1,967 research outputs found
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
A Type Language for Calendars
Time and calendars play an important role in databases,
on the Semantic Web, as well as in mobile computing. Temporal data
and calendars require (specific) modeling and processing tools. CaTTS
is a type language for calendar definitions using which one can model
and process temporal and calendric data. CaTTS is based on a "theory
reasoning" approach for efficiency reasons. This article addresses type
checking temporal and calendric data and constraints. A thesis underlying
CaTTS is that types and type checking are as useful and desirable
with calendric data types as with other data types. Types enable
(meaningful) annotation of data. Type checking enhances efficiency and
consistency of programming and modeling languages like database and
Web query languages
Temporalized logics and automata for time granularity
Suitable extensions of the monadic second-order theory of k successors have
been proposed in the literature to capture the notion of time granularity. In
this paper, we provide the monadic second-order theories of downward unbounded
layered structures, which are infinitely refinable structures consisting of a
coarsest domain and an infinite number of finer and finer domains, and of
upward unbounded layered structures, which consist of a finest domain and an
infinite number of coarser and coarser domains, with expressively complete and
elementarily decidable temporal logic counterparts.
We obtain such a result in two steps. First, we define a new class of
combined automata, called temporalized automata, which can be proved to be the
automata-theoretic counterpart of temporalized logics, and show that relevant
properties, such as closure under Boolean operations, decidability, and
expressive equivalence with respect to temporal logics, transfer from component
automata to temporalized ones. Then, we exploit the correspondence between
temporalized logics and automata to reduce the task of finding the temporal
logic counterparts of the given theories of time granularity to the easier one
of finding temporalized automata counterparts of them.Comment: Journal: Theory and Practice of Logic Programming Journal Acronym:
TPLP Category: Paper for Special Issue (Verification and Computational Logic)
Submitted: 18 March 2002, revised: 14 Januari 2003, accepted: 5 September
200
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
Reconciliation of temporal semantic heterogeneity in evolving information systems
The change in meaning of data over time poses significant challenges for the use of that data. These challenges exist in the use of an individual data source and are further compounded with the integration of multiple sources. In this paper, we identify three types of temporal semantic heterogeneity. We propose a solution based on extensions to the Context Interchange framework, which has mechanisms for capturing semantics using ontology and temporal context. It also provides a mediation service that automatically reconciles semantic conflicts. We show the feasibility of this approach with a prototype that implements a subset of the proposed extensions
Use-cases on evolution
This report presents a set of use cases for evolution and reactivity for data in the Web and
Semantic Web. This set is organized around three different case study scenarios, each of them
is related to one of the three different areas of application within Rewerse. Namely, the scenarios
are: “The Rewerse Information System and Portal”, closely related to the work of A3
– Personalised Information Systems; “Organizing Travels”, that may be related to the work
of A1 – Events, Time, and Locations; “Updates and evolution in bioinformatics data sources”
related to the work of A2 – Towards a Bioinformatics Web
Detecting anomalous longitudinal associations through higher order mining
The detection of unusual or anomalous data is an important
function in automated data analysis or data
mining. However, the diversity of anomaly detection
algorithms shows that it is often difficult to determine
which algorithms might detect anomalies given
any random dataset. In this paper we provide a partial
solution to this problem by elevating the search
for anomalous data in transaction-oriented datasets
to an inspection of the rules that can be produced
by higher order longitudinal/spatio-temporal association
rule mining. In this way we are able to apply
algorithms that may provide a view of anomalies that
is arguably closer to that sought by information analysts.Sydney, NS
Multi-Paradigm Reasoning for Access to Heterogeneous GIS
Accessing and querying geographical data in a uniform way has become easier in recent years. Emerging standards like WFS turn
the web into a geospatial web services enabled place. Mediation
architectures like VirGIS overcome syntactical and semantical heterogeneity
between several distributed sources. On mobile devices,
however, this kind of solution is not suitable, due to limitations,
mostly regarding bandwidth, computation power, and available storage
space. The aim of this paper is to present a solution for providing
powerful reasoning mechanisms accessible from mobile applications
and involving data from several heterogeneous sources.
By adapting contents to time and location, mobile web information
systems can not only increase the value and suitability of the
service itself, but can substantially reduce the amount of data delivered
to users. Because many problems pertain to infrastructures
and transportation in general and to way finding in particular, one
cornerstone of the architecture is higher level reasoning on graph
networks with the Multi-Paradigm Location Language MPLL. A
mediation architecture is used as a “graph provider” in order to
transfer the load of computation to the best suited component –
graph construction and transformation for example being heavy on
resources. Reasoning in general can be conducted either near the
“source” or near the end user, depending on the specific use case.
The concepts underlying the proposal described in this paper are
illustrated by a typical and concrete scenario for web applications
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