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The cutting edge of Industry 4.0 has driven everything to be converted to disruptive innovation and digitalized. This digital revolution is imprinted by modern and advanced technology that takes advantage of Big Data and Artificial Intelligence (AI) to nurture from automatic learning systems, smart city, smart energy, smart factory to the edge computing technology, and so on. To harness an appealing, noteworthy, and leading development in smart manufacturing industry, the modern industrial sciences and technologies such as Big Data, Artificial Intelligence, Internet of things, and Edge Computing have to be integrated cooperatively. Accordingly, a suggestion on the integration is presented in this paper. This proposed paper describes the design and implementation of big data platform for intelligence industrial internet of things sensor monitoring system and conveys a prediction of any upcoming errors beforehand. The architecture design is based on edge computing and artificial intelligence. To extend more precisely, industrial internet of things sensor here is about the condition monitoring sensor data — vibration, temperature, related humidity, and barometric pressure inside facility manufacturing factory.

Obesity is one of the most common chronic diseases in children and adolescents. Obesity in childhood and adolescence can lead to long-term physical complications. The purpose of this study is to investigate related diseases to obesity in children and adolescents through association analysis. The Apriori Algorithm was used to find the rules and the childhood complications which are consistent with previous studies' finding.

As soaring population of older people, the medical expense is also rising. Also the patients are moving for better medical treatment to Seoul or other cities. The goal of this paper is finding out the moving patterns of elderly patients and non-elderly patients with high medical cost in South Korea with the network analysis.

In recent years, many social network services have made lots of business models. However, it has big weakness related with personal information spill or attacks from spammers. There are many conventional approaches about spammer detection. Almost the whole studies suggest solutions by using vertex degrees or Local Clustering Coefficient. However, they have been caused false positive results. In other words, many normal vertices can be mistaken for spammers. Therefore, we propose a new approach by using the circuit of graph structure. And then we demonstrate the strengths of our work by employing the experiment results.

The recent social media use of the masses in the development of information technology and the popularization of smartphones continues to rapidly increase. In particular, to understand pending issue of society, there is a tendency for local anonymous entities to increase the use of social media to gather information. In this study, the pending issue of society was examined by analyzing the non-structured data of the ‘Sejong City dot-com’ in the social media of Sejong City. And with the help of the Naive Bayes in Machine Learning Algorithm, we attempted to derive the results of the Sentiment Analysis of each pending issue of society.

Having dataset in relational database published and accessed on the Web of Data is a hot issue. I n t his p aper, w e present implementation of publishing relational database as Linked Data with two open-source platforms, particularly D2RQ and Ontop. Both platforms provide their own way to translate databases into RDF Graphs. However, in case of querying data, Ontop shows much better performance than D2RQ.

The appearance of internet brought most part of our life from off-line into on-line. Also the appearance of SNS and blog became the factor to explosively increase data in website. It is very important to search the useful information through text analysis which takes up most of texts, photos and video. Accordingly this study applied the social network analysis method which is one of the Graph theory by extracting the company name from newspaper article. Based on the processed data, we formed the network of company in articles and analyzed through SNA method. As a result of test, we confirm the possibility of networking through news, we can confirm that sports sector can play the important role to form network by analyzing per category. Also we confirmed the possibility which can perform the analysis per group through clustering forming.

with rapid development of information and communication technology, people are very diverse in education, learning style, and knowledge improvement methods. This paper presents an approach of converting documents into knowledge of Chatbot system that enables users to make more benefits of it by asking and answering questions through the use of electronic documents integrated with simulate system. It is an integrated system for enrich contents of documents from popular format such as Portable Document Format (PDF) and digital photos. The workflow of this system is started from extracts texts using Optical Character Recognition (OCR) from files, then generates questions via Overgenerating Transformations and Ranking algorithm, and finally let Chatbot response to the user's question when it is matched with the String pattern.

