Informatica

One of the biggest difficulties in telecommunication industry is to retain the customers and prevent the churn. In this article, we overview the most recent researches related to churn detection for telecommunication companies. The selected machine learning methods are applied to the publicly available datasets, partially reproducing the results of other authors and then it is applied to the private Moremins company dataset. Next, we extend the analysis to cover the exiting research gaps: the differences of churn definitions are analysed, it is shown that the accuracy in other researches is better due to some false assumptions, i.e. labelling rules derived from definition lead to very good classification accuracy, however, it does not imply the usefulness for such churn detection in the context of further customer retention. The main outcome of the research is the detailed analysis of the impact of the differences in churn definitions to a final result, it was shown that the impact of labelling rules derived from definitions can be large. The data in this study consist of call detail records (CDRs) and other user aggregated daily data, 11000 user entries over 275 days of data was analysed. 6 different classification methods were applied, all of them giving similar results, one of the best results was achieved using Gradient Boosting Classifier with accuracy rate 0.832, F-measure 0.646, recall 0.769.

Information systems contain a lot of data regarding business process execution history. Use of this data, in the form of an event log, can greatly support business process management. The paper presents an approach to construct Bayesian belief network from an event log that could facilitate decision support in business process execution. The approach is evaluated against multiple event logs by inferring data probabilities occurring in the business processes. The results show that the approach is suitable for the task and could be used in decision support with future research focused on prediction and simulation of business processes.

Due to numerous public information sources and services, many methods to combine heterogeneous data were proposed recently. However, general end-to-end solutions are still rare, especially systems taking into account different context dimensions. Therefore, the techniques often prove insufficient or are limited to a certain domain. In this paper we briefly review and rigorously evaluate a general framework for data matching and merging. The framework employs collective entity resolution and redundancy elimination using three dimensions of context types. In order to achieve domain independent results, data is enriched with semantics and trust. However, the main contribution of the paper is evaluation on five public domain-incompatible datasets. Furthermore, we introduce additional attribute, relationship, semantic and trust metrics, which allow complete framework management. Besides overall results improvement within the framework, metrics could be of independent interest.

Nowadays, ontologies play a central role in many computer science problems such as data modelling, data exchange, integration of heterogeneous data and models or software reuse. Yet, if many methods of ontology based conceptual data modelling have been proposed, only few attempts have been made to ontology axioms based modelling of business rules, which make an integral part of each conceptual data model. In this paper, we present the approach how ontology axioms can be used for business rules implementation. Our proposal we apply for the transformation of PAL (Protege Axiom Language) constraints (ontology axioms), which is based on KIF (Knowledge Interchange Format) and is part of KIF ontology, into OCL (Object Constraint Language) constraints, which are part of a UML class diagram. Z language is used to formalise the proposal and describe the transformation. The Axiom2OCL plug-in is created for automation of the transformation and a case study is carried out.

In this paper, we describe a model for aligning books and documents from bilingual corpus with a goal to create “perfectly” aligned bilingual corpus on word-to-word level. Presented algorithms differ from existing algorithms in consideration of the presence of human translator which usage we are trying to minimize. We treat human translator as an oracle who knows exact alignments and the goal of the system is to optimize (minimize) the use of this oracle. The effectiveness of the oracle is measured by the speed at which he can create “perfectly” aligned bilingual corpus. By “Perfectly” aligned corpus we mean zero entropy corpus because oracle can make alignments without any probabilistic interpretation, i.e., with 100% confidence. Sentence level alignments and word-to-word alignments, although treated separately in this paper, are integrated in a single framework. For sentence level alignments we provide a dynamic programming algorithm which achieves low precision and recall error rate. For word-to-word level alignments Expectation Maximization algorithm that integrates linguistic dictionaries is suggested as the main tool for the oracle to build “perfectly” aligned bilingual corpus. We show empirically that suggested pre-aligned corpus requires little interaction from the oracle and that creation of perfectly aligned corpus can be achieved almost with the speed of human reading. Presented algorithms are language independent but in this paper we verify them with English–Lithuanian language pair on two types of text: law documents and fiction literature.

Business rules are relatively new addition in the field of Enterprise Resource Planning (ERP) systems, which are kind of business information systems, development. Recently some relevant enhancements of existing business information systems engineering methods were introduced, although there are still open issues of how business rules may be used and improve qualitative and quantitative attributes of such kind of information systems. The paper discusses existing business information systems engineering issues arising out of using business rules approach. The paper also introduces several ways of business rule involvement aiming at ensuring ERP systems development agility based on running researches in the field also carried out by the authors.

This paper examins approaches for translation between English and morphology-rich languages. Experiment with English–Russian and English–Lithuanian revels that “pure” statistical approaches on 10 million word corpus gives unsatisfactory translation. Then, several Web-available linguistic resources are suggested for translation. Syntax parsers, bilingual and semantic dictionaries, bilingual parallel corpus and monolingualWeb-based corpus are integrated in one comprehensive statistical model. Multi-abstraction language representation is used for statistical induction of syntactic and semantic transformation rules called multi-alignment templates. The decodingmodel is described using the feature functions, a log-linear modeling approach and A* search algorithm. An evaluation of this approach is performed on the English–Lithuanian language pair. Presented experimental results demonstrates that the multi-abstraction approach and hybridization of learning methods can improve quality of translation.

The main purpose of the paper is to compare ontology‐based reuse techniques in domain engineering and enterprise engineering. It discusses attempts to combine classical domain engineering techniques with ontology‐based techniques as well as the attempts to incorporate ontologies in enterprise engineering process and demonstrates that, on the one hand, both approaches still are not mature enough to solve practical reuse problems and, on the other hand, both propose ideas that can be used to develop more mature approach. The main contribution of the paper is the detail description of the problems of ontology‐based reuse of enterprise engineering assets.