Kyung Mi Lee

A genetic algorithm for general machine scheduling problems

This paper deals with the so-called general machine scheduling problems. In the general machine scheduling problems, job shop type jobs and open shop type jobs are scheduled together and the imposition of precedence constraints is allowed between operations belonging to either the same job or different jobs. This paper proposes a genetic algorithm to solve such general machine scheduling problems. Some experimental results are presented to show the applicability of the proposed method. The method can be used to solve traditional job shop scheduling, flow shop scheduling, and open shop scheduling as well as general machine scheduling problems.

Requirement Analysis and Architecture Design for Ubiquitous Healthcare Service Systems

Various kind of ubiquitous healthcare services have been developed and tried in patient care and health care fields. Due to technical restrictions and not enough application practices, the service systems have been developed somewhat in ad hoc way. This paper describes the requirements for ubiquitous healthcare service systems most of which need to have and presents a ubiquitous healthcare service system architecture with which various ubiquitous healthcare services can be developed. It also introduces an application system for ubiquitous benign prostatic hyperplasia (BPH) patient care which has been developed based on the architecture.

Similar pair identification using locality-sensitive hashing technique

Huge volumes of data pose many opportunities and challenges in business and information societies. The similar pair identification problem happens in various fields such as image retrieval, near-duplicate document identification, plagiarism analysis, entity resolution, and so on. With the increasing number of items, it is not efficient to make pair-wise similarity comparisons. To handle this problem in an efficient way, various techniques have been developed. The locality-sensitive hashing is one of such techniques to avoid pair-wise comparisons in avoiding similar pairs. This paper introduces a modified method of the projection-based locality sensitive hashing technique. The proposed method reduces the chances that similar pairs fall into different buckets which is one of major drawbacks in the projection-based technique. We have observed that the proposed method outperforms the conventional projection-based method in that it gets better recall rate with some additional memory and computation costs.

Density and Frequency-Aware Cluster Identification for Spatio-Temporal Sequence Data

Various wireless sensors and devices keep collecting data for their environments or owners. Such collected data are given in the form of spatio-temporal sequence data which are a sequence of data elements with spatial information and timestamp. Data clustering is useful in finding inherent underlying structures, natural or interesting groups in a collection of data. This paper proposes a new clustering method for spatio-temporal sequence data with respect to density and frequency. Density is a notion about how densely data elements are in a local region, and frequency is a notion about how many times sequences pass through a local region. The proposed method identifies three types of clusters: high density and high frequency clusters, high density and low frequency clusters, and low density and high frequency clusters. It first augments the data set by inserting dummy data elements for capturing frequency distribution in sparse local regions. Then it computes the densities and frequency for data elements and the frequencies for dummy data elements. It partitions data elements into the high density-high frequency data set, high density-low frequency data set, and low-density-high frequency data set. It clusters each data set individually using the clustering procedures that are similar to DBSCAN, which is a density-based clustering algorithm. The proposed method had been applied to the six spatial–temporal GPS sequence data sets for wildlife movements. The experiment results were compared with the results from DBSCAN and analyzed in terms of the number and characteristics of discovered clusters.

Efficient Identification of Frequent Family Subtrees in Tree Database

This paper introduces a new type of problem called the frequent common family subtree mining problem for a collection of leaf-labeled trees and presents some characteristics for the problem. It proposes an algorithm to find frequent common families in trees. To its applicability, the proposed method has been applied to both several synthetic data sets and a real data set.

A Locality Sensitive Hashing Technique for Categorical Data

The measured data may contain various types of attributes such as continuous, categorical, and set-valued attributes. Several locality-sensitive hashing techniques, which enable to find similar pairs of data in a fast and approximate way, have been developed for data with either numeric or set-valued attributes. This paper introduces a new locality sensitive-hashing technique applicable to data with categorical attributes.

Mining Frequent Common Families in Trees

This paper is concerned with mining the frequent common families from leaf-labeled tree database, in which supports for common families are established by not only exact family subtrees but also extended family subtrees. It proposes an algorithm to determine frequent common families with control over the coverage of extended family subtrees. The suggested method has been tested to both several synthetic data sets and a real data set.

Fuzzy Technique-based Identification of Close and Distant Clusters in Clustering

Due to advances in hardware performance, user-friendly interfaces are becoming one of the major concerns in information systems. Linguistic conversation is a very natural way of human communications. Fuzzy techniques have been employed to liaison the discrepancy between the qualitative linguistic terms and quantitative computerized data. This paper deals with linguistic queries using clustering results on data sets, which are intended to retrieve the close clusters or distant clusters from the clustering results. In order to support such queries, a fuzzy technique-based method is proposed. The method introduces distance membership functions, namely, close and distant membership functions which transform the metric distance between two objects into the degree of closeness or farness, respectively. In order to measure the degree of closeness or farness between two clusters, both cluster closeness measure and cluster farness measure which incorporate distance membership function and cluster memberships are considered. For the flexibility of clustering, fuzzy clusters are assumed to be formed. This allows us to linguistically query close or distant clusters by constructing fuzzy relation based on the measures.

Machine-part grouping for cellular manufacturing systems: a neural network approach

The machine cell formation problem is about grouping machines into machine families and parts into part families so as to minimize bottleneck machines, exceptional parts and inter-cell part movements in cellular and flexible manufacturing systems. This paper proposes a new machine cell formation method based on the adaptive Hamming net, which is a neural network model. To see the applicability of the method, this paper shows some experimental results and compares the proposed method with other cell formation methods. From the experiments, we can see that the proposed method can produce good cells for the machine cell formation problem.

Remote data integrity check for remotely acquired and stored stream data

Numerous sensors have been deployed to monitor processes or environments in various fields. These sensors produce stream data that are difficult to store in a central storage area owing to network bandwidth constraints and the massive amounts of storage necessary. Thus, it is sometimes efficient to store these data using remote storage services. However, integrity concerns arise when data are stored in remote storage. This paper presents a new integrity check method for remotely acquired and stored stream data. The proposed method uses a secure data acquisition and signature extraction module to produce integrity check metadata for the stream data. To share encryption keys used in signature generation and exchange messages like signatures, the module establishes a secure communication channel with the verifier that checks the integrity of the remote data. The signatures are the metadata of data records, which are used for integrity verification. Signatures for each data record are generated by a chain hash technique, but only some of them are kept in the signature store. The proposed method can successfully detect losses and modifications for remotely acquired and collected stream data.