Squential Step Towards Pattern Warehousing

With the massive increase in the data, the demand by the analysts hyped for the proper repositories where they could analyse the concerned specific data patterns in order to make smart and quick decisions for the welfare and benefit of the business, organization or some social work. Pattern warehouse proved to be the best solution. This paper focuses on the discussion of existing architecture and moreover on the algorithms that is needed for retrieving the optimal patterns from the pattern warehouse. It also includes the detailed study about the sequential emergence of association rule algorithms which initially derive out patterns and later on those patterns are being optimized according to the interest of the analyst

The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp)

The MESSAGE Integrated Assessment Model (IAM) developed by IIASA has been a central tool of energy-environment-economy systems analysis in the global scientific and policy arena. It played a major role in the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC); it provided marker scenarios of the Representative Concentration Pathways (RCPs) and the Shared Socio-Economic Pathways (SSPs); and it underpinned the analysis of the Global Energy Assessment (GEA). Alas, to provide relevant analysis for current and future challenges, numerical models of human and earth systems need to support higher spatial and temporal resolution, facilitate integration of data sources and methodologies across disciplines, and become open and transparent regarding the underlying data, methods, and the scientific workflow. In this manuscript, we present the building blocks of a new framework for an integrated assessment modeling platform; the \ecosystem" comprises: i) an open-source GAMS implementation of the MESSAGE energy++ system model integrated with the MACRO economic model; ii) a Java/database backend for version-controlled data management, iii) interfaces for the scientific programming languages Python & R for efficient input data and results processing workflows; and iv) a web-browser-based user interface for model/scenario management and intuitive \drag-and-drop" visualization of results. The framework aims to facilitate the highest level of openness for scientific analysis, bridging the need for transparency with efficient data processing and powerful numerical solvers. The platform is geared towards easy integration of data sources and models across disciplines, spatial scales and temporal disaggregation levels. All tools apply best-practice in collaborative software development, and comprehensive documentation of all building blocks and scripts is generated directly from the GAMS equations and the Java/Python/R source code

Supporting the grow-and-prune model for evolving software product lines

207 p.Software Product Lines (SPLs) aim at supporting the development of a whole family of software products through a systematic reuse of shared assets. To this end, SPL development is separated into two interrelated processes: (1) domain engineering (DE), where the scope and variability of the system is defined and reusable core-assets are developed; and (2) application engineering (AE), where products are derived by selecting core assets and resolving variability. Evolution in SPLs is considered to be more challenging than in traditional systems, as both core-assets and products need to co-evolve. The so-called grow-and-prune model has proven great flexibility to incrementally evolve an SPL by letting the products grow, and later prune the product functionalities deemed useful by refactoring and merging them back to the reusable SPL core-asset base. This Thesis aims at supporting the grow-and-prune model as for initiating and enacting the pruning. Initiating the pruning requires SPL engineers to conduct customization analysis, i.e. analyzing how products have changed the core-assets. Customization analysis aims at identifying interesting product customizations to be ported to the core-asset base. However, existing tools do not fulfill engineers needs to conduct this practice. To address this issue, this Thesis elaborates on the SPL engineers' needs when conducting customization analysis, and proposes a data-warehouse approach to help SPL engineers on the analysis. Once the interesting customizations have been identified, the pruning needs to be enacted. This means that product code needs to be ported to the core-asset realm, while products are upgraded with newer functionalities and bug-fixes available in newer core-asset releases. Herein, synchronizing both parties through sync paths is required. However, the state of-the-art tools are not tailored to SPL sync paths, and this hinders synchronizing core-assets and products. To address this issue, this Thesis proposes to leverage existing Version Control Systems (i.e. git/Github) to provide sync operations as first-class construct

Business Intelligence systems development in hospitals using an Agile Project Management approach

"Measure to manage" is a widely used expression to demonstrate that good governance must necessarily go through obtaining good data and information. These will allow managers to know the past and the momentum of the business and also to predict, estimate and take the best-informed decisions. The greater the complexity of the business, the greater this need. Healthcare units, specifically hospitals, are organizations that, due to their function and diversity of areas, are considered one of the most complex. In this context, projects for the development of business intelligence solutions, with huge impact and scope, undergo the need for continuous improvement and incremental evolution. Agile methods, by their nature and principles, are suitable to fulfil this need. The purpose of this dissertation is to support future research towards better models with agile tools to develop business intelligence system in hospitals and, manly, to understand how can Agile methodology improve a Business Intelligence System Implementation. This will be done mainly through bibliographical research on the covered topics, namely, Hospitals, Business Intelligence, Agile and Project Management. The expect results will be some clear practical guidelines, that any IT Project Manager could use for an efficient Business Intelligence System implementation using an Agile methodology. This will be done with the presentation of two use cases, from implementations in two hospitals in Portugal, where the Agile proposed model could be used to improve the outcomes of the projects. For that a deep analysis of the various phases of Business Intelligence development was carried out on the basis of information obtained in the literature and on the basis of information obtained in the practical development of Business Intelligence implementation projects. In the end it can be seen that the application of Agile can bring enormous benefits to the development of this kind of project, as, in addition to the advantages listed and widely known about Agile, it can help intensively to bring together and involve all the stakeholders of a project in a common goal of success and effectiveness

