Temporal aspects of semistructured data

Abstract

In many applications information about the history of data and their dynamic aspects are just as important as static information. During the last years the increasing amount of information accessible through the Web has presented new challenges to academic and industrial research on database. In this context, data are either structured, when coming from relational or object-oriented databases, or partially or completely unstructured, when they consist of simple collections of text or image files. In the context of semistructured data, models and query languages must be extended in order to consider dynamic aspects. We present a model based on labeled graphs for representing changes in semistructured data and a SQL-like query language for querying it

Modeling temporal dimensions of semistructured data

“…While they share the common goal of representing time-varying information, they consider different temporal dimensions and adopt different data models and strategies to capture the main features of the considered notion of time (Ali and Pokorný 2006;Chawathe et al 1998Chawathe et al , 1999Dyreson and Grandi 2009;Dyreson et al 2006;Grandi and Mandreoli 2000;Grandi et al 2005;Li et al 2010;Mandreoli et al 2006;Mendelzon and Rizzolo 2004;Noh et al 2008;Oliboni et al 2001;Rizzolo and Vaisman 2008;Rosado et al 2007;Wang and Zaniolo 2002;.…”

“…The Temporal Graphical Model (TGM) (Oliboni et al 2001) is a graphical model for representing semistructured data dynamics. This model uses temporal elements, instead of simple intervals, to keep trace of different time intervals when an object exists in the reality.…”

“…This model uses temporal elements, instead of simple intervals, to keep trace of different time intervals when an object exists in the reality. In Oliboni et al (2001) the authors consider only some preliminary issues (e.g. admitted operations and queries) related to the valid time representation.…”

“…As for the classical database field, also in the semistructured data context it has been considered the possibility of extending previously proposed models in order to take into account the evolution of data through time (see for example Amagasa et al 2000;Chawathe et al 1999;Oliboni et al 2001). Again, the development of methods for representing and querying changes in semistructured data have been often founded on graph-based data models (see, for example Amagasa et al 2000;Chawathe et al 1998Chawathe et al , 1999Dyreson et al 1999;Oliboni et al 2001;Rizzolo and Vaisman 2008). Even though the well-known concepts of valid and transaction times (Jensen et al 1998) have been considered as a starting point, these proposals focus on different aspects and propose different strategies to deal with temporal dimensions of information.…”

Temporal XML: modeling, indexing, and query processing

“…However, they do not apply this work to XML. Along the same lines, Oliboni et al [40] proposed a graphical data model and query language for semistructured data supporting transaction time, by means of attaching an interval of validity to the objects of the model. Dyreson et al [18] went further, allowing annotations on the edges of the database graph that can refer not only to valid or transaction times, but other kinds of metadata as well.…”