Discovery of frequent patterns is a very important data mining problem with numerous applications. Frequent pattern mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. A significant amount of research on efficient processing of frequent pattern queries has been done in recent years, focusing mainly on co...

Dynamically controllable simple temporal networks with uncertainty (STNU) are widely used to represent temporal plans or schedules with uncertainty and execution flexibility. While the problem of testing an STNU for dynamic controllability is well studied, many use cases – for example, problem relaxation or schedule robustness analysis – require optimising a function over STNU time bounds subje...

This paper addresses the optimal design and planning of sustainable chemical supply chains (SCs) in the presence of uncertainty in the damage model used to evaluate their environmental performance. The environmental damage is assessed through the Eco-indicator 99, which includes the recent advances made in Life Cycle Assessment (LCA). The overall problem is formulated as a bi-criterion stochast...

Blending biomass materials of different physical or chemical properties provides an opportunity to adjust the quality feedstock meet specifications conversion platform. We propose a model which identifies right mix optimize performance thermochemical process at minimum cost. This is chance-constraint programming (CCP) takes into account stochastic nature quality. The proposed CCP ensures that r...

The Markov decision process model is a powerful tool in planing tasks and sequential decision making problems. The randomness of state transitions and rewards implies that the performance of a policy is often stochastic. In contrast to the standard approach that studies the expected performance, we consider the policy that maximizes the probability of achieving a pre-determined target performan...

This article proposes a stochastic vehicle routing problem within the frame-wok of chance constrained programming where one or more parameters are presumed to be random variables with known distribution function. The reality is that once we convert some special form of probabilistic constraint into their equivalent deterministic form then a nonlinear constraint generates. Knowing that reliable ...

Optimization problems face random constraint violations when uncertainty arises in constraint parameters. Effective ways of controlling such violations include risk constraints, e.g., chance constraints and conditional Value-at-Risk (CVaR) constraints. This paper studies these two types of risk constraints when the probability distribution of the uncertain parameters is ambiguous. In particular...

This paper’s approach to semantic integrity management is that in order to maintain an integrity constraint, some variables of the constraint may be operated on while others may not This defines integrity dependencies between vsiables. Various examples of integrity dependencies and their meanings are discussed. In addition to corresponding to real world practice, integrity dependencies can be u...

This paper presents a novel approach for inducing causal rules by using deverbal nouns as a clue for finding causal relations. We collect verbs and their deverbal forms from FrameNet, and extract pairs of sentences in which event verbs and their corresponding deverbal forms co-occur in documents. The most challenging part of this work is to generalize an instance of causal relation into a rule....

We propose a safe approximation to joint chance-constrained programming, where the constraint functions are additively dependent on normally-distributed random vector. The is analytical, meaning that it requires neither numerical integrations nor sampling-based probability approximations. Under mild assumptions, standard nonlinear program. compare this new another analytical for programming bas...