Cloud service discovery and analysis: a unified framework

Over the past few years, cloud computing has been more and more attractive as a new computing paradigm due to high flexibility for provisioning on-demand computing resources that are used as services through the Internet. The issues around cloud service discovery have considered by many researchers in the recent years. However, in cloud computing, with the highly dynamic, distributed, the lack of standardized description languages, diverse services offered at different levels and non-transparent nature of cloud services, this research area has gained a significant attention. Robust cloud service discovery approaches will assist the promotion and growth of cloud service customers and providers, but will also provide a meaningful contribution to the acceptance and development of cloud computing. In this dissertation, we have proposed an automated cloud service discovery approach of cloud services. We have also conducted extensive experiments to validate our proposed approach. The results demonstrate the applicability of our approach and its capability of effectively identifying and categorizing cloud services on the Internet. Firstly, we develop a novel approach to build cloud service ontology. Cloud service ontology initially is built based on the National Institute of Standards and Technology (NIST) cloud computing standard. Then, we add new concepts to ontology by automatically analyzing real cloud services based on cloud service ontology Algorithm. We also propose cloud service categorization that use Term Frequency to weigh cloud service ontology concepts and calculate cosine similarity to measure the similarity between cloud services. The cloud service categorization algorithm is able to categorize cloud services to clusters for effective categorization of cloud services. In addition, we use Machine Learning techniques to identify cloud service in real environment. Our cloud service identifier is built by utilizing cloud service features extracted from the real cloud service providers. We determine several features such as similarity function, semantic ontology, cloud service description and cloud services components, to be used effectively in identifying cloud service on the Web. Also, we build a unified model to expose the cloud service’s features to a cloud service search user to ease the process of searching and comparison among a large amount of cloud services by building cloud service’s profile. Furthermore, we particularly develop a cloud service discovery Engine that has capability to crawl the Web automatically and collect cloud services. The collected datasets include meta-data of nearly 7,500 real-world cloud services providers and nearly 15,000 services (2.45GB). The experimental results show that our approach i) is able to effectively build automatic cloud service ontology, ii) is robust in identifying cloud service in real environment and iii) is more scalable in providing more details about cloud services.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201

Ontologies in Cloud Computing - Review and Future Directions

Cloud computing as a technology has the capacity to enhance cooperation, scalability, accessibility, and offers discount prospects using improved and effective computing, and this capability helps organizations to stay focused. Ontologies are used to model knowledge. Once knowledge is modeled, knowledge management systems can be used to search, match, visualize knowledge, and also infer new knowledge. Ontologies use semantic analysis to define information within an environment with interconnecting relationships between heterogeneous sets. This paper aims to provide a comprehensive review of the existing literature on ontology in cloud computing and defines the state of the art. We applied the systematic literature review (SLR) approach and identified 400 articles; 58 of the articles were selected after further selection based on set selection criteria, and 35 articles were considered relevant to the study. The study shows that four predominant areas of cloud computing—cloud security, cloud interoperability, cloud resources and service description, and cloud services discovery and selection—have attracted the attention of researchers as dominant areas where cloud ontologies have made great impact. The proposed methods in the literature applied 30 ontologies in the cloud domain, and five of the methods are still practiced in the legacy computing environment. From the analysis, it was found that several challenges exist, including those related to the application of ontologies to enhance business operations in the cloud and multi-cloud. Based on this review, the study summarizes some unresolved challenges and possible future directions for cloud ontology researchers.publishedVersio

Data-driven conceptual modeling: how some knowledge drivers for the enterprise might be mined from enterprise data

As organizations perform their business, they analyze, design and manage a variety of processes represented in models with different scopes and scale of complexity. Specifying these processes requires a certain level of modeling competence. However, this condition does not seem to be balanced with adequate capability of the person(s) who are responsible for the task of defining and modeling an organization or enterprise operation. On the other hand, an enterprise typically collects various records of all events occur during the operation of their processes. Records, such as the start and end of the tasks in a process instance, state transitions of objects impacted by the process execution, the message exchange during the process execution, etc., are maintained in enterprise repositories as various logs, such as event logs, process logs, effect logs, message logs, etc. Furthermore, the growth rate in the volume of these data generated by enterprise process execution has increased manyfold in just a few years. On top of these, models often considered as the dashboard view of an enterprise. Models represents an abstraction of the underlying reality of an enterprise. Models also served as the knowledge driver through which an enterprise can be managed. Data-driven extraction offers the capability to mine these knowledge drivers from enterprise data and leverage the mined models to establish the set of enterprise data that conforms with the desired behaviour. This thesis aimed to generate models or knowledge drivers from enterprise data to enable some type of dashboard view of enterprise to provide support for analysts. The rationale for this has been started as the requirement to improve an existing process or to create a new process. It was also mentioned models can also serve as a collection of effectors through which an organization or an enterprise can be managed. The enterprise data refer to above has been identified as process logs, effect logs, message logs, and invocation logs. The approach in this thesis is to mine these logs to generate process, requirement, and enterprise architecture models, and how goals get fulfilled based on collected operational data. The above a research question has been formulated as whether it is possible to derive the knowledge drivers from the enterprise data, which represent the running operation of the enterprise, or in other words, is it possible to use the available data in the enterprise repository to generate the knowledge drivers? . In Chapter 2, review of literature that can provide the necessary background knowledge to explore the above research question has been presented. Chapter 3 presents how process semantics can be mined. Chapter 4 suggest a way to extract a requirements model. The Chapter 5 presents a way to discover the underlying enterprise architecture and Chapter 6 presents a way to mine how goals get orchestrated. Overall finding have been discussed in Chapter 7 to derive some conclusions

