No abstract

Indoor navigation is an important research topic nowadays. The complexity of larger buildings, supermarkets, museums, etc. makes it necessary to use applications which can facilitate the orientation. While for outdoor navigation already exist tried and tested solutions, but few reliable ones are available for indoor navigation. In this paper we investigate the possible technologies for indoor navigation. Then, we present a general, cost effective system as a solution. This system uses the advantages of semantic web to store data and to compute the possible paths as well. Furthermore it uses Augmented Reality techniques and map view to provide interaction with the users. We made a prototype based on client-server architecture. The server runs in a cloud and provides the appropriate data to the client, which can be a smartphone or a tablet with Android operation system.

No abstract

Augmented Reality applications are more and more widely used nowadays. With help of it the real physical environment could be extended by computer generated virtual elements. These virtual elements can be for example important context-aware information. With Semantic Web it is possible among others to handle data which come from heterogeneous sources. As a result we have the opportunity to combine Semantic Web and Augmented Reality utilizing the benefits of combination of these technologies. The obtained system may be suitable for daily use with wide range of applications in field of tourism, entertainment, navigation, ambient assisted living, etc. The purpose of my research is to develop a prototype of general framework which satisfies the above criteria.

Social networks like Twitter and Facebook have gained a significant popularity with people from all parts of the society in the past decade, providing a new kind of data source for novel social-aware applications. A great majority of the users are online all the time, posting real-time information on various topics including unpredicted events. An accident or a natural disaster is often posted on social networks hours before appearing in traditional news. In this paper, we outline a framework for real-time event detection in Twitter data. In contrast to prior works where the absolute or relative changes in the frequencies of some predefined keywords are taken into account, we introduce a lifecycle for each keyword to be observed, expressing their average behavior (e.g. average frequency changes) over time. As a motivation, we show that some keywords exhibit periodic behavior that can be handled by our model. The proposed lifecycle model enables us to define novel temporal features used by our framework in real-time event detection.