=Paper=
{{Paper
|id=Vol-1383/paper16
|storemode=property
|title=Fast In-Memory Reasoner for Oracle NoSQL Database EE: Uncover hidden relationships that exist in your enterprise data
|pdfUrl=https://ceur-ws.org/Vol-1383/paper16.pdf
|volume=Vol-1383
|dblpUrl=https://dblp.org/rec/conf/semweb/PanRMW14
}}
==Fast In-Memory Reasoner for Oracle NoSQL Database EE: Uncover hidden relationships that exist in your enterprise data==
https://ceur-ws.org/Vol-1383/paper16.pdf
Fast In-Memory Reasoner for Oracle NoSQL
Database EE: Uncover Hidden Relationships
that Exist in Your Enterprise Data
Zhe Wu1 , Gabriela Montiel1 , Yuan Ren2 , and Jeff Z. Pan2
1
Oracle, US
2
Departmentof Computing Science, University of Aberdeen, UK
Graph databases and NoSQL databases, two very important topics in Big
Data, have gained popularity in recent years due to their unique characteristics
in their horizontally scale-out capability and flexible schema or schema-free de-
sign. The recent release of OWL-DBC 1 , an adaptor between Oracle Spatial and
Graph 2 and the TrOWL reasoner [2, 1], has built a tight integration between
one of the leading industrial graph databases and the cutting edge, in-memory,
semantic reasoner to achieve high quality and efficient semantic reasoning on
large scale enterprise data.
In this session we present OWL-NOSQL, which enhances the Oracle NoSQL
Database EE 3 with efficient in-memory reasoning capability from TrOWL. With
OWL-NOSQL, users are able to manage their enterprise data in the form of RDF
Graph stored in Oracle NoSQL Database EE and gain insight into their data
through powerful semantic reasoning.
Oracle NoSQL Database EE is a horizontally scaled, key-value database for
Web services and cloud. This system uses a simplistic key-value pair data model
to achieve efficiency and high scalability. Despite of its simplicity, such a data
model can be engineered to represent rather complex knowledge and structures
in data, including RDF graphs and OWL ontologies. In fact, key-value pair
databases have emerged as one of the promising solutions for semantic exploita-
tion in recent years. Such flexibility enables Oracle NoSQL Database EE to
expose its data to external semantic applications, including semantic reasoners,
to uncover hidden relationships in the stored data, especially those represent se-
mantic annotations. More concretely, such a semantic extension of Oracle NoSQL
Database EE is performed as follows:
1. Exporting RDF data stored in Oracle NoSQL Database EE into an ontology.
2. Performing reasoning using the semantic reasoner TrOWL to uncover hidden
relationships in the data.
3. Importing reasoning results into Oracle NoSQL Database EE to persistent
the uncovered relationships.
According to our previous experience with OWL-DBC, the most significant
performance challenge of such a solution rises from the data transferring be-
1
http://trowl.eu/owl-dbc/
2
http://www.oracle.com/technetwork/database/options/spatialandgraph/overview/index.html
3
http://www.oracle.com/us/products/database/nosql/overview/index.html
tween database and reasoner. To address such an issues and offer faster data
exploitation, the following means have been taken:
1. Performing reasoning and data export/import in memory. This minimises
the need to perform storage I/O.
2. Enable parallel processing of data. Such parallelisation can be realised on
two levels:
(a) Export, reasoning and import can be performed in parallel to each other.
Particularly, modules of exported data can be used by TrOWL for rea-
soning, while other modules of the data are being exported from Oracle
NoSQL Database EE. Once a reasoning result is being computed, it can
be directly imported into Oracle NoSQL Database EE, without having
to wait for the other reasoning results. Such parallelisation exploits the
parallel mechanism between storage, memory and CPU cores.
(b) Export, reasoning and import individually can be performed in parallel.
Particularly, Oracle NoSQL Database EE is capable of exporting and
importing data in parallel using multiple threads. TrOWL, on the other
hand, is capable of executing reasoning on several mutually independent
partitions. On a computer (cluster) with multiple storage I/O bandwidth
and multiple CPU cores, such parallelisation can make the best use of
all available hardware resources.
With the above solutions, we are able to improve the efficiency of data
transferring and reasoning. Together, OWL-NOSQL enhances Oracle NoSQL
Database EE with semantic reasoning, offering more flexibility to our clients in
terms of data storage, management and exploitation options.
We are optimistic about OWL-NOSQL because many industries have already
embraced Semantic Web and NoSQL technologies. In the past decade, we have
observed more and more enterprise applications built on top of RDF and OWL
standards; and NoSQL technologies at the same time play an ever-increasingly
critical role for the management and analysis of Big Data. There clearly is a
natural synergy between these two sets of technologies.
References
1. Ren, Y., Pan, J.Z., Zhao, Y.: Soundness Preserving Approximation for TBox Rea-
soning. In: the Proc. of the 25th AAAI Conference Conference (AAAI2010) (2010)
2. Thomas, E., Pan, J.Z., Ren, Y.: TrOWL: Tractable OWL 2 Reasoning Infrastruc-
ture. In: the Proc. of the Extended Semantic Web Conference (ESWC2010) (2010)