20 research outputs found
Operations on (ordered) interval sets
Intervals play an important role in various kinds of database-applications in practice, for example in historical, spatial, and temporal databases. As a consequence, there is a practical need for a clear and proper treatment of various useful operations on intervals and interval sets in a database context. However, the semantics of some important operations on interval sets are not always treated or not treated very clearly in the literature; e.g., often they are defined in an algorithmic rather than a declarative manner. Moreover, implementation proposals are often not as straightforward as they could be. This paper presents a declarative treatment of various operations on interval sets, also introducing some new notions (such as ordered interval sets, their visible points, and their surface). Then the paper formally ?links? such (mathematical) intervals to their database representations. Finally the paper provides straightforward translations from these formal database representations to standard SQL, without the need for SQL extensions.
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 Stream Algebra
Data stream management systems (DSMS) so far focus on
event queries and hardly consider combined queries to both
data from event streams and from a database. However,
applications like emergency management require combined
data stream and database queries. Further requirements are
the simultaneous use of multiple timestamps after different
time lines and semantics, expressive temporal relations between multiple time-stamps and
exible negation, grouping
and aggregation which can be controlled, i. e. started and
stopped, by events and are not limited to fixed-size time
windows. Current DSMS hardly address these requirements.
This article proposes Temporal Stream Algebra (TSA) so
as to meet the afore mentioned requirements. Temporal
streams are a common abstraction of data streams and data-
base relations; the operators of TSA are generalizations of
the usual operators of Relational Algebra. A in-depth 'analysis of temporal relations guarantees that valid TSA expressions are non-blocking, i. e. can be evaluated incrementally.
In this respect TSA differs significantly from previous algebraic approaches which use specialized operators to prevent
blocking expressions on a "syntactical" level
Performance Evaluation of Attribute and Tuple Timestamping In Temporal Relational Database
Modeling temporal database over relational database
using 1NF model is considered the most popular approach. This
is because of the easy implementation as well as the modeling and
querying power of 1NF model. In this paper, we compare a new
approach for representing valid-time temporal database (in
terms of structure and performance) to the main models in
literature with attribute and tuple timestamping. The
measurement of the performance is represented by the
processing time to get the required temporal data as well as the
size of the whole stored temporal data. A test has been performed
by running sample queries for the same data in the represented
models. Based on the tests, we have found that the new proposed
model required less time and used less disk space. Therefore, it is
more appropriate for modeling 1NF with interval-based
timestamping in relational data model
Degas: A Database of Autonomous objects
In this paper we introduce DEGAS (Dynamic Entities Get Autonomous Status), an active temporal data model based on autonomous objects. The natural combination of active and temporal databases is discussed. The active dimension of DEGAS means that we define the behaviour of objects in terms of production rules. The temporal dimension means that the history of an object is included in the DEGAS data model. Further novel features of DEGAS are the encapsulation of the complete behaviour of an object, both potential and actual. Thus, DEGAS combines dynamic and structural specifications in one model. In addition, DEGAS allows easy evolution of object capabilities through a clear distinction between inherent types and capabilities that can be acquired and lost. This addon mechanism makes DEGAS very suitable as a formalism for role modelling. Finally, the rule model in DEGAS is both simple, through the use of finite automata, and general, because it allows different strategies for dealing with constraints and reacting to events in other objects
Data Management in the LoanSTAR Program
This paper discusses the complexity of managing building energy usage data for many buildings. The history and methodology of data collection at the Texas LoanSTAR Monitoring and Analysis Program, a large multimillion dollar project, is given as an example. The differences in methodology of managing data for one/several buildings versus many buildings are given and discussed. Primary database design and quality assurance issues that should be thought out at the beginning of any large project of this type are given. The importance of intergroup communication throughout the project is stressed. Current areas of software development are discussed in detail, followed by future directions for the project
Algebraic Identities and Query Optimization In a Parametric Model For Relational Temporal Databases
This paper presents algebraic identities and algebraic query optimization for a parametric model for temporal databases. The parametric model has several features not present in the classical model. In this model a key is explicitly designated with a relation and an operator is available to change the key. The algebra for the parametric model is three-sorted; it includes relational expressions that evaluate to relations, domain expressions that evaluate to time domains, and boolean expressions that evaluate to TRUE or FALSE. The identities in the parametric model are classified as weak identities and strong identities. Weak identities in this model are largely counterparts of the identities in classical relational databases. Rather than establishing weak identities from scratch, a meta inference mechanism, introduced in the paper, allows weak identities to be induced from their classical counterpart. On the other hand, the strong identities will be established from scratch. An algorithm for algebraic optimization to transform a query to an equivalent query that will execute more efficiently is presented