Efficient Data Management with Applications to IoT

The Internet of Things (IoT) consists of a worldwide “network of networks,” composed by billions of interconnected heterogeneous devices denoted as things or “Smart Objects” (SOs). Significant research efforts have been dedicated to port the experience gained in the design of the Internet to the IoT, with the goal of maximizing interoperability, using the Internet Protocol (IP) and designing specific protocols like the Constrained Application Protocol (CoAP), which have been widely accepted as drivers for the effective evolution of the IoT. This first wave of standardization can be considered successfully concluded and we can assume that communication with and between SOs is no longer an issue. At this time, to favor the widespread adoption of the IoT, it is crucial to provide mechanisms that facilitate IoT data management and the development of services enabling a real interaction with things. Several reference IoT scenarios have real-time or predictable latency requirements, dealing with billions of device collecting and sending an enormous quantity of data. These features create a new need for architectures specifically designed to handle this scenario, hear denoted as “Big Stream”. In this thesis a new Big Stream Listener-based Graph architecture is proposed. Another important step, is to build more applications around the Web model, bringing about the Web of Things (WoT). As several IoT testbeds have been focused on evaluating lower-layer communication aspects, this thesis proposes a new WoT Testbed aiming at allowing developers to work with a high level of abstraction, without worrying about low-level details. Finally, an innovative SOs-driven User Interface (UI) generation paradigm for mobile applications in heterogeneous IoT networks is proposed, to simplify interactions between users and things.

A soft-computing-based method for the automatic discovery of fuzzy rules in databases:uses for academic research and management support in marketing

The study here highlights the potential that analytical methods based on Knowledge Discovery in Databases (KDD) methodologies have to aid both the resolution of unstructured marketing/business problems and the process of scholarly knowledge discovery. The authors present and discuss the application of KDD in these situations prior to the presentation of an analytical method based on fuzzy logic and evolutionary algorithms, developed to analyze marketing databases and uncover relationships among variables. A detailed implementation on a pre-existing data set illustrates the method. © 2012 Published by Elsevier Inc.

Knowledge assisted data management and retrieval in multimedia database systems

With the proliferation of multimedia data and ever-growing requests for multimedia applications, there is an increasing need for efficient and effective indexing, storage and retrieval of multimedia data, such as graphics, images, animation, video, audio and text. Due to the special characteristics of the multimedia data, the Multimedia Database management Systems (MMDBMSs) have emerged and attracted great research attention in recent years. Though much research effort has been devoted to this area, it is still far from maturity and there exist many open issues. In this dissertation, with the focus of addressing three of the essential challenges in developing the MMDBMS, namely, semantic gap, perception subjectivity and data organization, a systematic and integrated framework is proposed with video database and image database serving as the testbed. In particular, the framework addresses these challenges separately yet coherently from three main aspects of a MMDBMS: multimedia data representation, indexing and retrieval. In terms of multimedia data representation, the key to address the semantic gap issue is to intelligently and automatically model the mid-level representation and/or semi-semantic descriptors besides the extraction of the low-level media features. The data organization challenge is mainly addressed by the aspect of media indexing where various levels of indexing are required to support the diverse query requirements. In particular, the focus of this study is to facilitate the high-level video indexing by proposing a multimodal event mining framework associated with temporal knowledge discovery approaches. With respect to the perception subjectivity issue, advanced techniques are proposed to support users' interaction and to effectively model users' perception from the feedback at both the image-level and object-level.

An Examination of Current Approaches to Integrative Indigenous and Western Knowledge System Implementation in Water Research and Management: A Case Study Encompassing the Colonized Geographies of Canada, Australia, New Zealand, and the United States

The commodification of natural resources and the pursuit of continuous growth has resulted in environmental degradation, depletion, and disparity in access to these life-sustaining resources, including water. Utility-based objectification and exploitation of water in some societies has brought us to the brink of crisis through an apathetic disregard for present and future generations. The ongoing depletion and degradation of the world’s water sources, coupled with a reliance on Western knowledge and the continued omission of Indigenous knowledge to manage our relationship with water has unduly burdened many, but particularly so for Indigenous communities. The goal of my thesis research is to call attention to and advance the value and validity of using both Indigenous and Western knowledge systems (also known as Two-Eyed Seeing) in water research and management to better care for water. To achieve this goal, I used a combined systematic and realist review method to identify and synthesize the peer-reviewed, integrative water literature, followed by semi-structured interviews with first authors of the exemplars from the included literature to identify the challenges and insights that researchers have experienced in conducting integrative water research. Findings suggest that these authors recognize that many previous attempts to integrate Indigenous knowledges have been tokenistic rather than meaningful, and that new methods for knowledge implementation are needed. Community-based participatory research methods, and the associated tenets of balancing power, fostering trust, and community ownership over the research process, emerged as a pathway towards the meaningful implementation of Indigenous and Western knowledge systems. Data also indicate that engagement and collaborative governance structures developed from a position of mutual respect are integral to the realization of a given project. The recommendations generated from these findings offer support for future Indigenous-led research and partnerships through the identification and examination of approaches that facilitate the meaningful implementation of Indigenous and Western knowledge systems in water research and management. Asking Western science questions and seeking Indigenous science solutions does not appear to be working; instead, the co-design of research projects and asking questions directed at the problem rather than the solution better lends itself to the strengths of Indigenous science.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

