Stop writing repetitive code! Scaffolding a semantic data access layer to abstract developers from semantic technologies

This paper presents the Service Oriented Architecture (SOA)-based Semantic Data Access Layer (DAL) component approach which simplifies access to triple stores. Built with the benefits of web service and SOA, this component provides interoperability, loose coupling and programming-language independent access to different triple stores on various computer systems. Limitations of SPARQL query, SPARQL endpoint and the triples indexing mechanism as well as our solution are discussed. In this paper, particular attention is given to the performance comparison between our component and cache management, indexing facilities and native triple store vendors using the Lehigh University Benchmark (LUBM). The main contribution of our component is to provide an easy-one-stop common and reusable Application Programming Interface (API), to build high performance Semantic-based applications, without the need to develop yet another back-end system.

Introduction: Today, Internet of Things applications offer new opportunities in all domains like home automation, transportation, medical diagnosis, agriculture, etc. According to McKinsey Global Institute research, IoT will cover a market share of over $11.1 trillion by 2025. Moreover, Semantic web technology approaches are used in IoT applications so that machines can understand and interpret sensor-collected data. Method: Our proposed system uses a DHT11 sensor, NodeMCU for data collection, and ThingSpeak cloud for data analysis and visualization. It utilizes the Protégé tool to develop semantic data modelling using Ontology/RDF graphs and retrieval for future SPARQL queries. Result: This approach ensures the optimal presentation of sensor data and the meaning of data and controls the information for the Home Automation System. By semantic layer, we improved integration, interoperability, discovery, and data analysis. Conclusion: As far as applications are concerned, semantic technologies and IoT sensor data can be transformed into a more valuable and practical format, enabling intelligent applications and systems development across multiple fields, such as smart cities, industrial automation, healthcare, and environmental monitoring.

Semantic drug discovery has gained significant traction in recent years, with researchers becoming more aware that these technologies enable them to link together and query disparate datasets for information that cannot be extracted from a single dataset. This article provides a comprehensive reference source of the current knowledge available regarding Semantic Web technologies in drug discovery. The main aspects of Semantic Web technologies are explained, detailing the different ways in which they can be used in drug discovery. Over 1000 biomedical ontologies were reviewed as part of the work undertaken for this paper and 34 of the most relevant ontologies in the drug discovery field are categorized and described, followed by details of semantic applications and successes in drug discovery. Some core standards and guidelines have been established for sharing Semantic drug discovery data, both through making well established medical taxonomies available in a Semantic format, and by creating upper-level ontologies and guidelines for creating new ontologies in the biomedical domain. This article concludes that a majority of the prevalent ontologies in drug discovery follow these standards and provides advice for researchers wishing to use Semantic Web technologies in their drug discovery research.

A large-scale framework for storage, access and analysis of time series data in the manufacturing domain

This volume contains the Proceedings of The Third International Conference on Software, Services & Semantic Technologies (S3T) held in Bourgas, Bulgaria on September 1-3, 2011. It is the third S3T conference in a series of annually organized events supported by the F7 EU SISTER Project and hosted by Sofia University. The conference is aimed at providing a forum for researchers and practitioners to discuss the latest developments in the area of Software, Services and Intelligent Content and Semantics. The conference sessions and the contents of this volume are structured according to the conference track themes: Intelligent Content and Semantics (10 papers), Knowledge Management, Business Intelligence and Innovation (4 papers), Software and Services (6 papers), and Technology Enhanced Learning (9 papers). The papers published in this volume cover a wide range of topics related to the track themes. Particular emphasis is placed on applying intelligent semantic technologies in educational and professional environments with papers in the areas of Ontologies and Semantic Web Technologies, Web Data and Knowledge, Social Networks Analysis, Information Extraction and Visualisation, Semantic Search and Retrieval, E-learning, and User Modelling and Personalization.

Semantic Attribute-Based Access Control: A review on current status and future perspectives

It has been proposed that Semantic Web technologies would be key enablers in achieving context-aware computing in our everyday environments. In our vision of semantic technology empowered smart spaces, the whole interaction model is based on the sharing of semantic data via common blackboards. This approach allows smart space applications to take full advantage of semantic technologies. Because of its novelty, there is, however, a lack of solutions and methods for developing semantic smart space applications according to this vision. In this paper, we present solutions to the most relevant challenges we have faced when developing context-aware computing in smart spaces. In particular the paper describes (1) methods for utilizing semantic technologies with resource restricted-devices, (2) a solution for identifying real world objects in semantic technology empowered smart spaces, (3) a method for users to modify the behavior of context-aware smart space applications, and (4) an approach for content sharing between autonomous smart space agents. The proposed solutions include ontologies, system models, and guidelines for building smart spaces with the M3 semantic information sharing platform. To validate and demonstrate the approaches in practice, we have implemented various prototype smart space applications and tools.

Given the heterogeneous nature of biological data and their intensive use in many tools, in this paper we propose a semantic data access and integration (DAI) service, based on the grid paradigm, for the bioinformatics domain. This service uses ontologies for correlating different data sets. The DAI proposed in this work is a fundamental component of the ProGenGrid system, a grid-enabled platform, which aims at the design and implementation of a virtual laboratory where e-scientists could simulate complex "in silico" experiments, composing some popular analysis and visualization tools (e.g. Blast and Rasmol) available as Web services, into a workflow. The main goal of the DAI is to provide bioinformatics tools with advanced functionalities and data integration services for heterogeneous biological data banks, such as PDB and Swiss-Prot. A case study of our specialized data access service for locating similar protein sequences is presented.

