Big data warehouse framework for smart revenue management

Revenue Management’s most cited definitions is probably “to sell the right accommodation to the right customer, at the right time and the right price, with optimal satisfaction for customers and hoteliers”. Smart Revenue Management (SRM) is a project, which aims the development of smart automatic techniques for an efficient optimization of occupancy and rates of hotel accommodations, commonly referred to, as revenue management. One of the objectives of this project is to demonstrate that the collection of Big Data, followed by an appropriate assembly of functionalities, will make possible to generate a Data Warehouse necessary to produce high quality business intelligence and analytics. This will be achieved through the collection of data extracted from a variety of sources, including from the web. This paper proposes a three stage framework to develop the Big Data Warehouse for the SRM. Namely, the compilation of all available information, in the present case, it was focus only the extraction of information from the web by a web crawler – raw data. The storing of that raw data in a primary NoSQL database, and from that data the conception of a set of functionalities, rules, principles and semantics to select, combine and store in a secondary relational database the meaningful information for the Revenue Management (Big Data Warehouse). The last stage will be the principal focus of the paper. In this context, clues will also be giving how to compile information for Business Intelligence. All these functionalities contribute to a holistic framework that, in the future, will make it possible to anticipate customers and competitor’s behavior, fundamental elements to fulfill the Revenue Management

Using non-invasive techniques to examine black bear (Ursus americanus) abundance in the North Cascades Ecosystem, Washington State

Thesis (Master's)--University of Washington, 2013

Using data mining techniques to support DR programs definition in smart grids

In recent years, Power Systems (PS) have experimented many changes in their operation. The introduction of new players managing Distributed Generation (DG) units, and the existence of new Demand Response (DR) programs make the control of the system a more complex problem and allow a more flexible management. An intelligent resource management in the context of smart grids is of huge important so that smart grids functions are assured. This paper proposes a new methodology to support system operators and/or Virtual Power Players (VPPs) to determine effective and efficient DR programs that can be put into practice. This method is based on the use of data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 32 bus distribution network.

Demand response programs definition supported by clustering and classification techniques

The growing importance and influence of new resources connected to the power systems has caused many changes in their operation. Environmental policies and several well know advantages have been made renewable based energy resources largely disseminated. These resources, including Distributed Generation (DG), are being connected to lower voltage levels where Demand Response (DR) must be considered too. These changes increase the complexity of the system operation due to both new operational constraints and amounts of data to be processed. Virtual Power Players (VPP) are entities able to manage these resources. Addressing these issues, this paper proposes a methodology to support VPP actions when these act as a Curtailment Service Provider (CSP) that provides DR capacity to a DR program declared by the Independent System Operator (ISO) or by the VPP itself. The amount of DR capacity that the CSP can assure is determined using data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 33 bus distribution network.

A new approach for multi-agent coalition formation and management in the scope of electricity markets

This paper presents a new methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using the multi-agent market simulator MASCEM, taking advantage of its ability to provide the means to model and simulate VPP (Virtual Power Producers). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. The new features include the development of particular individual facilitators to manage the communications amongst the members of each coalition independently from the rest of the simulation, and also the mechanisms for the classification of the agents that are candidates to join the coalition. In addition, a global study on the results of the Iberian Electricity Market is performed, to compare and analyze different approaches for defining consistent and adequate strategies to integrate into the agents of MASCEM. This, combined with the application of learning and prediction techniques provide the agents with the ability to learn and adapt themselves, by adjusting their actions to the continued evolving states of the world they are playing in.

MV producers and consumers agents characterization with DSM techniques

This paper consist in the establishment of a Virtual Producer/Consumer Agent (VPCA) in order to optimize the integrated management of distributed energy resources and to improve and control Demand Side Management DSM) and its aggregated loads. The paper presents the VPCA architecture and the proposed function-based organization to be used in order to coordinate the several generation technologies, the different load types and storage systems. This VPCA organization uses a frame work based on data mining techniques to characterize the costumers. The paper includes results of several experimental tests cases, using real data and taking into account electricity generation resources as well as consumption data.

Intelligent Micro Grid Management using a Multi-Agent approach

Recent changes in electricity markets (EMs) have been potentiating the globalization of distributed generation. With distributed generation the number of players acting in the EMs and connected to the main grid has grown, increasing the market complexity. Multi-agent simulation arises as an interesting way of analysing players’ behaviour and interactions, namely coalitions of players, as well as their effects on the market. MASCEM was developed to allow studying the market operation of several different players and MASGriP is being developed to allow the simulation of the micro and smart grid concepts in very different scenarios This paper presents a methodology based on artificial intelligence techniques (AI) for the management of a micro grid. The use of fuzzy logic is proposed for the analysis of the agent consumption elasticity, while a case based reasoning, used to predict agents’ reaction to price changes, is an interesting tool for the micro grid operator.

Virtual Power Players Internal Negotiation and Management in MASCEM

Electricity Markets are not only a new reality but an evolving one as the involved players and rules change at a relatively high rate. Multi-agent simulation combined with Artificial Intelligence techniques may result in very helpful sophisticated tools. This paper presents a new methodology for the management of coalitions in electricity markets. This approach is tested using the multi-agent market simulator MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), taking advantage of its ability to provide the means to model and simulate Virtual Power Players (VPP). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market and internally, with their members in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. A case study using real data from the Iberian Electricity Market is performed to validate and illustrate the proposed approach.

