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Statistical Computing in Information Society

100%

Domański C. , Jędrzejczak A.

Folia Oeconomica Stetinensia

2015

tom 15

nr 2

144-152

In the presence of massive data coming with high heterogeneity we need to change our statistical thinking and statistical education in order to adapt both - classical statistics and software developments that address new challenges. Significant developments include open data, big data, data visualisation, and they are changing the nature of the evidence that is available, the ways in which it is presented and the skills needed for its interpretation. The amount of information is not the most important issue – the real challenge is the combination of the amount and the complexity of data. Moreover, a need arises to know how uncertain situations should be dealt with and what decisions should be taken when information is insufficient (which can also be observed for large datasets). In the paper we discuss the idea of computational statistics as a new approach to statistical teaching and we try to answer a question: how we can best prepare the next generation of statisticians.

Monitoring SOA Applications with SOOM Tools: A Competitive Analysis

80%

Zoraja I. , Trlin G. , Matijević M.

2013

tom 4

nr 1

21-35

Background: Monitoring systems decouple monitoring functionality from application and infrastructure layers and provide a set of tools that can invoke operations on the application to be monitored. Objectives: Our monitoring system is a powerful yet agile solution that is able to online observe and manipulate SOA (Service-oriented Architecture) applications. The basic monitoring functionality is implemented via lightweight components inserted into SOA frameworks thereby keeping the monitoring impact minimal. Methods/Approach: Our solution is software that hides the complexity of SOA applications being monitored via an architecture where its designated components deal with specific SOA aspects such as distribution and communication. Results: We implement an application-level and end-to-end monitoring with the end user experience in focus. Our tools are connected to a single monitoring system which provides consistent operations, resolves concurrent requests, and abstracts away the underlying mechanisms that cater for the SOA paradigm. Conclusions: Due to its flexible architecture and design our monitoring tools are capable of monitoring SOA application in Cloud environments without significant modifications. In comparisons with related systems we proved that our agile approaches are the areas where our monitoring system excels