EAGLE—A Scalable Query Processing Engine for Linked Sensor Data

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE

The Schema Editor of OpenIoT for Semantic Sensor Networks

Ontologies provide conceptual abstractions over data, in domains such as the Internet of Things, in a way that sensor data can be harvested and interpreted by people and applications. The Semantic Sensor Network (SSN) ontology is the de-facto standard for semantic representation of sensor observations and metadata, and it is used at the core of the open source platform for the Internet of Things, OpenIoT. In this paper we present a Schema Editor that provides an intuitive web interface for defining new types of sensors, and concrete instances of them, using the SSN ontology as the core model. This editor is fully integrated with the OpenIoT platform for generating virtual sensor descriptions and automating their semantic annotation and registration process

A scalable spatio-temporal query processing engine for linked sensor data

The ever-increasing amount of Internet of Things (IoT) data emanating from sensors and mobile devices is creating new capabilities and unprecedented economic opportunity for individuals, organizations, and states. To fully realize the potential benefits of these sensor datasets, two fundamental requirements need to be addressed, namely interoperability and an effective data management system. Fortunately, a suite of technologies developed in the Semantic Web effort, such as the RDF model, Linked Data, and SPARQL, can be used as some of the principal solutions to help sensor data from the challenge of poor interoperability. However, in this context, providing an effective data management system for sensor data that can combine the benefits of Semantic Web principles, and also be able to deal with the ''big spatio-temporal data'' nature of sensor data, is still an open challenge. Central to this problem is not only knowing how to store a massive volume of sensor data, but also being able to answer a complex spatio-temporal related query on large-scale sensor datasets in a timely manner. Researchers in the Semantic Web community have proposed a substantial number of works that use Semantic Web technologies for effectively managing and querying heterogeneous sensor data. However, our research survey revealed that these solutions primarily focused on semantic relationships and paid less attention to the temporal-spatial correlation of sensor data. Moreover, most semantic approaches do not have spatio-temporal support. Some of them have addressed limitations as regards providing full spatio-temporal support but have poor performance for complex spatio-temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this work, we propose a scalable spatio-temporal query engine for sensor data based on Linked Data model, called EAGLE. The ultimate goal of our approach is to provide an elastic and scalable system which allows fast searching and analysis on the relationships of space, time and semantic in sensor data. In order to support spatio-temporal computing, we introduce a set of new query operators which is compatible with SPARQL 1.1. For dealing with ''big data'' and a high update throughput of sensor data, EAGLE adopts a loosely hybrid architecture that consists of different clustered databases. This flexible architecture not only helps the engine with the overhead of ''big data'' processing but also allows us to make use of the existing spatio-temporal query functions provided by the underlying databases. The engine also provides a learning optimization approach that can predict query performance based on historical query execution plans. To demonstrate the advantages of the query processing engine in terms of performance, the thesis provides extensive experimental evaluations. The evaluations cover a comprehensive set of parameters that indicate the performance of spatio-temporal queries over Linked Sensor Data

A scalable spatio-temporal query processing engine for linked sensor data

The ever-increasing amount of Internet of Things (IoT) data emanating from sensors and mobile devices is creating new capabilities and unprecedented economic opportunity for individuals, organizations, and states. To fully realize the potential benefits of these sensor datasets, two fundamental requirements need to be addressed, namely interoperability and an effective data management system. Fortunately, a suite of technologies developed in the Semantic Web effort, such as the RDF model, Linked Data, and SPARQL, can be used as some of the principal solutions to help sensor data from the challenge of poor interoperability. However, in this context, providing an effective data management system for sensor data that can combine the benefits of Semantic Web principles, and also be able to deal with the \u27\u27big spatio-temporal data\u27\u27 nature of sensor data, is still an open challenge. Central to this problem is not only knowing how to store a massive volume of sensor data, but also being able to answer a complex spatio-temporal related query on large-scale sensor datasets in a timely manner. Researchers in the Semantic Web community have proposed a substantial number of works that use Semantic Web technologies for effectively managing and querying heterogeneous sensor data. However, our research survey revealed that these solutions primarily focused on semantic relationships and paid less attention to the temporal-spatial correlation of sensor data. Moreover, most semantic approaches do not have spatio-temporal support. Some of them have addressed limitations as regards providing full spatio-temporal support but have poor performance for complex spatio-temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this work, we propose a scalable spatio-temporal query engine for sensor data based on Linked Data model, called EAGLE. The ultimate goal of our approach is to provide an elastic and scalable system which allows fast searching and analysis on the relationships of space, time and semantic in sensor data. In order to support spatio-temporal computing, we introduce a set of new query operators which is compatible with SPARQL 1.1. For dealing with \u27\u27big data\u27\u27 and a high update throughput of sensor data, EAGLE adopts a loosely hybrid architecture that consists of different clustered databases. This flexible architecture not only helps the engine with the overhead of \u27\u27big data\u27\u27 processing but also allows us to make use of the existing spatio-temporal query functions provided by the underlying databases. The engine also provides a learning optimization approach that can predict query performance based on historical query execution plans. To demonstrate the advantages of the query processing engine in terms of performance, the thesis provides extensive experimental evaluations. The evaluations cover a comprehensive set of parameters that indicate the performance of spatio-temporal queries over Linked Sensor Data

