Vlado Stankovski

Grid-enabling data mining applications with DataMiningGrid: An architectural perspective

The DataMiningGrid system has been designed to meet the requirements of modern and distributed data mining scenarios. Based on the Globus Toolkit and other open technology and standards, the DataMiningGrid system provides tools and services facilitating the grid-enabling of data mining applications without any intervention on the application side. Critical features of the system include flexibility, extensibility, scalability, efficiency, conceptual simplicity and ease of use. The system has been developed and evaluated on the basis of a diverse set of use cases from different sectors in science and technology. The DataMiningGrid software is freely available under Apache License 2.0.

Experiences in building a mOSAIC of clouds

The diversity of Cloud computing services is challenging the application developers as various and non-standard interfaces are provided for these services. Few middleware solutions were developed until now to support the design, deployment and execution of service-independent applications as well as the management of resources from multiple Clouds. This paper focuses on one of these advanced middleware solutions, called mOSAIC. Written after the completion of its development, this paper presents an integrated overview of the mOSAIC approach and the use of its various software prototypes in a Cloud application development process. We are starting from the design concepts and arrive to various applications, as well as to the position versus similar initiatives.

Digging Deep into the Data Mine with DataMiningGrid

As modern data mining applications increase in complexity, so too do their demands for resources. Grid computing is one of several emerging networked computing paradigms promising to meet the requirements of heterogeneous, large-scale, and distributed data mining applications. Despite this promise, there are still too many issues to be resolved before grid technology is commonly applied to large-scale data mining tasks. To address some of these issues, the authors developed the DataMiningGrid system. It integrates a diverse set of programs and application scenarios within a single framework, and features scalability, flexible extensibility, sophisticated support for relevant standards and different users.

Application of decision trees to smart homes

This chapter aims to illustrate a possible way of using decision trees to make Smart Homes smarter. Decision trees are popular modelling technique, and the corresponding models are both predictive and descriptive. We formulate the modelling problem by defining the generic question “Is the undergoing activity or event in the Smart Home usual?” Then we explain how it is possible to gather appropriate data from the sensors and pre-process these data to form appropriate input for a decision tree algorithm. We further explain the mainstream approaches in decision trees algorithms rather then analysing them in detail, and we give short overview of available software. Finally, we explain some measures for quantitative and qualitative evaluation of the induced decision tree models (e.g. expert opinion, cross-validation, statistical tests etc.).

Developing and Operating Time Critical Applications in Clouds: The State of the Art and the SWITCH Approach☆

Cloud environments can provide virtualized, elastic, controllable and high quality on-demand services for supporting complex distributed applications. However, the engineering methods and software tools used for developing, deploying and executing classical time critical applications do not, as yet, account for the programmability and controllability provided by clouds, and so time critical applications cannot yet benefit from the full potential of cloud technology. This paper reviews the state of the art of technologies involved in developing time critical cloud applications, and presents the approach of a recently funded EU H2020 project: the Software Workbench for Interactive, Time Critical and Highly self-adaptive cloud applications (SWITCH). SWITCH aims to improve the existing development and execution model of time critical applications by introducing a novel conceptual model—the application-infrastructure co-programming and control model—in which application QoS and QoE, together with the programmability and controllability of cloud environments, is included in the complete application lifecycle.

A Software Workbench for Interactive, Time Critical and Highly Self-Adaptive Cloud Applications (SWITCH)

Time critical applications have very high requirements on network and computing services, in particular on well-tuned software architecture with sophisticated optimisation on data communication. Their development is often customised to dedicated infrastructure, and system performance is difficult to maintain when infrastructure changes. This fatal weakness in existing architecture and software tools causes very high development costs, and makes it difficult to fully utilise the virtualised, programmable and quality-on-demand services provided by networked Clouds to improve the system productivity. The Software Workbench for Interactive, Time Critical and Highly self-adaptive Cloud applications (SWITCH) is a newly funded project by EU H2020 to address this urgent industrial need, it aims at improving the existing development and execution model of time critical applications by introducing a novel conceptual model called application-infrastructure co-programming and control model, in which application QoS/QoE together with the programmability and controllability of Cloud environments can be all included in the complete lifecycle of applications.