Designing, Aligning, and Visualizing Service Systems

Service is a concept that separates the concerns of an organization into (1) the value created for users and (2) the way the organization manages its resources to provide this value. The discipline of management of information technology (IT) uses services to coordinate and to optimize the use of IT resources (servers, applications, databases, etc.) in a way that brings value to users. The concrete application of the service concept is challenging due to its abstract, interdependent and recursive nature. We experienced this challenge while collaborating with the IT department of our university (École Polytechnique Fédérale de Lausanne, EPFL) when the IT department adopted the IT Infrastructure Library (ITIL) best-practices framework for IT service management. As researchers, we have the goal of improving the understanding of services as a means to structuring what people and organizations do. In the context of the IT department, we studied how to apply the service concept internally within the IT department, and externally (as business services) in the overall organization. In this thesis, we model services by using systems thinking principles. In particular, we use and improve SEAM, the systemic service-modeling method developed in our laboratory. Our main result is an ontology for SEAM service modeling. Our contributions are the heuristics that define how the ontology relates to a perceived reality: for example, the heuristics focus on behavior rather than organization and they put an emphasis on service instances rather than service types. We also define alignment between service systems, based on the properties of the systems¿ behavior. We show how to model an organization by implementing the concept of service as defined by our ontology. This ontology supports the design of service systems that align across both IT and business services. During our work with over one hundred IT services, we developed several visualization prototypes of a service cartography; we use these prototypes to describe and to relate the different views required for managing services. Our results offer a concrete way to implement the abstract concept of services. This way could be of interest for any organization willing to embark on a large-scale service project

Information Systems as Representations: A Review of the Theory and Evidence

Representation theory proposes that the basic purpose of an information system (IS) is to faithfully represent certain real-world phenomena, allowing users to reason about these phenomena more cost- effectively than if they were observed directly. Over the past three decades, the theory has underpinned much research on conceptual modeling in IS analysis and design and, increasingly, research on other IS phenomena such as data quality, system alignment, IS security, and system use. The original theory has also inspired further development of its core premises and advances in methodological guidelines to improve its use and evaluation. Nonetheless, the theory has attracted repeated criticisms regarding its validity, relevance, usefulness, and robustness. Given the burgeoning literature on the theory over time, both positive and negative, the time is ripe for a narrative, developmental review. We review representation theory, examine how it has been used, and critically evaluate its contributions and limitations. Based on our findings, we articulate a set of recommendations for improving its application, development, testing, and evaluation

Extensibility of Enterprise Modelling Languages

Die Arbeit adressiert insgesamt drei Forschungsschwerpunkte. Der erste Schwerpunkt setzt sich mit zu entwickelnden BPMN-Erweiterungen auseinander und stellt deren methodische Implikationen im Rahmen der bestehenden Sprachstandards dar. Dies umfasst zum einen ganz konkrete Spracherweiterungen wie z. B. BPMN4CP, eine BPMN-Erweiterung zur multi-perspektivischen Modellierung von klinischen Behandlungspfaden. Zum anderen betrifft dieser Teil auch modellierungsmethodische Konsequenzen, um parallel sowohl die zugrunde liegende Sprache (d. h. das BPMN-Metamodell) als auch die Methode zur Erweiterungsentwicklung zu verbessern und somit den festgestellten Unzulänglichkeiten zu begegnen. Der zweite Schwerpunkt adressiert die Untersuchung von sprachunabhängigen Fragen der Erweiterbarkeit, welche sich entweder während der Bearbeitung des ersten Teils ergeben haben oder aus dessen Ergebnissen induktiv geschlossen wurden. Der Forschungsschwerpunkt fokussiert dabei insbesondere eine Konsolidierung bestehender Terminologien, die Beschreibung generisch anwendbarer Erweiterungsmechanismen sowie die nutzerorientierte Analyse eines potentiellen Erweiterungsbedarfs. Dieser Teil bereitet somit die Entwicklung einer generischen Erweiterungsmethode grundlegend vor. Hierzu zählt auch die fundamentale Auseinandersetzung mit Unternehmensmodellierungssprachen generell, da nur eine ganzheitliche, widerspruchsfreie und integrierte Sprachdefinition Erweiterungen überhaupt ermöglichen und gelingen lassen kann. Dies betrifft beispielsweise die Spezifikation der intendierten Semantik einer Sprache

