Incheon Paik

A Scalable Architecture for Automatic Service Composition

This paper addresses automatic service composition (ASC) as a means to create new value-added services dynamically and automatically from existing services in service-oriented architecture and cloud computing environments. Manually composing services for relatively static applications has been successful, but automatically composing services requires advances in the semantics of processes and an architectural framework that can capture all stages of an applications lifecycle. A framework for ASC involves four stages: planning an execution workflow, discovering services from a registry, selecting the best candidate services, and executing the selected services. This four-stage architecture is the most widely used to describe ASC, but it is still abstract and incomplete in terms of scalable goal composition, property transformation for seamless automatic composition, and integration architecture. We present a workflow orchestration to enable nested multilevel composition for achieving scalability. We add to the four-stage composition framework a transformation method for abstract composition properties. A general model for the composition architecture is described herein and a complete and detailed composition framework is introduced using our model. Our ASC architecture achieves improved seamlessness and scalability in the integrated framework. The ASC architecture is analyzed and evaluated to show its efficacy.

Improving efficiency of service discovery using Linked data-based service publication

It is considered that Web services have had a tremendous impact on the web as a potential silver bullet for supporting a distributed service-based economy on a global scale. However, despite the outstanding progress, their uptake on a web scale has been significantly less than initially anticipated due to higher usage thresholds. For instance, it is a hard task for service provider to seek appropriate semantic information such as OWL ontologies for service annotation in the service publication stage due to the fact that nowadays we are suffering from serious lack of available and ubiquitous ontologies for global consensus. Also it is not realistic for query users who do not possess much semantic knowledge to specify their requests with associated semantic information in the service discovery stage. In this paper, we propose a methodology to build a global social service network based on Link data principles for reducing the using thresholds. First, we propose Linked social service which is published on the open web by following Linked data principles with social link, and then we suggest a new platform for constructing a global social service network based on Linked social service. Then, an approach is proposed to enable exploitation of global social service network, providing Linked Social Service as a Service. Finally, experimental results show that the Linked social service can reduce the using threshold by enabling exploring service to service based on the global social service network.

Automatic Web Services Composition Using Combining HTN and CSP

Semantic Web services and their composition portend a future where the Web can behave more intelligently for solving general real-life problems. To solve such real-life problems requires a set of appropriate services to be composed via planning, scheduled, and then executed: those are logical services composition, physical composition, and execution. Web service composition is the most difficult aspect and is our focus. In this paper, we propose a framework combining logical composition (using HTN) and physical composition (using CSP) for automatic services composition. The framework covers the entire procedures to deal with a users request, domain analysis of the request, task flow decision and CSP creation by the planner, and solving the CSP by a distributed CSP solver for intelligent Web service composition. Implementation of the framework and an evaluation of it in terms of various problem solving types are then explained. We also discuss the necessity and synergy effect of the combined HTN and CSP for a framework that automates Web service composition and execution.

Web-Service Clustering with a Hybrid of Ontology Learning and Information-Retrieval-Based Term Similarity

Organizing Web services into functionally similar clusters, is an efficient approach to discovering Web services efficiently. An important aspect of the clustering process is calculating the semantic similarity of Web services. Most current clustering approaches are based on similarity-distance measurement, including keyword, ontology and information-retrieval-based methods. Problems with these approaches include a shortage of high quality ontologies and a loss of semantic information. In addition, there has been little fine-grained improvement in existing approaches to service clustering. In this paper, we present a new approach to grouping Web services into functionally similar clusters by mining Web service documents and generating an ontology via hidden semantic patterns present within the complex terms used in service features to measure similarity. If calculating the similarity using the generated ontology fails, the similarity is calculated by using an information-retrieval-based term-similarity method that adopts term-similarity measuring techniques used by thesaurus and search engines. Another important aspect of high performance in clustering is identifying the most suitable cluster center. To improve the utility of clusters, we propose an approach to identifying the cluster center that combines service similarity with the term frequency-inverse document frequency values of service names. Experimental results show that our clustering approach performs better than existing approaches.

Web Service Clustering using a Hybrid Term-Similarity Measure with Ontology Learning

Clustering Web services into functionally similar clusters is a very efficient approach to service discovery. A principal issue for clustering is computing the semantic similarity between services. Current approaches use similarity-distance measurement methods such as keyword, information-retrieval or ontology based methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information and a shortage of high-quality ontologies. In this paper, the authors present a method that first adopts ontology learning to generate ontologies via the hidden semantic patterns existing within complex terms. If calculating similarity using the generated ontology fails, it then applies an information-retrieval-based method. Another important issue is identifying the most suitable cluster representative. This paper proposes an approach to identifying the cluster center by combining service similarity with term frequency—inverse document frequency values of service names. Experimental results show that our term-similarity approach outperforms comparable existing approaches. They also demonstrate the positive effects of our cluster-center identification approach.

