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Knowledge and Information Systems |
Abstract. The discovery of association rules is a very
efficient data mining technique that is especially suitable for large
amounts of categorical data. This paper shows how the discovery of
association rules can be of benefit for numeric data as well. Based on
a review of previous approaches we introduce Q2, a faster algorithm
for the discovery of multi-dimensional association rules over ordinal
data. We experimentally compare the new algorithm with the previous
approach, obtaining performance improvements of more than an order of
magnitude on supermarket data. In addition, a new absolute measure for
the interestingness of quantitative association rules is
introduced. It is based on the view that quantitative association
rules have to be interpreted with respect to their Boolean
generalizations. This measure has two major benefits compared to the
previously used relative interestingness measure; first, it speeds up
rule extraction and evaluation and second, it is easier to interpret
for a user. Finally we introduce a rule browser which supports the
exploration of ordinal data with quantitative association rules.
Abstract. This paper identifies an axiom foundation for
uncertain reasonings in rule-based expert systems: a near topological
algebra (NT-algebra for short), which holds some basic notions hidden
behind the uncertain reasoning models in rule-based expert
systems. According to the basic means of topological connection in an
inference network, an NT-algebraic structure has five basic operators,
i.e. AND, OR, NOT, Sequential combination
and Parallel combination, which obey some axioms. An
NT-algebraic structure is defined on a near-degree space introduced by
the authors, which is a special topological space. The continuities of
real functions, of fuzzy functions and functions in other senses can
be uniformly considered in the framework of a near-degree space. This
paper also proves that EMYCIN's and PROSPECTOR's uncertain reasoning
models correspond to good NT-algebras. Moreover, the existence of any
finite NT-algebraic structure is constructively proved. Compared to
other related research efforts, the NT-algebra as an axiom foundation
has the following characteristics: (1) various cases of assessments
for uncertainties of evidence and rules are put into a unified
algebraic structure; and (2) major emphasis has been placed on the
basic laws of the propagation for them in an inference network,
especially the continuity of propagation operations and the
relationships between propagation operations.
Abstract. We study the performance of various run placement
policies on disks for the merge phase of concurrent mergesorts using
parallel prefetching. The initial sorted runs (input) of a merge and
its final sorted run (output) are stored on multiple disks but each
run resides only on a single disk. In this paper, we examine through
detailed simulations three different run placement policies and the
impact of buffer thrashing. The results show that, with buffer
thrashing avoidance, the best performance can be achieved by a run
placement policy that uses a proper subset of the disks dedicated for
writing the output runs while the rest of the disks are used for
prefetching the input runs in parallel. However, the proper number of
write disks is workload dependent, and if not carefully chosen, it can
adversely affect the system performance. In practice, a reasonably
good performance can be achieved by a run placement policy that does
not place the output run of a merge on any of the disks that store its
own input runs but allows the output run to share the same disk with
some of the input runs of other merges.
Abstract. This paper discusses the important aspects of the
reliability of systems with an imprecise general model of the
structure function. It is assumed that the information about
reliability behavior of components is restricted by the mean levels of
component performance. In this case the classical reliability theory
cannot provide a way for analyzing the reliability of systems. The
theory of imprecise probabilities may be a basis in developing a
general reliability theory which allows us to solve such problems. The
basic tool for computing new reliability measures is the natural
extension which can be regarded as a linear optimization
problem. However, the linear programming computations will become
impracticable when the number of components in the system is
large. Therefore, the main aim of this paper is to obtain explicit
expressions for computing the system reliability measures. We analyze
the reliability of general structures and typical systems. The
numerical examples illustrate the usefulness of the presented approach
to reliability analyzing.
Abstract. In this paper, we generalize conventional join
indexes to a cluster-based join index, in which objects are grouped
into clusters based on proximity. Each record of our join index
represents a pair of clusters in which the join condition is satisfied
by some members of the cluster. This strategy is especially useful
for spatial and high-dimensional databases because of their typically
large data volume and complex operations. Our approach leverages on
the structure of R-trees by exploiting the internal nodes of an R-tree
in effectively determining the precomputed clusters which can be used
in our join index. By varying the size of the cluster, we are able to
fine-tune the join index to achieve a balance between update cost and
retrieval cost to suit individual applications. Different
implementations of the join index are examined to determine how the
join index can be efficiently maintained. To this end, we also conduct
a number of experiments on intersection join and window queries, and
the results confirm that semi-precomputation of join results is a
robust and cost effective approach to join processing.
An Axiom Foundation for Uncertain Reasonings in Rule-Based Expert
Systems: NT-Algebra
Xudong Luo and Chengqi ZhangRun Placement Policies for Concurrent Mergesorts Using Parallel
Prefetching
Kun-Lung Wu, Philip S. Yu and James Z. TengImprecise Reliability of General Structures
Lev V. Utkin and Sergey V. GurovEfficient Join Processing Using Partial Precomputation
Kian-Lee Tan, Cheng Hian Goh, Mong Li Lee, and Beng Chin Ooi
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