131 research outputs found
On the Semantics of "Now" in Databases
While "now" is expressed in SQL as CURRENT-TIMESTAMP within queries, this value cannot be
stored in the database. However, this notion of an ever-increasing current-time value has been
reflected in some temporal data models by inclusion of database-resident variables, such as
"now," "until-changed," "â," "@" and "-." Time variables are very desirable, but their use
also leads to a new type of database, consisting of tuples with variables, termed a variable
database.
This paper proposes a framework for defining the semantics of the variable databases of temporal
relational data models. A framework is presented because several reasonable meanings
may be given to databases that use some of the specific temporal variables that have appeared
in the literature. Using the framework, the paper defines a useful semantics for such databases.
Because situations occur where the existing time variables are inadequate, two new types of
modeling entities that address these shortcomings, timestamps which we call now-relative and
now-relative indeterminate, are introduced and defined within the framework. Moreover, the paper
provides a foundation, using algebraic bind operators, for the querying of variable databases
via existing query languages. This transition to variable databases presented here requires minimal
change to the query processor. Finally, to underline the practical feasibility of variable
databases, we show that database variables can be precisely specified and efficiently implemented
in conventional query languages, such as SQL, and in temporal query languages, such
as TSQL2.Information Systems Working Papers Serie
6 Access Methods and Query Processing Techniques
The performance of a database management system (DBMS) is fundamentally dependent on the access methods and query processing techniques available to the system. Traditionally, relational DBMSs have relied on well-known access methods, such as the ubiquitous B +-tree, hashing with chaining, and, in som
Development of cloud removal and land cover Change extraction algorithms for remotely-sensed Landsat imagery
Land cover change monitoring requires the analysis of remotely-sensed data. In the tropics this is difficult because of persistent cloud cover, and data availability. This research focuses on the elimination of cloud cover as an important step towards addressing the issue of change detection. The result produced clearer images, whereas some persistent cloud remains. This persistent cloud and the cloud adjacency effects diminish the quality of image product and affect the change detection quality
Relational model of temporal data based on 6th normal form
Ovaj rad povezuje dva različita područja istraživanja, tj. Temporalne podatke i Relacijsko modeliranje. Temporalni podaci su podaci koji predstavljaju stanje u vremenu, a temporalna baza podataka je baza podataka s ugrađenom podrškom za baratanje s podacima koji uključuju vrijeme. Većina temporalnih sustava pruža dovoljno temporalnih karakteristika, ali su relacijski modeli nepravilno normalizirani, a pristupi modeliranju nedostaju ili nisu uvjerljivi. Ovim se prijedlogom daju prednosti modeliranja temporalne baze podataka, prvenstveno korištene u analitici i izvještavanju, gdje tipična pretraživanja uključuju mali podniz atributa i veliku količinu zapisa. U radu se definira posebni logički model koji podržava temporalne podatke i konzistenciju, zasnovan na vertikalnoj dekompoziciji i šestoj normalnoj formi (6NF). Primjena 6NF omogućuje neovisnost u promjeni atributnih vrijednosti i tako sprečava redundanciju i anomalije. Naš je model uspoređen s drugim temporalnim modelima i demonstrirano je super brzo pretraživanje postignuto eliminacijom spajanja baze podataka (database join elimination). Svrha je rada pomoći stručnjacima koji se bave bazama podataka u primjeni temporalnog modeliranja.This paper brings together two different research areas, i.e. Temporal Data and Relational Modelling. Temporal data is data that represents a state in time while temporal database is a database with built-in support for handling data involving time. Most of temporal systems provide sufficient temporal features, but the relational models are improperly normalized, and modelling approaches are missing or unconvincing. This proposal offers advantages for a temporal database modelling, primarily used in analytics and reporting, where typical queries involve a small subset of attributes and a big amount of records. The paper defines a distinctive logical model, which supports temporal data and consistency, based on vertical decomposition and sixth normal form (6NF). The use of 6NF allows attribute values to change independently of each other, thus preventing redundancy and anomalies. Our proposal is evaluated against other temporal models and super-fast querying is demonstrated, achieved by database join elimination. The paper is intended to help database professionals in practice of temporal modelling
BiTRDF: Extending RDF for BiTemporal Data
The Internet is not only a platform for communication, transactions, and cloud storage, but it is also a large knowledge store where people as well as machines can create, manipulate, infer, and make use of data and knowledge. The Semantic Web was developed for this purpose. It aims to help machines understand the meaning of data and knowledge so that machines can use the data and knowledge in decision making. The Resource Description Framework (RDF) forms the foundation of the Semantic Web which is organized as the Semantic Web Layer Cake. RDF is limited and can only express a binary relationship with the format of . However, expressing higher order relationships requires reification which is very cumbersome. Naturally, time varying data is very common and cannot be represented by only binary relationships. We first surveyed approaches that use reification or extend RDF for higher order relationships. Then we proposed a new data model, BiTemporal RDF (BiTRDF), that incorporates both valid time and transaction time explicitly into standard RDF resources. We defined the BiTRDF model with its elements, vocabulary, semantics, and entailment, and the BiTemporal SPARQL (BiT-SPARQL) query language. We discussed the foundation for implementing BiTRDF and we also explored different approaches to implement the BiTRDF model. We concluded this thesis with potential research directions. This thesis lays the foundation for a new approach to easily embed any or more dimensions, such as temporal data, spatial data, probabilistic data, confidence levels, etc