Knowledge Warehouse: An Architectural Integration of Knowledge Management, Decision Support, Artificial Intelligence and Data Warehousing

Decision support systems (DSS) are becoming increasingly more critical to the daily operation of organizations. Data warehousing, an integral part of this, provides an infrastructure that enables businesses to extract, cleanse, and store vast amounts of data. The basic purpose of a data warehouse is to empower the knowledge workers with information that allows them to make decisions based on a solid foundation of fact. However, only a fraction of the needed information exists on computers; the vast majority of a firm’s intellectual assets exist as knowledge in the minds of its employees. What is needed is a new generation of knowledge-enabled systems that provides the infrastructure needed to capture, cleanse, store, organize, leverage, and disseminate not only data and information but also the knowledge of the firm. The purpose of this paper is to propose, as an extension to the data warehouse model, a knowledge warehouse (KW) architecture that will not only facilitate the capturing and coding of knowledge but also enhance the retrieval and sharing of knowledge across the organization. The knowledge warehouse proposed here suggests a different direction for DSS in the next decade. This new direction is based on an expanded purpose of DSS. That is, the purpose of DSS in knowledge improvement. This expanded purpose of DSS also suggests that the effectiveness of a DS will, in the future, be measured based on how well it promotes and enhances knowledge, how well it improves the mental model(s) and understanding of the decision maker(s) and thereby how well it improves his/her decision making

Natural Language Processing – Finding the Missing Link for Oncologic Data, 2022

Oncology like most medical specialties, is undergoing a data revolution at the center of which lie vast and growing amounts of clinical data in unstructured, semi-structured and structed formats. Artificial intelligence approaches are widely employed in research endeavors in an attempt to harness electronic medical records data to advance patient outcomes. The use of clinical oncologic data, although collected on large scale, particularly with the increased implementation of electronic medical records, remains limited due to missing, incorrect or manually entered data in registries and the lack of resource allocation to data curation in real world settings. Natural Language Processing (NLP) may provide an avenue to extract data from electronic medical records and as a result has grown considerably in medicine to be employed for documentation, outcome analysis, phenotyping and clinical trial eligibility. Barriers to NLP persist with inability to aggregate findings across studies due to use of different methods and significant heterogeneity at all levels with important parameters such as patient comorbidities and performance status lacking implementation in AI approaches. The goal of this review is to provide an updated overview of natural language processing (NLP) and the current state of its application in oncology for clinicians and researchers that wish to implement NLP to augment registries and/or advance research projects

The Impact of Climate Change on the United States Whiskey Industry

Climate change is one of the most pressing issues of our time and will impact the lives and livelihood of billions of people around the world. As the Earth’s climate continues to warm, the whiskey industry will need to acknowledge and adapt to the challenges that climate change will bring. While other alcoholic beverage industries have already reckoned with the effects of climate change, the American whiskey industry has been relatively insulated. This is in part due to the lack of government regulation and having access to a large geographic area with ample resources. This thesis reviewed literature regarding climate change, whiskey, and the intersection of the two. Then, the cognac and beer industry were investigated to examine the severity of climate change’s effects on comparable industries. Next, interviews with five American whiskey distillers provided data on areas where the industry would be impacted the most. Qualitative data from the interviews were coded to derive five main themes: climate change’s impact on grain variety, whiskey aging, wood used for barrels, freshwater access, and adaptation methods. Finally, adaptation recommendations were provided for the distilleries based on the literature review and data from the qualitative interviews to mitigate the adverse effects of climate change on the American whiskey industry.Business Administratio

COCAINE SEIZURES AND CRIME: DATA ANALYTICS USING BIG DATA TOOLS

Includes Supplementary MaterialColombia's status as the largest cocaine producer in the world has prompted its government's strategies to combat drug trafficking. One of these strategies is to seize cocaine in the Colombian jurisdictional territory. The unintended consequences of this strategy on crime rates, particularly homicides, remain uncertain. Web scraping methods and big data tools were used to gather and construct a time series dataset on cocaine seizures from three distinct websites, while the homicides dataset was supplied by the Colombian Ministry of Defense (MDN). This study aims to investigate, from a quantitative standpoint, whether there is a link between cocaine seizures and homicides in the Colombian Pacific region, utilizing an exploratory data analysis (EDA) method and machine learning techniques. The study recognizes the constraints of the sample size and opts to reveal valuable insights through data analysis and modeling instead. Despite the constraints, two models were developed to partially explicate the significance of this correlation. The study's findings provide value for policymakers, military personnel, government officials, and academics, offering essential perspectives to devise improved policies and strategies to mitigate drug trafficking in the Colombian Pacific region without exacerbating homicide rates. Future research endeavors could consider expanding the sample size of the cocaine seizure time-series dataset to conduct a more robust correlation analysis.Approved for public release. Distribution is unlimited.Outstanding ThesisCapitan de Corbeta, Colombian National Nav