RonPub -- Research Online Publishing
Not a member yet
199 research outputs found
Sort by
Advances in Cloud and Ubiquitous Computing
Get PDFCloud computing provides on-demand access to a shared pool of configurable and dynamically reallocated computing resources typically located in third-party data centers. Ubiquitous computing aims at providing computing resources anytime and everywhere by using any device, in any location, and in any format. This special issue, Advances in Cloud and Ubiquitous Computing (ACUC), aims at addressing the challenges and reporting the latest research findings in the fields of Cloud computing and Ubiquitous Computing respectively, and how new technologies of Cloud Computing and Ubiquitous Computing complete each other
Distributed Join Approaches for W3C-Conform SPARQL Endpoints
Get PDFCurrently many SPARQL endpoints are freely available and accessible without any costs to users: Everyone can submit SPARQL queries to SPARQL endpoints via a standardized protocol, where the queries are processed on the datasets of the SPARQL endpoints and the query results are sent back to the user in a standardized format. As these distributed execution environments for semantic big data (as intersection of semantic data and big data) are freely accessible, the Semantic Web is an ideal playground for big data research. However, when utilizing these distributed execution environments, questions about the performance arise. Especially when several datasets (locally and those residing in SPARQL endpoints) need to be combined, distributed joins need to be computed. In this work we give an overview of the various possibilities of distributed join processing in SPARQL endpoints, which follow the SPARQL specification and hence are "W3C conform". We also introduce new distributed join approaches as variants of the Bitvector-Join and combination of the Semi- and Bitvector-Join. Finally we compare all the existing and newly proposed distributed join approaches for W3C conform SPARQL endpoints in an extensive experimental evaluation
Epilogue: Summary and Outlook
Get PDFOpen Journal of Big Data (OJBD) is an open access journal addressing aspects of Big Data, including new methodologies, processes, case studies, poofs-of-concept, scientific demonstrations, industrial applications and adoption. This editorial presents three articles in the second issue. The first paper is on Big Data in the Cloud. The second paper is on Statistical Machine Learning in Brain State Classification using EEG Data. The third article is on Data Transfers in Hadoop. OJBD has a rising reputation thanks to the support of research communities, which has helped us set up the First International Conference on Internet of Things and Big Data (IoTBD 2016), in Rome, Italy, between 23 and 25 April 2016. OJBD is published by RonPub (www.ronpub.com), which is an academic publisher of online, open access, peer-reviewed journals
Achieving Business Practicability of Model-Driven Cross-Platform Apps
Get PDFDue to the incompatibility of mobile device platforms such as Android and iOS, apps have to be developed separately for each target platform. Cross-platform development approaches based on Web technology have significantly improved over the last years. However, since they do not lead to native apps, these frameworks are not feasible for all kinds of business apps. Moreover, the way apps are developed is cumbersome. Advanced cross-platform approaches such as MD2, which is based on model-driven development (MDSD) techniques, are a much more powerful yet less mature choice. We discuss business implications of MDSD for apps and introduce MD2 as our proposed solution to fulfill typical requirements. Moreover, we highlight a business-oriented enhancement that further increases MD2's business practicability. We generalize our findings and sketch the path towards more versatile MDSD of apps
Relationship between Externalized Knowledge and Evaluation in the Process of Creating Strategic Scenarios
Get PDFSocial systems are changing so rapidly that it is important for humans to make decisions considering uncertainty. A scenario is information about the series of events/actions, which supports decision makers to take actions and reduce risks. We propose Action Planning for refining simple ideas into practical scenarios (strategic scenarios). Frameworks and items on Action Planning Sheets provide participants with organized constraints, to lead to creative and logical thinking for solving real issues in businesses or daily life. Communication among participants who have preset roles leads the externalization of knowledge. In this study, we set three criteria for evaluating strategic scenarios; novelty, utility, and feasibility, and examine the relationship between externalized knowledge and the evaluation values, in order to consider factors which affect the evaluations. Regarding a word contained in roles and scenarios as the smallest unit of knowledge, we calculate Relativeness between roles and scenarios. The results of our experiment suggest that the lower the relativeness of a strategic scenario, the higher the strategic scenario is evaluated in novelty. In addition, in the evaluation of utility, a scenario satisfying a covert requirement tends to be estimated higher. Moreover, we found the externalization of stakeholders may affect the realization of strategic scenarios
Deriving Bounds on the Size of Spatial Areas
