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Data architecture

Data architecture
In information technology, data architecture is composed of models, policies, rules or standards that govern which data is collected, and how it is stored, arranged, integrated, and put to use in data systems and in organizations.[1] Data is usually one of several architecture domains that form the pillars of an enterprise architecture or solution architecture.[2] Overview[edit] A data architecture should[neutrality is disputed] set data standards for all its data systems as a vision or a model of the eventual interactions between those data systems. Data integration, for example, should be dependent upon data architecture standards since data integration requires data interactions between two or more data systems. A data architecture, in part, describes the data structures used by a business and its computer applications software. Essential to realizing the target state, Data Architecture describes how data is processed, stored, and utilized in an information system. Technology drivers

Modelo em cascata Origem: Wikipédia, a enciclopédia livre. O modelo em cascata é um modelo de desenvolvimento de software seqüencial no qual o desenvolvimento é visto como um fluir constante para frente (como uma cascata) através das fases de análise de requisitos, projeto, implementação, testes (validação), integração, e manutenção de software. A origem do termo cascata é frequentemente citado como sendo um artigo publicado em 1970 por W. W. Royce; ironicamente, Royce defendia um abordagem iterativa para o desenvolvimento de software e nem mesmo usou o termo cascata. História do modelo em cascata[editar | editar código-fonte] Em 1970 Royce propôs o que é agora popularmente designado no modelo em cascata como um conceito inicial, um modelo no qual ele argumentava ser defeituoso. Uso do modelo cascata[editar | editar código-fonte] O Modelo em cascata estático. No modelo em cascata original de Royce, as seguintes fases são seguidas em perfeita ordem:

Business intelligence Business intelligence (BI) is the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. BI technologies are capable of handling large amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities. The goal of BI is to allow for the easy interpretation of these large volumes of data. BI technologies provide historical, current and predictive views of business operations. BI can be used to support a wide range of business decisions ranging from operational to strategic. Components[edit] Business intelligence is made up of an increasing number of components including: History[edit] In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. Business intelligence as it is understood today is said to have evolved from the decision support systems (DSS) that began in the 1960s and developed throughout the mid-1980s. Data warehousing[edit]

Processo de desenvolvimento de software Origem: Wikipédia, a enciclopédia livre. Um processo de desenvolvimento de software é um conjunto de atividades, parcialmente ordenadas, com a finalidade de obter um produto de software. É estudado dentro da área de Engenharia de Software, sendo considerado um dos principais mecanismos para se obter software de qualidade e cumprir corretamente os contratos de desenvolvimento, sendo uma das respostas técnicas adequadas para resolver a Crise do software. Passos/Atividades Processo[editar | editar código-fonte] Análise Econômica[editar | editar código-fonte] Análise de requisitos de software[editar | editar código-fonte] A extração dos requisitos de um cliente Especificação[editar | editar código-fonte] A especificação é a tarefa de descrever precisamente o software que será escrito, preferencialmente de uma forma matematicamente rigorosa. Arquitetura de Software[editar | editar código-fonte] A arquitetura de um sistema de software remete a uma representação abstrata daquele sistema.

Predictive analytics Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.[1][2] In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.[3] Predictive analytics is used in actuarial science,[4] marketing,[5] financial services,[6] insurance, telecommunications,[7] retail,[8] travel,[9] healthcare,[10] pharmaceuticals[11] and other fields. One of the most well known applications is credit scoring,[1] which is used throughout financial services. Definition[edit] Types[edit] Predictive models[edit] Descriptive models[edit] Decision models[edit] Applications[edit] Collection analytics[edit]

Enterprise architecture planning Levels of Enterprise Architecture Planning.[1] Enterprise Architecture Planning (EAP) in Enterprise Architecture is the planning process of defining architectures for the use of information in support of the business and the plan for implementing those architectures.[2] Overview[edit] One of the earlier professional practitioners in the field of system architecture Steven H. Spewak in 1992 defined Enterprise Architecture Planning (EAP) as "the process of defining architectures for the use of information in support of the business and the plan for implementing those architectures. This hierarchy of activity is represented in the figure above, in which the layers are implemented in order, from top to bottom. EAP topics[edit] Zachman framework[edit] EAP defines the blueprint for subsequent design and implementation and it places the planning/defining stages into a framework. EAP components[edit] Enterprise Architecture Planning model consists of four levels: EAP methodology[edit] See also[edit]

XSLT XSLT (Extensible Stylesheet Language Transformations) is a language for transforming XML documents into other XML documents,[1] or other objects such as HTML for web pages, plain text or into XSL Formatting Objects which can then be converted to PDF, PostScript and PNG.[2] The original document is not changed; rather, a new document is created based on the content of an existing one.[3] Typically, input documents are XML files, but anything from which the processor can build an XQuery and XPath Data Model can be used, for example relational database tables, or geographical information systems.[1] XSLT is a Turing-complete language, meaning it can specify any computation that can be performed by a computer.[4][5] History[edit] Design and processing model[edit] Diagram of the basic elements and process flow of Extensible Stylesheet Language Transformations. Processor implementations[edit] Performance[edit] Most early XSLT processors were interpreters. XSLT and XPath[edit] XSLT media types[edit] <?

Business process modeling Business process modeling (BPM) in systems engineering is the activity of representing processes of an enterprise, so that the current process may be analyzed or improved. BPM is typically performed by business analysts, who provide expertise in the modeling discipline; by subject matter experts, who have specialized knowledge of the processes being modeled; or more commonly by a team comprising both. The business objective is often to increase process speed or reduce cycle time; to increase quality; or to reduce costs, such as labor, materials, scrap, or capital costs. In practice, a management decision to invest in business process modeling is often motivated by the need to document requirements for an information technology project. Change management programs are typically involved to put any improved business processes into practice. History[edit] BPM topics[edit] Business model[edit] A business model is a framework for creating economic, social, and/or other forms of value.

XML Extensible Markup Language (XML) is a markup language that defines a set of rules for encoding documents in a format which is both human-readable and machine-readable. It is defined by the W3C's XML 1.0 Specification[2] and by several other related specifications,[3] all of which are free open standards.[4] The design goals of XML emphasize simplicity, generality and usability across the Internet.[5] It is a textual data format with strong support via Unicode for different human languages. Several schema systems exist to aid in the definition of XML-based languages, while many application programming interfaces (APIs) have been developed to aid the processing of XML data. Applications of XML[edit] XML has come into common use for the interchange of data over the Internet. RFC 7303 also recommends that XML-based languages be given media types ending in +xml; for example image/svg+xml for SVG. Key terminology[edit] The material in this section is based on the XML Specification. Tag Element <?