Anomaly free search using multi table entity attribute value data model

Abstract

In this paper, the authors are proposing a principled extension of dynamic tables (DT). It is termed multi table entity attribute value (MTEAV) model which offers a search efficient avenue for storing database. The authors present precise semantics of MTEAV and demonstrate the following: 1) MTEAV possesses consistency and availability; 2) MTEAV outperforms other existing models (entity attribute value model, dynamic tables, optimised entity attribute value and optimised column oriented model) under various query scenarios and varying datasets size; 3) MTEAV retains flavour of EAV in terms of handling sparseness and self-adapting schema changing capability. To heighten adaptability of MTEAV, authors implement a translation layer over existing SQL engine in a non-intrusive way. Translation layer transforms conventional SQL query (as per horizontal row representation) to a new SQL query (as per MTEAV structure) to maintain user friendliness. Translation layer makes user feel as if he/she is interacting with the conventional horizontal row approach. The authors also critically analyse maximum percentage of non-null density appropriate for choosing MTEAV as a storage option.