A middleware framework for scalable management of linked streams

The Web has long exceeded its original purpose of a distributed hypertext system and has become a global, data sharing and processing platform. This development is confirmed by remarkable milestones such as the Semantic Web, Web services, social networks and mashups. In parallel with these developments on the Web, the Internet of Things (IoT), i.e., sensors and actuators, has matured and has become a major scientific and economic driver. Its potential impact cannot be overestimated–for example, in logistics, cities, electricity grids and in our daily life, in the form of sensor-laden mobile phones–and rivals that of the Web itself. While the Web provides ease of use of distributed resources and a sophisticated development and deployment infrastructure, the IoT excels in bringing real-time information from the physical world into the picture. Thus a combination of these players seems to be the natural next step in the development of even more sophisticated systems of systems. While only starting, there is already a significant amount of sensor-generated, or more generally dynamic information, available on the Web. However, this information is not easy to access and process, depends on specialised gateways and requires significant knowledge on the concrete deployments, for example, resource constraints and access protocols. To remedy these problems and draw on the advantages of both sides, we try to make dynamic, online sensor data of any form as easily accessible as resources and data on the Web, by applying well-established Web principles, access and processing methods, thus shielding users and developers from the underlying complexities. In this paper we describe our Linked Stream Middleware (LSM, http://lsm.deri.ie/), which makes it easy to integrate time-dependent data with other Linked Data sources, by enriching both sensor sources and sensor data streams with semantic descriptions, and enabling complex SPARQL-like queries across both dataset types through a novel query processing engine, along with means to mashup the data and process results. Most prominently, LSM provides (1) extensible means for real-time data collection and publishing using a cloud-based infrastructure, (2) a Web interface for data annotation and visualisation, and (3) a SPARQL endpoint for querying unified Linked Stream Data and Linked Data. We describe the system architecture behind LSM, provide details of how Linked Stream Data is generated, and demonstrate the benefits and efficiency of the platform by showcasing some experimental evaluations and the system’s interface

Defining the stack for service delivery models and interoperability in the internet of things: a practical case with OpenIoT-VDK

This paper introduces the stack for service delivery models and interoperability in the Internet of Things. The main characteristics and functional layers of the IoT stack are described. The applicability of the IoT stack is described based on particular use cases and deployed pilots. The validation of the IoT stack in terms of functionality and adaptation at different IoT particular areas is based on the Virtual Development Kit (VDK) developed and implemented within the framework of the OpenIoT project—OpenIoT project is the awarded Internet of Things open-source rookie of the year by BlackDuck Software Co. (www.github.com/OpenIotOrg). The methods and standards that boosted OpenIoT-VDK implementation are described in this paper. An instance of the OpenIoT-VDK process is described as the practical use case demonstrating being an IoT platform with autonomic behavior. OpenIoT-VDK creates IoT instances, analyzes the IoT stack dependence, and resolves them following interoperability principles. The OpenIoT-VDK instance deploys IoT service delivery models facilitating the validation of use cases by using the OpenIoT platform. As proof of concept, a delivered IoT service using open data from OpenIoT local instantiation is described.This work was supported in part by the ICT OpenIoT Project, which is cofunded by the European Commission through FP7 Program, under Contract FP7-ICT-2011-7-287305-OpenIoT; by the Fed4FIRE Federation for FIRE under Grant FP7-ICT-2011-8-318389; and by the Science Foundation Ireland (SFI) under Grant SFI/12/RC/2289