Liquid stream processing on the web: a JavaScript framework

The Web is rapidly becoming a mature platform to host distributed applications. Pervasive computing application running on the Web are now common in the era of the Web of Things, which has made it increasingly simple to integrate sensors and microcontrollers in our everyday life. Such devices are of great in- terest to Makers with basic Web development skills. With them, Makers are able to build small smart stream processing applications with sensors and actuators without spending a fortune and without knowing much about the technologies they use. Thanks to ongoing Web technology trends enabling real-time peer-to- peer communication between Web-enabled devices, Web browsers and server- side JavaScript runtimes, developers are able to implement pervasive Web ap- plications using a single programming language. These can take advantage of direct and continuous communication channels going beyond what was possible in the early stages of the Web to push data in real-time. Despite these recent advances, building stream processing applications on the Web of Things remains a challenging task. On the one hand, Web-enabled devices of different nature still have to communicate with different protocols. On the other hand, dealing with a dynamic, heterogeneous, and volatile environment like the Web requires developers to face issues like disconnections, unpredictable workload fluctuations, and device overload. To help developers deal with such issues, in this dissertation we present the Web Liquid Streams (WLS) framework, a novel streaming framework for JavaScript. Developers implement streaming operators written in JavaScript and may interactively and dynamically define a streaming topology. The framework takes care of deploying the user-defined operators on the available devices and connecting them using the appropriate data channel, removing the burden of dealing with different deployment environments from the developers. Changes in the semantic of the application and in its execution environment may be ap- plied at runtime without stopping the stream flow. Like a liquid adapts its shape to the one of its container, the Web Liquid Streams framework makes streaming topologies flow across multiple heterogeneous devices, enabling dynamic operator migration without disrupting the data flow. By constantly monitoring the execution of the topology with a hierarchical controller infrastructure, WLS takes care of parallelising the operator execution across multiple devices in case of bottlenecks and of recovering the execution of the streaming topology in case one or more devices disconnect, by restarting lost operators on other available devices

Requirements model driven adaption and evolution of Internetware

Today’s software systems need to support complex business operations and processes. The development of the web-based software systems has been pushing up the limits of traditional software engineering methodologies and technologies as they are required to be used and updated almost real-time, so that users can interact and share the same applications over the internet as needed. These applications have to adapt quickly to the diversified and dynamic changing requirements in the physical, technological, economical and social environments. As a consequence, we are expecting a major paradigm shift in software engineering to reflect such changes in computing environment in order to better address the fundamental needs of organisations in this new era. Existing software technologies, such as model driven development, business process engineering, online (re-)configuration, composition and adaptation of managerial functionalities are being repurposed to reduce the time taken for software development by reusing software codes. The ability to dynamically combine contents from numerous web sites and local resources, and the ability to instantly publish services worldwide have opened up entirely new possibilities for software development. In retrospect to the ten years applied research on Internetware, we have witnessed such a paradigm shift, which brings about many changes to the developmental experience of conventional web applications. Several related technologies, such as cloud computing, service computing, cyber-physical systems and social computing, have converged to address this emerging issue with emphasis on different aspects. In this paper, we first outline the requirements that the Internetware software paradigm should meet to excel at web application adaptation; we then propose a requirement model driven method for adaptive and evolutionary applications; and we report our experiences and case studies of applying it to an enterprise information system. Our goal is to provide high-level guidelines to researchers and practitioners to meet the challenges of building adaptive industrial-strength applications with the spectrum of processes, techniques and facilities provided within the Internetware paradigm

Experiencing OptiqueVQS: A Multi-paradigm and Ontology-based Visual Query System for End Users

This is author's post-print version, published version available on http://link.springer.com/article/10.1007%2Fs10209-015-0404-5Data access in an enterprise setting is a determining factor for value creation processes, such as sense-making, decision-making, and intelligence analysis. Particularly, in an enterprise setting, intuitive data access tools that directly engage domain experts with data could substantially increase competitiveness and profitability. In this respect, the use of ontologies as a natural communication medium between end users and computers has emerged as a prominent approach. To this end, this article introduces a novel ontology-based visual query system, named OptiqueVQS, for end users. OptiqueVQS is built on a powerful and scalable data access platform and has a user-centric design supported by a widget-based flexible and extensible architecture allowing multiple coordinated representation and interaction paradigms to be employed. The results of a usability experiment performed with non-expert users suggest that OptiqueVQS provides a decent level of expressivity and high usability and hence is quite promising

