5,844 research outputs found
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
Query processing in temporal object-oriented databases
This PhD thesis is concerned with historical data management in the context of objectoriented
databases. An extensible approach has been explored to processing temporal object queries within a uniform query framework. By the uniform framework, we mean
temporal queries can be processed within the existing object-oriented framework that is extended from relational framework, by extending the existing query processing
techniques and strategies developed for OODBs and RDBs.
The unified model of OODBs and RDBs in UmSQL/X has been adopted as a basis for this purpose. A temporal object data model is thereby defined by incorporating a time
dimension into this unified model of OODBs and RDBs to form temporal relational-like cubes but with the addition of aggregation and inheritance hierarchies. A query algebra,
that accesses objects through these associations of aggregation, inheritance and timereference, is then defined as a general query model /language. Due to the extensive
features of our data model and reducibility of the algebra, a layered structure of query processor is presented that provides a uniforrn framework for processing temporal object
queries. Within the uniform framework, query transformation is carried out based on a set of transformation rules identified that includes the known relational and object rules plus those pertaining to the time dimension. To evaluate a temporal query involving a path with timereference, a strategy of decomposition is proposed. That is, evaluation of an enhanced path, which is defined to extend a path with time-reference, is decomposed by initially dividing the path into two sub-paths: one containing the time-stamped class that can be optimized by
making use of the ordering information of temporal data and another an ordinary sub-path (without time-stamped classes) which can be further decomposed and evaluated using
different algorithms. The intermediate results of traversing the two sub-paths are then joined together to create the query output. Algorithms for processing the decomposed query components, i. e., time-related operation algorithms, four join algorithms (nested-loop forward join, sort-merge forward join, nested-loop reverse join and sort-merge reverse join) and their modifications, have been presented with cost analysis and implemented with stream processing techniques using C++. Simulation results are also provided. Both cost analysis and simulation show the effects of time on the query processing algorithms: the join time cost is linearly increased with the expansion in the number of time-epochs (time-dimension in the case of a regular TS). It is also shown that using heuristics that make use of time information can lead to a significant time cost saving. Query processing with incomplete temporal data has also been discussed
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 reIationa1 completeness, analogous to Codd's notion of relational
completeness, one for each type of model. We show that the temporally ungrouped models
are less expressive 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 a 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
Constructing a concept of number
Numbers are concepts whose content, structure, and organization are influenced by the material forms used to represent and manipulate them. Indeed, as argued here, it is the inclusion of multiple forms (distributed objects, fingers, single- and two-dimensional forms like pebbles and abaci, and written notations) that is the mechanism of numerical elaboration. Further, variety in employed forms explains at least part of the synchronic and diachronic variability that exists between and within cultural number systems. Material forms also impart characteristics like linearity that may persist in the form of knowledge and behaviors, ultimately yielding numerical concepts that are irreducible to and functionally independent of any particular form. Material devices used to represent and manipulate numbers also interact with language in ways that reinforce or contrast different aspects of numerical cognition. Not only does this interaction potentially explain some of the unique aspects of numerical language, it suggests that the two are complementary but ultimately distinct means of accessing numerical intuitions and insights. The potential inclusion of materiality in contemporary research in numerical cognition is advocated, both for its explanatory power, as well as its influence on psychological, behavioral, and linguistic aspects of numerical cognition
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
In domains like bioinformatics, information retrieval and social network
analysis, one can find learning tasks where the goal consists of inferring a
ranking of objects, conditioned on a particular target object. We present a
general kernel framework for learning conditional rankings from various types
of relational data, where rankings can be conditioned on unseen data objects.
We propose efficient algorithms for conditional ranking by optimizing squared
regression and ranking loss functions. We show theoretically, that learning
with the ranking loss is likely to generalize better than with the regression
loss. Further, we prove that symmetry or reciprocity properties of relations
can be efficiently enforced in the learned models. Experiments on synthetic and
real-world data illustrate that the proposed methods deliver state-of-the-art
performance in terms of predictive power and computational efficiency.
Moreover, we also show empirically that incorporating symmetry or reciprocity
properties can improve the generalization performance
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