Languages of games and play: A systematic mapping study

Digital games are a powerful means for creating enticing, beautiful, educational, and often highly addictive interactive experiences that impact the lives of billions of players worldwide. We explore what informs the design and construction of good games to learn how to speed-up game development. In particular, we study to what extent languages, notations, patterns, and tools, can offer experts theoretical foundations, systematic techniques, and practical solutions they need to raise their productivity and improve the quality of games and play. Despite the growing number of publications on this topic there is currently no overview describing the state-of-the-art that relates research areas, goals, and applications. As a result, efforts and successes are often one-off, lessons learned go overlooked, language reuse remains minimal, and opportunities for collaboration and synergy are lost. We present a systematic map that identifies relevant publications and gives an overview of research areas and publication venues. In addition, we categorize research perspectives along common objectives, techniques, and approaches, illustrated by summaries of selected languages. Finally, we distill challenges and opportunities for future research and development

SNACH a new framework to support business process improvement.

Business processes are central to any organisation. They coordinate activities, roles, resources, systems and constraints within and across organisational boundaries to achieve predefined business goals. The demand for dynamic business environments, customer satisfaction, global competition, system integration, operational efficiency, innovation and adaptation to market changes necessitates the need for continuous process improvement. In order to adequately respond to these demands, business processes are designed in two approaches: Business Process Re-engineering (BPR) and Business Process Improvement (BPI). This thesis follows the BPI approach which considers existing infrastructure in an organization to improve operational efficiency and achieve organisational goals. Many methodologies have been developed for conducting BPI projects, but they provide little support for the actual act of systematically improving a business process. We adopted case study as the research strategy to examine a collaborative business process, specifically the UK Higher Education Institutions (HEI) admission process. The design science research methodology was used to answer the research questions and satisfy the research objectives. The Map technique was employed to construct the new BPI artefact based on the Mandatory Elements of Method (MEM) from Method Engineering. The new BPI framework comprises of a number of elements to support analysts and practitioners in process improvement activities. We present a novel approach to BPI, the SNACH (Simulation Network Analysis Control flow complexity and Heuristics) framework that supports the actual act of process improvement using a combination of process analysis techniques with integrated quantitative measurable concepts to measure and visualize improvement in four dimensions: cost, cycle time, flexibility and complexity. A simulation technique was employed to analyse the process models in terms of time and cost; and Control Flow Complexity was used to calculate the logical complexity of the process model. A complex network analysis approach was used to provide information about the structural relationship and information exchange between process activities. Using a complex network analysis approach to reduce a process model to a network of nodes and links so that its structural properties are analysed to provide information about the structural complexity and flexibility of the network. To achieve this higher level of abstraction, an algorithm was defined and validated using four disparate process models. The complex network analysis technique is integrated into the SNACH framework and its significance lies in the study of the nature of the individual nodes and the pattern of connections in the network. These characteristics are assessed using network metrics to quantitatively analyse the structure of the network, thereby providing insight into the interaction and behavioural structure of the business process activities. To conclude the design science research process phases, the artefact was evaluated in terms of its effectiveness and efficiency to systematically improve a business process by conducting an experiment using another use case

Building information modeling – A game changer for interoperability and a chance for digital preservation of architectural data?

Digital data associated with the architectural design-andconstruction process is an essential resource alongside -and even past- the lifecycle of the construction object it describes. Despite this, digital architectural data remains to be largely neglected in digital preservation research – and vice versa, digital preservation is so far neglected in the design-and-construction process. In the last 5 years, Building Information Modeling (BIM) has seen a growing adoption in the architecture and construction domains, marking a large step towards much needed interoperability. The open standard IFC (Industry Foundation Classes) is one way in which data is exchanged in BIM processes. This paper presents a first digital preservation based look at BIM processes, highlighting the history and adoption of the methods as well as the open file format standard IFC (Industry Foundation Classes) as one way to store and preserve BIM data