1,215 research outputs found
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 \u201cnearly periodic events\u201d, i.e., eventsacts
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
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
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
Temporal Data Modeling and Reasoning for Information Systems
Temporal knowledge representation and reasoning is a major research field in Artificial
Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to
model and process time and calendar data is essential for many applications like appointment
scheduling, planning, Web services, temporal and active database systems, adaptive
Web applications, and mobile computing applications. This article aims at three complementary
goals. First, to provide with a general background in temporal data modeling
and reasoning approaches. Second, to serve as an orientation guide for further specific
reading. Third, to point to new application fields and research perspectives on temporal
knowledge representation and reasoning in the Web and Semantic Web
ON PERIODICITY IN TEMPORAL DATABASES
The issue of periodicity is generally understood to be a desirable property of temporal
data that should be supported by temporal database models and their query
languages. Nevertheless, there has so far not been any systematic examination of how
to incorporate this concept into a temporal DBMS. In this paper we describe two concepts
of periodicity, which we call strong periodicity and near periodicity, and discuss
how they capture formally two of the intuitive meanings of this term. We formally
compare the expressive power of these two concepts, relate them to existing temporal
query languages, and show how they can be incorporated into temporal relational
database query languages, such as the proposed temporal extension to SQL, in a clean
and straightforward manner.Information Systems Working Papers Serie
Application of the Temporal Database Technology in the Development of Latvian Railway Information Systems
The paper presents the research of temporal data usage in the information systems (IS) for railway transport. In the research the IS artifact building phase has been executed. The models and methods oriented at the operation with the temporal objects of the railway transport system have been designed. The work takes into consideration the problems of the temporal objects presentation in the relational databases, as well as the problems of provision of their integrity and the questions of interaction with them. The investigation results have been employed in the development of the interactive passenger train schedule IS for the Latvian Railway
Supporting Temporal Reasoning by Mapping Calendar Expressions to Minimal Periodic Sets
In the recent years several research efforts have focused on the concept of
time granularity and its applications. A first stream of research investigated
the mathematical models behind the notion of granularity and the algorithms to
manage temporal data based on those models. A second stream of research
investigated symbolic formalisms providing a set of algebraic operators to
define granularities in a compact and compositional way. However, only very
limited manipulation algorithms have been proposed to operate directly on the
algebraic representation making it unsuitable to use the symbolic formalisms in
applications that need manipulation of granularities.
This paper aims at filling the gap between the results from these two streams
of research, by providing an efficient conversion from the algebraic
representation to the equivalent low-level representation based on the
mathematical models. In addition, the conversion returns a minimal
representation in terms of period length. Our results have a major practical
impact: users can more easily define arbitrary granularities in terms of
algebraic operators, and then access granularity reasoning and other services
operating efficiently on the equivalent, minimal low-level representation. As
an example, we illustrate the application to temporal constraint reasoning with
multiple granularities.
From a technical point of view, we propose an hybrid algorithm that
interleaves the conversion of calendar subexpressions into periodical sets with
the minimization of the period length. The algorithm returns set-based
granularity representations having minimal period length, which is the most
relevant parameter for the performance of the considered reasoning services.
Extensive experimental work supports the techniques used in the algorithm, and
shows the efficiency and effectiveness of the algorithm
Research and Development of a General Purpose Instrument DAQ-Monitoring Platform applied to the CLOUD/CERN experiment
The current scientific environment has experimentalists and system administrators allocating large amounts of time for data access, parsing and gathering as well as instrument management. This is a growing challenge since there is an increasing number of large collaborations with significant amount of instrument resources, remote instrumentation sites and continuously improved and upgraded scientific instruments. DAQBroker is a new software designed to monitor networks of scientific instruments while also providing simple data access methods for any user. Data can be stored in one or several local or remote databases running on any of the most popular relational databases (MySQL, PostgreSQL, Oracle). It also provides the necessary tools for creating and editing the metadata associated with different instruments, perform data manipulation and generate events based on instrument measurements, regardless of the user’s know-how of individual instruments. Time series stored in a DAQBroker database also benefit from several statistical methods for time series classification, comparison and event detection as well as multivariate time series analysis methods to determine the most statistically relevant time series, rank the most influential time series and also determine the periods of most activity during specific experimental periods. This thesis presents the architecture behind the framework, assesses the performance under controlled conditions and presents a use-case under the CLOUD experiment at CERN, Switzerland. The univariate and multivariate time series statistical methods applied to this framework are also studied.O processo de investigação científica moderno requer que tanto experimentalistas como administradores de sistemas dediquem uma parte significativa do seu tempo a criar estratégias para aceder, armazenar e manipular instrumentos científicos e os dados que estes produzem. Este é um desafio crescente considerando o aumento de colaborações que necessitam de vários instrumentos, investigação em áreas remotas e instrumentos científicos com constantes alterações. O DAQBroker é uma nova plataforma desenhada para a monitorização de instrumentos científicos e ao mesmo tempo fornece métodos simples para qualquer utilizador aceder aos seus dados. Os dados podem ser guardados em uma ou várias bases de dados locais ou remotas utilizando os gestores de bases de dados mais comuns (MySQL, PostgreSQL, Oracle). Esta plataforma também fornece as ferramentas necessárias para criar e editar versões virtuais de instrumentos científicos e manipular os dados recolhidos dos instrumentos, independentemente do grau de conhecimento que o utilizador tenha com o(s) instrumento(s) utilizado(s). Séries temporais guardadas numa base de dados DAQBroker beneficiam de um conjunto de métodos estatísticos para a classificação, comparação e detecção de eventos, determinação das séries com maior influência e os sub-períodos experimentais com maior actividade. Esta tese apresenta a arquitectura da plataforma, os resultados de diversos testes de esforço efectuados em ambientes controlados e um caso real da sua utilização na experiência CLOUD, no CERN, Suíça. São estudados também os métodos de análise de séries temporais, tanto singulares como multivariadas aplicados na plataforma
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