Coherent Integration of Databases by Abductive Logic Programming
We introduce an abductive method for a coherent integration of independent
data-sources. The idea is to compute a list of data-facts that should be
inserted to the amalgamated database or retracted from it in order to restore
its consistency. This method is implemented by an abductive solver, called
Asystem, that applies SLDNFA-resolution on a meta-theory that relates
different, possibly contradicting, input databases. We also give a pure
model-theoretic analysis of the possible ways to `recover' consistent data from
an inconsistent database in terms of those models of the database that exhibit
as minimal inconsistent information as reasonably possible. This allows us to
characterize the `recovered databases' in terms of the `preferred' (i.e., most
consistent) models of the theory. The outcome is an abductive-based application
that is sound and complete with respect to a corresponding model-based,
preferential semantics, and -- to the best of our knowledge -- is more
expressive (thus more general) than any other implementation of coherent
integration of databases
Temporal reasoning in a logic programming language with modularity
Actualmente os Sistemas de Informação Organizacionais (SIO) lidam cada vez mais com informação que tem dependências temporais. Neste trabalho concebemos um ambiente de trabalho para construir e manter SIO Temporais. Este ambiente assenta sobre um linguagem lógica denominada Temporal Contextua) Logic Programming que integra modularidade com raciocínio temporal fazendo com que a utilização de um módulo dependa do tempo do contexto. Esta linguagem é a evolução de uma outra, também introduzida nesta tese, que combina Contextua) Logic Programming com Temporal Annotated Constraint Logic Programming, na qual a modularidade e o tempo são características ortogonais. Ambas as linguagens são formalmente discutidas e exemplificadas.
As principais contribuições do trabalho descrito nesta tese incluem:
• Optimização de Contextua) Logic Programming (CxLP) através de interpretação abstracta.
• Sintaxe e semântica operacional para uma linguagem que combina de um modo independente as linguagens Temporal Annotated Constraint Logic Programming (TACLP) e CxLP. É apresentado um compilador para esta linguagem.
• Linguagem (sintaxe e semântica) que integra de um modo inovador modularidade (CxLP) com raciocínio temporal (TACLP). Nesta linguagem a utilização de um dado módulo está dependente do tempo do contexto. É descrito um interpretador e um compilador para esta linguagem.
• Ambiente de trabalho para construir e fazer a manutenção de SIO Temporais. Assenta sobre uma especificação revista da linguagem ISCO, adicionando classes e manipulação de dados temporais. É fornecido um compilador em que a linguagem resultante é a descrita no item anterior. ABSTRACT- Current Organisational Information Systems (OIS) deal with more and more Infor-mation that, is time dependent. In this work we provide a framework to construct and maintain Temporal OIS. This framework builds upon a logical language called Temporal Contextual. Logic Programming that deeply integrates modularity with tem-poral reasoning making the usage of a module time dependent. This language is an evolution of another one, also introduced in this thesis, that combines Contextual Logic Programming with Temporal Annotated Constraint Logic Programming where modularity and time are orthogonal features. Both languages are formally discussed and illustrated.
The main contributions of the work described in this thesis include:
• Optimisation of Contextual Logic Programming (CxLP) through abstract interpretation.
• Syntax and operational semantics for an independent combination of the temporal framework Temporal Annotated Constraint Logic Programming (TACLP) and CxLP. A compiler for this language is also provided.
• Language (syntax and semantics) that integrates in a innovative way modularity
(CxLP) with temporal reasoning (TACLP). In this language the usage of a given
module depends of the time of the context. An interpreter and a compiler for
this language are described.
• Framework to construct and maintain Temporal Organisational Information Systems. It builds upon a revised specification of the language ISCO, adding temporal classes and temporal data manipulation. A compiler targeting the language presented in the previous item is also given
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
Change detection is one of the central problems in earth observation and was
extensively investigated over recent decades. In this paper, we propose a novel
recurrent convolutional neural network (ReCNN) architecture, which is trained
to learn a joint spectral-spatial-temporal feature representation in a unified
framework for change detection in multispectral images. To this end, we bring
together a convolutional neural network (CNN) and a recurrent neural network
(RNN) into one end-to-end network. The former is able to generate rich
spectral-spatial feature representations, while the latter effectively analyzes
temporal dependency in bi-temporal images. In comparison with previous
approaches to change detection, the proposed network architecture possesses
three distinctive properties: 1) It is end-to-end trainable, in contrast to
most existing methods whose components are separately trained or computed; 2)
it naturally harnesses spatial information that has been proven to be
beneficial to change detection task; 3) it is capable of adaptively learning
the temporal dependency between multitemporal images, unlike most of algorithms
that use fairly simple operation like image differencing or stacking. As far as
we know, this is the first time that a recurrent convolutional network
architecture has been proposed for multitemporal remote sensing image analysis.
The proposed network is validated on real multispectral data sets. Both visual
and quantitative analysis of experimental results demonstrates competitive
performance in the proposed mode
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