Semantic Challenges for Sensor Plug and Play

Bröring, Arne; Janowicz, Krzysztof; Stasch, Christoph; Kuhn, Walther

doi:10.1007/978-3-642-10601-9_6

Cited by 43 publications

(37 citation statements)

References 13 publications

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“…An approach to 'semantically-enabled sensor plug & play' was proposed by Bröring et al (2009), who identified challenges to achieving sensor plug-and-play based on semantic knowledge of sensor observations. They subsequently proposed a method for automatic plug-and-play functionality by making use of a Sensor Bus (Bröring et al, 2011) that matches services to sensors.…”

Section: Semantic Integrationmentioning

confidence: 99%

Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation

Leng¹

2017

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A lot of today's data is generated incrementally over time by a large variety of producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, making sense of these streams of data through reasoning is challenging. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in a physical environment. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and its refinement an important problem. Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on wellknown database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this thesis, we integrate techniques for logic-based spatio-temporal stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over streaming data and the problem of robustly managing streaming data and its refinement. The main contributions of this thesis are (1) a logic-based spatio-temporal reasoning technique that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt in situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in the context of a case study on run-time adaptive reconfiguration. The results show that the proposed system-by combining reasoning over and reasoning about streams-can robustly perform spatio-temporal stream reasoning, even when the availability of streaming resources changes.

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Section: Semantic Integrationmentioning

confidence: 99%

Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation

Leng¹

2017

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“…The SWE working group defines data models, encodings, and Web service specifications to overcome issues raised by syntactic heterogeneities [4]. Yet, one of the biggest challenges in this field is dealing with semantic heterogeneities [6]. The Semantic Sensor Web provides a framework for the interoperable exchange and processing of observation data from sensor networks of different types.…”

Section: Background Conceptsmentioning

confidence: 99%

“…thesis [41] a system to convert real-time heterogeneous sensor data into an integrated stream of abstractions (also called features in the thesis), and to reason over them by using background knowledge. Patni's system is called Real-Time Feature Streams Infrastructure (RTFS) and converts raw sensor data to streams of Resource Description Framework (RDF) 6 triples.…”

Section: Previous Work On Semantic Event Processingmentioning

confidence: 99%

An event abstraction layer for the integration of geosensor data

Llaves¹,

Kuhn²

2014

International Journal of Geographical Information Science

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Abstract. Time series of observations reflect the status of environmental properties. Variations in these properties can be considered events when they potentially affect the stability of the monitored environment. Organisations dedicated to analyse environmental change use institutionalised descriptions of events to define the observable conditions under which events happen. This also applies to the methods used to classify and model changes in environmental monitoring. The heterogeneity of representations often causes interoperability problems when such communities exchange geospatial information. To enhance interoperability among diverse communities, it is required to develop models that do not restrict the representation of events, but allow integrating different perspectives on changes in the environment. The goal of the Event Abstraction Layer is to facilitate the analysis and integration of geosensor data by inferring events from time series of observations. For the analysis of geosensor data, we use event processing to detect event patterns in time series of observations. Spatio-temporal properties of the event are inferred from the geosensor location and the observation timestamps. For the data integration, we represent event-related information extracted from multiples sources under a common event model. Additionally, domain knowledge is modelled in a multilevel ontology structure.

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“…For example, the missing capabilities for semantic matching are among the main obstacles towards a plug & play infrastructure for the Sensor Web (Bröring et al, 2009a). The Semantic Web promises to address exactly these shortcomings.…”

Section: The Semantic Web and Linked Datamentioning

confidence: 99%

A RESTful proxy and data model for linked sensor data

Janowicz

Bröring

Stasch

et al. 2013

International Journal of Digital Earth

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The vision of a Digital Earth calls for more dynamic information systems, new sources of information, and stronger capabilities for their integration. Sensor networks have been identified as a major information source for the Digital Earth, while Semantic Web technologies have been proposed to facilitate integration.. So far, sensor data is stored and published using the Observations & Measurements standard of the Open Geospatial Consortium (OGC) as data model. With the advent of Volunteered Geographic Information and the Semantic Sensor Web, work on an ontological model gained importance within Sensor Web Enablement (SWE). In contrast to data models, an ontological approach abstracts from implementation details by focusing on modeling the physical world from the perspective of a particular domain. Ontologies restrict the interpretation of vocabularies towards their intended meaning. The ongoing paradigm shift to Linked Sensor Data complements this attempt. Two questions have to be addressed: (i) how to refer to changing and frequently updated data sets using Uniform Resource Identifiers, and (ii) how to establish meaningful links between those data sets, i.e., observations, sensors, features of interest, and observed properties? In this paper, we present a Linked Data model and a RESTful proxy for OGC's Sensor Observation Service to improve integration and inter-linkage of observation data for the Digital Earth.Keywords: Digital Earth, Geospatial data integration, Spatial Data Infrastructure, Earth Observation, Data exchange models, Linked Data, Semantic Enablement * Corresponding author: Krzysztof Janowicz; email: jano@geog.ucsb.edu 1 MotivationThe initial vision of a Digital Earth was first formulated by former US Vice President Al Gore as a multi-resolution, three-dimensional representation of the planet, into which we can embed vast quantities of geo-referenced data (Gore, 1998). Ten years after this speech, Craglia et al. published a position paper to argue that this vision has not yet been achieved (Craglia et al., 2008). In parallel to the growing availability of information, the need to better understand the interplay of environmental and social phenomena has also increased, thus requiring more dynamic systems, new sources of information, and stronger capacities for their integration. The Sensor Web has been identified as a central building block to address these challenges (De Longueville et al., 2010). A digital nervous system for the globe has been suggested as a vibrant approach for the Digital Earth. An implementation based on Spatial Data Infrastructures (SDI), especially on the Sensor Web Enablement (SWE) standards of the Open Geospatial Consortium (OGC), has been proposed. These infrastructures do not only deliver data but also offer geospatial processing capabilities and the final rendering on a virtual globe. Grounded in spatial and temporal reference systems, the outcomes of a variety of services can be combined into a multi-layered representation of the Earth's surface and help to an...

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Semantic Challenges for Sensor Plug and Play

Cited by 43 publications

References 13 publications

Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation

Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation

An event abstraction layer for the integration of geosensor data

A RESTful proxy and data model for linked sensor data

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