Use of multi-sensory marketing techniques and its influence on brand experience in retail and HORECA sector

Double degree. A Work Project presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA- School of Business and Economics and Warsaw School of Economics

The management of brain metastases

Brain metastases occur in 20-50% of NSCLC and 50-80% of SCLC. In this review, we will look at evidence-based medicine data and give some perspectives on the management of BM. We will address the problems of multiple BM, single BM and prophylactic cranial irradiation. Recursive Partitioning Analysis (RPA) is a powerful prognostic tool to facilitate treatment decisions. Dealing with multiple BM, the use of corticosteroids was established more than 40 years ago by a unique randomized trial (RCT). Palliative effect is high (_80%) as well as side-effects. Whole brain radiotherapy (WBRT) was evaluated in many RCTs with a high (60-90%) response rate; several RT regimes are equivalent, but very high dose per fraction should be avoided. In multiple BM from SCLC, the effect of WBRT is comparable to that in NSCLC but chemotherapy (CXT) although advocated is probably less effective than RT. Single BM from NSCLC occurs in 30% of all BM cases; several prognostic classifications including RPA are very useful. Several options are available in single BM: WBRT, surgery (SX), radiosurgery (RS) or any combination of these. All were studied in RCTs and will be reviewed: the addition of WBRT to SX or RS gives a better neurological tumour control, has little or no impact on survival, and may be more toxic. However omitting WBRT after SX alone gives a higher risk of cerebro-spinal fluid dissemination. Prophylactic cranial irradiation (PCI) has a major role in SCLC. In limited disease, meta-analyses have shown a positive impact of PCI in the decrease of brain relapse and in survival improvement, especially for patients in complete remission. Surprisingly, this has been recently confirmed also in extensive disease. Experience with PCI for NSCLC is still limited, but RCT suggest a reduction of BM with no impact on survival. Toxicity of PCI is a matter of debate, as neurological or neuro-cognitive impairment is already present prior to PCI in almost half of patients. However RT toxicity is probably related to total dose and dose per fraction. Perspectives : Future research should concentrate on : 1) combined modalities in multiple BM. 2) Exploration of treatments in oligo-metastases. 3) Further exploration of PCI in NSCLC. 4) Exploration of new, toxicity-sparing radiotherapy techniques (IMRT, Tomotherapy etc).

Corporate Social Responsibility in the Oil and Gas Industry: Environmental Management Systems as a Source of Sustainable Development

The environmental aspect of corporate social responsibility (CSR) expressed through the process of the EMS implementation in the oil and gas companies is identified as the main subject of this research. In the theoretical part, the basic attention is paid to justification of a link between CSR and environmental management. The achievement of sustainable competitive advantage as a result of environmental capital growth and inclusion of the socially responsible activities in the corporate strategy is another issue that is of special significance here. Besides, two basic forms of environmental management systems (environmental decision support systems and environmental information management systems) are explored and their role in effective stakeholder interaction is tackled. The most crucial benefits of EMS are also analyzed to underline its importance as a source of sustainable development. Further research is based on the survey of 51 sampled oil and gas companies (both publicly owned and state owned ones) originated from different countries all over the world and providing reports on sustainability issues in the open access. To analyze their approach to sustainable development, a specifically designed evaluation matrix with 37 indicators developed in accordance with the General Reporting Initiative (GRI) guidelines for non-financial reporting was prepared. Additionally, the quality of environmental information disclosure was measured on the basis of a quality – quantity matrix. According to results of research, oil and gas companies prefer implementing reactive measures to the costly and knowledge-intensive proactive techniques for elimination of the negative environmental impacts. Besides, it was identified that the environmental performance disclosure is mostly rather limited, so that the quality of non-financial reporting can be judged as quite insufficient. In spite of the fact that most of the oil and gas companies in the sample claim the EMS to be embedded currently in their structure, they often do not provide any details for the process of their implementation. As a potential for the further development of EMS, author mentions possible integration of their different forms in a single entity, extension of existing structure on the basis of consolidation of the structural and strategic precautions as well as development of a unified certification standard instead of several ones that exist today in order to enhance control on the EMS implementation.

The self-management approach of health education in the treatment of chronic illnesses

Health education is essential to the successful treatment of individuals with chronic illnesses. Self-management is a philosophical model of health education that has been shown to be effective in teaching individuals with chronic arthritis to manage their illness as part of their daily lives. Despite the proven results of arthritis self-management programs, some limitations of this form of health education were apparent in the literature. The present study attempted to address the problems of the self-management approach of health education such as reasons for lack of participation in programs and poor course outcomes. In addition, the study served to investigate the relationship between course outcomes and participation in programs with the theory upon which arthritis self-management programs are based, known as self-efficacy theory. Through a combination of qualitative and quantitative methodologies, data collection, and analysis, a deeper understanding of the self-management phenomenon in the treatment of chronic arthritic conditions was established. Findings of the study confirm findings of previous studies that suggest that arthritis self-management programs result in enhanced levels of self-efficacy and are effective in teaching individuals with arthritis to self-manage their health and health care. Findings of the study suggest that there are many factors that determine the choice of participants to participate in programs and the outcomes for the individuals who do choose to participate in programs. Some of the major determinants of enrollment and outcomes of programs include: the participant's personality, beliefs, attitudes and abilities, and the degree of emotional acceptance of the illness. Other determinants of course enrollment and outcomes included class size and length of time, timing of participation, and ongoing support after the program. The results of the study are consistent with the self-management literature and confirm the relationship between the underlying philosophies of adult education and Freire's model of education and self-management.

