A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Automating data preparation with statistical analysis

Data preparation is the process of transforming raw data into a clean and consumable format. It is widely known as the bottleneck to extract value and insights from data, due to the number of possible tasks in the pipeline and factors that can largely affect the results, such as human expertise, application scenarios, and solution methodology. Researchers and practitioners devised a great variety of techniques and tools over the decades, while many of them still place a significant burden on human’s side to configure the suitable input rules and parameters. In this thesis, with the goal of reducing human manual effort, we explore using the power of statistical analysis techniques to automate three subtasks in the data preparation pipeline: data enrichment, error detection, and entity matching. Statistical analysis is the process of discovering underlying patterns and trends from data and deducing properties of an underlying distribution of probability from a sample, for example, testing hypotheses and deriving estimates. We first discuss CrawlEnrich, which automatically figures out the queries for data enrichment via web API data, by estimating the potential benefit of issuing a certain query. Then we study how to derive reusable error detection configuration rules from a web table corpus, so that end-users get results with no efforts. Finally, we introduce AutoML-EM, aiming to automate the entity matching model development process. Entity matching is to find the identical entities in real-world. Our work provides powerful angles to automate the process of various data preparation steps, and we conclude this thesis by discussing future directions

CREATING A BIOMEDICAL ONTOLOGY INDEXED SEARCH ENGINE TO IMPROVE THE SEMANTIC RELEVANCE OF RETREIVED MEDICAL TEXT

Medical Subject Headings (MeSH) is a controlled vocabulary used by the National Library of Medicine to index medical articles, abstracts, and journals contained within the MEDLINE database. Although MeSH imposes uniformity and consistency in the indexing process, it has been proven that using MeSH indices only result in a small increase in precision over free-text indexing. Moreover, studies have shown that the use of controlled vocabularies in the indexing process is not an effective method to increase semantic relevance in information retrieval. To address the need for semantic relevance, we present an ontology-based information retrieval system for the MEDLINE collection that result in a 37.5% increase in precision when compared to free-text indexing systems. The presented system focuses on the ontology to: provide an alternative to text-representation for medical articles, finding relationships among co-occurring terms in abstracts, and to index terms that appear in text as well as discovered relationships. The presented system is then compared to existing MeSH and Free-Text information retrieval systems. This dissertation provides a proof-of-concept for an online retrieval system capable of providing increased semantic relevance when searching through medical abstracts in MEDLINE

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

A DevOps approach to infrastructure on demand

As DevOps grows in importance in companies, there is an increasing interest in automating the process of building and deploying infrastructure, having as an objective reduce the complexity for non DevOps engineers and making it so that infrastructure is less error prone, which is not the case when doing it manually. This work aims to explore how to build a solution that allows to manage infrastructure on demand while supporting specific services that are relevant for git profiles analysis, such as Sonarqube and Jenkins. Firstly, this work starts by introducing its context, the problem that the solution is trying to solve and the methodology used to develop the solution. On the State-of-the-Art various topics are presented in order to give all the information needed to understand the implementation of the solution, including concepts such as DevOps and Automation, while going over specific technologies such as GraphQL, Docker, Terraform and Ansible. A value analysis was also done to explore what are the main concerns for stakeholders when managing their infrastructure and to define the value of the solution being developed. Lastly, the solution was implemented making use of various technologies and with scalability in mind that would allow it to grow in the amount of services supported with minimum changes. The work is interesting for someone that is interested in DevOps, Infrastructure-as-Code and automation in general.Com o crescimento da importância de DevOps em empresas existe um interesse acrescido em automatizar o processo de construir e de dar deploy de infra-estrutura, tendo como objectivo reduzir a complexidade para engenheiros menos proficientes em DevOps, e construir infraestrutura que é menos propensa a erros, o que não acontece quando feito manualmente. Este trabalho visa implementar uma solução capaz de gerir infra-estrutura a pedido e ao mesmo tempo suportar serviços específicos relevantes para a análise de perfis git, como por exemplo Sonarqube e Jenkins. Em primeiro lugar, este trabalho começa por introduzir o seu contexto, o problema que a solução está a tentar resolver e a metodologia utilizada para desenvolver a solução. No estado da arte são apresentados vários tópicos com a finalidade de fornecer toda a informação necessária para compreender a implementação da solução, incluindo conceitos como DevOps e automação, são também exploradas tecnologias específicas como GraphQL, Docker, Terraform e Ansible. Foi também feita uma análise de valor para explorar quais são as principais preocupações das partes interessadas na gestão das infra-estruturas das suas empresas e para definir o valor da solução que está a ser desenvolvida. Finalmente, a solução foi implementada, recorrendo a várias tecnologias e tendo em mente a escalabilidade da solução que permitiria crescer na quantidade de serviços suportados requerendo alterações mínimas. O trabalho é interessante para alguém que esteja interessado em DevOps, Infraestrutura como código e automatização em geral

A situation risk awareness approach for process systems safety

Promoting situation awareness is an important design objective for a wide variety of domains, especially for process systems where the information flow is quite high and poor decisions may lead to serious consequences. In today's process systems, operators are often moved to a control room far away from the physical environment, and increasing amounts of information are passed to them via automated systems, they therefore need a greater level of support to control and maintain the facilities in safe conditions. This paper proposes a situation risk awareness approach for process systems safety where the effect of ever-increasing situational complexity on human decision-makers is a concern. To develop the approach, two important aspects - addressing hazards that arise from hardware failure and reducing human error through decision-making - have been considered. The proposed situation risk awareness approach includes two major elements: an evidence preparation component and a situation assessment component. The evidence preparation component provides the soft evidence, using a fuzzy partitioning method, that is used in the subsequent situation assessment component. The situation assessment component includes a situational network based on dynamic Bayesian networks to model the abnormal situations, and a fuzzy risk estimation method to generate the assessment result. A case from US Chemical Safety Board investigation reports has been used to illustrate the application of the proposed approach. © 2013 Elsevier Ltd