Temporal Relational Databases Research Articles

Building ontology-based temporal databases for data reuse: An applied example on hospital organizational structures.

Keeping track of data semantics and data changes in the databases is essential to support retrospective studies and the reproducibility of longitudinal clinical analysis by preventing false conclusions from being drawn from outdated data. A knowledge model combined with a temporal model plays an essential role in organizing the data and improving query expressiveness across time and multiple institutions. This paper presents a modelling framework for temporal relational databases using an ontology to derive a shareable and interoperable data model. The framework is based on: OntoRela an ontology-driven database modelling approach and Unified Historicization Framework a temporal database modelling approach. The method was applied to hospital organizational structures to show the impact of tracking organizational changes on data quality assessment, healthcare activities and data access rights. The paper demonstrated the usefulness of an ontology to provide a formal, interoperable, and reusable definition of entities and their relationships, as well as the adequacy of the temporal database to store, trace, and query data over time.

τSQWRL: A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data

Temporal ontologies allow to represent not only concepts, their properties, and their relationships, but also time-varying information through explicit versioning of definitions or through the four-dimensional perdurantist view. They are widely used to formally represent temporal data semantics in several applications belonging to different fields (e.g., Semantic Web, expert systems, knowledge bases, big data, and artificial intelligence). They facilitate temporal knowledge representation and discovery, with the support of temporal data querying and reasoning. However, there is no standard or consensual temporal ontology query language. In a previous work, we have proposed an approach named τJOWL (temporal OWL 2 from temporal JSON, where OWL 2 stands for “OWL 2 Web Ontology Language” and JSON stands for “JavaScript Object Notation”). τJOWL allows (1) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (2) to manage its incremental maintenance accommodating their evolution, in a temporal and multi-schema-version environment. In this paper, we propose a temporal ontology query language for rJOWL, named rSQWRL (temporal SQWRL), designed as a temporal extension of the ontology query language-Semantic Query-enhanced Web Rule Language (SQWRL). The new language has been inspired by the features of the consensual temporal query language TSQL2 (Temporal SQL2), well known in the temporal (relational) database community. The aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any (complex) temporal query on time-varying ontologies generated from time-varying big data. Some examples, in the Internet of Healthcare Things (IoHT) domain, are provided to motivate and illustrate our proposal.

Modeling and Transformation from Temporal Object Relational Database into Mongodb: Rules

Modeling and Transformation from Temporal Object Relational Database into Mongodb: Rules

Migration of the Temporal RDB into Temporal ORDB including

This paper proposes an approach for migrating existing relational database (TRDB) according to SQL: 2011 standard into temporal object relational database (TORDB) including Bitemporal data. This is done with methods that can extract various functions from a TRDB, which is based on the different kind of the relationships between tables. To do that we’re going to enhance a representation of a varying time database’s structure in order to make hidden semantic explicit. In contrast to other studies, our main goal here is to offer a first and better solution to mentioned limits to existing works, in order to provide the efficient method for the translation from TRDB to TORDB. We are going to take an existing RDB with bitemporal data features as input, we provide our meta-model in order to get a correct description of a data structure, enriches its metadata representation, and generates a bitemporal data Model (BTDM), which captures essential characteristics of temporal databases for migration. A prototype has been developed which proves the effectiveness of this solution, and temporal queries has been made with help of Oracle 12C.

A Comprehensive Approach to ‘Now’ in Temporal Relational Databases: Semantics and Representation

Now-related temporal data play an important role in many applications. Clifford et al.’s approach is a milestone to model the semantics of ‘now’ in temporal relational databases. Several relational representation models for now-related data have been presented; however, the semantics of such representations has not been explicitly studied. Additionally, the definition of a relational algebra to query now-related data is an open problem. We propose the first integrated approach that provides both a neat semantics for now-related data and a compact 1NF representation (data model and relational algebra) for them. Additionally, our approach also extends current approaches to consider (i) domains where it is not always possible to know when changes in the world are recorded in the database and (ii) now-related data with a bound on their persistency in the future. To do so, we explicitly model the notion of temporal indeterminacy in the future for now-related data. The properties of our approach are also analyzed both from a theoretical (semantic correctness and reducibility of the algebra) and from an experimental point of view. Experiments show that, despite the fact that our approach is a major extension to current temporal relational approaches, no significant overhead is added to deal with ‘now’.

Nearly Periodic Facts in Temporal Relational Databases

Despite the huge amount of work devoted to the treatment of time within the relational context, few relevant temporal phenomena still remain to be addressed. One of them is the treatment of “ nearly periodic events ”, i.e., events/facts that occur in intervals of time which repeat periodically (e.g., a meeting occurring twice each Monday , possibly not at regular times ). Nearly periodic events are quite frequent in everyday life, and thus in many applicative contexts. Their treatment within the relational model is quite challenging, since it involves the integrated treatment of three aspects: (i) the number of repetitions, (ii) their periodicity , and (iii) temporal indeterminacy . Coping with this problem requires an in-depth extension of current temporal relational database techniques. In this paper, we introduce a new data model, and new definitions of relational algebraic operators coping with the above issues. We ascertain the properties of the new model and algebra, with emphasis on the expressiveness of our representation model, on the reducibility property, and on the correctness of the algebraic operators.

