Ossi Nykänen

Catalysing the Development of a Conference Workspace

Modern Web introduces new means to support conference information retrieval and, moreover, social tools that enable conference delegates to actively contribute to a conference. Simultaneously, existing case studies show that collecting explicitly represented conference data is possible in real-life settings. From these grounds, methods and tools of component-based information visualisation can be used to process conference data, firstly, in order to create visualisations of the conference data, and, secondly, to produce data for populating interactive conference tools. We claim that when coupled with usage tracking and analysis, the automated creation of visualisations and population data can catalyse the iterative development of a conference workspace. In this article, we present case OPAALS 2008 where formal conference data was successfully harvested and utilised to partly automate the creation workflow of the conference workspace. Moreover, we introduce the methods that were used to gain insight on the dynamics of the workspace usage.

An implementation of a semantic, web-based virtual machine laboratory prototyping environment

Creation of virtual machine laboratories - simulated planning and learning environments demonstrating function and structure of working machines - often involve a lot of manual labor. A notable source of the labor is the programming required due to changes in structural and functional models of a system. As a result, rapid prototyping of a virtual machine laboratory becomes difficult, if not impossible. We argue that by using a combination of semantic modeling and prototyping with a web-based system, more rapid development of virtual machine laboratories can be achieved. In this paper, we present the design and implementation of a semantic, web-based virtual machine laboratory prototyping environment. Application of the environment to a case example is also described and discussed.

A Design for a Hypermedia-based Learning Environment

This paper presents ideas and a design for a Hypermedia-Based Learning Environment, HBLE for short. As the system is in implementation phase, we are also able to present some implementation techniques, problems and solutions. HBLE offers tools and methods for course development, teaching, maintenance, and different learner-centered study strategies. The system also has information acquisition functionality for research purposes.We study structuring the learning material and how to adapt it for individual students. As collaboration is an important aspect of learning process, the system also includes tools and research instruments for collaborative activities between the actors in the learning process. The concrete outcomes of the project are a platform for Web-based courses and experimental courseware.

Metadata for learning resources: technologies and directions of the Semantic Web - a brief review

The appearance of the Semantic Web technologies has a significant impact on the development of all semantically interoperable tools for the Web. This article provides an outline of the SW technologies and tools from the perspective of learning systems.The appearance of the Semantic Web technologies has a significant impact on the development of all semantically interoperable tools for the Web. This article provides an outline of the SW technologies and tools from the perspective of learning systems.

IoT-based interoperability framework for asset and fleet management

Service business renewal to support global fleet operations is one of the critical competitiveness factors of technology industries. This paper describes preliminary results of defining an IoT-based framework for fleet management systems (FMS). The main contribution is to provide a systematic outline to the ingredients of the framework and discussing their impact in an industrial setting. The overall aim of the research is to define a technical framework for fleet management solutions and to define the necessary set of functionalities that the framework should support as a base for FMS systems. The framework will be implemented and validated in collaboration with industrial partners.

Using context overlays to analyse the role of a priori information with Process Mining

Notwithstanding the significant advances in context-aware computing in pervasive computing and self-adaptive systems, there is still much more to be desired in providing better context services. The number of sensors deployed world-wide increases very rapidly. The Internet of Things, amongst others, generates vast amounts of data of many different data types. How data are used is essential to improve user experience and efficiencies of the systems in which they occur. We explain how familiar concepts of Process Mining strengthen generalised sensor context services. We present a laboratory case to explain the approach. By way of a real-world example, we confirm the viability of using Process Mining to strengthen context-aware computing.

Associating event logs with ontologies for semantic process mining and analysis

Process mining uses various forms of event logs to extract process-related information, in order to discover, analyze conformance, or to enhance (business) processes. The vast majority of process mining applications are based on event logs with flat, keyword-based activity and resource descriptions. Many human-designed processes, however, are based on explicit workflow or lifecycle models with associated product models, both of which can be described using taxonomies or more complicated ontologies. This additional information can be used to analyze and visualize the processes with better insight of and improved formal access to the data. In this paper, we introduce a generic approach for enriching process mining using events logs with associated ontology structures. The main contribution and benefit of this approach lies in the ability to analyze the models in different abstraction levels, which greatly helps understanding complicated processes. Our main application areas are related to engineering and documentation processes.

Social Approach for Context Analysis: Modelling and Predicting Social Network Evolution Using Homophily

Understanding the user’s context is important for mobile applications to provide personalized services. Such context is typically based on the user’s own information. In this paper, we show how social network analysis and the study of the individual in a social network can provide meaningful contextual information. According to the phenomenon of homophily, similar users tend to be connected more frequently than dissimilar. We model homophily in social networks over time. Such models strengthen context inference algorithms, which helps determine future status of the user, resulting in prediction accuracy improvements of up to 118 % with respect to a naive classifier.

Engineering intelligence - Product-service concepts and requirements in industry

European manufacturing and construction sector is wrestling with the fundamental and rapid changes in their business environment. Business environment is becoming more dynamic and distributed, thus old standardization and mass-customization methods can no longer support the companies. In order to survive the companies must be able to offer fully customized product-service concepts instead of technical solutions. This causes challenges relating to efficiency of collaboration, utilization of information flows, agility and interoperability of technical solutions and operation culture. The products, processes and services need to be designed “for humans by humans”. The competence development methods and enhanced learning have to be comprehensively taken into account. This paper summarizes the challenges among industry and provides a new approach Engineering Intelligence Ecosystem (EIE) that addresses aforementioned challenges of complex engineering systems.

Towards Traditional Simulation Models of Context Using Process Mining

Context (sensor) systems are hard to model: they require constant updating and insightful approaches, especially considering the increasing data volume, variety, and generation rate of contemporary networking paradigms, like the Internet of Things. In this paper, we argue that intelligent process models can be mined to look at the actual system activity from alternative context perspectives, i.e., Perspectives observable from the sensor attributes themselves. We explain how the close relationship between the models derived using Process Mining, and Event-Driven Simulation can be exploited to help not only better understand what is happening in such systems but also provide alternative models for the intelligent solutions they support, such as context inference. We demonstrate this using a real-world example and discuss the feasibility of extending these alternative process models to be viewed as simulation. We envision automated steps that would result in traditional simulation models of context using Process Mining.