Information visualisation and data analysis using web mash-up systems

A thesis submitted in partial fulfilment for the degree of Doctor of PhilosophyThe arrival of E-commerce systems have contributed greatly to the economy and have played a vital role in collecting a huge amount of transactional data. It is becoming difficult day by day to analyse business and consumer behaviour with the production of such a colossal volume of data. Enterprise 2.0 has the ability to store and create an enormous amount of transactional data; the purpose for which data was collected could quite easily be disassociated as the essential information goes unnoticed in large and complex data sets. The information overflow is a major contributor to the dilemma. In the current environment, where hardware systems have the ability to store such large volumes of data and the software systems have the capability of substantial data production, data exploration problems are on the rise. The problem is not with the production or storage of data but with the effectiveness of the systems and techniques where essential information could be retrieved from complex data sets in a comprehensive and logical approach as the data questions are asked. Using the existing information retrieval systems and visualisation tools, the more specific questions are asked, the more definitive and unambiguous are the visualised results that could be attained, but when it comes to complex and large data sets there are no elementary or simple questions. Therefore a profound information visualisation model and system is required to analyse complex data sets through data analysis and information visualisation, to make it possible for the decision makers to identify the expected and discover the unexpected. In order to address complex data problems, a comprehensive and robust visualisation model and system is introduced. The visualisation model consists of four major layers, (i) acquisition and data analysis, (ii) data representation, (iii) user and computer interaction and (iv) results repositories. There are major contributions in all four layers but particularly in data acquisition and data representation. Multiple attribute and dimensional data visualisation techniques are identified in Enterprise 2.0 and Web 2.0 environment. Transactional tagging and linked data are unearthed which is a novel contribution in information visualisation. The visualisation model and system is first realised as a tangible software system, which is then validated through different and large types of data sets in three experiments. The first experiment is based on the large Royal Mail postcode data set. The second experiment is based on a large transactional data set in an enterprise environment while the same data set is processed in a non-enterprise environment. The system interaction facilitated through new mashup techniques enables users to interact more fluently with data and the representation layer. The results are exported into various reusable formats and retrieved for further comparison and analysis purposes. The information visualisation model introduced in this research is a compact process for any size and type of data set which is a major contribution in information visualisation and data analysis. Advanced data representation techniques are employed using various web mashup technologies. New visualisation techniques have emerged from the research such as transactional tagging visualisation and linked data visualisation. The information visualisation model and system is extremely useful in addressing complex data problems with strategies that are easy to interact with and integrate

EXPRESS: Resource-oriented and RESTful Semantic Web services

This thesis investigates an approach that simplifies the development of Semantic Web services (SWS) by removing the need for additional semantic descriptions.The most actively researched approaches to Semantic Web services introduce explicit semantic descriptions of services that are in addition to the existing semantic descriptions of the service domains. This increases their complexity and design overhead. The need for semantically describing the services in such approaches stems from their foundations in service-oriented computing, i.e. the extension of already existing service descriptions. This thesis demonstrates that adopting a resource-oriented approach based on REST will, in contrast to service-oriented approaches, eliminate the need for explicit semantic service descriptions and service vocabularies. This reduces the development efforts while retaining the significant functional capabilities.The approach proposed in this thesis, called EXPRESS (Expressing RESTful Semantic Services), utilises the similarities between REST and the Semantic Web, such as resource realisation, self-describing representations, and uniform interfaces. The semantics of a service is elicited from a resource’s semantic description in the domain ontology and the semantics of the uniform interface, hence eliminating the need for additional semantic descriptions. Moreover, stub-generation is a by-product of the mapping between entities in the domain ontology and resources.EXPRESS was developed to test the feasibility of eliminating explicit service descriptions and service vocabularies or ontologies, to explore the restrictions placed on domain ontologies as a result, to investigate the impact on the semantic quality of the description, and explore the benefits and costs to developers. To achieve this, an online demonstrator that allows users to generate stubs has been developed. In addition, a matchmaking experiment was conducted to show that the descriptions of the services are comparable to OWL-S in terms of their ability to be discovered, while improving the efficiency of discovery. Finally, an expert review was undertaken which provided evidence of EXPRESS’s simplicity and practicality when developing SWS from scratch

