Author:
Alattar Munqath,Sali Attila
Abstract
AbstractMissing data value is an extensive problem in both research and industrial developers. Two general approaches are there to deal with the problem of missing values in databases; they could be either ignored (removed) or imputed (filled in) with new values (Farhangfar et al. in IEEE Trans Syst Man Cybern-Part A: Syst Hum 37(5):692–709, 2007). For some SQL tables, it is possible that some candidate key of the table is not null-free and this needs to be handled. Possible keys and certain keys to deal with this situation were introduced in Köhler et al. (VLDB J 25(4):571–596, 2016). In the present paper, we introduce an intermediate concept called strongly possible keys that is based on a data mining approach using only information already contained in the SQL table. A strongly possible key is a key that holds for some possible world which is obtained by replacing any occurrences of nulls with some values already appearing in the corresponding attributes. Implication among strongly possible keys is characterized, and Armstrong tables are constructed. An algorithm to verify a strongly possible key is given applying bipartite matching. Connection between matroid intersection problem and system of strongly possible keys is established. For the cases when no strongly possible keys hold, an approximation notion is introduced to calculate the closeness of any given set of attributes to be considered as a strongly possible key using the $$g_3$$
g
3
measure, and we derive its component version $$g_4$$
g
4
. Analytical comparisons are given between the two measures.
Funder
ELKH Alfréd Rényi Institute of Mathematics
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Networks and Communications,Information Systems
Reference28 articles.
1. Acurna E, Rodriguez C (2007) The treatment of missing values and its effect in the classifier accuracy classification, clustering, and data mining applications, pp 639–648
2. Beeri C, Dowd M, Fagin R, Statman R (1984) On the structure of Armstrong relations for functional dependencies. J ACM 31(1):30–46
3. Date CJ (1990) NOT is not“not”! (notes on three-valued logic and related matters) in relational database writings 1985–1989. Addison-Wesley, Reading, MA
4. Dhevi ATS (2014) Imputing missing values using inverse distance weighted interpolation for time series data. In: Sixth international conference on advanced computing (ICoAC), pp 255–259
5. Chang G, Ge T (2011) Comparison of missing data imputation methods for traffic flow. In: International conference on transportation, mechanical, and electrical engineering (TMEE), pp 639–642
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献