Search

Technologies of the Internet of Things (IoT) and of smart spaces support creating smart museums based on digitized infrastructures and information systems already deployed in modern museums. Cultural heritage knowledge in such a museum is used by interested visitors as well as by personnel. This work continues the authors' research on the smart museum concept and its case study of everyday life history in the History Museum of Petrozavodsk State University (PetrSU). The authors develop an ontological model for the needs of studying the everyday life history. The ontology supports integrating descriptions of collected exhibits into a semantic network, where the links reflect meaningful relations between exhibits and other historical objects. They apply the wiki technology within the smart spaces-based architecture of a smart museum. The wiki implements an ontology-enabled system that experts use to extract and represent knowledge hidden in the museum collection. The authors discuss possible semantic algorithms for data mining in the museum semantic network.

Nowadays many museums are being transformed to some “smart” variants: a cultural space in a digitally-equipped environment is formed where cultural heritage knowledge can be easily and collaboratively usable and creatable by interested visitors as well as by professionals. By using such innovative technologies as Internet of Things (IoT) and Smart Spaces it is possible to shorten the distance between museum exhibits and collected historical information on one side and consumers and providers of this information on the other side. In this paper, we study the smart museum concept for development of software that realizes connection between the involved actors and shared cultural heritage knowledge. Our research and development are focused on designing a semantic layer in the form of a software infrastructure. This infrastructure is a middleware on top of which such advanced museum information services can be constructed as personal visit planning, virtual exhibition preparation, and collaborative enrichment the museum collection. We introduce a suit of smart space-based solutions to create this type of software infrastructures. The generic solutions are theoretically analyzed based on the case study of the History Museum of Petrozavodsk State University (PetrSU).

The existing wide spectrum of embedded, multimedia, and mobile equipment provides an effective base for making museums smart or intelligent. By using the Internet of Things technology and the smart spaces paradigm the distance can be shortened between exhibits and their descriptive information on one side and consumers and providers of this information on the other side. In this paper, the authors discuss a smart museum concept and present a semantic approach to design of advanced information services for smart museums. The central point is introduction of the semantic layer to create a semantic network. The proposed approach reduces the semantic layer development to the following components: software infrastructure, semantic layer ontology, and mobile user access. For these components the authors provide design solutions, which are analyzed in respect to particular services for the History Museum of Petrozavodsk State University. The semantic approach can be applied to development of many museum services as well as in various digital environments of museums and cultural heritage areas.

The amount of digital data about regional history is increasing rapidly. These data might be effectively used to promote regional historical and cultural tourism as well as to design special recommendation services for trip developers and individual tourists who are interested in regional history. We propose the technology for the creation of such a recommendation service on the basis of smart space approach. This approach employs multi-agent, event-oriented methods of service design with central information hub. Each of the agents exchanges information by means of subscribing and publication actions. An important feature of the smart space technology is the use of ontological formats to keep semantically described information and to infer new knowledge from it. This technology is scrutinized employing data on the history of Latgale.

Nowadays the amount of “smart” services in e-Tourism grows rapidly. This is due to widespread using mobile devices with new input methods and large amount of digital data. Together with that, the smart services require complex methods and high cost of their creation. Thereby we have relevant problem of estimating smart service's efficiency. This paper presents evaluation of Cultural trip planning service based on defined smart attributes. For each of used attributes the execution scenario, ordinary service for comparison and used estimates are defined. Each estimate is calculated with taking into account the user experience. The evaluation results allow to compare Cultural trip planning service with some ordinary services.

e-Tourism covers a wide niche of the digital services market. The existing services, although being presented in the large amount in today's Internet, do not achieve high intelligence level. The user still needs to perform a lot of operations manually: to solve a given problem she/he finds and accesses appropriate Internet services or uses mobile applications. A lot of information fragments are linked by the user her/himself, in the mind. In this paper, we discuss the problem of smart service development for the e-Tourism domain. The considered use-case scenario comes from cultural heritage tourism, which is an area of growing application interest We identify smart service attributes that allow a service to achieve a new level of the intelligence. The user becomes more focused on the substantial aspects of solving the problem. We contribute architectural solutions to service design. For the given use-case we show how the service-oriented system can be implemented in the form of a smart space deployed on user-surrounding devices based on the usage of external Internet services and multiple data sources.