The XCREAM Framework and Collaboration Validity Tests

Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication

Kim

Chang

2014

Self Cite

With the rapid growth of network-enabled environment, the range of intelligent application services is widely expanding with continuous attempts to fully utilize highly accurate personal or environmental information. In parallel, a variety of professional knowledge data have been built up and provided to the public in the form of semantically linked ontology files. This phenomenon encourages us to focus on developing more flexible data mixing bowl, where the individual data are used to support their inherent functions and associated with different data to trigger inter-related complex services. This research has been evolved from the existing collaboration framework for a context-aware environment by integrating ontology gateway, the XOnt agent, as its ontologyenabled context-aware knowledge option. The ontology-enabled semantic reasoning scheme includes the XOntology combined with the Context-Aware Inference (CAI) model of the existing collaboration framework. Based on the properties of the ontology and behavioral activities of individual instances of the classes, the XOnt agent semantically evaluates current situation and controls the proposed ontology-enabled collaboration framework.

Section: Introductionmentioning

confidence: 99%

Interoperable context sharing in an ontology-enabled collaboration framework

Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication

Kim

Chang

2014

Self Cite

“…The framework must be a robust and reliable infrastructure and be able to handle a huge amount of tag and sensor data and propagate them to the appropriate services according to predefined scenarios. These requirements led us to develop a scenario-based collaborative framework, the XCREAM (XLogic Collaborative RFID/USN-Enabled Adaptive Middleware) framework, which seamlessly integrates numerous heterogeneous application services and plays an organizing role in today's pervasive computing environment [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Inference (CAI) Model on Smart Computing Environment

2012 International Conference on Information Science and Applications

Byun

Yun

et al. 2012

Self Cite

With the proliferation of smart computing equipment, the range of providing highly intelligent services is widely expanding in an attempt to include the present condition of each individual data source. Furthermore, broadly adopted RFID (Radio Frequency Identification) technology has led to the widespread application of automatic identification in numerous pervasive computing services including logistics, emergency control, and medical services. Another remarkable phenomenon is the explosively growing number of smartphone users, which allows them to stay connected to the Internet. The environment created by these factors, increases the need to make a decision out of the current context which can be inferred from various sensors and mobile devices. In response to the growing demand for the need to make a decision in the current context, the new XOnt Agent has been integrated into our collaboration middleware framework, the XCREAM (XLogic Collaborative RFID/USN-Enabled Adaptive Middleware). All the collected information should be examined too see if they meet any condition that triggers correspondent actions of a specific rule through reasoning process of a rule engine. We also suggest the context-aware inference (CAI) model to effectively support the reasoning and handling processes.

“…The existing framework was first implemented as a scenario-based collaborative framework where numerous heterogeneous application services collaborate with each other according to the predefined service scenarios depending on specific event(s) from sensor network environments [4]. Particularly, rapid progress in wireless telecommunication and RFID/USN sensor technology encouraged us to apply this advanced smart computing power to the existing IT solutions.…”

Section: Introductionmentioning

confidence: 99%

“…In order to provide end-users in a smart city environment with the highly sophisticated services of interconnected International Journal of Distributed Sensor Networks 3 applications, we need to consider an orchestrating service framework performing comprehensive analysis about the acquired contextual information. This requirement results in integrating ontology-enabled semantic reasoning scheme within the context-aware collaboration manager which adopts many features from the initial scenario-based collaborative framework [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Semantic Reasoning with Contextual Ontologies on Sensor Cloud Environment

International Journal of Distributed Sensor Networks

Kim

Chang

2014

Self Cite

This research first focused on processing enormous number of sensor events from a variety of city-wide sensor networks. As a solution, the well-known big data handling scheme, Hadoop cluster framework, drew, unquestionably, our attention. The acquired sensor events are to be used to immediately detect a certain abnormal situation within the framework. Accordingly, we integrated our existing context-aware collaboration framework with Hadoop cluster framework by interfacing data collection and contextaware reasoning parts of the existing framework with the Hadoop cluster framework. This approach enabled us to effectively process massive sensor events and semantically analyze the big data within the cluster environment. The proposed smart city sensor cloud framework provides ontology-enabled semantic reasoning scheme with the XOntology in combination with the Context-Aware Inference (CAI) model. By applying the ontology technology, the proposed framework enhances the availability and interoperability of the contextual information across many cooperating parties according to semantic reasoning results. Further, this framework is flexible enough to integrate any heterogeneous platforms including many existing IT solutions as well as mobile platforms. In addition, this approach presents the direction of progressive migration of many existing sensor network solutions into big data handling sensor cloud framework.