Get PDFMany application domains such as surveillance, environmental monitoring or sensor-data processing need upper and lower bounds on areas that are covered by a certain feature. For example, a smart-city infrastructure might need bounds on the size of an area polluted with fine-dust, to re-route combustion-engine traffic. Obtaining such bounds is challenging, because in almost any real-world application, information about the region of interest is incomplete, e.g., the database of sensor data contains only a limited number of samples. Existing approaches cannot provide upper and lower bounds or depend on restrictive assumptions, e.g., the area must be convex. Our approach in turn is based on the natural assumption that it is possible to specify a minimal diameter for the feature in question. Given this assumption, we formally derive bounds on the area size, and we provide algorithms that compute these bounds from a database of sensor data, based on geometrical considerations. We evaluate our algorithms both with a real-world case study and with synthetic data
Cognitive Spam Recognition Using Hadoop and Multicast-Update
Get PDFIn today's world of exponentially growing technology, spam is a very common issue faced by users on the internet. Spam not only hinders the performance of a network, but it also wastes space and time, and causes general irritation and presents a multitude of dangers - of viruses, malware, spyware and consequent system failure, identity theft, and other cyber criminal activity. In this context, cognition provides us with a method to help improve the performance of the distributed system. It enables the system to learn what it is supposed to do for different input types as different classifications are made over time and this learning helps it increase its accuracy as time passes. Each system on its own can only do so much learning, because of the limited sample set of inputs that it gets to process. However, in a network, we can make sure that every system knows the different kinds of inputs available and learns what it is supposed to do with a better success rate. Thus, distribution and combination of this cognition across different components of the network leads to an overall improvement in the performance of the system. In this paper, we describe a method to make machines cognitively label spam using Machine Learning and the Naive Bayesian approach. We also present two possible methods of implementation - using a MapReduce Framework (hadoop), and also using messages coupled with a multicast-send based network - with their own subtypes, and the pros and cons of each. We finally present a comparative analysis of the two main methods and provide a basic idea about the usefulness of the two in various different scenarios
Using Nuisance Telephone Denial of Service to Combat Online Sex Trafficking
Get PDFOver the past few years, sex trafficking has been linked to online classified ads sites such as Craigslist.com and Backpage.com. However, to date technology-based solutions have not been used to attack classified ad sites or the advertisers. This paper proposes and tests a new approach to combating online sex trafficking promulgated via online classified ad sites - nuisance telephone denial of service (TDoS) attacks on the advertisers. The method of attack is described and implications are discussed
Emerging Software as a Service and Analytics
Get PDFThis special issue of Open Journal of Cloud Computing (OJCC) (www.ronpub.com/journals/ojcc) reports work in the field of emerging software as a service and analytics, and presents innovative approaches to delivering software services in research and enterprise communities. It contains extended versions of papers selected from the international workshop on Emerging Software as a Service and Analytices (ESaaSA) in association with the international conference on cloud computing and serviced science taken place in Barcelona, Spain during April 2014. OJCC is published by RonPub (www.ronpub.com), which is an academic publisher of online, open access, peer-reviewed journals
Causal Consistent Databases
Get PDFMany consistency criteria have been considered in databases and the causal consistency is one of them. The causal consistency model has gained much attention in recent years because it provides ordering of relative operations. The causal consistency requires that all writes, which are potentially causally related, must be seen in the same order by all processes. The causal consistency is a weaker criteria than the sequential consistency, because there exists an execution, which is causally consistent but not sequentially consistent, however all executions satisfying the sequential consistency are also causally consistent. Furthermore, the causal consistency supports non-blocking operations; i.e. processes may complete read or write operations without waiting for global computation. Therefore, the causal consistency overcomes the primary limit of stronger criteria: communication latency. Additionally, several application semantics are precisely captured by the causal consistency, e.g. collaborative tools. In this paper, we review the state-of-the-art of causal consistent databases, discuss the features, functionalities and applications of the causal consistency model, and systematically compare it with other consistency models. We also discuss the implementation of causal consistency databases and identify limitations of the causal consistency model