2,932 research outputs found
A Process Framework for Semantics-aware Tourism Information Systems
The growing sophistication of user requirements in tourism due to the advent of new technologies such as the Semantic Web and mobile computing has imposed new possibilities for improved intelligence in Tourism Information Systems (TIS). Traditional software engineering and web engineering approaches cannot suffice, hence the need to find new product development approaches that would sufficiently enable the next generation of TIS. The next generation of TIS are expected among other things to: enable
semantics-based information processing, exhibit natural language capabilities, facilitate inter-organization exchange of information in a seamless way, and
evolve proactively in tandem with dynamic user requirements. In this paper, a product development approach called Product Line for Ontology-based Semantics-Aware Tourism Information Systems (PLOSATIS) which is a novel
hybridization of software product line engineering, and Semantic Web engineering concepts is proposed. PLOSATIS is presented as potentially effective, predictable and amenable to software process improvement initiatives
Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research
This paper reviews the published articles on eTourism in the past 20 years. Using a wide variety of sources, mainly in the tourism literature, this paper comprehensively reviews and analyzes prior studies in the context of Internet applications to Tourism. The paper also projects future developments in eTourism and demonstrates critical changes that will influence the tourism industry structure. A major contribution of this paper is its overview of the research and development efforts that have been endeavoured in the field, and the challenges that tourism researchers are, and will be, facing
Customer empowerment in tourism through Consumer Centric Marketing (CCM)
We explain Consumer Centric Marketing (CCM) and adopt this new technique to travel context. Benefits and disadvantages of the CCM are outlined together with warnings of typical caveats
Value: CCM will be expected as the norm in the travel industry by customers of the future, yet it is only the innovators who gain real tangible benefits from this development. We outline current and future opportunities to truly place your customer at the centre and provide the organisation with some real savings/gains through the use of ICT
Practical Implications: We offer tangible examples for travel industry on how to utilise this new technology. The technology is already available and the ICT companies are keen to establish ways how consumers can utilise it, i.e. by providing ‘content’ for these ICT products the travel industry can fully gain from these developments and also enhance consumers’ gains from it. This can result in more satisfied customers for the travel (as well as ICT) companies thus truly adopting the basic philosophy of marketin
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Generic system architecture for context-aware, distributed recommendation
In the existing literature on recommender systems, it is difficult to find an architecture for large-scale implementation. Often, the architectures proposed in papers are specific to an algorithm implementation or a domain. Thus, there is no clear architectural starting point for a new recommender system. This paper presents an architecture blueprint for a context-aware recommender system that provides scalability, availability, and security for its users. The architecture also contributes the dynamic ability to switch between single-device (offline), client-server (online), and fully distributed implementations. From this blueprint, a new recommender system could be built with minimal design and implementation effort regardless of the application.Electrical and Computer Engineerin
Principal Patterns on Graphs: Discovering Coherent Structures in Datasets
Graphs are now ubiquitous in almost every field of research. Recently, new
research areas devoted to the analysis of graphs and data associated to their
vertices have emerged. Focusing on dynamical processes, we propose a fast,
robust and scalable framework for retrieving and analyzing recurring patterns
of activity on graphs. Our method relies on a novel type of multilayer graph
that encodes the spreading or propagation of events between successive time
steps. We demonstrate the versatility of our method by applying it on three
different real-world examples. Firstly, we study how rumor spreads on a social
network. Secondly, we reveal congestion patterns of pedestrians in a train
station. Finally, we show how patterns of audio playlists can be used in a
recommender system. In each example, relevant information previously hidden in
the data is extracted in a very efficient manner, emphasizing the scalability
of our method. With a parallel implementation scaling linearly with the size of
the dataset, our framework easily handles millions of nodes on a single
commodity server
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