Yoo-Mi Park

An Integrated Data Model and a Query Language for Content-Based Retrieval of Video

As video data have been used in multimedia information systems, the need for the database support that provides an efficient way for users to retrieve video data is growing. In this paper, we propose a video data model that integrates feature based model and annotation based model for the purpose of improving content-based retrieval of video data. The proposed video data can act as a generic video data model for multimedia applications, and support free annotations, image features, and structure information of video data within the same framework. The proposed video query language provides semantics to access arbitrary segments in the video data. It can formalize various kinds of queries based on the video contents.

Employing collective intelligence for user driven service creation

With advances in computing technologies and active user participation through smart devices such as the iPhone and Android, user needs are becoming varied and complex. It is quite natural, then, that a single Web service may not be sufficient to fully satisfy the diverse goals of users in their daily lives. A set of cohesively connected Web services/mashups may be able to deal with these goals. An increasing number of open APIs can facilitate various types of service compositions with users as the service creators. Recently, Internet, telecommunications, and third-party providers have opened their services to the public in the form of open APIs, a trend following the Web 2.0 paradigm. However, most service creation environments do not have sufficient knowledge (particularly, available services and their functionality) to support service creation by users. The problem of knowledge scarcity is that users may have difficulty in finding relevant open APIs for a given situation, finally resulting in rather straightforward types of service. In this article we present two kinds of collective intelligence for user-driven service creation: the users own experiences in service composition, and activity knowledge from the web. These collective intelligence types will aid in creating enduser service compositions by enforcing knowledge support in terms of user experiences and activity-aware functional semantics, and will finally accelerate the development of various kinds of converged applications. Using the beneficial roles of collective intelligence as key enablers of future service creation environments, this article also shows a new potential for user-driven composite services within the next few years.

Automatic Tagging of Functional-Goals for Goal-Driven Semantic Service Discovery

With the growing number of Web services available on the Web, finding services that match users query becomes more difficult. To deal with the problem, numerous approaches employed semantic techniques in terms of annotation (or tagging) and service discovery algorithms. However, the problem of semantic service discovery based on the identification of fine-grained goals of users and services is still challenging due to the lack of semantic information in Web services. To enable the goal-driven semantic service discovery, we propose an automatic functional-goals tagging approach which employs a set of natural language processing (NLP) procedures based on the descriptions of Web services. In addition, to check the effectiveness of the functional-goals tagged, we designed a goal-driven semantic service discovery algorithm and compared it with other approaches: keyword-based, ontology-based, and topic-based service discovery. As results, our proposed goal-driven semantic service discovery achieved 75.7% of precision and 59.6% of recall (finally, F1=62%) that outperform other discovery approaches.

An Efficient Context Model for Fast Responsiveness of Context-Aware Services in Mobile Networks

In order to provide context-aware services, context modeling is one of the most important tasks. Ontology has been widely accepted for the context modeling but it brings some overheads in the process of reasoning large-scale and dynamic information. In mobile networks, especially, ontology should be verified to be applicable for Context-aware Services since real-time responsiveness for large scale moving users is highly required to be guaranteed. This paper proposes an efficient context model to overcome the limitation of ontology. For the purpose, we design a conceptual context model and compare the processing time of the context-aware mobile service system implying the three different context modeling approaches - ontology, database, and proposed hybrid modeling. As the result of performance evaluations, the proposed hybrid model shows faster processing time among them and lesser affected by the major metrics of mobile services - changeability and number of users - than the two existing models. Based on the experimental results, we expect that the proposed hybrid model is very useful and efficient for fast responsiveness to support Context-aware Services for large scale users in mobile networks.

Presence-based call reservation service using open API on the Web-service architecture in broadband converged network

Open API in telecommunication is a service control technologies and is a set of abstract standard operations which can provide capabilities to access telecom network for the 3rd party service provider in a secure manner. This paper describes presence-based call reservation service which is a case study of converged service with open API on the Web-service architecture in broadband converged network. The presence-based call reservation service could provide the enterprise with combining capabilities of accessing network resources and manipulating enterprise data resources. We also propose a simple control mechanism for realization of this service with open API on the Web-service architecture

Semantic service discovery and matching for semi-automatic service mashup

A service mashup goes through several processes, which it takes much time and efforts for developers to mashup of many heterogeneous web services. To mitigate the complexity of a service mashup and automate the mashup process, the present paper proposes semantic service discovery and matching technologies. The semantic service discovery technology is capable of finding out more appropriate and ranked services with a given query, and the semantic service matching technology enables searching for compatible and interoperable services automatically across a number of heterogeneous web services. The semantic service discovery and matching technologies are based on the service ontology and service metadata that play important roles in relieving the semantic gap between a users natural query and the technical service description. To verify the usability and effectiveness of the proposed technologies on this environment, experiments and simple use cases are shown. The results indicate that the proposed technologies help developers create new mashup applications more effectively and conveniently.

Ontology based keyword dictionary server for semantic service discovery

Web service discovery relies on matching between the description of service functionality and the user requirement. To minimize the gap between service description and user requirement, it is effective to expand query keywords with domain-specific ontologies and make use of the extended keywords in service discovery. In this paper, we propose the keyword dictionary server that supports ontology modeling, keyword expansion, and similarity calculation between ontology terms with reasoning. It expands the synonyms and hyponyms of query keywords and can help to discover the suitable services to meet the users query intention. Also we present the performance measurement result of keyword dictionary server to verify deployment performance of server that provides REST interfaces. Our keyword dictionary server can support semantic service discovery by matching between the user query and service description through the keywords expansion.

Semantic web service processes enabling goal-driven application creation

We propose a novel semantic web service process which consists of service annotation, discovery, and composition for a goal-driven computing. The proposed web service process is the key enabler of our goal-driven application creation environment that helps software developers to make new application semi-automatically by accomplishing their programming goals. Especially, in the goal-driven computing, we employ goal ontology and goal pattern library to bridge the gap between concrete services on the Web and users declarative goals in the real world. The proposed semantic service process can be performed under the existing web services environment without new service models proposed for semantic and goal-driven computing by previous works, such as OWL-S and WSMO. Ultimately, we expect that the proposed process can promote the growth of converged applications across the domains and service providers.

Semi-automatic Semantic Service Annotation for SOAP and REST Web Services

Until now, most service annotation has been added the semantics to WSDL, that is a description of SOAP based web services. But REST services have been widely used because of simplicity and usability. In order to discover the most suitable services among various web services and compose them, the integrated service annotation of SOAP and REST web services is necessary. In this paper, we present semantic service framework which is designed for user’s goal based service discovery and composition. And we present the semi-automatic service annotation for SOAP and REST web services. The proposed annotation approach deals with functional semantics, non-functional semantics, and data semantics from web service description and web service portal. Based on this service annotation, it is useful to discover and compose services that satisfy the user’s goal.

Higher order knowledge management platform for the personalized services in next generation networks

We propose higher order knowledge management platform (HKMP) that can support to provide the personalized services for the end users by means of evolving lower order network knowledge. HKMP helps 3rd party service providers to create personalized services considering users context through the open interfaces for providing higher order knowledge. In this paper, we classify lower order network knowledge and build ontology to represent them including user profiles and users preference. Furthermore, we propose the efficient algorithms of leaner and recommender to evolve higher order knowledge in HKMP. Finally, we simulated to evaluate the precision of proposed algorithms with data sets of UCI depository. As the result of evaluation, we expect that the HKMP would be an essential component for providing personalized services in next generation networks.