Evolution of Decision Support Systems Research Field in Numbers

The scientific production in a certain field shows, in great extent, the research interests in that field. Decision Support Systems are a particular class of information systems which are gaining more popularity in various domains. In order to identify the evolution in time of the publications number, authors, subjects, publications in the Decision Support Systems (DSS) field, and therefore the scientific world interest for this field, in November 2010 there have been organized a series of queries on three major international scientific databases: ScienceDirect, IEEE Xplore Digital Library and ACM Digital Library. The results presented in this paper shows that, even the decision support systems research field started in 1960s, the interests for this type of systems grew exponentially with each year in the last decades.DSS, Numbers, Research, Materials

Detecting Emotions in Comments on Forums

The paper presents one of the most important issues in Natural Language Processing (NLP), emotion identification and classification to implement a computational technology based on existing resources, open-source or freely available for research purposes. Furthermore, we are interested to use it for establishing Gold standards in sentiment analysis area, such as SentiWordNet. In this sense, we propose to recognize and classify the emotions (sentiments) of the public consumer from the written texts which appeared on the various Forums. We analyse the writing style which refers to how consumers construct sentences together when they write comments to indicate their passion about an entity (persons, brand, location, etc.). We present in this paper a method for integrating Romanian lexical resources from motional perspective, in developing, which can be used in sentiment analysis. This study is intend to help direct beneficiaries (public consumer, marketing managers, PR firms, politicians, investors), but, also, specialists and researchers in the field of natural language processing, linguists, psychologists, sociologists, economists, etc

Disaster Prevention Integrated into Commonly Used Web Rendered Systems with GIS Capabilities

The end of the 20th century brought a remarkable increase in the field of positioning techniques and communications, making them visible and available to the public, which led to an unprecedented interconnectivity. At the same time, disasters are part of our life. Regardless of their nature, measures can be taken, in order to prevent and mitigate their effects, by anticipative preparation or by avoiding the calamity area (if possible). To this end, this paper presents an integrated system, composed of a software component, a hardware component, and a decision-making human element, all having the declared role of diminishing or eliminating human and material losses

Semantic Web-based E-business Applications

The paper presents the opportunities given by the Semantic Web technologies for the enterprise integration in the context of development of e-business software applications

Improving service-level agreements for critical systems using big data monitoring techniques

The proliferation of big data in virtually every branch of society and industry comes with the need to adapt and develop monitoring and alerting systems in such a way that the system can cope with any kind of data stream, whilst also ensuring rapid response times. This paper presents a framework based on modern open-source technologies that can be used to improve the quality and reliability of a connected system (such as an industrial control system), through effective monitoring and alerting. Service level agreements are crucial in our modern society, where failures need to be detected quickly and effectively, especially when one is providing a service and every moment of downtime means a large quantity of lost money and potential customers, thus monitoring is essential. Benefits in terms of responsiveness and lower downtime are also discussed, with an emphasis on a prototype implementation for a major non-profit organization

An implementation of a fault-tolerant database system using the actor model

Fault-tolerant systems are an important discussion subject in our world of interconnected devices. One of the major failure points of every distributed infrastructure is the database. A data migration or an overload of one of the servers could lead to a cascade of failures and service downtime for the users. NoSQL databases sacrifice some of the consistency provided by traditional SQL databases while privileging availability and partition tolerance. This paper presents the design and implementation of a distributed in-memory database that is based on the actor model. The benefits of the actor model and development using functional languages are detailed, and suitable performance metrics are presented. A case study is also performed, showcasing the system’s capacity to quickly recover from the loss of one of its machines and maintain functionality

A System for Sustainable Usage of Computing Resources Leveraging Deep Learning Predictions

In this paper, we present the benefit of using deep learning time-series analysis techniques in order to reduce computing resource usage, with the final goal of having greener and more sustainable data centers. Modern enterprises and agile ways-of-working have led to a complete revolution of the way that software engineers develop and deploy software, with the proliferation of container-based technology, such as Kubernetes and Docker. Modern systems tend to use up a large amount of resources, even when idle, and intelligent scaling is one of the methods that could be used to prevent waste. We have developed a system for predicting and influencing computer resource usage based on historical data of real production software systems at CERN, allowing us to scale down the number of machines or containers running a certain service during periods that have been identified as idle. The system leverages recurring neural network models in order to accurately predict the future usage of a software system given its past activity. Using the data obtained from conducting several experiments with the forecasted data, we present the potential reductions on the carbon footprint of these computing services, from the perspective of CPU usage. The results show significant improvements to the computing power usage of the service (60% to 80%) as opposed to just keeping machines running or using simple heuristics that do not look too far into the past