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: it Templog in the context of temporal deductive databases, and it 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

ON COMPLETENESS OF HISTORICAL RELATIONAL DATA MODELS

Several proposals for extending the relational data model to incorporate the temporal dimension of data have appeared in the past several years. These proposals have differed considerably in the way that the temporal dimension has been incorporated both into the structure of the extended relations that are defined as part of these extended model, and into the operations of the extended relational algebra or calculus component of the models. Because of these differences it has been difficult to compare the proposed models and to make judgements as to which of them is "better" or indeed, the "best." In this paper we propose a notion of historical relational completeness, analogous to Codd's notion of relational completeness, and examine several historical relational proposals in light of this standard.Information Systems Working Papers Serie

A Critical Review of Temporal Database Management Systems

There have been significant research activities in Temporal Databases during the last decade. However, the developments of a semantics of time, a temporal model for efficient database systems and temporal query languages still need much study. Based on the researches of the TDB group [Snodgrass 1987], the review of research about TDBMS in this dissertation mainly emphasises three aspects as follows. 1) The formulation of a semantics of time at the conceptual level. A topology of time and types of time attributes are introduced. A new taxonomy for time attributes is presented: assertion time, event time, and recording time. 2) The development of a model for TDBMS analogous to relational databases. Based on Snodgrass' classification, four kinds of databases: snapshot, rollback, historical and temporal are discussed in depth. But the discussion distinguishes some important differences from the representation of the TDB model: - historical relation for most enterprises is an interval relation, but not a sequence of snapshot slices indexed by valid time. The term "tuple" no longer simply refers to an entity as in traditional relational databases. It refers to different level representations of an object: entity, entity state, observation of entity, and observation of entity state in different types of databases. 3) The design of temporal query languages. We do not present a new temporal query language in this dissertation, but we discuss a Quel-like temporal query language, TQuel, in some depth. TQuel is compared with two other temporal query languages TOSQL and Legol 2.0. We centre the main discussion on TQuel's semantics for tuple calculus. The classification for the relationships between overlapping intervals suggests an approach using temporal logic to classify the derived tuples in tuple calculus. Under such an approach, a new presentation for tuple modification calculus is proposed, not only for interval relations, but also for event relations

ON COMPLETENESS OF HISTORICAL RELATIONAL QUERY LANGUAGES

Numerous proposals for extending the relational data model to incorporate the temporal dimension of data have appeared in the past several years. These proposals have differed considerably in the way that the temporal dimension has been incorporated both into the structure of the extended relations of these temporal models, and consequently into the extended relational algebra or calculus that they define. Because of these differences it has been difficult to compare the proposed models and to make judgments as to which of them might in some sense be equivalent or even better. In this paper we define the notions of temporally grouped and temporally ungrouped historical data models and propose two notions of historical relational completeness, analogous to Codd's notion of relational completeness, one for each type of model. We show that the temporally ungrouped models are less powerful than the grouped models, but demonstrate a technique for extending the ungrouped models with a grouping mechanism to capture the additional semantic power of temporal grouping. For the ungrouped models we define three different languages, a temporal logic, a logic with explicit reference to time, and a temporal algebra, and show that under certain assumptions all three are equivalent in power. For the grouped models we define a many-sorted logic with variables over ordinary values, historical values, and times. Finally, we demonstrate the equivalence of this grouped calculus and the ungrouped calculus extended with the proposed grouping mechanism. We believe the classification of historical data models into grouped and ungrouped provides a useful framework for the comparison of models in the literature, and furthermore the exposition of equivalent languages for each type provides reasonable standards for common, and minimal, notions of historical relational completeness.Information Systems Working Papers Serie

ON THE SEMANTICS OF TRANSACTION TIME AND VALID TIME IN BITEMPORAL DATABASES

Numerous proposals for extending the relational data model to incorporate the temporal dimension of data have appeared in the past several years. While most of these have been historical databases, incorporating in some fashion a valid time dimension to the data model and the query languages, others have been rollback databases, incorporating a transaction time dimension, or bitemporal databases, incorporating both of these temporal dimensions. In this paper we address an issue that has been lacking in many of these papers, namely, a formal specification of the precise semantics of these temporal dimensions of data. We introduce the notion of reference time - the time that any operation is applied to the database state - and provide a logical analysis of the interrelationships among these three temporal dimensions. We also provide an analysis of the meaning of various variables such as now and â which have been used in many of these models without a complete specification of their semantics.Information Systems Working Papers Serie

The archaeological database—New relations?

