Jongsoo Sohn

A real time process management system using RFID data mining

Abstract Recently, there have been numerous efforts to fuse the latest Radio Frequency Identification (RFID) technology with the Enterprise Information System (EIS). However, in most cases these attempts are centered mainly on the simultaneous multiple reading capability of RFID technology, and thus neglect the management of massive data generated from the RFID reader. As a result, it is difficult to obtain flow information for RFID data mining related to real time process control. In this paper, we propose an advanced process management method, called ‘Procedure Tree’ (PT), for RFID data mining. Using the suggested PT, we are able to manage massive RFID data effectively, and perform real time process management efficiently. Then we evaluate the efficiency of the proposed method, after applying it to a real time process control system connected to the RFID-based EIS. For the verification of the suggested system, we collect an enormous amount of data in the Enterprise Resource Planning (ERP) database, analyze characteristics of the collected data, and then compute the elapsed time on each stage in process control. The suggested system was able to perform what the traditional RFID-based process control systems failed to do, such as predicting and tracking of real time process and inventory control.

Contents Recommendation Method Using Social Network Analysis

With the recent tremendous increase in the volume of Web 3.0 content, content recommendation systems (CRS) have emerged as an important aspect of social network services and computing. Thus, several studies have been conducted to investigate content recommendation methods (CRM) for CRSs. However, traditional CRMs are limited in that they cannot be used in the Web 3.0 environment. In this paper, we propose a novel way to recommend high-quality web content using degree of centrality and term frequency–inverse document frequency (TF–IDF). In the proposed method, we analyze the TF–IDF and degree of centrality of collected RDF site summary and friend-of-a-friend data and then generate content recommendations based on these two analyzed values. Results from the implementation of the proposed system indicate that it provides more appropriate and reliable contents than traditional CRSs. The proposed system also reflects the importance of the role of content creators.

Improved View Selection Algorithm in Data Warehouse

In order to minimize the query processing time, a data warehouse maintains materialized views of aggregate data derived from a fact table. However, due to the expensive computing and space costs materializing the whole relations instead of part of the relations results in much worse performance. Consequently, proper selection of appropriate views to be materialized is very important to get a precise and fast response in the data warehouse. However, this view selection problem is NP-hard problem, and there have been many research works on the selection of materialized views. In this paper we propose an improved algorithm to overcome problems of existing view selection algorithms. In the presented algorithm, we first construct the reduced tables in the data warehouse using clustering method among data mining techniques, and then we consider the combination of reduced tables as the materialized views instead of combination of the original base relations. For the justification of the suggested idea, we show the experimental results in which time as well as space costs are about 1.7 times better than the conventional approaches which considered all the tuples in a relation to materialize.

An Improved Social Network Analysis Method for Social Networks

Recently, Social Network Service (SNS) users are rapidly increasing, and Social Network Analysis (SNA) methods are used to analyze the structure of user relationship or messages in many fields. However, the SNA methods based on the shortest distance among nodes is time-consuming in measuring computation time. In order to solve this problem, we present a heuristic method for the shortest path search using SNS user graphs. Our proposed method consists of three steps. First, it sets a start node and a goal node in the Social Network (SN), which is represented by trees. Second, the goal node sets a temporary node starting from a skewed tree, if there is a goal node on a leaf node of the skewed tree. Finally, the betweenness and closeness centralities are computed with the heuristic shortest path search. For verification of the proposed method, we demonstrate an experimental analysis of betweenness centrality and closeness centrality, with 164,910 real data in an SNS. In the experimental results, the method shows that the computation time of betweenness centrality and closeness centrality is faster than the traditional method. This heuristic method can be used to analyze social phenomena and trends in many fields.

