Kyungeun Park

The Prototype of the Massive Events Streams Service Architecture and its Application

This paper describes the prototype of the Massive Events Streams Service Architecture (MESSA), which is used as a framework of various RFID-enabled application systems. The MESSA captures events from a number of RFID readers, refines the events, processes the continuous queries, and delivers the event query results to the applications systems. In addition, the MESSA conforms to the ALE 1.0 interfaces from the EPCglobal in order to make the MESSA widely applicable to general events handling applications. The underlying data manipulation schemes of the MESSA support the continuous queries to manage the events streams from external data sources during specified periods of time. The main contribution of this research lies in its seamless integration of the event handling processes within a unified framework, MESSA, and realization of air cargo handling business logics into the RFID-enabled air cargo management system based on the MESSA.

The XCREAM Framework and Collaboration Validity Tests

The purpose of the XCREAM (XLogic Collaborative RFID/USN-Enabled Adaptive Middleware) is to enable collaboration among many RFID/USN-enabled appplications by providing them with flexible interface to the XCREAM through a web-based service scheme, called the Enterprise Manager, and XML infrastructure language, the XLogic script language. Rapidly growing demand for collaboration among numerous and heterogeneous business applications draws attention to this kind of framework, especially in the ubiquitous computing environment. The XCREAM framework gathers massive events from a variety of event sources and distributes them to the appropriate service parties depending on the predefined business scenarios. The scenarios are written in the XLogic script language and registered to the XCREAM framework. The paper includes simulation result which shows excellence in performance and validity of collaboration suppport of the XCREAM. Reviewing the test results, XCREAM works well, especially in the collaborative environment. This approach makes it possible to integrate many heterogeneous services with various data sources and to present a collaborative service framework.

Design and Implementation of Scenario-Based Collaborative Framework: XCREAM

The XCREAM (XLogic Collaborative RFID/USN-Enabled Adaptive Middleware), as a mediator between smart physical objects and collaborative cyber services, gathers massive events from a variety of event origins and distributes them to the appropriate service parties depending on the predefined business scenarios. Demand for collaboration among numerous business applications draws attention to this kind of middleware platform especially in the highly networked environment. The XCREAM platform, the scenario-based collaborative framework, integrates various kinds of RFID/USN enabled application services and provides more advanced services than the individual services. The goal of this research is to construct well-organized systematic RFID/USN prototyping framework, to which physical RFID readers or sensor devices are directly connected. Additionally, tag data and sensor signals from the devices are propagated to the proper services registered to the framework. The study encourages us to develop the XCREAM platform and build prototyping environment for presenting further comprehensive services. This approach makes it possible to integrate many heterogeneous services and to present collaborative service framework.

Semantic Reasoning with Contextual Ontologies on Sensor Cloud Environment

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 context-aware 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.

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

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.

Mobile Real-Time Tracking System Based on the XCREAM (XLogic Collaborative RFID/USN-Enabled Adaptive Middleware)

The main objective of this paper is to provide people who use smartphones with a convenient way of getting useful information by making their smartphones communicate with the XCREAM (XLogic Collaborative RFID/USN-Enabled Adaptive Middleware). We have developed the XCREAM, which is a smart mediator between many types of sensor devices and applications. It gathers a variety of raw data from many types of USN devices, such as temperature or humidity sensors and RFID readers. It also distributes the collected event data to the appropriate applications according to the pre-registered scenarios. In addition, we have been extending the external interface of the XCREAM, where the XCREAM receives data from smartphones as well as ordinary USN devices. Furthermore, the extension enables the XCREAM to deliver the sensor data to the corresponding smartphone(s) in order to make applications for the smartphone(s) be more flexible and interactive. In this paper, we present a simple but feasible scenario that helps a package receiver simply track the current location of its delivery truck for the package.

XCREAM: collaborative middleware framework for RFID/USN-enabled applications

The purpose of the XCREAM (XLogic Collaborative RFID/USN-Enabled Adaptive Middleware) is to enable collaboration among many RFID/USN-enabled applications by providing them with flexible interface to the XCREAM through a web-based service scheme, called the Enterprise Manager, and XML infrastructure language, the XLogic script language. The XCREAM framework gathers massive events from a variety of event sources and distributes them to the appropriate service parties depending on the predefined business scenarios. The scenarios are written in the XLogic script language and registered to the XCREAM framework. The paper includes simulation result which includes the performance and collaboration validity of the XCREAM. This approach makes it possible to integrate many heterogeneous services and to present a collaborative service framework.

Interoperable context sharing in an ontology-enabled collaboration framework

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 ontology-enabled 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.