XML data update management in XML-enabled database

With the increasing demand for a proper and efficient XML data storage, XML-Enabled Database (XEnDB) has emerged as one of the popular solutions. It claims to combine the pros and limit the cons of the traditional Database Management Systems (DBMS) and Native XML Database (NXD). In this paper, we focus on XML data update management in XEnDB. Our aim is to preserve the conceptual semantic constraints and to avoid inconsistencies in XML data during update operations. In this current era when XML data interchange mostly occurs in a commercial setting, it is highly critical that data exchanged be correct at all times, and hence data integrity in XML data is paramount. To achieve our goal, we firstly classify different constraints in XML documents. Secondly, we transform these constraints into XML Schema with embedded SQL annotations. Thirdly, we propose a generic update methodology that utilizes the proposed schema. We then implement the method in one of the current XEnDB products. Since XEnDB has a Relational Model as the underlying data model, our update method uses the SQL/XML as a standard language. Finally, we also analyze the processing performance.     

Mobile service oriented architectures for NN-queries

The ever-increasing number of mobile device users has also raised the users’ expectation of mobile services accordingly. This phenomenon has given pressures to the mobile service providers to improve their services in order to stay competitive in the market. The service oriented approach is seen to be a promising scheme for mobile services. This paper presents mobile service oriented architectures for Nearest-Neighbor (NN) queries that are classified into five categories, namely (i) intermittent query mobile services, (ii) continuous query mobile services, (iii) context-aware mobile services, (iv) continuous moving object query mobile services, and (v) data broadcast mobile services. These services incorporate query, location and context-aware services, ontological context model, and broadcast. The proposed architectures are concerned with mobile services for clients on the move requesting services based on their current location, which is arguably the most important feature in a wireless environment. Furthermore, we also discuss the Quality-of-Service (QoS) requirement for mobile services in which request latency time is one of the most important parameters to consider. Some analytical models for query latency measurement are presented and the results are compared with the simulation experiments.     

Ontology as a Service (OaaS): a case for sub-ontology merging on the cloud

Cloud computing is a revolution in the information technology industry. It allows computing services provided as utilities. The traditional cloud services include Software as a Service, Platform as a Service, Hardware/Infrastructure as a Service, and Database as a Service. In this paper, we introduce the notion of Ontology as a Service (OaaS), whereby the ontology tailoring process is a service in the cloud. This is particularly relevant as we are moving toward Cloud 2.0—multi-cloud providers to provide an interoperable service to customers. To illustrate OaaS, in this paper we propose sub-ontology extraction and merging, whereby multiple sub-ontologies are extracted from various source ontologies, and then these extracted sub-ontologies are merged to form a complete ontology to be used by the user. We use the Minimum extraction method to facilitate this. A walkthrough case study using the UMLS meta-thesaurus ontology is elaborated, and its performance in the cloud is also discussed.     

Ontology tailoring in the Semantic Grid

This paper presents a Service Oriented Architecture (SOA) approach to a distributed framework for reusing, extracting and extending large domain ontologies in the Semantic Grid environment. The conceptual level of the framework describes how sub-ontologies are extracted and extended with new features, while the architectural level of the framework describes the components of the framework. These components allow the sub-ontology extraction and extension process to be performed using shared resources in the Semantic Grid environment. A prototype of the framework is built using Web Services and a complexity evaluation measure is presented. The results of several simulations show that the sub-ontology extraction and extension process in the Semantic Grid is a viable solution and can be optimized by using better quality, initial labeling set.     

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.     

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.     

Voronoi-based range and continuous range query processing in mobile databases

With the wide availability of mobile devices (smart phones, iPhones, etc.), mobile location-based queries are increasingly in demand. One of the most frequent queries is range search which returns objects of interest within a pre-defined area. Most of the existing methods are based on the road network expansion method – expanding all nodes (intersections and objects) and computing the distance of each node to the query point. Since road networks are extremely complex, node expansion approaches are inefficient. In this paper, we propose a method, Voronoi Range Search (VRS) based on the Voronoi diagram, to process range search queries efficiently and accurately by partitioning the road networks to some special polygons. Then we further propose Voronoi Continuous Range (VCR) to satisfy the requirement for continuous range search queries (moving queries) based on VRS. Our empirical experiments show that VRS and VCR surpass all their rivals for both static and moving queries.