MOVE: A Distributed Framework for Materialized Ontology View Extraction

The use of ontologies lies at the very heart of the newly emerging era of semantic web. Ontologies provide a shared conceptualization of some domain that may be communicated between people and application systems. As information on the web increases significantly in size, web ontologies also tend to grow bigger, to such an extent that they become too large to be used in their entirety by any single application. Moreover, because of the size of the original ontology, the process of repeatedly iterating the millions of nodes and relationships to form an optimized sub-ontology becomes very computationally extensive. Therefore, it is imperative that parallel and distributed computing techniques be utilized to implement the extraction process. These problems have stimulated our work in the area of sub-ontology extraction where each user may extract optimized sub-ontologies from an existing base ontology. The extraction process consists of a number of independent optimization schemes that cover various aspects of the optimization process, such as ensuring consistency of the user-specified requirements for the sub-ontology, ensuring semantic completeness of the sub-ontology, etc. Sub-ontologies are valid independent ontologies, known as materialized ontologies, that are specifically extracted to meet certain needs. Our proposed and implemented framework for the extraction process, referred to as Materialized Ontology View Extractor (MOVE), has addressed this problem by proposing a distributed architecture for the extraction/optimization of a sub-ontology from a large-scale base ontology. We utilize coarse-grained data-level parallelism inherent in the problem domain. Such an architecture serves two purposes: (a) facilitates the utilization of a cluster environment typical in business organizations, which is in line with our envisaged application of the proposed system, and (b) enhances the performance of the computationally extensive extraction process when dealing with massively sized realistic ontologies. As ontologies are currently widely used, our proposed approach for distributed ontology extraction will play an important role in improving the efficiency of ontology-based information retrieval.     

Correction to: AnonSURP: an anonymous and secure ultralightweight RFID protocol for deployment in internet of vehicles systems

The Journal of Supercomputing -     

A Taxonomy of Indexing Schemes for Parallel Database Systems

In this paper, we present a taxonomy of indexing schemes in parallel database systems. Index partitioning is not recognized widely as yet. One of the reasons is that most of index structures are trees, not flat structures like tables, and consequently, index partitioning imposes some degree of complexity compared with common data partitioning for tables. We present three parallel indexing schemes, and discuss their maintenance strategies. We also analyze their storage requirements.     

IG-Tree: an efficient spatial keyword index for planning best path queries on road networks

World Wide Web - Due to the popularity of Spatial Databases, many search engine providers have started to expand their text searching capability to include geographical information. Because of this...     

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Reverse Approximate Nearest Neighbor Queries on Road Network

World Wide Web - Reverse k Nearest Neighbor (RkNN) queries retrieve all objects that consider the query as one of their k most influential objects. Given a set of user U, a set of facilities F and...     

Partitioning methods for multi-version XML data warehouses

Due to an explosive increase of XML documents, it is imperative to manage XML data in an XML data warehouse. XML warehousing imposes challenges, which are not found in the relational data warehouses. In this paper, we firstly present a framework to build an XML data warehouse schema. For the purpose of scalability due to the increase of data volume, we propose a number of partitioning techniques for multi-version XML data warehouses, including document based partitioning, schema based partitioning, and cascaded (mixed) partitioning model. Finally, we formulate cost models to evaluate various types of queries for an XML data warehouse.     

AnonSURP: an anonymous and secure ultralightweight RFID protocol for deployment in internet of vehicles systems

The Journal of Supercomputing - With the rapid development in world-wide technologies affecting large-scale applications, the Internet of Things (IoT) has gained a lot of attention from such...