Formal Specification of Relational Model of Data in Z-Notation

Владимир Димитров - Целта на настоящия доклад е формалната спецификация на релационния модел на данни. Тази спецификация след това може да бъде разширена към Обектно-релационния модел на данни и към Потоците от данни.

Analysing Conjoint Analysis Data by a Random Coefficient Regression Model

2000 Mathematics Subject Classification: 62J12, 62K15, 91B42, 62H99.

Implementation of the EM Algorithm for Maximum Likelihood Estimation of a Random Effects Model for One Longitudinal Ordinal Outcome

2010 Mathematics Subject Classification: 62J99.

Em Algorithm for MLE of a Probit Model for Multiple Ordinal Outcomes

The correlated probit model is frequently used for multiple ordered data since it allows to incorporate seamlessly different correlation structures. The estimation of the probit model parameters based on direct maximization of the limited information maximum likelihood is a numerically intensive procedure. We propose an extension of the EM algorithm for obtaining maximum likelihood estimates for a correlated probit model for multiple ordinal outcomes. The algorithm is implemented in the free software environment for statistical computing and graphics R. We present two simulation studies to examine the performance of the developed algorithm. We apply the model to data on 121 women with cervical or endometrial cancer. Patients developed normal tissue reactions as a result of post-operative external beam pelvic radiotherapy. In this work we focused on modeling the effects of a genetic factor on early skin and early urogenital tissue reactions and on assessing the strength of association between the two types of reactions. We established that there was an association between skin reactions and polymorphism XRCC3 codon 241 (C>T) (rs861539) and that skin and urogenital reactions were positively correlated. ACM Computing Classification System (1998): G.3.

Intelligent Model of User Behavior in Distributed Systems

We present a complex neural network model of user behavior in distributed systems. The model reflects both dynamical and statistical features of user behavior and consists of three components: on-line and off-line models and change detection module. On-line model reflects dynamical features by predicting user actions on the basis of previous ones. Off-line model is based on the analysis of statistical parameters of user behavior. In both cases neural networks are used to reveal uncharacteristic activity of users. Change detection module is intended for trends analysis in user behavior. The efficiency of complex model is verified on real data of users of Space Research Institute of NASU-NSAU.

Preserving Linked Data

PRELIDA (PREserving LInked DAta) is an FP7 Coordination Action funded by the European Commission under the Digital Preservation Theme. PRELIDA targets the particular stakeholders of the Linked Data community, including data providers, service providers, technology providers and end user communities. These stakeholders have not been traditionally targeted by the Digital Preservation community, and are typically not aware of the digital preservation solutions already available. So an important task of PRELIDA is to raise awareness of existing preservation solutions and to facilitate their uptake. At the same time, the Linked Data cloud has specific characteristics in terms of structuring, interlinkage, dynamicity and distribution that pose new challenges to the preservation community. PRELIDA organises in-depth discussions among the two communities to identify which of these characteristics require novel solutions, and to develop road maps for addressing the new challenges. PRELIDA will complete its lifecycle at the end of this year, and the talk will report about the major findings.