Irregular Indeterminate Repeated Facts in Temporal Relational Databases

Time is pervasive of reality, and many relational database approaches have been developed to cope with it. In practical applications, facts can repeat several times, and only the overall period of time containing all the repetitions may be known (consider, e.g., On January, John attended five meetings of the Bioinformatics project ). While some temporal relational databases have faced facts repeated at (known) periodic time, or single facts occurred at temporally indeterminate time, the conjunction of non-periodic repetitions and temporal indeterminacy has not been faced yet. Coping with this problem requires an in-depth extension of current techniques. In this paper, we have introduced a new data model, and new definitions of relational algebraic operators coping with the above issues. We have studied the properties of the new model and algebra (with emphasis on the reducibility property), and how it can be integrated with other models in the literature.

Dynamic class hierarchy management for multi-version ontology-based personalization

We introduce a storage scheme that allows the representation and management of the evolving hierarchical structure of a multi-version ontology in a temporal relational database. The proposed scheme is aimed at supporting ontology-based personalization and temporal access to large collections of resources (data, documents, procedures etc.) stored in a dynamic environment. Whereas in previous works we considered tree-shaped ontologies only, in this work we consider ontologies with a class hierarchy structured as a general directed graph, that is also supporting multiple inheritance and intersection classes. We will also show how multi-version ontologies must be dealt with for the processing of ontology-based personalization queries.

Coping with Events in Temporal Relational Databases

Event relations are used in many temporal relational database approaches to represent facts occurring at time instants. However, to the best of our knowledge, none of such approaches fully copes with the definition of events as provided, e.g., by the “consensus” temporal database glossary. We propose a new approach which overcomes such a limitation, allowing one to cope with multiple events occurring in the same temporal granule. This move involves major extensions to current approaches, since indeterminacy about the time and number of occurrences of events need to be faced. Specifically, we have introduced a new data model, and new definitions of relational algebraic operators coping with the above issues, and we have studied their reducibility. Last, but not least, we have shown that our approach can be easily extended in order to cope with a general form of temporal indeterminacy. Such an extension further increases the applicability of our approach.

Extending BCDM to Cope with Proposals and Evaluations of Updates

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 BCDMPV, 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 BCDMPV 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.

Managing proposals and evaluations of updates to medical knowledge: Theory and applications

The process of keeping up-to-date the medical knowledge stored in relational databases is of paramount importance. Since quality and reliability of medical knowledge are essential, in many cases physicians’ proposals of updates must undergo experts’ evaluation before possibly becoming effective. However, until now no theoretical framework has been provided in order to cope with this phenomenon in a principled and non-ad hoc way. Indeed, such a framework is important not only in the medical domain, but in all Wikipedia-like contexts in which evaluation of update proposals is required. In this paper we propose GPVM (General Proposal Vetting Model), a general model to cope with update proposal⧹evaluation in relational databases. GPVM extends the current theory of temporal relational databases and, in particular, BCDM – Bitemporal Conceptual Data Model – “consensus” model, providing a new data model, new operations to propose and accept⧹reject updates, and new algebraic operators to query proposals. The properties of GPVM are also studied. In particular, GPVM is a consistent extension of BCDM and it is reducible to it.These properties ensure consistency with most relational temporal database frameworks, facilitating implementation on top of current frameworks and interoperability with previous approaches.

A modelling framework for social media monitoring

This paper describes a hierarchical, three-level modelling framework for monitoring social media. Immediate social reality is modelled through the first level of the models. They represent various virtual communities at social media sites and adhere to the social world models of the sites, i.e., the site ontologies. The second-level model is a temporal multirelational graph that captures the static and dynamic properties of the first-level models from the perspective of the monitoring site. The third-level model consists of a temporal relational database scheme that models the temporal multirelational graph within the database. The models are specified and instantiated at the monitoring site. An important contribution of the paper is the description of the mappings between the modelling levels and their schematic algorithmic implementation within the monitoring site. The paper also describes theoretical limits for the accuracy and timeliness of monitoring activity, assuming that the monitoring is performed remotely over the internet.

Overview of methods of creating systems based on temporal relational database

Overview of methods of creating systems based on temporal relational database

Querying temporal clinical databases on granular trends

This paper focuses on the identification of temporal trends involving different granularities in clinical databases, where data are temporal in nature: for example, while follow-up visit data are usually stored at the granularity of working days, queries on these data could require to consider trends either at the granularity of months (“find patients who had an increase of systolic blood pressure within a single month”) or at the granularity of weeks (“find patients who had steady states of diastolic blood pressure for more than 3 weeks”).Representing and reasoning properly on temporal clinical data at different granularities are important both to guarantee the efficacy and the quality of care processes and to detect emergency situations. Temporal sequences of data acquired during a care process provide a significant source of information not only to search for a particular value or an event at a specific time, but also to detect some clinically-relevant patterns for temporal data.We propose a general framework for the description and management of temporal trends by considering specific temporal features with respect to the chosen time granularity. Temporal aspects of data are considered within temporal relational databases, first formally by using a temporal extension of the relational calculus, and then by showing how to map these relational expressions to plain SQL queries. Throughout the paper we consider the clinical domain of hemodialysis, where several parameters are periodically sampled during every session.