Automated Rhythmic Transformation of Drum Recordings

Within the creative industries, music information retrieval techniques are now being applied in a variety of music creation and production applications. Audio artists incorporate techniques from music informatics and machine learning (e.g., beat and metre detection) for generative content creation and manipulation systems within the music production setting. Here musicians, desiring a certain sound or aesthetic influenced by the style of artists they admire, may change or replace the rhythmic pattern and sound characteristics (i.e., timbre) of drums in their recordings with those from an idealised recording (e.g., in processes of redrumming and mashup creation). Automated transformation systems for rhythm and timbre can be powerful tools for music producers, allowing them to quickly and easily adjust the different elements of a drum recording to fit the overall style of a song. The aim of this thesis is to develop systems for automated transformation of rhythmic patterns of drum recordings using a subset of techniques from deep learning called deep generative models (DGM) for neural audio synthesis. DGMs such as autoencoders and generative adversarial networks have been shown to be effective for transforming musical signals in a variety of genres as well as for learning the underlying structure of datasets for generation of new audio examples. To this end, modular deep learning-based systems are presented in this thesis with evaluations which measure the extent of the rhythmic modifications generated by different modes of transformation, which include audio style transfer, drum translation and latent space manipulation. The evaluation results underscore both the strengths and constraints of DGMs for transformation of rhythmic patterns as well as neural synthesis of drum sounds within a variety of musical genres. New audio style transfer (AST) functions were specifically designed for mashup-oriented drum recording transformation. The designed loss objectives lowered the computational demands of the AST algorithm and offered rhythmic transformation capabilities which adhere to a larger rhythmic structure of the input to generate music that is both creative and realistic. To extend the transformation possibilities of DGMs, systems based on adversarial autoencoders (AAE) were proposed for drum translation and continuous rhythmic transformation of bar-length patterns. The evaluations which investigated the lower dimensional representations of the latent space of the proposed system based on AAEs with a Gaussian mixture prior (AAE-GM) highlighted the importance of the structure of the disentangled latent distributions of AAE-GM. Furthermore, the proposed system demonstrated improved performance, as evidenced by higher reconstruction metrics, when compared to traditional autoencoder models. This implies that the system can more accurately recreate complex drum sounds, ensuring that the produced rhythmic transformation maintains richness of the source material. For music producers, this means heightened fidelity in drum synthesis and the potential for more expressive and varied drum tracks, enhancing the creativity in music production. This work also enhances neural drum synthesis by introducing a new, diverse dataset of kick, snare, and hi-hat drum samples, along with multiple drum loop datasets for model training and evaluation. Overall, the work in this thesis increased the profile of the field and hopefully will attract more attention and resources to the area, which will help drive future research and development of neural rhythmic transformation systems

Concealment and Discovery: The Role of Information Security in Biomedical Data Re-Use

This paper analyses the role of information security (IS) in shaping the dissemination and re-use of biomedical data, as well as the embedding of such data in the material, social and regulatory landscapes of research. We consider the data management practices adopted by two UK-based data linkage infrastructures: the Secure Anonymised Information Linkage, a Welsh databank that facilitates appropriate re-use of health data derived from research and routine medical practice in the region; and the Medical and Environmental Data Mash-up Infrastructure, a project bringing together researchers from the University of Exeter, the London School of Hygiene and Tropical Medicine, the Met Office and Public Health England to link and analyse complex meteorological, environmental and epidemiological data. Through an in-depth analysis of how data are sourced, processed and analysed in these two cases, we show that IS takes two distinct forms: epistemic IS, focused on protecting the reliability and reusability of data as they move across platforms and research contexts; and infrastructural IS, concerned with protecting data from external attacks, mishandling and use disruption. These two dimensions are intertwined and mutually constitutive, and yet are often perceived by researchers as being in tension with each other. We discuss how such tensions emerge when the two dimensions of IS are operationalised in ways that put them at cross purpose with each other, thus exemplifying the vulnerability of data management strategies to broader governance and technological regimes. We also show that whenever biomedical researchers manage to overcome the conflict, the interplay between epistemic and infrastructural IS prompts critical questions concerning data sources, formats, metadata and potential uses, resulting in an improved understanding of the wider context of research and the development of relevant resources. This informs and significantly improves the re-usability of biomedical data, while encouraging exploratory analyses of secondary data sources

Platforms for deployment of scalable on- and off-line data analytics.

The ability to exploit the intelligence concealed in bulk data to generate actionable insights is increasingly providing competitive advantages to businesses, government agencies, and charitable organisations. The burgeoning field of Data Science, and its related applications in the field of Data Analytics, finds broader applicability with each passing year. This expansion of users and applications is matched by an explosion in tools, platforms, and techniques designed to exploit more types of data in larger volumes, with more techniques, and at higher frequencies than ever before. This diversity in platforms and tools presents a new challenge for organisations aiming to integrate Data Science into their daily operations. Designing an analytic for a particular platform necessarily involves “lock-in” to that specific implementation – there are few opportunities for algorithmic portability. It is increasingly challenging to find engineers with experience in the diverse suite of tools available as well as understanding the precise details of the domain in which they work: the semantics of the data, the nature of queries and analyses to be executed, and the interpretation and presentation of results. The work presented in this thesis addresses these challenges by introducing a number of techniques to facilitate the creation of analytics for equivalent deployment across a variety of runtime frameworks and capabilities. In the first instance, this capability is demonstrated using the first Domain Specific Language and associated runtime environments to target multiple best-in-class frameworks for data analysis from the streaming and off-line paradigms. This capability is extended with a new approach to modelling analytics based around a semantically rich type system. An analytic planner using this model is detailed, thus empowering domain experts to build their own scalable analyses, without any specific programming or distributed systems knowledge. This planning technique is used to assemble complex ensembles of hybrid analytics: automatically applying multiple frameworks in a single workflow. Finally, this thesis demonstrates a novel approach to the speculative construction, compilation, and deployment of analytic jobs based around the observation of user interactions with an analytic planning system

