Extending BCDM to Cope with Proposals and Evaluations of Updates

Abstract

Abstract-The cooperative construction of data/knowledge bases has recently had a significant impulse (see, e.g., Wikipedia [1]). In cases in which data/knowledge quality and reliability are crucial, proposals of update/insertion/deletion need to be evaluated by experts. To the best of our knowledge, no theoretical framework has been devised to model the semantics of update proposal/evaluation in the relational context. Since time is an intrinsic part of most domains (as well as of the proposal/evaluation process itself), semantic approaches to temporal relational databases (specifically, Bitemporal Conceptual Data Model (henceforth, BCDM) [2]) are the starting point of our approach. In this paper, we propose BCDM PV , a semantic temporal relational model that extends BCDM to deal with multiple update/insertion/deletion proposals and with acceptances/rejections of proposals themselves. We propose a theoretical framework, defining the new data structures, manipulation operations and temporal relational algebra and proving some basic properties, namely that BCDM PV is a consistent extension of BCDM and that it is reducible to BCDM. These properties ensure consistency with most relational temporal database frameworks, facilitating implementations.

A Mathematical Model for Comparing Memory Storage of Three Interval-Based Parametric Temporal Database Models

“…That is why varieties of techniques have been proposed to address this problem from different viewpoints [1]- [3]. Modeling temporal database in relational framework differs in many dimensions [4]- [11].…”

“…Models under TTSR approach are discussed by [1], [4], [5], [12]- [14]. An example of some of these temporal data models are LEGOL 2.0 by Jones [15], Temporally Oriented Data Model by Ariav [16], HSQL by Sarda [17], and TQuel by Snodgrass [18].…”

A 1NF temporal relational model and algebra coping with valid-time temporal indeterminacy

“…Indeed, this contribution is in the line of research that we have been following for several years, aiming at extending temporal relational databases to cope with new temporal phenomena, such as the telic/atelic distinction (Terenziani et al 2007;Terenziani and Snodgrass 2004), periodically repeated data (Terenziani 2003;Stantic et al 2012), proposal vetting (Anselma et al 2013a), instantaneous events (Terenziani 2013) and (semantics of) temporal indeterminacy (Anselma et al 2013c), proposing general (domain-independent) and theoretically grounded solutions.…”

“…Notably, our group has a tradition in the application of AI techniques to cope with complex temporal phenomena in relational databases (Terenziani 2012;Anselma et al 2013aAnselma et al , 2013bTerenziani 2003;Terenziani and Snodgrass 2004;Terenziani 2013). …”

An ontological knowledge and multiple abstraction level decision support system in healthcare

“…One of such approaches is GLARE (Guideline Acquisition, Representation and Execution), which started from 1997 in a long-term cooperation between the Department of Computer Science of the University of Eastern Piedmont Alessandria, Italy, and the Azienda Ospedaliera San Giovanni Battista in Turin (one of the largest hospitals in Italy). Besides supporting CIG acquisition, representation, storage and execution, GLARE is characterized by the adoption of advanced Artificial Intelligence and Temporal Database formal techniques to provide advanced supports for different tasks, including reasoning about temporal constraints (Anselma et al 2006), the treatment of periodic data (Stantic et al 2012), guideline versioning (Anselma et al 2013), model-checking verification (Bottrighi et al 2010), decision support (based on Decision Theory) (Anselma et al 2011), contextualization (Terenziani et al 2004).…”