Applying Machine Learning to Study Infrastructure Anomalies in a Mid-size Data Center -- Preliminary Considerations

Today, data centers deal with fast growing data volumes. To deliver services, they deploy growing amount of heterogeneous hardware. As a result, it becomes practically impossible to apply human-based data center management. For instance, in a real-world data center, with 500+ computers, delivering data, computational, and network services, it becomes impossible to visualize, and understand, causal relationships among variables describing performance of monitored resources. However, it is possible to collect data describing behavior of individual nodes. Hence, such data may be used to analyze/model system performance. In particular, it may be applied to recognize and predict anomalies in system behavior. Furthermore, collected data should allow finding the cause(s) of anomalies. Therefore, “data-driven approaches” have been applied to the real-world data, to find, so called, Root Cause of anomalies

Implementing the Duty Trip Support Application

We are in the process of developing an agent and ontology-based Duty Trip Support application. The goal of this paper is to consider issues arising when implementing such a system. In addition to the description of our current implementation, which is also critically analyzed, other possible approaches are considered as well.software agents, agent systems, ontologies, transport objects, agent-non-agent integration.

Feature Extraction for Polish Language Named Entities Recognition in Intelligent Office Assistant

The purpose of this contribution is to present a feature extractor that was designed as a part of a Named Entity Recognition (NER) system, which is to be used in a Robotic Process Automation application with a self-learning ability. The NER system has a screen of the user interface as its input, and tries to recognize and categorize all the named entities that can be located within this screen. The set of features that can be extracted from the input, is discussed in the article. The local context features appear to be very important in the considered problem. Experiments show that the entities are recognized with a rate that is satisfactory from the business perspective

Topical Classification of Food Safety Publications with a Knowledge Base

The vast body of scientific publications presents an increasing challenge of finding those that are relevant to a given research question, and making informed decisions on their basis. This becomes extremely difficult without the use of automated tools. Here, one possible area for improvement is automatic classification of publication abstracts according to their topic. This work introduces a novel, knowledge base-oriented publication classifier. The proposed method focuses on achieving scalability and easy adaptability to other domains. Classification speed and accuracy are shown to be satisfactory, in the very demanding field of food safety. Further development and evaluation of the method is needed, as the proposed approach shows much potential

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference

Application of genetic algorithm to load balancing in networks with a homogeneous traffic flow

The concept of extended cloud requires efficient network infrastructure to support ecosystems reaching form the edge to the cloud(s). Standard approaches to network load balancing deliver static solutions that are insufficient for the extended clouds, where network loads change often. To address this issue, a genetic algorithm based load optimizer is proposed and implemented. Next, its performance is experimentally evaluated and it is shown that it outperforms other existing solutions.Comment: Accepted for the conference -- The International Conference on Computational Science ICCS202

Multi-Domain Named Entity Recognition for Robotic Process Automation

To make Robotic Process Automation more attractive, it needs to become more ``intelligent''. In this context, a modification of the Form-to-Rule approach, based on identifying data types of form fields, is proposed. Moreover, multi-domain named entity recognition is used, for field value identification. These techniques, used jointly, allow software robots to adapt to interface changes. Experimental results are reported and verify viability of the proposed approach