Concealment and discovery: the role of information security in biomedical data re-use

This is the author accepted manuscript. The final version is available from SAGE Publications via the DOI in this record.This paper analyses the role of information security (IS) in shaping the dissemination and re-use of biomedical data, as well as the embedding of such data in the material, social and regulatory landscapes of research. We consider the data management practices adopted by two UK-based data linkage infrastructures: the Secure Anonymised Information Linkage, a Welsh databank that facilitates appropriate re-use of health data derived from research and routine medical practice in the region; and the Medical and Environmental Data Mash-up Infrastructure, a project bringing together researchers from the University of Exeter, the London School of Hygiene and Tropical Medicine, the Met Office and Public Health England to link and analyse complex meteorological, environmental and epidemiological data. Through an in-depth analysis of how data are sourced, processed and analysed in these two cases, we show that IS takes two distinct forms: epistemic IS, focused on protecting the reliability and reusability of data as they move across platforms and research contexts; and infrastructural IS, concerned with protecting data from external attacks, mishandling and use disruption. These two dimensions are intertwined and mutually constitutive, and yet are often perceived by researchers as being in tension with each other. We discuss how such tensions emerge when the two dimensions of IS are operationalised in ways that put them at cross purpose with each other, thus exemplifying the vulnerability of data management strategies to broader governance and technological regimes. We also show that whenever biomedical researchers manage to overcome the conflict, the interplay between epistemic and infrastructural IS prompts critical questions concerning data sources, formats, metadata and potential uses, resulting in an improved understanding of the wider context of research and the development of relevant resources. This informs and significantly improves the re-usability of biomedical data, while encouraging exploratory analyses of secondary data sources.This research was funded by ERC grant award 335925 (DATA_SCIENCE), the Australian Research Council (Discovery Project DP160102989) and a MEDMI pilot project funded through MEDMI by MRC and NERC (MR/K019341/1)

Self-adaptive mobile web service discovery framework for dynamic mobile environment

The advancement in mobile technologies has undoubtedly turned mobile web service (MWS) into a significant computing resource in a dynamic mobile environment (DME). The discovery is one of the critical stages in the MWS life cycle to identify the most relevant MWS for a particular task as per the request's context needs. While the traditional service discovery frameworks that assume the world is static with predetermined context are constrained in DME, the adaptive solutions show potential. Unfortunately, the effectiveness of these frameworks is plagued by three problems. Firstly, the coarse-grained MWS categorization approach that fails to deal with the proliferation of functionally similar MWS. Secondly, context models constricted by insufficient expressiveness and inadequate extensibility confound the difficulty in describing the DME, MWS, and the user’s MWS needs. Thirdly, matchmaking requires manual adjustment and disregard context information that triggers self-adaptation, leading to the ineffective and inaccurate discovery of relevant MWS. Therefore, to address these challenges, a self-adaptive MWS discovery framework for DME comprises an enhanced MWS categorization approach, an extensible meta-context ontology model, and a self-adaptive MWS matchmaker is proposed. In this research, the MWS categorization is achieved by extracting the goals and tags from the functional description of MWS and then subsuming k-means in the modified negative selection algorithm (M-NSA) to create categories that contain similar MWS. The designing of meta-context ontology is conducted using the lightweight unified process for ontology building (UPON-Lite) in collaboration with the feature-oriented domain analysis (FODA). The self-adaptive MWS matchmaking is achieved by enabling the self-adaptive matchmaker to learn MWS relevance using a Modified-Negative Selection Algorithm (M-NSA) and retrieve the most relevant MWS based on the current context of the discovery. The MWS categorization approach was evaluated, and its impact on the effectiveness of the framework is assessed. The meta-context ontology was evaluated using case studies, and its impact on the service relevance learning was assessed. The proposed framework was evaluated using a case study and the ProgrammableWeb dataset. It exhibits significant improvements in terms of binary relevance, graded relevance, and statistical significance, with the highest average precision value of 0.9167. This study demonstrates that the proposed framework is accurate and effective for service-based application designers and other MWS clients