A critical review of habitat use by feral cats and key directions for future research and management

Feral cats (Felis catus) have a wide global distribution and cause significant damage to native fauna. Reducing their impacts requires an understanding of how they use habitat and which parts of the landscape should be the focus of management. We reviewed 27 experimental and observational studies conducted around the world over the last 35 years that aimed to examine habitat use by feral and unowned cats. Our aims were to: (1) summarise the current body of literature on habitat use by feral and unowned cats in the context of applicable ecological theory (i.e. habitat selection, foraging theory); (2) develop testable hypotheses to help fill important knowledge gaps in the current body of knowledge on this topic; and (3) build a conceptual framework that will guide the activities of researchers and managers in reducing feral cat impacts. We found that feral cats exploit a diverse range of habitats including arid deserts, shrublands and grasslands, fragmented agricultural landscapes, urban areas, glacial valleys, equatorial to sub-Antarctic islands and a range of forest and woodland types. Factors invoked to explain habitat use by cats included prey availability, predation/competition, shelter availability and human resource subsidies, but the strength of evidence used to support these assertions was low, with most studies being observational or correlative.Wetherefore provide a list of key directions that will assist conservation managers and researchers in better understanding and ameliorating the impact of feral cats at a scale appropriate for useful management and research. Future studies will benefit from employing an experimental approach and collecting data on the relative abundance and activity of prey and other predators. This might include landscape-scale experiments where the densities of predators, prey or competitors are manipulated and then the response in cat habitat use is measured. Effective management of feral cat populations could target high-use areas, such as linear features and structurally complex habitat. Since our review shows often-divergent outcomes in the use of the same habitat components and vegetation types worldwide, local knowledge and active monitoring of management actions is essential when deciding on control programs.

Copyright and Data

ANDS Guides http://ands.org.au/guides/index.html These guides provide information about ANDS services and some fundamental issues in data-intensive research and research data management. These are not rules, prescriptions or proscriptions. They are guidelines and checklists to inform and broaden the range of possibilities for researchers, data managers, and research organisations.

Coordinated research support services at Queensland University of Technology, Australia

Queensland University of Technology (QUT) is a large multidisciplinary university located in Brisbane, Queensland, Australia. QUT is increasing its research focus and is developing its research support services. It has adopted a model of collaboration between the Library, High Performance Computing and Research Support (HPC) and more broadly with Information Technology Services (ITS). Research support services provided by the Library include the provision of information resources and discovery services, bibliographic management software, assistance with publishing (publishing strategies, identifying high impact journals, dealing with publishers and the peer review process), citation analysis and calculating authors’ H Index. Research data management services are being developed by the Library and HPC working in collaboration. The HPC group within ITS supports research computing infrastructure, research development and engagement activities, researcher consultation, high speed computation and data storage systems , 2D/ 3D (immersive) visualisation tools, parallelisation and optimization of research codes, statistics/ data modeling training and support (both qualitative and quantitative) and support for the university’s central Access Grid collaboration facility. Development and engagement activities include participation in research grants and papers, student supervision and internships and the sponsorship, incubation and adoption of new computing technologies for research. ITS also provides other services that support research including ICT training, research infrastructure (networking, data storage, federated access and authorization, virtualization) and corporate systems for research administration. Seminars and workshops are offered to increase awareness and uptake of new and existing services. A series of online surveys on eResearch practices and skills and a number of focus groups was conducted to better inform the development of research support services. Progress towards the provision of research support is described within the context organizational frameworks; resourcing; infrastructure; integration; collaboration; change management; engagement; awareness and skills; new services; and leadership. Challenges to be addressed include the need to redeploy existing operational resources toward new research support services, supporting a rapidly growing research profile across the university, the growing need for the use and support of IT in research programs, finding capacity to address the diverse research support needs across the disciplines, operationalising new research support services following their implementation in project mode, embedding new specialist staff roles, cross-skilling Liaison Librarians, and ensuring continued collaboration between stakeholders.

Metadata aggregation : a critical component of research infrastructure for the future

The development of research data management infrastructure and services and making research data more discoverable and accessible to the research community is a key priority at the national, state and individual university level. This paper will discuss and reflect upon a collaborative project between Griffith University and the Queensland University of Technology to commission a Metadata Hub or Metadata Aggregation service based upon open source software components. It will describe the role that metadata aggregation services play in modern research infrastructure and argue that this role is a critical one.

