An integrated approach to deliver OLAP for multidimensional Semantic Web Databases

Semantic Webs (SW) and web data have become increasingly important sources to support Business Intelligence (BI), but they are difficult to manage due to the exponential increase in their volumes, inconsistency in semantics and complexity in representations. On-Line Analytical Processing (OLAP) is an important tool in analysing large and complex BI data, but it lacks the capability of processing disperse SW data due to the nature of its design. A new concept with a richer vocabulary than the existing ones for OLAP is needed to model distributed multidimensional semantic web databases. A new OLAP framework is developed, with multiple layers including additional vocabulary, extended OLAP operators, and usage of SPARQL to model heterogeneous semantic web data, unify multidimensional structures, and provide new enabling functions for interoperability. The framework is presented with examples to demonstrate its capability to unify existing vocabularies with additional vocabulary elements to handle both informational and topological data in Graph OLAP. The vocabularies used in this work are: the RDF Cube Vocabulary (QB) – proposed by the W3C to allow multi-dimensional, mostly statistical, data to be published in RDF; and the QB4OLAP – a QB extension introducing standard OLAP operators. The framework enables the composition of multiple databases (e.g. energy consumptions and property market values etc.) to generate observations through semantic pipe-like operators. This approach is demonstrated through Use Cases containing highly valuable data collected from a real-life environment. Its usability is proved through the development and usage of semantic pipe-like operators able to deliver OLAP specific functionalities. To the best of my knowledge there is no available data modelling approach handling both informational and topological Semantic Web data, which is designed either to provide OLAP capabilities over Semantic Web databases or to provide a means to connect such databases for further OLAP analysis. The thesis proposes that the presented work provides a wider understanding of: ways to access Semantic Web data; ways to build specialised Semantic Web databases, and, how to enrich them with powerful capabilities for further Business Intelligence

A Framework to Support Developers in the Integration and Application of Linked and Open Data

In the last years, the number of freely available Linked and Open Data datasets has multiplied into tens of thousands. The numbers of applications taking advantage of it, however, have not. Thus, large portions of potentially valuable data remain unexploited and are inaccessible for lay users. Therefore the upfront investment in releasing data in the first place is hard to justify. The lack of applications needs to be addressed in order not to undermine efforts put into Linked and Open Data. In existing research, strong indicators can be found that the dearth of applications is due to a lack of pragmatic, working architectures supporting these applications and guiding developers. In this thesis, a new architecture for the integration and application of Linked and Open Data is presented. Fundamental design decisions are backed up by two studies: firstly, based on real-world Linked and Open Data samples, characteristic properties are identified. A key finding is the fact that large amounts of structured data display tabular structures, do not use clear licensing and involve multiple different file formats. Secondly, following on from that study, a comparison of storage choices in relevant query scenarios is made. It includes the de-facto standard storage choice in this domain, Triples Stores, as well as relational and NoSQL approaches. Results show significant performance deficiencies of some technologies in certain scenarios. Consequently, when integrating Linked and Open Data in scenarios with application-specific entities, the first choice of storage is relational databases. Combining these findings and related best practices of existing research, a prototype framework is implemented using Java 8 and Hibernate. As a proof-of-concept it is employed in an existing Linked and Open Data integration project. Thereby, it is shown that a best practice architectural component is introduced successfully, while development effort to implement specific program code can be simplified. Thus, the present work provides an important foundation for the development of semantic applications based on Linked and Open Data and potentially leads to a broader adoption of such applications

Enabling Cross Constraint Satisfaction in RDF-Based Heterogeneous Database Integration

Abstract The problem of database integration has been widely tackled through different approaches. While data transformation based systems, such as Data Warehouses, reached the acceptation of the industry during the 80's, in the last decade query translation based approaches have gained popularity given their adequacy to dynamic domains

Distributed RDF query processing and reasoning for big data / linked data

Title from PDF of title page, viewed on August 27, 2014Thesis advisor: Yugyung LeeVitaIncludes bibliographical references (pages 61-65)Thesis (M. S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2014The Linked Data Movement is aimed at converting unstructured and semi-structured data on the documents to semantically connected documents called the "web of data." This is based on Resource Description Framework (RDF) that represents the semantic data and a collection of such statements shapes an RDF graph. SPARQL is a query language designed specifically to query RDF data. Linked Data faces the same challenge that Big Data does. We now lead the way to a new wave of a new paradigm, Big Data and Linked Data that identify massive amounts of data in a connected form. Indeed, utilizing Linked Data and Big Data continue to be in high demand. Therefore, we need a scalable and accessible query system for the reusability and availability of existing web data. However, existing SPAQL query systems are not sufficiently scalable for Big Data and Linked Data. In this thesis, we address an issue of how to improve the scalability and performance of query processing with Big Data / Linked Data. Our aim is to evaluate and assess presently available SPARQL query engines and develop an effective model to query RDF data that should be scalable with reasoning capabilities. We designed an efficient and distributed SPARQL engine using MapReduce (parallel and distributed processing for large data sets on a cluster) and the Apache Cassandra database (scalable and highly available peer to peer distributed database system). We evaluated an existing in-memory based ARQ engine provided by Jena framework and found that it cannot handle large datasets, as it only works based on the in-memory feature of the system. It was shown that the proposed model had powerful reasoning capabilities and dealt efficiently with big datasetsAbstract -- Illistrations -- Tables -- Introduction -- Background and related work -- Graph-store based SPARQL model -- Graph-store based SPARQL model implementation -- Results and evaluation -- Conclusion and future work -- Reference

Video on the semantic web : experiences with media streams

In this paper, we report our experiences with the use of SemanticWeb technology for annotating digital video material.Web technology is used to transform a large, existing video ontology embedded in an annotation tool into a commonly accessible format. The recombination of existing video material is then used as an example application, in which the video metadata enables the retrieval of video footage based on both content descriptions and cinematographic concepts, such as establishing and reaction shots. The paper focuses on the practical issues of porting ontological information to the Semantic Web, the multimedia-specific issues of video annotation, and requirements for Semantic Web query and access patterns. It thereby explicitly aims at providing input to the two new W3C Semantic Web Working Groups (Best Practices and Deployment; Data Access)

Structuring visual exploratory analysis of skill demand

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on