Evolving features-algorithms knowledge map to support NIDS data intelligence and learning loop architecting – a generalised approach to NIDS pattern feature space knowledge processing

This paper describes a proposed new approach to the Computer Network Security Intrusion Detection Systems (NIDS) application domain knowledge processing focused on a topic map technology-enabled representation of features of the threat pattern space as well as the knowledge of situated efficacy of alternative candidate algorithms for pattern recognition within the NIDS domain. Thus an integrative knowledge representation framework for virtualisation, data intelligence and learning loop architecting in the NIDS domain is described together with specific aspects of its deployment.

Growth hacking model for B2B saas startups

The following project introduces a model of Growth Hacking strategies for business-tobusiness Software-as-a-Service startups that was developed in collaboration with and applied to a Portuguese startup called Liquid. The work addresses digital marketing channels such as content marketing, email marketing, social marketing and selling. Further, the company’s product, pricing strategy, partnerships and website communication are examined. Applying best case practices, competitor benchmarks and interview insights from numerous industry influencers and experts, areas for improvement are deduced and procedures for each of those channels recommended.

Rikkiyhdisteiden laskenta simuloinneissa

Diplomityön tarkoituksena on tutkia eri laskentamenetelmien soveltuvuutta kevyiden rikkiyhdisteiden laskentaan ja kuinka mitatusta kaasu-neste tasapainotiedoista sovitetut binääriset vuorovaikutusparametrit parantavat kaasu-neste tasapainojen laskentaa simuloinneissa. Kirjallisuusosassa paneudutaan kevyisiin rikkiyhdisteisiin ja niiden aineominaisuuksiin. Lisäksi käsitellään öljynjalostuksessa nykyisin käytettäviä ja uusia kehitteillä olevia rikinpoistomenetelmiä.Kokeellisessa osassa tarkastellaan eri laskentamenetelmien soveltuvuutta rikkiyhdisteiden ja kevyiden hiilivetyjen kaasu-neste tasapainon laskentaan. Mitatusta rikkiyhdisteiden ja hiilivetyjen kaasu-neste tasapainoista sovitetaan binäärisiä vuorovaikutusparametrejä tarkentamaan käytettäviä laskentamenetelmiä. Osassa verrataan binääristen seosten mittaustuloksia eri laskentamenetelmillä saatuihin simulointituloksiin. Tarkasteluiden perusteella tehdään johtopäätöksiä laskentamenetelmien soveltuvuudesta kevyiden hiilivetyjen ja rikkiyhdisteiden laskentaan. Tarkastellaan kahden prosessin (rikkivetystripperi ja butaaninpoistokolonni) rikkiyhdisteiden laskentaa. Prosesseille tehdään taseajot, joista saatuja analyysituloksia verrataan simulointien antamiin tuloksiin. Työssä tarkastellaan myös veden liukoisuuden laskentaa ja mahdollisten laskentamenetelmien käytön vaikutusta rikkiyhdisteiden laskentaan.

An architecture for an effective usage of data mining in business intelligence systems

Business Intelligence (BI) is one emergent area of the Decision Support Systems (DSS) discipline. Over the last years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. Therefore, a lack of an effective usage of DM in BI can be found in some BI systems. An architecture that pretends to conduct to an effective usage of DM in BI is presented.

Data mining languages for business intelligence

Doctoral Thesis in Information Systems and Technologies Area of Engineering and Manag ement Information Systems

Stratigraphic interpretation of Well-Log data of the Athabasca Oil Sands of Alberta Canada through Pattern recognition and Artificial Intelligence

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Data fusion with computational intelligence techniques: a case study of fuzzy inference for terrain assessment

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

From a NoSQL data source to a business intelligence solution: An experiment

We are living in the era of Big Data. A time which is characterized by the continuous creation of vast amounts of data, originated from different sources, and with different formats. First, with the rise of the social networks and, more recently, with the advent of the Internet of Things (IoT), in which everyone and (eventually) everything is linked to the Internet, data with enormous potential for organizations is being continuously generated. In order to be more competitive, organizations want to access and explore all the richness that is present in those data. Indeed, Big Data is only as valuable as the insights organizations gather from it to make better decisions, which is the main goal of Business Intelligence. In this paper we describe an experiment in which data obtained from a NoSQL data source (database technology explicitly developed to deal with the specificities of Big Data) is used to feed a Business Intelligence solution.

Integração da área de negócio de retalho do grupo Riopele no Data Warehouse corporativo

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

A collaborative approach for incorporating forensic case data into crime investigation using criminal intelligence analysis and visualisation

There is an increasing awareness that the articulation of forensic science and criminal investigation is critical to the resolution of crimes. However, models and methods to support an effective collaboration between these partners are still poorly expressed or even lacking. Three propositions are borrowed from crime intelligence methods in order to bridge this gap: (a) the general intelligence process, (b) the analyses of investigative problems along principal perspectives: entities and their relationships, time and space, quantitative aspects and (c) visualisation methods as a mode of expression of a problem in these dimensions. Indeed, in a collaborative framework, different kinds of visualisations integrating forensic case data can play a central role for supporting decisions. Among them, link-charts are scrutinised for their abilities to structure and ease the analysis of a case by describing how relevant entities are connected. However, designing an informative chart that does not bias the reasoning process is not straightforward. Using visualisation as a catalyser for a collaborative approach integrating forensic data thus calls for better specifications.

Análisis de datos de accidentes de tráfico mediante soluciones big data y business intelligence

Este proyecto de final de carrera corresponde al área de inteligencia artificial y representa un caso de uso que pretende utilizar datos reales referentes a accidentes de tráfico (datos de accidentes, muertos, heridos, etc.) y analizarlas conjuntamente con datos que puedan tener una posible relación con los accidentes como el parque de vehículos, las temperaturas de la zona de los accidentes, etc. con la finalidad de poder obtener las posibles relaciones causa-efecto.

Deterpenation of Bergamot Essential Oil Using Liquid-Liquid Extraction: Equilibrium Data of Model Systems at 298.2 K

The deterpenation of bergamot essential oil can be performed by liquid liquid extraction using hydrous ethanol as the solvent. A ternary mixture composed of 1-methyl-4-prop-1-en-2-yl-cydohexene (limonene), 3,7-dimethylocta-1,6-dien-3-yl-acetate (linalyl acetate), and 3,7-dimethylocta-1,6-dien-3-ol (linalool), three major compounds commonly found in bergamot oil, was used to simulate this essential oil. Liquid liquid equilibrium data were experimentally determined for systems containing essential oil compounds, ethanol, and water at 298.2 K and are reported in this paper. The experimental data were correlated using the NRTL and UNIQUAC models, and the mean deviations between calculated and experimental data were lower than 0.0062 in all systems, indicating the good descriptive quality of the molecular models. To verify the effect of the water mass fraction in the solvent and the linalool mass fraction in the terpene phase on the distribution coefficients of the essential oil compounds, nonlinear regression analyses were performed, obtaining mathematical models with correlation coefficient values higher than 0.99. The results show that as the water content in the solvent phase increased, the kappa value decreased, regardless of the type of compound studied. Conversely, as the linalool content increased, the distribution coefficients of hydrocarbon terpene and ester also increased. However, the linalool distribution coefficient values were negatively affected when the terpene alcohol content increased in the terpene phase.