Preparing library staff for research support services at QUT

INTRODUCTION: Queensland University of Technology (QUT) Library is partnering with High Performance Computing (HPC) services and the Division of Research and Commercialisation to develop and deliver a range of integrated research support services and systems designed to enhance the research capabilities of the University. Existing and developing research support services include - support for publishing strategies including open access, bibliographic citation and ranking services, research data management, use of online collaboration tools, online survey tools, quantitative and qualitative data analysis, content management and storage solutions. In order to deliver timely and effective research referral and support services, it is imperative that library staff maintain their awareness of, and develop expertise in new eResearch methods and technologies. ---------- METHODS: In 2009/10 QUT Library initiated an online survey for support staff and researchers and a series of focus groups for researchers aimed at gaining a better understanding of current and future eresearch practices and skills. These would better inform the development of a research skills training program and the development of new research support services. The Library and HPC also implemented a program of seminars and workshops designed to introduce key library staff to a broad range of eresearch concepts and technologies. Feedback was obtained after each training session. A number of new services were implemented throughout 2009 and 2010. ---------- RESULTS: Key findings of the survey and focus groups are related to the development of the staff development program. Feedback from program attendees is provided and evaluated. The staff development program is assessed in terms of its success to support the implementation of new research support services. --------- CONCLUSIONS QUT Library has embarked on an ambitious awareness and skills development program to assist Library staff transition a period of rapid change and broadening scope for the Library. Successes and challenges of the program are discussed. A number of recommendations are made in retrospect and also looking forward to the future training needs of Library staff to support the University’s future research goals.

Enabling access to and re-use of publicly funded research data as Open Educational Resources : a strategy for overcoming the legal barriers to data access and re-use

Open Educational Resources (OER) are teaching, learning and research materials that have been released under an open licence that permits online access and re-use by others. The 2012 Paris OER Declaration encourages the open licensing of educational materials produced with public funds. Digital data and data sets produced as a result of scientific and non-scientific research are an increasingly important category of educational materials. This paper discusses the legal challenges presented when publicly funded research data is made available as OER, arising from intellectual property rights, confidentiality and information privacy laws, and the lack of a legal duty to ensure data quality. If these legal challenges are not understood, addressed and effectively managed, they may impede and restrict access to and re-use of research data. This paper identifies some of the legal challenges that need to be addressed and describes 10 proposed best practices which are recommended for adoption to so that publicly funded research data can be made available for access and re-use as OER.

Library research support in Queensland : a survey

University libraries worldwide are reconceptualising the ways in which they support the research agenda in their respective institutions. This paper is based on a survey completed by member libraries of the Queensland University Libraries Office of Cooperation (QUL OC), the findings of which may be informative for other university libraries. After briefly examining major emerging trends in research support, the paper discusses the results of the survey specifically focussing on support for researchers and the research agenda in their institutions. All responding libraries offer a high level of research support, however, eResearch support, in general, and research data management support, in particular, have the highest variance among the libraries, and signal possible areas for growth. Areas for follow-up, benchmarking and development are suggested.

Enabling open access to and re-use of publicly funded research data in Malaysian public universities : a legal and policy analysis

Numerous statements and declarations have been made over recent decades in support of open access to research data. The growing recognition of the importance of open access to research data has been accompanied by calls on public research funding agencies and universities to facilitate better access to publicly funded research data so that it can be re-used and redistributed as public goods. International and inter-governmental bodies such as the ICSU/CODATA, the OECD and the European Union are strong supporters of open access to and re-use of publicly funded research data. This thesis focuses on the research data created by university researchers in Malaysian public universities whose research activities are funded by the Federal Government of Malaysia. Malaysia, like many countries, has not yet formulated a policy on open access to and re-use of publicly funded research data. Therefore, the aim of this thesis is to develop a policy to support the objective of enabling open access to and re-use of publicly funded research data in Malaysian public universities. Policy development is very important if the objective of enabling open access to and re-use of publicly funded research data is to be successfully achieved. In developing the policy, this thesis identifies a myriad of legal impediments arising from intellectual property rights, confidentiality, privacy and national security laws, novelty requirements in patent law and lack of a legal duty to ensure data quality. Legal impediments such as these have the effect of restricting, obstructing, hindering or slowing down the objective of enabling open access to and re-use of publicly funded research data. A key focus in the formulation of the policy was the need to resolve the various legal impediments that have been identified. This thesis analyses the existing policies and guidelines of Malaysian public universities to ascertain to what extent the legal impediments have been resolved. An international perspective is adopted by making a comparative analysis of the policies of public research funding agencies and universities in the United Kingdom, the United States and Australia to understand how they have dealt with the identified legal impediments. These countries have led the way in introducing policies which support open access to and re-use of publicly funded research data. As well as proposing a policy supporting open access to and re-use of publicly funded research data in Malaysian public universities, this thesis provides procedures for the implementation of the policy and guidelines for addressing the legal impediments to open access and re-use.