Over two decades have passed since the foundations of the relational data model were formalised (Codd 1970) and today a large number of Database Management Systems (DBMS) based on its principles are readily available. The better of these have attained a high degree of sophistication, running in a variety of environments — micros, workstations, minis and mainframes — and have achieved some standardisation through the adoption of Standard (or Structured) Query Language (SQL). As such, the user who invests much time in learning to use a DBMS and its development tools, for example INGRES, will have little problem when the present micro is dumped and a workstation appears on the desk. More importantly for archaeological information, the data, its structure, and application programs will also transfer with minimal upheaval. This is a salutary warning to those investing a great deal of resources in non-upwardly mobile micro-based DBMS and they are urged to consider employing either ORACLE or INGRES (the current flagships of the 4th generation language multi- environment relational DBMS) if they wish to ensure the longevity of their work. The reference to work rather than just to data is deliberate and the cornerstone of this paper, for information is not just data values; it is the context and meaning of those values that ultimately determine the usefulness of the data. Data structure, user interfaces, validation procedures, help systems and applications are inextricably linked with the raw data, giving it context and providing a crude but non-trivial 'knowledge base' without which data files may be useless, or even a negative resource, if misunderstood. Although high-quality relational DBMS did not come into general use as commercial products until the late 1980s, deficiencies in the relational model had already been noted in the previous decade. Important new products are likely to become generally available soon. Many of the major research areas of general DBMS have direct application in the management of archaeological data. The aim of this paper is to discuss some of the limitations and deficiencies of currently available relational DBMS, to review informally the most relevant areas of development (and one area which has yet to be developed), and to consider the implications for mainstream archaeology

Portinari: A Data Exploration Tool to Personalize Cervical Cancer Screening

Socio-technical systems play an important role in public health screening programs to prevent cancer. Cervical cancer incidence has significantly decreased in countries that developed systems for organized screening engaging medical practitioners, laboratories and patients. The system automatically identifies individuals at risk of developing the disease and invites them for a screening exam or a follow-up exam conducted by medical professionals. A triage algorithm in the system aims to reduce unnecessary screening exams for individuals at low-risk while detecting and treating individuals at high-risk. Despite the general success of screening, the triage algorithm is a one-size-fits all approach that is not personalized to a patient. This can easily be observed in historical data from screening exams. Often patients rely on personal factors to determine that they are either at high risk or not at risk at all and take action at their own discretion. Can exploring patient trajectories help hypothesize personal factors leading to their decisions? We present Portinari, a data exploration tool to query and visualize future trajectories of patients who have undergone a specific sequence of screening exams. The web-based tool contains (a) a visual query interface (b) a backend graph database of events in patients' lives (c) trajectory visualization using sankey diagrams. We use Portinari to explore diverse trajectories of patients following the Norwegian triage algorithm. The trajectories demonstrated variable degrees of adherence to the triage algorithm and allowed epidemiologists to hypothesize about the possible causes.Comment: Conference paper published at ICSE 2017 Buenos Aires, at the Software Engineering in Society Track. 10 pages, 5 figure

USING QUERY-DRIVEN SIMULATIONS FOR QUERYING OUTCOMES OF BUSINESS PROCESSES

When decision makers want to know outcomes of business processes in their organizations, they often use simulations to do this. This paper describes how a new Query-Driven Simulation (QDS) approach can be used by decision makers to obtain information about future outcomes of business processes in a more declarative, flexible, and interactive way than the traditional approach of running simulations and then gathering statistics about simulation outcomes. The paper also describes the types of questions decision makers ask about outcomes of business processes and studies how easy it is to express these questions in terms of an SQL-like query language SimQL designed for Query-Driven Simulations. It also identifies the types of applications that are especially well-suited for QDS. Finally, the paper describes the Query-Driven Simulation Modeling Lifecycle and how QDS provides a feedback loop in the model development process.Information Systems Working Papers Serie

A TEMPORAL RELATIONAL ALGEBRA AS A BASIS FOR TEMPORAL RELATIONAL COMPLETENESS

We define a temporal algebra that is applicable to any temporal relational data model supporting discrete linear bounded time. This algebra has the five basic relational algebra operators extended to the temporal domain and an operator of linear recursion. We show that this algebra has the expressive power of a safe temporal calculus based on the predicate temporal logic with the until and since temporal operators. In [CrC189], a historical calculus was proposed as a basis for historical relational completeness. We propose the temporal algebra defined in this paper and the equivalent temporal calculus as an alternative basis for temporal relational completeness.Information Systems Working Papers Serie