Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids

Abstract. Semantic Web allows us to model and query time-invariant or slowly evolving knowledge using ontologies. Emerging applications in Cyber Physical Systems such as Smart Power Grids that require contin-uous information monitoring and integration present novel opportunities and challenges for Semantic Web technologies. Semantic Web is promis-ing to model diverse Smart Grid domain knowledge for enhanced situa-tion awareness and response by multi-disciplinary participants. However, current technology does pose a performance overhead for dynamic anal-ysis of sensor measurements. In this paper, we combine semantic web and complex event processing for stream based semantic querying. We illustrate its adoption in the USC Campus Micro-Grid for detecting and enacting dynamic response strategies to peak power situations by di-verse user roles. We also describe the semantic ontology and event query model that supports this. Further, we introduce and evaluate caching techniques to improve the response time for semantic event queries to meet our application needs and enable sustainable energy management

A New Approach for Fast Processing of SPARQL Queries on RDF Quadruples

Title from PDF of title page, viewed on July 7, 2015Dissertation advisor: Praveen R. RaoVitaIncludes bibliographic references (pages 87-92)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2015The Resource Description Framework (RDF) is a standard model for representing data on the Web. It enables the interchange and machine processing of data by considering its semantics. While RDF was first proposed with the vision of enabling the Semantic Web, it has now become popular in domain-specific applications and the Web. Through advanced RDF technologies, one can perform semantic reasoning over data and extract knowledge in domains such as healthcare, biopharmaceuticals, defense, and intelligence. Popular approaches like RDF-3X perform poorly on RDF datasets containing billions of triples when the queries are large and complex. This is because of the large number of join operations that must be performed during query processing. Moreover, most of the scalable approaches were designed to operate on RDF triples instead of quads. To address these issues, we propose to develop a new approach for fast and cost-effective processing of SPARQL queries on large RDF datasets containing RDF quadruples (or quads). Our approach employs a decrease-and-conquer strategy: Rather than indexing the entire RDF dataset, it identifies groups of similar RDF graphs and indexes each group separately. During query processing, it uses a novel filtering index to first identify candidate groups that may contain matches for the query. On these candidates, it executes queries using a conventional SPARQL processor to produce the final results. A query optimization strategy using the candidate groups to further improve the query processing performance is also used.Introduction -- Background and motivations -- The design of RIQ -- Implementation of RIQ -- Evaluation -- Conclusion and future work -- Appendix A. Queries -- Appendix B. SPARQL gramma

Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams

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.Comment: 20pages, 12 figure

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Utilising semantic technologies for intelligent indexing and retrieval of digital images

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion