Informatica

Data involving spatial and/or temporal attributes are often represented at different levels of granularity in different source schemata. In this work, a model of such multigranular data is developed, which supports not only the usual order structure on granules, but also lattice-like join and disjointness operators for relating such granules in much more complex ways. In addition, a model for multigranular thematic attributes, to which aggregation operators are applied, is provided. Finally, the notion of a thematic multigranular comparison dependency, generalizing ordinary functional and order dependencies but specifically designed to model the kinds of functional and order dependencies which arise in the multigranular context, and in particular incorporating aggregation into the definition of the constraint, is developed.

Computational thinking is an increasingly important focus in computer science or informatics curricula around the world, and ways of incorporating it into the school curricula are being sought. The Bebras contest on informatics, which originated 12 years ago and now involves around 50 countries, consists of short problem-solving tasks based on topics in informatics. Bebras tasks engender the development of computational thinking skills by incorporating abstraction, algorithmic thinking, decomposition, evaluation and generalization. Bebras tasks cover a range of informatics concepts including algorithms and data structures, programming, networking, databases and social and ethical issues. Having built up a substantial number of Bebras tasks over 12 years it is important to be able to categorize them so that they can be easily accessed by the Bebras community and teachers within schools. The categorization of tasks within Bebras is important as it ensures that tasks span a wide range of topics; there have been several categorization schemes suggested to date. In this paper we present a new two-dimensional categorization system that takes account of computational thinking skills as well as content knowledge. Examples are given from recent tasks that illustrate the role that Bebras can play in the development of computational thinking skills.

Similarity searching has become widely available in many on-line archives of multimedia data. Users accessing such systems look for data items similar to their specific query object and typically refine results by re-running the search with a query from the results. We study this issue and propose a mechanism of approximate kNN query evaluation that incorporates statistics of accessing index data partitions. Apart from the distance between database objects, it also considers the prior query answers to prioritize index partitions containing frequently retrieved data, so evaluating repetitive similar queries more efficiently. We verify this concept in a number of experiments.

A new method is proposed to solve the interactive group decision making problem in which the preference information takes the form of intuitionistic fuzzy preference relations. Firstly, we aggregate all individual intuitionistic fuzzy preference relations into a collective one. Then, a method to determine the experts’ weights by utilizing the compatibility measures of the individual intuitionistic fuzzy preference relations and the collective one is proposed. Furthermore, a practical interactive procedure is developed, in which the intuitionistic fuzzy association coefficient is used to rank the given alternatives. Finally, this study presents a numerical example to illustrate the availability of the developed approach and compare it to another method.

Statistical modelling plays a central role for any prediction problem of interest. However, predictive models may give misleading results when the data contain outliers. In many real-world applications, it is important to identify and treat the outliers without direct elimination. To handle such issues, a hybrid computational method based on conic quadratic programming is introduced and employed on earthquake ground motion dataset. This method aims to minimize the impact of the outliers on regression estimators as well as handling the nonlinearity in the dataset. Results are compared against widely used parametric and nonparametric ground motion prediction models.

This paper investigates a kind of hybrid multiple attribute decision making (MADM) problems with incomplete attribute weight information and develops a hesitant fuzzy programming method based on the linear programming technique for multidimensional analysis of preference (LINMAP). In this method, decision maker (DM) gives preferences over alternatives by the pair-wise comparison with hesitant fuzzy truth degrees and the evaluation values are expressed as crisp numbers, intervals, intuitionistic fuzzy sets (IFSs), linguistic variables and hesitant fuzzy sets (HFSs). First, by calculating the relative projections of alternatives on the positive ideal solution (PIS) and negative ideal solution (NIS), the overall relative closeness degrees of alternatives associated with attribute weights are derived. Then, the hesitant fuzzy consistency and inconsistency measures are defined. Through minimizing the inconsistency measure and maximizing the consistency measure simultaneously, a new bi-objective hesitant fuzzy programming model is constructed and a novel solution method is developed. Thereby, the weights of attributes are determined objectively. Subsequently, the ranking order of alternatives is generated based on the overall relative closeness degrees of alternatives. Finally, a supplier selection example is provided to show the validity and applicability of the proposed method.

This paper deals with the problem of selecting a suitable design pattern when necessary. The number of design patterns has been rapidly rising, but management and searching facilities appear to be lagging behind. In this paper we will present a platform, which is used to search for suitable design patterns and for design patterns knowledge exchange. We are introducing a novel design pattern proposing approach: the developer no longer searches for an appropriate design pattern, but rather the intelligent component asks the developer questions. We do not want to invest extra effort in terms of maintaining a special expert system. Guided dialogues consist of independent questions from different sources and authors that are automatically combined. The enabling algorithm and formulas are discussed in detail. This paper also presents our comparison with human-created expert systems via a decision tree. Experiments were executed in order to verify our approach performance. The control group used a human-created expert system, while others were given a proposing component to find appropriate design patterns.

We present a new root cause analysis algorithm for discovering the most likely causes of differences found in testing results of two versions of the same software. Problematic points in test and environment attribute hierarchies are presented to a user in a compact way which in turn allows saving time on test result processing. We have proven that for clearly separated problem causes our algorithm gives an exact solution. Practical application of described method is discussed.

We present an algorithm to solve multistage stochastic convex problems, whose objective function and constraints are nonlinear. It is based on the twin-node-family concept involved in the Branch-and-Fix Coordination method. These problems have 0–1 mixed-integer and continuous variables in all the stages. The non-anticipativity constraints are satisfied by means of the twin-node-family strategy.