Appearance-Order-Based Schema Matching

Ding, Guohui,Cao, Keyan,Wang, Guoren,Han, Dong Korean Institute of Information Scientists and Eng 2014 Journal of Computing Science and Engineering Vol.8 No.2

Schema matching is widely used in many applications, such as data integration, ontology merging, data warehouse and dataspaces. In this paper, we propose a novel matching technique that is based on the order of attributes appearing in the schema structure of query results. The appearance order embodies the extent of the importance of an attribute for the user examining the query results. The core idea of our approach is to collect statistics about the appearance order of attributes from the query logs, to find correspondences between attributes in the schemas to be matched. As a first step, we employ a matrix to structure the statistics around the appearance order of attributes. Then, two scoring functions are considered to measure the similarity of the collected statistics. Finally, a traditional algorithm is employed to find the mapping with the highest score. Furthermore, our approach can be seen as a complementary member to the family of the existing matchers, and can also be combined with them to obtain more accurate results. We validate our approach with an experimental study, the results of which demonstrate that our approach is effective, and has good performance.

Time Series Data Cleaning Method Based on Optimized ELM Prediction Constraints

Guohui Ding,Yueyi Zhu,Chenyang Li,Jinwei Wang,Ru Wei,Zhaoyu Liu 한국정보처리학회 2023 Journal of information processing systems Vol.19 No.2

Affected by external factors, errors in time series data collected by sensors are common. Using the traditionalmethod of constraining the speed change rate to clean the errors can get good performance. However, they areonly limited to the data of stable changing speed because of fixed constraint rules. Actually, data with unevenchanging speed is common in practice. To solve this problem, an online cleaning algorithm for time series databased on dynamic speed change rate constraints is proposed in this paper. Since time series data usually changesperiodically, we use the extreme learning machine to learn the law of speed changes from past data and predictthe speed ranges that change over time to detect the data. In order to realize online data repair, a dual-windowmechanism is proposed to transform the global optimal into the local optimal, and the traditional minimumchange principle and median theorem are applied in the selection of the repair strategy. Aiming at the problemthat the repair method based on the minimum change principle cannot correct consecutive abnormal points,through quantitative analysis, it is believed that the repair strategy should be the boundary of the repaircandidate set. The experimental results obtained on the dataset show that the method proposed in this paper canget a better repair effect.

Appearance-Order-Based Schema Matching

Guohui Ding,Keyan Cao,Guoren Wang,Dong Han 한국정보과학회 2014 Journal of Computing Science and Engineering Vol.8 No.2

Schema matching is widely used in many applications, such as data integration, ontology merging, data warehouse and dataspaces. In this paper, we propose a novel matching technique that is based on the order of attributes appearing in the schema structure of query results. The appearance order embodies the extent of the importance of an attribute for the user examining the query results. The core idea of our approach is to collect statistics about the appearance order of attributes from the query logs, to find correspondences between attributes in the schemas to be matched. As a first step, we employ a matrix to structure the statistics around the appearance order of attributes. Then, two scoring functions are considered to measure the similarity of the collected statistics. Finally, a traditional algorithm is employed to find the mapping with the highest score. Furthermore, our approach can be seen as a complementary member to the family of the existing matchers, and can also be combined with them to obtain more accurate results. We validate our approach with an experimental study, the results of which demonstrate that our approach is effective, and has good performance.

Crystallization Morphology Transition of Poly(3-hydroxybutyrate) Films Depending on Nucleation Temperature under Temperature Gradient

Qing Chen,Guohui Zhang,Kaixuan Wang,Chao Wang,Guangzhu Ding,Jieping Liu 한국고분자학회 2017 Macromolecular Research Vol.25 No.4

The growth of spherulites is an intricate process and the final morphology is dependent on many process conditions. The spherulitic morphologies of poly(3-hydroxybutyrate) (PHB) crystallized from melt film are observed under various crystallization temperature gradient (the non-isothermal crystallization) in this work. Characterization reveals that the crystal morphologies (banded or nonbanded spherulites) of PHB film are determined significantly by nucleation temperature (not growth temperature). Interestingly, banded spherulites, nucleated between 60-135 oC, are able to grow completely above growth temperature 135 oC or below 60 oC yet and non-banded shperulites nucleated at above 135 oC can also develop steadily till below growth temperature 135 oC under temperature gradient condition. Therefore, the conclusion drawn from the temperature gradient or non-isothermal process is novel and different from the result of isothermal crystallization (morphology mainly depending on the growth temperature). In addition, the growth and evolution kinetics of banded and non-banded spherulites are both investigated for some polymer thin films under the temperature gradient in detail.