Community Topical “Fingerprint” Analysis Based on Social Semantic Networks

Community analysis of social networks is a widely used technique in many fields. There have been many studies on community detection where the detected communities are attached to a single topic. However, an overall topical analysis for a community is required since community members are often concerned with multiple topics. In this paper, we propose a semantic method to analyze the topical community “fingerprint” in a social network. We represent the social network data as an ontology, and integrate with two other ontologies, creating a Social Semantic Network (SSN) context. Then, we take advantage of previous topological algorithms to detect the communities and retrieve the topical “fingerprint” using SPARQL. We extract about 210,000 Twitter profiles, detect the communities, and demonstrate the topical “fingerprint”. It shows human-friendly as well as machine-readable results, which can benefit us when retrieving and analyzing communities according to their interest degrees in various domains.

Content Recommendation Method Using FOAF and SNA

With the rapid growth of user-created contents and wide use of community-based websites, content recommendation systems have attracted the attention of users. However, most recommendation systems have limitations in properly reflecting each user’s characteristics, and difficulty in recommending appropriate contents to users. Therefore, we propose a content recommendation method using Friend-Of-A-Friend (FOAF) and Social Network Analysis (SNA). First, we extract user tags and characteristics using FOAF, and generate graphs with the collected data, with the method. Next, we extract common characteristics from the contents, and hot tags using SNA, and recommend the appropriate contents for users. For verification of the method, we analyzed an experimental social network with the method. From the experiments, we verified that the more users that are added into the social network, the higher the quality of recommendation increases, with comparison to an item-based method. Additionally, we can provide users with more relevant recommendation of contents.

An Improved Method for Measurement of Gross National Happiness Using Social Network Services

Studies on the measurement of happiness have been utilized in a variety of areas; in particular, it has played an important role in the measurement of society stability. As the number of users of Social Network Services (SNSs) increase, efforts are being made to measure human well-being by analyzing user messages in SNSs. Most previous works mainly counted positive and negative words; they did not consider the grammar and emotion. In this paper, we reorganize the mechanism to harness the advantages of (a) Part-Of-Speech (POS) tagging for grammatical analysis, and (b) the SentiWordNet lexicon for the assignment of sentiment scores for emotion degree. We suggest a modified formula for calculating the Gross National Happiness (GNH). To verify the method, we gather a real-world dataset from 405,700 Twitter users, measure the GNH, and compare it with the Gallup well-being release. We demonstrate that the method has more precise computation ability for GNH.

Collaborative Ontology Generation Method Using an Ant Colony Optimization Model

Ontology has been regarded as the core technology of the semantic web. However, non-experts still have difficulties in participating in ontology generation. So, the growing need for public participation in ontology generation has arisen. We propose a method in which the public may participate in ontology generation by adopting the ACO (Ant Colony Optimization) algorithm. We demonstrate that the ontology generated by the proposed method is satisfactory to justify our method: precision and recall of the ontology are about 94.44 and 99.6 % respectively. The suggested method enables the construction of the semantic web environment with non-experts in the field of ontology engineering.

An Intelligent Dynamic Context-Aware System Using Fuzzy Semantic Language

The prevalence of smart devices and the wireless Internet environment have enabled users to exploit environmental sensor data in a variety of fields. This has engendered various research issues in the development of context-awareness technology. In this paper, we propose a novel method where semantic web technology and the fuzzy concept are used to perform tasks that express and infer the user’s dynamic context, in distributed heterogeneous computing environments. The proposed method expresses environmental information using numerical values, and converts them into fuzzy OWL. Then, we make inferences based on the user context, using FiRE, a fuzzy inference engine. The suggested method allows us to describe user context information in heterogeneous environments. Because we use fuzzy concepts to represent contextual information, we can easily express its degree or status.

Ontology-based ID3 tag management system

Since music service providers offer different ID3 tags, it is difficult for users to maintain a consistent multimedia environment. To overcome this difficulty, we propose a system that uses an open API, a social network service, and ontology. The system collects ID3 tag information using the open API and maintains the consistency of the MP3 metadata through the ontology. In an implementation, we achieved a 93.5% success rate when we automatically entered ID3 tags into MP3 files and an 84.7% success rate when we inserted the information into ID3 tag frames.