| Logical inference starts with concluding that if B implies A, and B is true, then A is true as well. To describe probabilistic inference rules, we must therefore deene the probability of an implication \A if B". There exist two diierent approaches to deening this probability, and these approaches lead to diierent probabilistic inference rules: We may interpret the probability of an implicatio...

Dependencies play an important role in database theory. We study order dependencies (ODs)—and unidirectional order dependencies (UODs), sub-class of ODs—which describe the relationships among lexicographical orderings of sets of tuples. We investigate the inference problem for order dependencies. We establish the following: (i) a sound and complete chase procedure for ODs for testing logical im...

some applications are critical and must designed fault tolerant system. usually voting algorithm is one of the principle elements of a fault tolerant system. two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted average algorithm these algorithms have some problems. majority confronts with the problem of threshold limits and voter of weight...

We adapt the Fast Causal Inference (FCI) algorithm of Spirtes et al. (2000) to the problem of inferring causal relationships from time series data and evaluate our adaptation and the original FCI algorithm, comparing them to other methods including Granger causality. One advantage of FCI based approaches is the possibility of taking latent confounding variables into account, as opposed to metho...

We target the problem of accuracy and robustness in causal inference from finite data sets. Our aim is to combine the inherent robustness of the Bayesian approach with the theoretical strength and clarity of constraint-based methods. We use a Bayesian score to obtain probability estimates on the input statements used in a constraint-based procedure. These are subsequently processed in decreasin...

The implication problem is the problem of deciding whether a given set of dependencies entails other dependencies. Up to now, the entailment of excluded dependencies or independencies is only regarded on a metalogical level, which is not suitable for an automatic inference process. But, the inference of independencies is of great importance for new topics in database research like knowledge dis...

We target the problem of accuracy and robustness in causal inference from finite data sets. Some state-of-the-art algorithms produce clear output complete with solid theoretical guarantees but are susceptible to propagating erroneous decisions, while others are very adept at handling and representing uncertainty, but need to rely on undesirable assumptions. Our aim is to combine the inherent ro...

In [14], we have presented a fuzzy forward reasoning methodology for rule-based systems using the functional representation of rules (fuzzy implications). In this paper, we extend methodology for selecting relevant fuzzy implications from [14] in backward reasoning. The proposed methodology takes full advantage of the functional representation of fuzzy implications and the algebraic properties ...

This paper is concerned with proposing the fuzzy cognitive map (FCM)-driven inference ampli®cation mechanism in the ®eld of web-mining. As the recent advent of the Internet, most of the modern ®rms are now geared towards using the web technology in their daily as well as strategic activities. The web-mining technology provides them with unprecedented ability to analyze web-log data, which are s...