Enabling query technologies for the semantic sensor web

Sensor networks are increasingly being deployed in the environment for many different purposes. The observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse this data, for other purposes than those for which they were originally set up. The authors propose an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. In this article, the authors describe the theoretical foundations and technologies that enable exposing semantically enriched sensor metadata, and querying sensor observations through SPARQL extensions, using query rewriting and data translation techniques according to mapping languages, and managing both pull and push delivery modes

Optimal processing node discovery algorithm for distributed computing in IoT

© 2015 IEEE.The number of Internet-connected sensing and control devices is growing. Some anticipate them to number in excess of 212 billion by 2020. Inherently, these devices generate continuous data streams, many of which need to be stored and processed. Traditional approaches, whereby all data are shipped to the cloud, may not continue to be effective as cloud infrastructure may not be able to handle myriads of data streams and their associated storage and processing needs. Using cloud infrastructure alone for data processing significantly increases latency, and contributes to unnecessary energy inefficiencies, including potentially unnecessary data transmission in constrained wireless networks, and on cloud computing facilities increasingly known to be significant consumers of energy. In this paper we present a distributed platform for wireless sensor networks which allows computation to be shifted from the cloud into the network. This reduces the traffic in the sensor network, intermediate networks, and cloud infrastructure. The platform is fully distributed, allowing every node in a homogeneous network to accept continuous queries from a user, find all nodes satisfying the users query, find an optimal node (Fermat-Weber point) in the network upon which to process the query, and provide the result to the user. Our results show that the number of required messages can be decreased up to 49% and processing latency by 42% in comparison with state-of-the-art approaches, including Innet

Lean Six Sigma maturity model within Saudi Arabian organisations : an empirical study

Lean Six Sigma (LSS) is a continuous improvement methodology that aims to reduce the costs of poor quality, improve the bottom-line results and create value for both customers and shareholders. LSS has been deployed in organisations in the Western countries for more than two decades. However, its implementation in Middle Eastern countries has only just begun to emerge. Furthermore, there is a lack of empirical studies in the area of understanding the current status of LSS in these countries. Therefore, the purpose of this research is to investigate the current status of Lean Six Sigma (LSS) in Saudi Arabian organisations and subsequently develop a Lean Six Sigma Maturity Model (LSSMM) which can be used to assess their current level of LSS maturity. The study is based on a systematic literature review of 45 papers that were published on LSS in high ranking journals in the field and other specialist journals, from 2000 to 2015. LSS themes identified include: LSS benefits, critical success factors, motivational factors, tools and techniques, critical failure factors, and limitations. A descriptive survey via a questionnaire was conducted in the second phase of the data collection process and multiple case studies were conducted in the third phase. Based on the literature review and the findings of the empirical research, a LSSMM was developed and used to assess the current level of LSS deployment maturity in five organisations in Saudi Arabia. The results of the empirical study show that LSS is in the early stages of implementation and that organisations in Saudi Arabia have only recently started to recognise the importance of LSS to their business. This finding was also supported by the evaluation of LSS maturity level that was assessed using the model developed in this study. This study contributes to understanding the current status of LSS in Saudi Arabian organisations and provides recommendations to guide the future of LSS in Saudi organisations by comparisons with the LSS literature and best practice. The contribution to knowledge and theory in this study is through validating and extending current operations management theories to LSS deployment, including organisational learning theory, theory of motivation and goal theory. The adoption of a mixed method approach contributes to the advancement of the methodology applied within LSS research in Saudi Arabian organisations. This study adds value for academics and practitioners in the field of LSS in Saudi Arabia by providing an intensive study on the current status of LSS deployment together with the LSSMM

Stream WatDiv - A Streaming RDF Benchmark

Modern applications are required to process stream data which are semantically tagged. Sometimes static background data interlinked with stream data are also needed to answer the query. To meet these requirements, streaming RDF processing (SRP) engines emerged in recent years. Although most SRP engines adopt the same streaming RDF data model in which a streaming RDF triple is an RDF triple annotated with a timestamp, there is no standard query language, which means every engine has their own language syntax. In addition, these engines are quite primitive, different engines support limited and different query operation sets. What's more, they are fragile in face of complex query, high stream rate or large static dataset. This poses a lot of challenges to evaluate the SRP engines. In our work, we show that previous streaming RDF benchmarks do not have a sufficient workload to understand engine's performance. The queries in those workloads are either not executable on existing engines, or very limited in terms of number. The goal of this work is to propose a benchmark which provides diversified datasets and workloads. In our work, we extend WatDiv to generate streaming data and streaming query, and propose a new streaming RDF benchmark, called Stream WatDiv. WatDiv is an RDF benchmark designed for diversified stress testing of RDF data management engines. It introduces a collection of query features, which is used to assess the diversity of dataset and workloads. Through proper data schema design and query generation, WatDiv shows a good coverage of values of these query features. We demonstrate the feasibility of applying the same idea in streaming RDF domain. Stream WatDiv benchmark suits contain a data generator to generate scalable streaming data and static data, a query generator to generate scalable workloads, and a testbed to monitor the engine's output. We evaluate two engines, C-SPARQL and CQELS, and measure the correctness of engine output, latency and memory consumption. The findings contain two parts. First, we validate the result related to these two engines in previous works. (1) CQELS is more robust and efficient than C-SPARQL at processing streaming RDF queries in most cases. (2) increasing streaming rate and integrating static data will significantly degrade C-SPARQL's performance, while CQELS is marginally affected. (3) C-SPARQL is more memory-efficient than CQELS. Second, the diversity of Stream WatDiv workloads helps detect engines' issues that are not captured before. Queries can be grouped into different types based on the query features. These types of queries can be used to evaluate a specific engine features. (1) Triple pattern count of a query influences C-SPARQL's performance. (2) Both C-SPARQL and CQELS show a significant latency increase when the query has larger result cardinality. (3) Neither of these two engines are biased toward processing linear, star or snowflake queries. (4) CQELS is more efficient at handling queries with variously selective triple patterns, while C-SPARQL performs better for queries with equally selective triple patterns than queries with variously selective triple patterns