We proposed a LanLinux-based cloud system for ClustalW-MPI, a parallel implementation of Clustal-W based on MPI, where researchers can submit their sequence data online for multiple sequence alignment. ClustalW is one of the most widely used programs for multiple sequence alignment (MSA) in bioinformatics. However, current in-silico environmental conditions for MSAs are facing computing power problems. The proposed system uses the MPICH2 (a standard message-passing interface for distributed-memory applications used in parallel computing) for handling all the tasks associated with the multiple sequence alignment on the Web. It provides sufficient computing power for aligning large number of sequences at a time, with real-time monitoring capabilities to ensure correctness, efficiency and effectiveness.

Due to rapid improvement of information technology, the emergence of various information channels such as mobile devices and social media has been producing tremendous amount of data. The evolution of smartphones and social network services (SNS) leads to the big data era. The research for unstructured, large and varied data, has been going on for more systematic and appropriate ways of collection and analysis. In this paper, Twitter data has been collected, stored and analyzed in a multi-dimensional fashion on top of Hadoop platform in order to find out what kind of factors can affect the customer preference for the smartphones. About 600,000 Twitter data has been collected for one month and the analysis result shows the most popular smartphone, the most interesting attributes in the smartphones, and the maker the customers most interested in.

The amount of web information is increasing rapidly with advanced wireless networks and emergence of diverse smart devices like i-Phone, i-Pad and so on. The information is continuously being produced and updated in anywhere and anytime by means of easy web platforms, and social networks. Now, it is becoming a hot issue how frequently updated web data has to be refreshed in data integration and retrieval domain. In this paper, we propose dynamic web-data crawling methods, which include sensitive checking of web site changes, and dynamic retrieving of web pages from target web sites. Furthermore, we implemented a java-based web crawling application and compared performance between conventional static approaches and our proposed dynamic ones. Our experiment results showed 59% performance benefits compared to static crawling method

Biomedical entities such as proteins and nucleic acids and its corresponding interactions is considered as one of the fundamental building block in understanding biological processes of organisms. Majority of this information is stored in an overwhelming amount of scientific literatures. Written in unstructured text format, this collection also employs natural language. Additionally, the demands for such extraction and analysis over a huge amount of biomedical abstracts and full texts requires effective means of data management and high performance computing systems. A key in efficiently acquiring this information involves an intelligent and effective system of information extraction using techniques in natural language processing, information retrieval and knowledge management. With this regard, we developed a straight-forward biomolecular mining system that recognizes the biological named entities, simple heuristic pattern rules in identifying molecular interactions and utilization of high performance cloud computing technology.

Recently, the amount of genome sequence is increasing rapidly due to advanced computational techniques and experimental tools in the biological area. Sequence comparisons are very useful operations to predict the functions of the genes or proteins. However, it takes too much time to compare long sequence data and there are many research results for fast sequence comparisons. In this paper, we propose a LanLinux-based grid system to improve the performance of the sequence comparisons. Compared with conventional approaches, LanLinux-based grid is easy to construct, maintain, and manage because there is no need to install SWs for every node. As a real experiment, we constructed an orthologous database for 89 prokaryotes just in a week by using the grid system; note that it requires 33 weeks on a single computer.

BLAST is one of the most widely used tools in bio informatics to find biologically similar sequences from the genome databases for a given query sequence. Although elaborate dynamic programming and heuristic search in the BLAST rapidly identify significant similar sequences, performance is still an important issue as the size of genome databases and sequences to be compared increases. We propose HGBS (hardware-oriented grid BLAST system) that significantly improves the BLAST performance by applying the grid computing technique. HGBS utilizes a PC-based grid environment constructed by a specialized hardware chip, not a grid middleware such as the Globus toolkit, to provide enhanced performance, good extensibility, and lower implementation cost. Performance analysis shows that there is a significant performance advantage in HGBS; for huge number of BLAST operations in 89-prokaryote genomes comparison, HGBS connecting 27 PCs needs just a week to complete the comparisons, but a single server system takes about 33 weeks.