Sungkwang Eom

A Pub/Sub-Based Fog Computing Architecture for Internet-of-Vehicles

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 control scenario using a smart traffic light system which operates on top of the proposed architecture. Furthermore, we propose an upper-level domain ontology in order to enhance the expressivity of knowledge and describe a variety of semantic properties which interlink spatial information in IoV. Finally, we present an active rule where supports the exchange of event-driven messages between publishing and subscribing fog nodes.

Spatiotemporal query processing for semantic data stream

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 should be performed with consideration into both temporal and spatial factors. However, existing researches have a limitation of processing spatial queries since they focus on the temporal processing of streaming data. To support spatiotemporal query processing on semantic data streams, we propose a query language, which integrates temporal and geospatial properties. Specifically, we construct a spatiotemporal index to process the proposed spatiotemporal query language efficiently. The experimental results with a prototype implementation show that the proposed method processes spatiotemporal queries in an acceptable time.

Keyword Based Semantic Search for Mobile Data

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 with mobile ontology. Experimental results of the proposed method show that our method provides accurate search results and is suitable for a mobile environment.

Q-ASSF: Query-adaptive semantic stream filtering

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 of sensing devices become available, there is an increasing amount of the stream data, resulting in the performance degradation of a query engine. To remedy this problem, we propose a query-adaptive method of filtering semantic streams. The proposed method filters out sensors and semantic streaming data, which are not related with queries registered in a semantic stream query engine. The approach fairly reduces the data size necessary to answer semantic stream queries and consequently improves the performance of the query processing. To demonstrate the efficiency of our proposal, we present extensive experimental performance evaluations under a variety of sensor streams and query types. Experimental results show that the proposed method dramatically improves the performance of query processing compared to a non-filtering approach.

Semantically enhanced keyword search for smartphones

To apply semantic search to smartphones, we propose an efficient semantic search method based on a lightweight mobile ontology. Through a prototype implementation of a semantic search engine on an android smartphone, experimental results show that the proposed method provides more accurate search results and a better user experience compared to the conventional method.

Augmenting Mobile Search Engine with Semantic Web and Context Awareness

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, we find that it provides more meaningful search results semantically and contextually, compared with the conventional keyword-based search engines of mobile devices.

Keyword-based mobile semantic search using mobile ontology

A large volume of mobile data is being generated and shared among mobile devices such as smartphones. Most of the mobile platforms provide a user with a keyword-based full text search (FTS) in order to search for mobile data. However, FTS only returns the data corresponding to the keywords given by users as results without considering a user’s query intention. To overcome this limitation, we propose a semantically enhanced keyword-based search method. Although there are various semantic search techniques, it is hard to apply existing methods to mobile devices just as they are. This is caused by the characteristics of mobile devices such as isolated database structures and limited computing resources. To enable semantic search on mobile devices, we also propose a lightweight mobile ontology. Experimental results from the prototype implementation of the proposed method show that the proposed method provides a better user experience than the conventional FTS and returns accurate search results in an acceptable response time.

Reliable TF-based recommender system for capturing complex correlations among contexts

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 influence on users and items. However, due to the limitation of the contextual expression power, they have difficulties in effectively capturing complex correlations among multiple contexts, and also the meaning of each context is diluted. To address the issue, we propose a reliable TF-based recommender system based on a proposed context tensor (CT-CARS), which incorporates a variety of correlations among contexts. CT-CARS contains a novel recommendation rating function and a learning algorithm. Specifically, the proposed context tensor elaborately captures the influences of both individual contexts and context combinations. Moreover, we introduce a novel parameter initialization based on past-learned results to improve the reliability of recommendations. The overall time complexity of our parameter learning algorithm grows linearly as dataset size increases. Experiments on six real-world datasets including two large-scaled datasets show that CT-CARS outperforms the existing state-of-the-art models in terms of both accuracy and reliability.

Job-Optimized Map-Side Join Processing Using MapReduce and HBase with Abstract RDF Data

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 is that intermediate query results from join operations in a MapReduce framework are so massive that network bandwidth and hard disk drive I/O speeds may not keep pace with the processing speed. In this paper, we present an efficient SPARQL processing system that uses MapReduce and HBase. The system runs a job optimized query plan using our proposed abstract RDF data to decrease the amount of intermediate data, thus resulting in faster query processing performance. We also present an efficient algorithm of using Map-side joins while also using the abstract RDF data to filter out unneeded RDF data. Experimental results show that the proposed approach demonstrates better performance when processing queries with a large set of inputs than those found in previous works.

Enriching mobile semantic search with web services

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 mobile devices. Therefore, it is quite difficult to figure out and to provide what a user really wants. To overcome this limitation, we propose a semantic search method for mobile platforms. The proposed method augments the results of semantic search on local databases with their related useful Web information according to the intention and context information of a user. Although there are various semantic search techniques, it is hard to apply the existing methods to mobile devices due to the characteristics of mobile devices such as isolated database structures and limited computing resources. To enable semantic search on mobile devices, we also propose a lightweight mobile ontology. The proposed mobile ontology is also aligned with related Web information to enrich search results. Experimental results from prototype implementation of the proposed method verify that our approach provides more accurate results than the conventional FTS does. In addition, the proposed method shows an acceptable amount of response time and battery consumption.