Undergraduate Student Anxiety-Management in Academia: Appraising the Value of Services and Strategies

This study explored strategies that Brock University undergraduate students value the most for managing anxiety in academia. Although previous literature indicates services and techniques such as academic advising, physical activity, and educator engagement help students, few if any have ranked students’ perceived value of anxiety-management strategies. The researcher recruited 54 undergraduate student participants (primarily from the Department of Community Health Sciences) through online invitation. Participants completed an online survey to rate their previous experience with anxiety-management strategies discussed in the literature. Survey findings identified the 4 most valuable resources students used to manage anxiety in academia: (a) educators who post academic material posted online (e.g., on Sakai) early in the term, (b) physical activity, (c) socialization, and (d) breaking large assignments into smaller portions. Conversely, student participants found disability services, counseling, and medication to be the least valuable resources. Results suggest higher-education facilities should ensure that the most valuable services are readily available to students seeking them. The study contributes to the field by identifying a broad set of strategies that students find highly valuable in their management of academic related anxiety.

Sequential Machine learning Approaches for Portfolio Management

Cette thèse envisage un ensemble de méthodes permettant aux algorithmes d'apprentissage statistique de mieux traiter la nature séquentielle des problèmes de gestion de portefeuilles financiers. Nous débutons par une considération du problème général de la composition d'algorithmes d'apprentissage devant gérer des tâches séquentielles, en particulier celui de la mise-à-jour efficace des ensembles d'apprentissage dans un cadre de validation séquentielle. Nous énumérons les desiderata que des primitives de composition doivent satisfaire, et faisons ressortir la difficulté de les atteindre de façon rigoureuse et efficace. Nous poursuivons en présentant un ensemble d'algorithmes qui atteignent ces objectifs et présentons une étude de cas d'un système complexe de prise de décision financière utilisant ces techniques. Nous décrivons ensuite une méthode générale permettant de transformer un problème de décision séquentielle non-Markovien en un problème d'apprentissage supervisé en employant un algorithme de recherche basé sur les K meilleurs chemins. Nous traitons d'une application en gestion de portefeuille où nous entraînons un algorithme d'apprentissage à optimiser directement un ratio de Sharpe (ou autre critère non-additif incorporant une aversion au risque). Nous illustrons l'approche par une étude expérimentale approfondie, proposant une architecture de réseaux de neurones spécialisée à la gestion de portefeuille et la comparant à plusieurs alternatives. Finalement, nous introduisons une représentation fonctionnelle de séries chronologiques permettant à des prévisions d'être effectuées sur un horizon variable, tout en utilisant un ensemble informationnel révélé de manière progressive. L'approche est basée sur l'utilisation des processus Gaussiens, lesquels fournissent une matrice de covariance complète entre tous les points pour lesquels une prévision est demandée. Cette information est utilisée à bon escient par un algorithme qui transige activement des écarts de cours (price spreads) entre des contrats à terme sur commodités. L'approche proposée produit, hors échantillon, un rendement ajusté pour le risque significatif, après frais de transactions, sur un portefeuille de 30 actifs.

Numérisation des dossiers de santé et protection des renseignements personnels:impératifs techniques, intérêts économiques, considérations politiques et émergence de nouvelles normes

Dans son texte, l’auteur tente de répondre à quatre grandes questions : le cadre juridique actuel qui régi la gestion des renseignements personnels dans le réseau de la santé et des services sociaux protège–t–il adéquatement ces données sensibles ? Peut–on tenter d’améliorer cette protection ? Quels facteurs contribuent à l’émergence des nouvelles normes ? Quel rôle les exigences techniques, les intérêts économiques et les considérations politiques jouent–ils dans l’émergence de ces nouvelles normes ? À l’égard de ces interrogations, l’auteur montre que le régime actuel, qui se distingue particulièrement par sa complexité et son opacité, ne protège pas adéquatement les renseignements personnels appelés à circuler dans l’univers numérique en émergence. Il examine donc les facteurs qui contribuent À l’émergence des nouvelles normes alors que les règles juridiques actuelles se trouvent en porte–à–faux par rapport aux développements technologiques. Par la suite, il tente de situer ou de mesurer l’influence des intérêts économiques, des considérations politiques et des impératifs techniques dans l’émergence de ces nouvelles normes. Finalement, il met de l’avant un concept nouveau, celui des aires de partage, qu’il présente comme un moyen de mieux protéger les renseignements personnels et confidentiels dans l’ère de l’information.