Provenance-based computing

Relying on computing systems that become increasingly complex is difficult: with many factors potentially affecting the result of a computation or its properties, understanding where problems appear and fixing them is a challenging proposition. Typically, the process of finding solutions is driven by trial and error or by experience-based insights. In this dissertation, I examine the idea of using provenance metadata (the set of elements that have contributed to the existence of a piece of data, together with their relationships) instead. I show that considering provenance a primitive of computation enables the exploration of system behaviour, targeting both retrospective analysis (root cause analysis, performance tuning) and hypothetical scenarios (what-if questions). In this context, provenance can be used as part of feedback loops, with a double purpose: building software that is able to adapt for meeting certain quality and performance targets (semi-automated tuning) and enabling human operators to exert high-level runtime control with limited previous knowledge of a system's internal architecture. My contributions towards this goal are threefold: providing low-level mechanisms for meaningful provenance collection considering OS-level resource multiplexing, proving that such provenance data can be used in inferences about application behaviour and generalising this to a set of primitives necessary for fine-grained provenance disclosure in a wider context. To derive such primitives in a bottom-up manner, I first present Resourceful, a framework that enables capturing OS-level measurements in the context of application activities. It is the contextualisation that allows tying the measurements to provenance in a meaningful way, and I look at a number of use-cases in understanding application performance. This also provides a good setup for evaluating the impact and overheads of fine-grained provenance collection. I then show that the collected data enables new ways of understanding performance variation by attributing it to specific components within a system. The resulting set of tools, Soroban, gives developers and operation engineers a principled way of examining the impact of various configuration, OS and virtualization parameters on application behaviour. Finally, I consider how this supports the idea that provenance should be disclosed at application level and discuss why such disclosure is necessary for enabling the use of collected metadata efficiently and at a granularity which is meaningful in relation to application semantics.CHESS Scholarship Scheme EPSR

The effectiveness of efficiently managing the process lifecycle of medium and low voltage equipment in building services

Abstract: The rotating cylinder electrode (RCE) is one rig constructed and designed to study the behaviour of the material under the effect of flow. The behaviour of hot and cold rolled UNS S32205 duplex stainless steel (DSS) in erosion-corrosion (EC) conditions were studied to monitor the material loss and surface degradation. The impact is highlighted and explained with the aim to offer better understanding regarding degradation phenomena. Mine water solution containing silica sand particles (SiO2) was the environment of interest for this study. Both gravimetric and electrochemical methods (potentiodynamic polarisation (PP) and open circuit potential (OCP)) were adopted for the erosion-corrosion behaviour of the alloys. The cold rolled material displayed better resistance to erosion-corrosion at all speed tested in comparison with hot rolled material. The surface morphology for both materials revealed erosion dominating the erosion-corrosion process with minimal evidence of pitting. The hardness of cold rolled sample showed higher hardness as compared to hot rolled due to the impingement by sand particle hence better resistance to erosion-corrosion. The wear corrosion mechanism displayed grooving on the material surface especially at a higher speed. The effect of particles in wear mechanism play an important role. The electrochemical results showed increasing corrosion rate as the rotating speed of the electrode increased. The higher pH of mine water solution resulted in the passivation of the sample. The analysis trend was not surprising as the vulnerability of the surface layer increased with increasing velocity under the subjected conditions. Aggressive conditions and sand concentration are good enough to prove relation between wear rate and corrosion rate. The environmental impact was not aggressive enough to create much interferences hence the surface deterioration was not severe in all analysis.M.Ing. (Engineering Management

Benchmarking RDF Storage Engines

In this deliverable, we present version V1.0 of SRBench, the first benchmark for Streaming RDF engines, designed in the context of Task 1.4 of PlanetData, completely based on real-world datasets. With the increasing problem of too much streaming data but not enough knowledge, researchers have set out for solutions in which Semantic Web technologies are adapted and extended for the publishing, sharing, analysing and understanding of such data. Various approaches are emerging. To help researchers and users to compare streaming RDF engines in a standardised application scenario, we propose SRBench, with which one can assess the abilities of a streaming RDF engine to cope with a broad range of use cases typically encountered in real-world scenarios. We offer a set of queries that cover the major aspects of streaming RDF engines, ranging from simple pattern matching queries to queries with complex reasoning tasks. To give a first baseline and illustrate the state of the art, we show results obtained from implementing SRBench using the SPARQLStream query-processing engine developed by UPM

Simulation and performance analysis of a telecommunication system based on advanced intelligent network architecture

This thesis describes the modelling and simulation of a telecommunication system based on Advanced Intelligent Network (AIN) architecture for provision of telecom services. It also presents an analysis of the performance of the Service Control Point under overload conditions with the implementation of Automatic Code Gap (ACG) controls. A telecommunications system was developed to demonstrate the operation of an intelligent network Further, a set of services including Call Forward, Call Back, Call Transfer and Automatic Call Distribution were implemented on the network. This was done to exhibit the relative ease with which new services can be developed and implemented on the AIN network in a system independent manner. Further, to evaluate the performance of the Service Control Point (SCP) under overload conditions an another system was developed. The functionality for implementing ACG controls on the system was also realised. In addition, an algorithm for selection of controls was developed. An extensive set of simulations were performed on the system to determine the effectiveness of the controls in preventing overloading at the SCP. A considerable improvement in SCP performance was observed with the deployment of ACG controls OPNET, a network simulation tool, was used for developing the system