SysPIMP: the web-based systematical platform for identifying human disease-related mutated sequences from mass spectrometry

Xi, Hong,Park, Jongsun,Ding, Guohui,Lee, Yong-Hwan,Li, Yixue Oxford University Press 2009 Nucleic acids research Vol.37 No.1

<P>Some mutations resulting in protein sequence change might be tightly related to certain human diseases by affecting its roles, such as sickle cell anemia. Until now several databases, such as PMD, OMIM and HGMD, have been developed, providing useful information about human disease-related mutation. Tandem mass spectrometry (MS) has been used for characterizing proteins in various conditions; however, there is no system in place for finding disease-related mutated proteins within the MS results. Here, a Systematical Platform for Identifying Mutated Proteins (SysPIMP; http://pimp.starflr.info/) was developed to efficiently identify human disease-related mutated proteins within MS results. SysPIMP comprises of three layers: (i) a standardized data warehouse, (ii) a pipeline layer for maintaining human disease databases and X!Tandem and BLAST and (iii) a web-based interface. From OMIM AV part, PMD and SwissProt databases, 35 497 non-redundant human disease-related mutated sequences were collected with disease information described by OMIM terms. With the interfaces to browse sequences archived in SysPIMP, X!Tandem, an open source database-search engine used to identify proteins within MS data, was integrated into SysPIMP to help support the detection of potential human disease-related mutants in MS results. In addition, together with non-redundant disease-related mutated sequences, original non-mutated sequences are also provided in SysPIMP for comparative research. Based on this system, SysPIMP will be the platform for efficiently and intensively studying human diseases caused by mutation.</P>

Overexpression of Uridine-Cytidine Kinase 2 Correlates with Breast Cancer Progression and Poor Prognosis

Guosong Shen,Pingya He,Yingying Mao,Peipei Li,Frank Luh,Guohui Ding,Xi Yong Liu,Yun Yen 한국유방암학회 2017 Journal of breast cancer Vol.20 No.2

Purpose: Uridine-cytidine kinase (UCK) 2 is a rate-limiting enzyme involved in the salvage pathway of pyrimidine-nucleotide biosynthesis. Recent studies have shown that UCK2 is overexpressed in many types of cancer and may play a crucial role in activating antitumor prodrugs in human cancer cells. In the current study, we evaluated the potential prognostic value of UCK2 in breast cancer. Methods: We searched public databases to explore associations between UCK2 gene expression and clinical parameters in patients with breast cancer. Gene set enrichment analysis (GSEA) was performed to identify biological pathways associated with UCK2 gene expression levels. Survival analyses were performed using 10 independent large-scale breast cancer microarray datasets. Results: We found that UCK2 mRNA expression was elevated in breast cancer tissue compared with adjacent nontumorous tissue or breast tissue from healthy controls. High UCK2 levels were correlated with estrogen receptor negativity (p<0.001), advanced tumor grade (p<0.001), and poor tumor differentiation (p<0.001). GSEA revealed that UCK2-high breast cancers were enriched for gene sets associated with metastasis, progenitor-like phenotypes, and poor prognosis. Multivariable Cox proportional hazards regression analyses of microarray datasets verified that high UCK2 gene expression was associated with poor overall survival in a dose-response manner. The prognostic power of UCK2 was superior to that of TNM staging and comparable to that of multiple gene signatures. Conclusion: These findings suggest that UCK2 may be a promising prognostic biomarker for patients with breast cancer.