In the last decade, substantial progress has been made in multimedia creation, transmission, presentation and analysis to facilitate the development of large-scale multimedia information systems. Together with the maturation and deployment of semantic web technologies, it is now possible to build a new generation of multimedia applications that enables large-scale semantic representation, analysis, and delivery of multimedia data from heterogeneous data sources. However, there is still a long way to go for mature solutions of multimedia database systems that are capable of processing semantics-rich, large-volume of multimedia data. It could be even more challenging if such systems are under stringent functional and nonfunctional (e.g., QoS) requirements. The goal of this special issue is to invite high quality scientific contributions in multimedia data semantics, with a focus on how to apply semantic technologies to the acquisition, generation, transmission, storage, processing, and retrieval of large-scale multimedia information. Discussions on future challenges in multimedia information processing, as well as practical solutions for the design and implementation of multimedia database software systems have also been encouraged. The topics of interest include, but are not limited to, practical areas that span both semantic technologies and multimedia processing and computing. This special issue follows the very successful IEEE International Workshop on Data Semantics for Multimedia Systems and Applications (IEEE-DSMSA) held in 2009 and for which the best authors were invited to submit extended versions of their papers. An open Multimed Tools Appl DOI 10.1007/s11042-011-0808-z

Summary form only given. In this session you will learn: to leverage semantic technology to bring information and intelligence from around the Web, inside your operation. Semantic technology can improve on your traditional data management methods through better data identification, classification, mapping and evaluation. Semantic Web technology can provide a window into how people, places, things and events come together into both threats and opportunities, adding a semantic layer to your existing intelligence platform supports the strategic process of intelligence gathering and data analysis. Semantics can help in cyber security and threat detection with semantic-based classification, filtering, data mining, and meta-tagging to expose non-obvious relationships.

This paper describes the implementation of a Smart Campus application prototype that integrates heterogeneous data using semantic technologies. The prototype is based on a layered semantic architecture that facilitates semantic data access and integration using OWL, SWRL and SPARQL. The focus of the paper is on the prototype implementation and the lessons learned from its development.

This paper presents a general Semantic Smart World framework (SSWF), to cover the Migratory birds’ paths. This framework combines semantic and big data technologies to support meaning for big data. In order to build the proposed smart world framework, technologies such as cloud computing, semantic technology, big data, data visualization, and the Internet of Things are hybrid. We demonstrate the proposed framework through a case study of automatic prediction of air quality index and different weather phenomena in the different locations in the world. We discover the association between air pollution and increasing weather conditions. The experimental results indicate that the framework performance is suitable for heterogeneous big data.

The incorporation of semantics and the necessary interoperability within these aspects is essential for the domain's proper operation as well as execution. Healthcare systems have become an ideal arena of IoT because they tackle the problems of humanity, especially of an older population whilst providing secure and high-quality home care and support. The use of IoT technologies in healthcare will improve the quality of human life, chronic illness monitoring, hazard detection, and life-saving measures. To get more useful information from biomedical big data, it must have interoperability. In the latest times, an increasing count of organizations and businesses have expressed interest in combining semantic web technologies alongside healthcare big data to transform data into knowledge and understanding. Even though we can see a systematic acceptance of semantic technologies-based applications in the IoT domain and across the Internet, the cumulative actual implementations are insufficient to provide real-world rooted standards and guidelines to follow. This sets the stage for this work, which attempts to describe current developments in the application of semantic technologies in the IoT domain. This motivates the authors to examine and highlight some of the developing developments in semantic technology, its effects in the IoT area, and how they are together seen in the health-care. Over the last several times, there has been a lot of emphasis on using SWT to enhance the uptake of sensor networks, IoT, and WoT. Indeed, to tackle semantic interoperability and other issues in health care domains, there is a need to comprehend its means of construction.

Conflating multiple geospatial data sets into a single dataset is challenging. It requires resolving spatial and aspatial attribute conflicts between source data sets so the best value can be retained and duplicate features removed. Domain experts are able to conflate data using manual comparison techniques, but the task it is labour intensive when dealing with large data sets. This paper demonstrates how semantic technologies can be used to automate the geospatial data conflation process by showcasing how three Points of Interest (POI) data sets can be conflated into a single data set. First, an ontology is generated based on a multipurpose POI data model. Then the disparate source formats are transformed into the RDF format and linked to the designed POI Ontology during the conversion. When doing format transformations, SWRL rules take advantage of the relationships specified in the ontology to convert attribute data from different schemas to the same attribute granularity level. Finally, a chain of SWRL rules are used to replicate human logic and reasoning in the filtering process to find matched POIs and in the reasoning process to automatically make decisions where there is a conflict between attribute values. A conflated POI dataset reduces duplicates and improves the accuracy and confidence of POIs thus increasing the ability of emergency services agencies to respond quickly and correctly to emergency callouts where times are critical.

The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed.