Dependency-preserving normalization of relational and XML data

Abstract

Having a database design that avoids redundant information and update anomalies is the main goal of normalization techniques. Ideally, data as well as constraints should be preserved. However, this is not always achievable: while BCNF eliminates all redundancies, it may not preserve constraints, and 3NF, which achieves dependency preservation, may not always eliminate all redundancies. Our first goal is to investigate how much redundancy 3NF tolerates in order to achieve dependency preservation. We apply an information-theoretic measure and show that only prime attributes admit redundant information in 3NF, but their information content may be arbitrarily low. Then we study the possibility of achieving both redundancy elimination and dependency preservation by a hierarchical representation of relational data in XML. We provide a characterization of cases when an XML normal form called XNF guarantees both. Finally, we deal with dependency preservation in XML and show that like in the relational case, normalizing XML documents to achieve non-redundant data can result in losing constraints.

Ethnopharmacological field studies: A critical assessment of their conceptual basis and methods

“…A well-structured schema that includes all relevant tables and correct relationships between them, is not only easier to maintain, but also facilitates analysis of data. Normalisation is the process, or series of steps, which database developers use to refine the schema so that data and data dependencies are preserved, data dependencies make sense, and that redundant data are eliminated (Kolahi, 2007). These steps reduce the amount of space a database uses, ensure that data are logically stored, and reduce the chance of data inconsistency.…”

Constraint acquisition for Entity-Relationship models

“…Potentially interesting data models include those that do not conform to the first-normal form proposal, such as nested data models [98][99][100][101][102]23] or XML [46,47,[103][104][105][106][107][108]. This includes also databases that permit null values [48,55].…”

Empirical evidence for the usefulness of Armstrong relations in the acquisition of meaningful functional dependencies

“…Functional dependencies have also received considerable attention in other data models such as models with nulls [34,35], nested data models [36][37][38][39], object-oriented data models [40,41], temporal data models [42,43], fuzzy data models [44], description logics [45] and XML [46][47][48][49][50][51][52][53][54].…”

“…Functional dependencies between sets of attributes have always played a central role in the study of relational databases [21,22,84], and seem to be central for the study of database design in other data models as well [35,[39][40][41]43,45,46,53,54].…”