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Expressing knowledge as expert experience and discovering knowledge implied in data are two important ways for knowledge acquisition. Consistent combination of these two kinds of knowledge has attracted much attention due to the potential applications to knowledge fusion and wide requirements of decision support. In this paper, we focus on the probabilistic modeling of expert experience represented as logical predicate formulas, aiming at the effective fusion of logical and probabilistic knowledge. Taking qualitative probabilistic network (QPN) as the underlying framework of probabilistic knowledge implied in data as well as the abstraction of general Bayesian networks (BNs), we are to construct the probabilistic graphical model for both the given predicate formulas and the ultimate result of knowledge fusion. We first propose the concept and the construction algorithm of predicate graph (PG) to describe the dependence relations among predicate formulas, and discuss PG’s probabilistic semantics correspondingly. We then prove that PG is a probability dependency model and has the same semantics with a general probabilistic graphical model. Consequently, we give the method for fusing PG and QPN. Experimental results show the effectiveness of our methods.

Modeling and analysis of time-series data attract much attention in data mining and knowledge discovery community due to their many applications in financial analysis, automation control, etc. In such applications, time-series data usually contain several attributes that may be causally dependent in historical time slices. Dangerous feedback loops of attributes' dependent relationships can make the system collapse due to amplification or oscillation of attribute values. Motivated by efficient analysis of causalities in time-series data, we propose a temporal qualitative probabilistic graphical model in this paper. From given time-series sample data, we construct the structure of the temporal qualitative probabilistic network (TQPN) and derive the corresponding qualitative influences on directed edges. We then present the approach for TQPN reasoning with time-series features. Consequently, positive time-series feedback loops are defined, and the approach to identify them is proposed. Preliminary experiments show that our proposed method is not only feasible but also efficient.

A temporal nodes Bayesian network (TNBN) is an alternative graphical representation of temporal probabilistic information.The temporal functional dependency is an important data dependencyin temporal relational databases. In this paper temporal data dependencies are extended into delayed conditions, called asynchronous dependencies. The purpose of this paper is to set up a connection between these asynchronous data dependencies and TNBNs. We show that we can obtain the maximum conditionalin dependencies implied by given temporal functional dependencies. Furthermore, we propose an approach to construct a TNBN from a set of temporal functional dependencies.

Finding similar sequences in time series has received much attention and is a widely studied topic. Most existing approaches in the time series area focus on the efficiency of algorithms but seldom provide a means to handle imprecise data. In this paper, a more general approach is proposed to measure the distance of time sequences containing crisp values, intervals, and fuzzy intervals as well. The concept of distance measurement and its associated dynamic-programming-based algorithms are described. In addition to finding the sequences with similar evolving trends, a means of finding the sequences with opposite evolving tendencies is also proposed, which is usually omitted in current related research but could be of great interest to many users.

Bayesian networks have become a popular technique for representing and reasoning with probabilistic information. The fuzzy functional dependency is an important kind of data dependencies in relational databases with fuzzy values. The purpose of this paper is to set up a connection between these data dependencies and Bayesian networks. The connection is done through a set of methods that enable people to obtain the most information of independent conditions from fuzzy functional dependencies.

This paper presets an expression of the semantic proximity. Based on the concept of the semantic proximity, an evaluated method of the fuzzy association degree is given. It is shown that the method is reasonable and effective. Particularly, by means of the fuzzy association degree, we can discover the connection between a plant and some ecological environment, which will be helpful to the exploitation of rare plants.

In this paper, we want to set up a series of design methods for relational databases based on the fuzzy inheritance relationship. We look upon the relationship between a class and its subclass as a scheme constraint. We present the concept of fuzzy inheritance dependencies and show that the inference rules for fuzzy inheritance dependencies are sound and complete. Furthermore, we give the method of revising the scope of the attribute value in the superclass.

Due to the governments' supports and aggressive business worldwide expansion, the supply has gradually exceeded the demand in solar cell industry, which causes a serious competition in market and thus profit drops. This study proposes a feasible solution to re-layout base on the idea of cellular manufacturing, and uses the multiple criteria decision-making method (MCDM) to evaluate the best alternative. An empirical result about a leading solar cell manufacturer in Taiwan was conducted and led to 9.68% cycle time reduction and 18.17% decrease of WIP level.

In this paper the relational model with fuzzy attribute values is discussed. Based on the concepts of the semantic distance, the definitions of the fuzzy relational operations and the fuzzy functional dependency are given. Making use of fuzzy functional dependencies, we can compress the domain of the fuzzy data and make the fuzzy data ‘clearer’. Furthermore, an algorithm for testing the satisfiability of a fuzzy instance to the set of fuzzy functional dependencies is given.

This paper presents a new method for learning Bayesian networks from functional dependencies (FD) and third normal form (3NF) tables in relational databases. The method sets up a linkage between the theory of relational databases and probabilistic reasoning models, which is interesting and useful especially when data are incomplete and inaccurate. The effectiveness and practicability of the proposed method is demonstrated by its implementation in a mobile commerce system.