MILPRIT*

In this article, we consider a new kind of temporal pattern where both interval and punctual time representation are considered. These patterns, which we call temporal point-interval patterns, aim at capturing how events taking place during different time periods or at different time instants relate to each other. The datasets where these kinds of patterns may appear are temporal relational databases whose relations contain point or interval timestamps. We use a simple extension of Allen’s Temporal Interval Logic as a formalism for specifying these temporal patterns. We also present the algorithm MILPRIT* for mining temporal point-interval patterns, which uses variants of the classical levelwise search algorithms. In addition, MILPRIT* allows a broad spectrum of constraints to be incorporated into the mining process. An extensive set of experiments of MILPRIT* executed over synthetic and real data is presented, showing its effectiveness for mining temporal relational patterns.

Expressiveness of temporal query languages: on the modelling of intervals, interval relationships and states

Storing and retrieving time-related information are important, or even critical, tasks on many areas of computer science (CS) and in particular for artificial intelligence (AI). The expressive power of temporal databases/query languages has been studied from different perspectives, but the kind of temporal information they are able to store and retrieve is not always conveniently addressed. Here we assess a number of temporal query languages with respect to the modelling of time intervals, interval relationships and states, which can be thought of as the building blocks to represent and reason about a large and important class of historic information. To survey the facilities and issues which are particular to certain temporal query languages not only gives an idea about how useful they can be in particular contexts, but also gives an interesting insight in how these issues are, in many cases, ultimately inherent to the database paradigm. While in the area of AI declarative languages are usually the preferred choice, other areas of CS heavily rely on the extended relational paradigm. This paper, then, will be concerned with the representation of historic information in two well known temporal query languages: Templog in the context of temporal deductive databases, and TSQL2 in the context of temporal relational databases. We hope the results highlighted here will increase cross-fertilisation between different communities. This article can be related to recent publications drawing the attention towards the different approaches followed by the Databases and AI communities when using time-related concepts.

Temporal Functional Dependencies and Temporal Nodes Bayesian Networks

A temporal nodes Bayesian network (TNBN) is an alternative graphical representation of temporal probabilistic information.The temporal functional dependency is an important data dependencyin temporal relational databases. In this paper temporal data dependencies are extended into delayed conditions, called asynchronous dependencies. The purpose of this paper is to set up a connection between these asynchronous data dependencies and TNBNs. We show that we can obtain the maximum conditionalin dependencies implied by given temporal functional dependencies. Furthermore, we propose an approach to construct a TNBN from a set of temporal functional dependencies.

Reconciling point-based and interval-based semantics in temporal relational databases: a treatment of the telic/atelic distinction

The analysis of the semantics of temporal data and queries plays a central role in the area of temporal databases. Although many different algebrae and models have been proposed, almost all of them are based on a point-based (snapshot) semantics for data. On the other hand, in the areas of linguistics, philosophy, and recently, artificial intelligence, an oft-debated issue concerns the use of an interval-based versus a point-based semantics. In this paper, we first show some problems inherent in the adoption of a point-based semantics for data, then argue that these problems arise because there is no distinction drawn in the data between telic and atelic facts. We then introduce a three-sorted temporal model and algebra including coercion functions for transforming relations of one sort into relations of the other at query time which properly copes with these issues.

On handling time-varying data in the relational data model

The article addresses the key issues in modeling and querying temporal data within the relational framework. These issues include representation of temporal data, temporal grouping (object) identifiers and primary keys of temporal relations, temporal integrity constraints, and fundamental operations of a temporal query language. The paper develops taxonomy for temporal relational databases and establishes criteria to evaluate various proposed extensions to the relational data model. We expect that it will benefit the researchers and guide the practitioners in choosing the right approach for managing temporal data in their applications.

T-ODMG: an ODMG compliant temporal object model supporting multiple granularity management

In this paper we investigate some issues arising from the introduction of multiple temporal granularities in an object-oriented data model. Although issues concerning temporal granularities have been investigated in the context of temporal relational database systems, no comparable amount of work has been done in the context of object-oriented models. Moreover, the main drawback of the existing proposals is the lack of a formal basis—which we believe is essential to manage the inherent complexity of the object-oriented data model. In this paper, we define a comprehensive temporal object-oriented data model supporting multiple temporal granularities. We formally define the main notions of the data model such as types, legal values, classes, and objects. We address issues related to inheritance, type refinement, and substitutability. Finally, we describe the implementation of the presented model on top of an ODMG compliant DBMS.