A Framework for Intelligent Web Services: Combined HTN and CSP Approach

Solving general real-life problems requires a set of appropriate services to be composed via planning, scheduled, and then executed. Web service composition is the most difficult aspect and is our focus. In this paper, we describe a new framework for intelligent semantic Web services that supports the planning and scheduling aspects by a combined HTN planner and CSP. The framework covers all of the procedures needed to deal with a users request, including domain analysis of the request, task flow decisions and CSP creation by the planner, and solving the CSP by a distributed CSP solver

Software Component Architecture for an Information Infrastructure to Support Innovative Product Design in a Supply Chain

Existing methodologies to increase the efficiency of design of supply-chain management (SCM) processes consider cost, quality, function, and technology attributes of products separately. However, there is no integrated information infrastructure to consider these attributes together. We have devised new table structures to combine the 4 attributes and their relationships organically. We introduced simple ontology concepts for this information infrastructure, considering the supply-chain tree and the structure of attribute–relationship tables, with some restrictions from a general ontology for the semantic Web. We created basic database schemas using the designed ontology for the information infrastructure for SCM collaboration. We also developed a software component architecture that provides interface services to clients and other business logic in future semantic Web services based on this information infrastructure and using the component-based software development methodology. We built Servlet classes for Web presentation and used Enterprise JavaBeans, entity beans, and session beans for business logic. We also describe examples to demonstrate our information infrastructure. Experiments using the 3 platforms provide performance data that show the capability of our server-side software component system.

Toward Better Quality of Service Composition Based on a Global Social Service Network

Automatic service composition can create new value-added services dynamically and automatically from existing services in an envisioned service-oriented architecture. However, despite considerable progress, web-scale uptake has been significantly less than initially anticipated because of several challenging issues, such as poor scalability, exponentially expanding search time in large search spaces, and the lack of service sociability caused by the isolation of services. In this paper, we propose an innovative methodology for moving from isolated service islands to a global social service network (GSSN) by developing a network model that supports service sociability. First, we propose the construction of a GSSN based on the quality of social links. We then propose an algorithm that maps the GSSN into a service cluster network to reduce the search space, and a quality-driven composition approach that enables exploitation of the service cluster network by providing workflow as a service. Finally, experimental results show that our GSSN-based approach can solve the service composition problem well, improving not only the response time but also the success rate.

Ontology-Based Workflow Generation for Intelligent Big Data Analytics

Big Data analytics provide support for decision making by discovering patterns and other useful information from large set of data. Organizations utilizing advanced analytics techniques to gain real value from Big Data will grow faster than their competitors and seize new opportunities. Cross-Industry Standard Process for Data Mining (CRISP-DM) is an industry-proven way to build predictive analytics models across the enterprise. However, the manual process in CRISP-DM hinders faster decision making on real-time application for efficient data analysis. In this paper, we present an approach to automate the process using Automatic Service Composition (ASC). Focusing on the planning stage of ASC, we propose an ontology-based workflow generation method to automate the CRISP-DM process. Ontology and rules are designed to infer workflow for data analytics process according to the properties of the datasets as well as user needs. Empirical study of our prototyping system has proved the efficiency of our workflow generation method.

Intelligent Big Data Analysis Architecture Based on Automatic Service Composition

Big Data contains massive information, which are generating from heterogeneous, autonomous sources with distributed and anonymous platforms. Since, it raises extreme challenge to organizations to store and process these data. Conventional pathway of store and process is happening as collection of manual steps and it is consuming various resources. An automated real-time and online analytical process is the most cognitive solution. Therefore it needs state of the art approach to overcome barriers and concerns currently facing by the Big Data industry. In this paper we proposed a novel architecture to automate data analytics process using Nested Automatic Service Composition (NASC) and CRoss Industry Standard Platform for Data Mining (CRISP-DM) as main based technologies of the solution. NASC is well defined scalable technology to automate multi-disciplined problems domains. Since CRISP-DM also a well-known data science process which can be used as innovative accumulator of multi-dimensional data sets. CRISP-DM will be mapped with Big Data analytical process and NASC will automate the CRISP-DM process in an intelligent and innovative way.