Biological data cleaning: A case study

被引：8
作者：
Herbert, Katherine G. [1 ]
Wang, Jason T.L. [2 ]
机构：
[1] Department of Computer Science, Montclair State University, Montclair, NJ
[2] Bioinformatics Center and Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark
来源：
International Journal of Information Quality | 2007年 / 1卷 / 01期
关键词：
Bioinformatics; Biological data quality; Data cleaning; Data quality; Information quality; Phylogenetic data;
D O I：
10.1504/IJIQ.2007.013376
中图分类号：
学科分类号：
摘要：
As databases become more pervasive through the biological sciences, various data quality concerns are emerging. Biological databases tend to develop data quality issues regarding data legacy, data uniformity and data duplication. Due to the nature of this data, each of these problems is non-trivial and can cause many problems for the database. For biological data to be corrected and standardised, methods and frameworks must be developed to handle both structural and traditional data. This paper discusses issues concerning biological data quality with respect to data cleaning. It presents BIO-AJAX, a framework developed to address these issues. It finally describes BIO-JAX for TreeBASE and BIO-AJAX for Lineage Path, two implementations of BIO-AJAX on phylogenetic data sets. Copyright © 2007 Inderscience Enterprises Ltd.
引用
收藏
页码：60 / 82
页数：22
相关论文
共 32 条
  • [1] Benson D.A., Karsch-Mizrachi I., Lipman D.J., Ostell J., Rapp B.A., Wheeler D.L., GenBank, Nuc. Acids Res., 28, 1, pp. 15-18, (2000)
  • [2] Bitton D., Dewitt D.J., Duplicate record elimination in large data files, ACM Transactions on Databases, 8, 2, pp. 255-265, (1983)
  • [3] Caruso F., Cochinwala M., Ganapathy U., Lalk G., Missier P., Telcordia's database reconciliation and data quality analysis tool, Proc. 26th International Conference on Very Large Data Bases, pp. 615-618, (2000)
  • [4] Chaudhuri S., Ganjam K., Ganti V., Motwani R., Robust and efficient fuzzy match for online data cleaning, Proc. 2003 ACM SIGMOD International Conference on Management of Data, pp. 313-324, (2003)
  • [5] Chu F., Wang Y., Parker D.S., Zaniolo C., Data cleaning using belief propagation, Proc. Second International ACM SIGMOD Workshop on Information Quality in Information Systems, pp. 99-104, (2005)
  • [6] Cochinwala M., Kurien V., Lalk G., Shasha D., Efficient data reconciliation, Information Sciences, 137, 1-4, pp. 1-15, (2001)
  • [7] Dasu T., Johnson T., Problems, solutions and research in data quality, Proc. SIAM International Conference on Data Mining, (2002)
  • [8] Dasu T., Johnson T., Exploratory Data Mining and Data Cleaning, (2003)
  • [9] Federhen S., Harrison I., Hotton C., Leipe D., Soussov V., Sternberg R., Turner S., NCBI Taxonomy Homepage, (2004)
  • [10] Fuxman A., Fazil E., Miller R.J., ConQuer: Efficient management of inconsistent databases, Proc. 2005 ACM SIGMOD International Conference on Management of Data, pp. 155-166, (2005)
1234