Ontology-to-Source
mappings. The algebra is an extension of the one defined for SNEEql[9], a con-
tinuous query language that has expressive window and window-to-stream op-
erations, and a semantics that incorporates both streaming and stored data.
Once the transformations are made, the distributed query processor for streams
is in charge of the logical rewriting and physical optimisations that take into ac-
count rewriting rules (push of operators, order of joins, etc) and cost models
to find the best distributed query plan that will be executed by the evaluator,
dispatched to the participating processors and then integrated[14]. Note that the
execution in sources such as sensor networks may include in-network query pro-
cessing, pull or push based data delivery and other data source specific settings.
4
Fig. 1. Ontology-based Streaming Data Access service
5 Evaluation
In order to evaluate the proposed solution we will first identify the main targets
of the evaluation, which will be focused in the following points: performance of
the query rewriting, performance of the query execution, expressive power of
the mappings, expressiveness of the query language. For this purpose we will use
the well-known Linear Road benchmark to compare our results to non-ontology-
based approaches in terms of performance and expressiveness. Moreover and in
order to validate the usability of our approach to real applications we will use
our proposal in order to provide integrated streaming and stored data sources for
the use cases of the SemSorGrid4Env[13] project for environmental monitoring.
These include a Coastal and Estuarine Flood Warning system in southern UK
and a Fire Risk Monitoring and Warning system in northern Spain.
6 Future Work
Although we have shown initial results querying the underlying streaming engine
with basic queries[10], we expect to consider in the near future more complex
query expressions including aggregates, and joins involving both streaming and
stored data sources. Another important strand of future work is the optimisation
of distributed query processing [14] and the streaming queries [1, 12]. In the scope
of a larger streaming and sensor networks integration framework, we intend to
achieve the following goals: i) integrating streaming and stored data sources
through an ontological unified view; ii) combining data from event-based and
acquisition-based streams, and stored data sources. The present work can be
considered as a first step to our goal of providing an ontology-based integration
platform for continuous heterogeneous data sources.
5
Acknowledgments. This work is supported by the European Commission
project SemSorGrid4Env (FP7-223913).
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