Workflows in a dashboard: a new generation of usability

In the last 20 years quite a few mature workflow engines and workflow editors have been developed to support communities in managing workflows. While there is a trend followed by the providers of workflow engines to ease the creation of workflows tailored to their specific workflow system, the management tools still often necessitate much understanding of the workflow concepts and languages. This paper describes the approach targeting various workflow systems and building a single user interface for editing and monitoring workflows under consideration of aspects such as optimization and provenance of data. The design allots agile Web frameworks and novel technologies to build a workflow dashboard offered in a web browser and connecting seamlessly to available workflow systems and external resources like Cloud infrastructures. The user interface eliminates the need to become acquainted with diverse layouts. Thus, the usability is immensely increased for various aspects of managing workflows.

Improving the performance of Federated Digital Library services

The number of Digital Libraries (DLs) accessible over the Open Archives Initiative-Protocol for Metadata Harvesting (OAI-PMH) has been constantly increasing in the past years. Earlier efforts in the DL area have concentrated on metadata harvesting and provisioning of value-added Federated Digital Library (FDL) services to the users. FDL services, however, have to meet significant performance and scalability requirements, which is difficult to achieve in centralized metadata harvesting systems. The goal of the present study was to evaluate the benefits of using Web Services Resource Framework (WSRF) compliant grid middleware infrastructure for providing efficient and reliable FDL services. The presented FDL application allows for parallel harvesting of OAI-PMH compliant DLs. The results show that this approach efficiently solves the performance related problems, while it also contributes to greater flexibility of the system. The quality of service is improved as metadata can be updated frequently, and the system does not exhibit a single point of failure.

Editorial: special section: Data mining in grid computing environments

Data mining can be viewed as the formulation, analysis, and implementation of an induction process proceeding from specific data to general patterns that facilitates the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. Data mining ranges from highly theoretical mathematical work in areas like statistics, machine learning, knowledge representation, and algorithms to systems solutions for problems like fraud detection, modeling of cancer and other complex diseases, network intrusion, information retrieval on the Web, and monitoring of grid systems. Data mining techniques are increasingly employed in traditional scientific discovery disciplines, such as biological, medical, biomedical, chemical, physical, and social research, and a variety of other knowledge industries, such as government, education, high-tech engineering, and process automation. Thus, data mining is playing an increasingly important role in structuring and shaping future knowledge-based industries and businesses. The effective and efficient management and use of stored data, and in particular the transformation of these data into information and knowledge, is considered a key requirement for success in such domains. In the past, research in data mining has mainly been concerned with small to moderately sized data sets and knowledge-weak domains (e.g., market and retail applications) with focus on largely homogeneous and localized computing environments. These assumptions are no longer met in modern scientific and industrial complex problem-solving environments, which are increasingly relying on the sharing of geographically dispersed computing resources. Such

Using the mOSAIC's semantic engine to design and develop civil engineering cloud applications

The development of applications for the Cloud requires programming skills and knowledge about the several programming models, APIs and underlying infrastructures, which are provided by Cloud vendors. The European Project mOSAIC aims at developing an API, Platform and a set of tools to facilitate language and platform agnostic application development and deployment on a variety of Infrastructures as a Service offers. Within the mOSAIC project, appropriate ontologies, a knowledge base and an associated Semantic Engine [10] have been developed to support the Cloud application developer in the tasks of discovering the needed functionalities and resources for application development through vendor independent representations of such application components, and representation of generic programming concepts and patterns, including application domain related ones. In this paper the use of the Semantic Engine, its ontologies and knowledge base is illustrated by following the design and implementation of an application for analysis of structures under static loading that is based on the Finite Element Method.