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Context-aware recommender systems (CARS) exploit multiple contexts to improve user experience in embracing new information and services. Tensor factorization (TF), a type of latent factor model, has achieved remarkable performance in CARS. TF learns latent representations of contexts by decomposing an observed rating tensor and combines the latent representations as a vector form to represent contextual...
Fog computing is a promising paradigm in terms of extending cloud computing to an edge network. In a broad sense, fog computing in Internet-of-Vehicles(IoV) provides low-latency services since fog nodes are closely located with moving cars and are locally distributed. In this paper, we propose a fog computing architecture based on a publish/subscribe model. After that, we describe a traffic congestion...
The amount of RDF data being published on the Web is increasing at a massive rate. MapReduce-based distributed frameworks have become the general trend in processing SPARQL queries against the RDF data. Currently, query processing systems that use MapReduce have not been able to keep up with increases in semantic annotated data, resulting in non-interactive SPARQL query processing. The principal reason...
In this paper, we address the problem of processing semantic data streams. The semantic annotation of sensor data is one of the solutions to the heterogeneous nature of sensor data streams. Existing systems for publishing semantic streaming data collect stream data and transmit the semantic streaming data to query engines regardless of the queries registered in the query engines. As a large number...
With the increasing number of mobile devices, there have been many researches on searching and managing a large volume of mobile data. Most of the mobile platforms today provide users with keyword-based full text search (FTS) in order to search for mobile data. Recently, voice search interfaces have been deployed. These search methods, however, query only the keywords given as an input to local databases...
In this paper, we propose a method for processing spatiotemporal queries on semantic data streams generated from diverse sensors. On the Internet of Things (IoT) environment, the number of mobile sensors greatly increases and their locations are becoming more important. IoT services may not be fully supported when only considering the temporal feature of streaming data. Accordingly, stream processing...
Most of the mobile platforms provide a keyword based full text search (FTS) for users to find what they want. However, FTS has difficulties in dealing with the cases where a user cannot remember the exact keywords about target data or the number of search results is too many. To overcome these limitations of FTS, we propose a semantically enhanced method of searching for data on mobile devices along...
This paper proposes a mobile search engine for smart devices, which effectively augments the result of local semantic search with useful Web information according to the intent and context of a mobile user. To support an intuitive query, we employ the conventional natural language user interface, which supports voice recognition. Through the prototype implementation of the proposed search engine,...
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