Towards semantic search in building sensor data

Wireless sensor networks (WSN) generate large volumes of raw data which possess natural heterogeneity. WSNs are normally application specific with no sharing or reusability of sensor data among applications. In order for applications and services to be developed independently of particular WSNs, sensor data need to be enriched with semantic information. In this paper, we propose a Semantic Web Architecture for Sensor Networks (SWASN). This information oriented architecture allows the sensor data to be understood and processed in a meaningful way by a variety of applications with different purposes. We develop ontologies for sensor data and use the Jena API for processing which includes querying and inference over sensor data. By studying a building fire emergency scenario, we show that semantic web technologies can provide high level information extraction and inference of sensor data.

The emergence of dynamic information sources -including sensor networks -has led to large streams of real-time data on the Web. Research studies suggest, these dynamic networks have created more data in the last three years than in the entire history of civilization, and this trend will only increase in the coming years . With this coming data explosion, real-time analytics software must either adapt or die [2]. This paper focuses on the task of integrating and analyzing multiple heterogeneous streams of sensor data with the goal of creating meaningful abstractions, or features. These features are then temporally aggregated into feature streams. We will demonstrate an implemented framework, based on Semantic Web technologies, that creates feature-streams from sensor streams in real-time, and publishes these streams as Linked Data. The generation of feature streams can be accomplished in reasonable time and results in massive data reduction.

2018

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Proceedings of the 11th …, 2008

Next-Generation Analyst, 2013

More and more sensors are getting Internet connected. Examples are cameras on cell phones, CCTV cameras for traffic control as well as dedicated security and defense sensor systems. Due to the steadily increasing data volume, human exploitation of all this sensor data is impossible for effective mission execution. Smart access to all sensor data acts as enabler for questions such as "Is there a person behind this building" or "Alert me when a vehicle approaches".

Informes de la Construcción, 2015

Construcción se distribuyen bajo una licencia de uso y distribución Creative Commons Reconocimiento no Comercial 3.0. España (cc-by-nc).

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '99, 1999

There has been much recent interest in retrieval of time series data. Earlier work has used a fixed similarity metric (e.g., Euclidean distance) to determine the similarity between a userspecified query and items in the database. Here, we describe a novel approach to retrieval of time series data by using relevance feedback from the user to adjust the similarity metric. This is important because the Euclidean distance metric does not capture many notions of similarity between time series. In particular, Euclidean distance is sensitive to various "distortions" such as offset translation, amplitude scaling, etc. Depending on the domain and the user, one may wish a query to be sensitive or insensitive to these distortions to varying degrees. This paper addresses this problem by introducing a profile that encodes the user's subjective notion of similarity in a domain. These profiles can be learned continuously from interaction with the user. We further show how the user profile may be embedded in a system that uses relevance feedback to modify the query in a manner analogous to the familiar text retrieval algorithms.

Automation in Construction, 2017

The surge of interest in digital building models combined with increasing sensorization of spaces is prompting an integration of Building Information Modelling (BIM) with real-time sensor data. However, current approaches reported in literature either remain theoretical or target very specific application domains, showing that there is no generic approach to assist in the creation of service and application software that combines sensor data with BIM models. The solution presented in this article addresses the engineering complexity associated with integrating sensor data with BIM by leveraging an advanced software engineering technique known as a Domain-Specific Language (DSL). We demonstrate also that the language herein proposed, named Building Information Modeling Sensor Language (BIMSL) provides substantial gains of expressiveness and ease of use in developing queries that process sensor data with complex conditions over BIM models. BIMSL is validated with experienced software developers according to a consistent evaluation protocol for DSLs focused on effectiveness, efficiency, satisfaction, and usability attributes. The results outperform the standard existing alternatives, indicating that our proposal contributes to reducing the human effort associated with integrating BIM with sensor data.

International journal of Web & Semantic Technology, 2014

Wildfires are frequent, devastating events in Australia that regularly cause significant loss of life and widespread property damage. Fire weather indices are a widely-adopted method for measuring fire danger and they play a significant role in issuing bushfire warnings and in anticipating demand for bushfire management resources. Existing systems that calculate fire weather indices are limited due to low spatial and temporal resolution. Localized wireless sensor networks, on the other hand, gather continuous sensor data measuring variables such as air temperature, relative humidity, rainfall and wind speed at high resolutions. However, using wireless sensor networks to estimate fire weather indices is a challenge due to data quality issues, lack of standard data formats and lack of agreement on thresholds and methods for calculating fire weather indices. Within the scope of this paper, we propose a standardized approach to calculating Fire Weather Indices (a.k.a. fire danger ratings) and overcome a number of the challenges by applying Semantic Web Technologies to the processing of data streams from a wireless sensor network deployed in the Springbrook region of South East Queensland. This paper describes the underlying ontologies, the semantic reasoning and the Semantic Fire Weather Index (SFWI) system that we have developed to enable domain experts to specify and adapt rules for calculating Fire Weather Indices. We also describe the Web-based mapping interface that we have developed, that enables users to improve their understanding of how fire weather indices vary over time within a particular region. Finally, we discuss our evaluation results that indicate that the proposed system outperforms state-of-the-art techniques in terms of accuracy, precision and query performance.

1998

We study a set of linear transformations on the Fourier series representation of a sequence that can be used as the basis for similarity queries on time-series data. We show that our set of transformations is rich enough to formulate operations such as moving average and time warping. We present a query processing algorithm that uses the underlying R-tree index of a multidimensional data set to answer similarity queries e ciently. Our experiments show that the performance of this algorithm is competitive to that of processing ordinary (exact match) queries using the index, and much faster than sequential scanning. We relate our transformations to the general framework for similarity queries of Jagadish et al.