Publications

The class of a priori algorithms are popular association rule mining techniques. However, these algorithms are computationally expensive. The authors propose another novel approach to extract association rules. The method represents an itemset information as a cell of a hypercube. The hypercube encodes associations between the items of each transaction. Apart from proposing the main result, we also propose linguistic association rules. Linguistic association rules encode fuzzy information and represent summarized rules.

It is known that bisymmetry generalizes the simultaneous commutativity and associativity in the framework of the unit interval. In this work, we will completely characterize two classes of bisymmetric aggregation operators: one with a neutral element and the other with the vertical and horizontal sections of the idempotent elements being smooth on a finite chain, but not necessarily smooth and commutative. Thus, the previous results, based on the smoothness that is known as a very restrictive condition, are improved. For example, there is only one smooth Archimedean t-norm on a finite chain. In this paper, the discrete bisymmetric aggregation operators are explored without the limit of the smoothness. As a by-product, it is deduced that for smooth aggregation operators on a finite chain, the bisymmetry is equivalent to the commutativity and associativity, which improves the conclusion obtained by Mas et al. that associativity and bisymmetry are equivalent for commutative smooth aggregation operators on a finite chain.

ion as a key to knowledge processing (acquisition, integration, reuse...) Granular Computing: diversity of formal environments Set theory, interval analysis Fuzzy sets Rough sets Probabilistic granules

Summary form only given. Information granules and their computing, which give rise to the framework of Granular Computing, deliver interesting opportunities to endow processing with an important facet of human-centricity. This facet directly implies that the underlying processing supports non-numeric data inherently associated with the variable perception of humans and generates results being seamlessly comprehended by users. Given that systems, which are quite commonly become distributed and hierarchical, managing granular information in hierarchical and distributed architectures is of growing interest, especially when invoking mechanisms of knowledge generation and knowledge sharing. The outstanding feature of human centricity of Granular Computing along with essential fuzzy set-based constructs constitutes the crux of our study. We elaborate on some new directions of knowledge elicitation and quantification realized in the setting of fuzzy sets. With this regard, we concentrate on an idea of knowledge-based clustering, which aims at the seamless realization of the data-expertise design of information granules. It is also emphasized that collaboration and reconciliation of locally available knowledge give rise to the concept of higher type information granules. The other interesting directions enhancing human centricity of computing with fuzzy sets, deals with non-numeric, semi-qualitative characterization of information granules (fuzzy sets) as well as inherent evolving capabilities of associated human-centric systems. We discuss a suite of algorithms facilitating a qualitative assessment of fuzzy sets, formulate a series of associated optimization tasks guided by well-formulated performance indexes, and discuss the underlying essence of the resulting solutions.

Scheduling big data processing workflows involves both large-scale tasks and transmission of massive intermediate data among tasks, thus optimizing their completion time and monetary cost becomes a challenging issue. Besides, data streams are continuously generated, and dynamically submitted to clouds for real-time or near real-time processing. Naturally, responsive schedules are required to keep pace with such dynamic environments and this further aggravates the difficulty of the workflow scheduling problem. To address these issues, we first derive two theorems to minimize the completion time of a set of parallel workflow tasks and the start time of each workflow task, and then define the latest finish time for workflow tasks, which is also proved its advantage in reducing costs without delaying the completion of workflows. On the basis of these theorems, we propose a novel real-time scheduling algorithm using task-duplication, RTSATD, such that minimizing both the completion time and monetary cost of processing big data workflows in clouds. The performance of RTSATD is analyzed by using both synthesized and real-world workflows. The experimental results demonstrate the superiority of the proposed algorithm with respect to completion time (up to 28.73 percent) and resource utilization (up to 46.31 percent) over two existing approaches.

Software cost estimation techniques predict the amount of effort required to develop a software system. Cost estimates are needed throughout the software lifecycle to determine feasibility of software projects and to provide for appropriate allocation or reallocation of available resources. To assess the effect of imprecise evaluations, a comprehensive sensitivity analysis was performed on a major cost estimation model, COCOMO II. Results of this analysis are described and explicated in this paper. To reduce risk of drawing biased conclusions, three different methods for sensitivity analysis were employed: the mathematical analysis of the estimating equation, Monte Carlo simulation, and error propagation. The results of the first two methods are very consistent and confirm expected highest sensitivity of the model to the imprecision of the size estimate. Error propagation allows determination of the combined impact of imprecision in multiple inputs and it is therefore most valuable from the practical point of view. The results obtained by this technique also indicate very strong sensitivity to the imprecision in size estimates. A possible way to cope with imprecise information in software cost estimation is also indicated.