Rapid application development Rapid Application Development (RAD) Model Rapid application development (RAD) is a software development methodology that uses minimal planning in favor of rapid prototyping. The "planning" of software developed using RAD is interleaved with writing the software itself. The lack of extensive pre-planning generally allows software to be written much faster, and makes it easier to change requirements. History[edit] Rapid Application Development (RAD) is a term originally used for describing a software development process first developed and successfully deployed during the mid-1970s by the New York Telephone Co's Systems Development Center under the direction of Dan Gielan. Rapid application development is a response to processes developed in the 1970s and 1980s, such as the Structured Systems Analysis and Design Method and other Waterfall models. Four phases of RAD[edit] Relative effectiveness[edit] Criticism[edit] Practical implications[edit] References[edit] Jump up ^ Maurer and S.
Big Data Analytics Big data analytics: From data scientists to business analysts - Data The growing popularity of Big Data management tools (Hadoop; MPP, real-time SQL, NoSQL databases; and others1) means many more companies can handle large amounts of data. But how do companies analyze and mine their vast amounts of data? The cutting-edge (social) web companies employ teams of data scientists2 who comb through data using different Hadoop interfaces and use custom analysis and visualization tools. Other companies integrate their MPP databases with familiar Business Intelligence tools. For companies that already have large amounts of data in Hadoop, there’s room for even simpler tools that would allow business users to directly interact with Big Data. A startup aims to expose Big Data to analysts charged with producing most routine reports. Datameer’s workflow uses the familiar spreadsheet interface as a data processing pipeline. What’s intriguing about DAS is that it opens up Big Data analysis to large sets of business users.
Microsoft SharePoint 2010, más implantación pero sin estrategia La cuota de mercado de la plataforma de colaboración de Microsoft crece. Así lo revela un estudio de OpenText donde se pone de relieve la creciente preocupación por la falta de estrategia clara de las empresas a la hora de implantar SharePoint. El informe, realizado entre 362 personas habituadas a utilizar Microsoft SharePoint, demuestra que cada día es más frecuente dentro de las corporaciones, sobre todo, en la gestión de procesos de negocio y el flujo de trabajo. Sin embargo, el alcance del despliegue todavía no es claro. En este sentido, Lubor Ptacek, vicepresidente de marketing estratégico y director general de soluciones Microsoft en OpenText explica que “Con esta información tenemos un mejor conocimiento de las necesidades de nuestros clientes lo que nos permite mejorar nuestros productos, resolver los problemas de los clientes y aumentar la presencia de OpenText en el mercado SharePoint”. Las principales conclusiones del estudio son las siguientes:
Основана в 2003 г., большая компания - 560 Market basket analysis - identifying products and content that go well together Affinity analysis and association rule learning encompasses a broad set of analytics techniques aimed at uncovering the associations and connections between specific objects: these might be visitors to your website (customers or audience), products in your store, or content items on your media site. Of these, “market basket analysis” is perhaps the most famous example. In a market basket analysis, you look to see if there are combinations of products that frequently co-occur in transactions. For example, maybe people who buy flour and casting sugar, also tend to buy eggs (because a high proportion of them are planning on baking a cake). Store layout (put products that co-occur together close to one another, to improve the customer shopping experience) Marketing (e.g. target customers who buy flour with offers on eggs, to encourage them to spend more on their shopping basket) Online retailers and publishers can use this type of analysis to: Terminology Rules are statements of the form ! ! ! ! !
Estudio de mercado sharepoint en chile Big Data: 9 Steps to Extract Insight from Unstructured Data The increasing digitization of information in recent years, coupled with the proliferation of multi-channel processes and transactions, has resulted in a data deluge. The ever-increasing pace of digital information has led the world's aggregate creation of data to double in even shorter intervals than ever before. According to Gartner, about 80% of data held by an organization is unstructured data, comprised of information from customer calls, emails and social media feeds. This is in addition to the voluminous diagnostic information logged by embedded and user devices. While it would be a daunting to even make a proper analysis from organized data, it is very difficult to make sense of unstructured data. As a result, organizations have to study both structured and unstructured data to arrive at meaningful business decisions, including determining customer sentiment, cooperating with e-discovery requirements and personalizing their product for their customers. 1. 2. 3. 4. 5. 6. 7. 8.
Scalable Machine Learning for Big Data Using R and H2O - Data Science Las Veg... Part I Part II H2O is an open source parallel processing engine for machine learning on Big Data. This prediction engine is designed by, h20, a Mountain View-based startup that has implemented a number of impressive statistical and machine learning algorithms to run on HDFS, S3, SQL and NoSQL. We were honored to have Tom Kraljevic (Vice President of Engineering at H2O) demonstrate how this prediction engine is suited for machine learning on Big Data from within R. “R tells H2O to perform a task…and then H2O returns the result back to R, which is a tiny result….but you never actually transfer the data to R…That’s the magic behind the scalability of H2O with R.” This feature appealed to me. You can find the slides to this presentation by clicking here or copy and paste the following URL into your web browser (
Big Data and Apache Hadoop for Financial Services How Big Data and Hadoop Help Financial Services Firms Manage Risk and Stay Competitive Financial services organizations around the world are experiencing drastic change. The global financial crisis of 2008 resulted in the failing of scores of banks, which also impacted incomes, jobs, and wealth. As a result, financial institutions need to work hard to avoid the repeat of such a crisis. Additionally, financial sector companies realize that in order to thrive in a market that has changed so dramatically, they need to be able to improve their operational efficiencies, detect fraud quicker and more accurately, model and manage their risk, and reduce customer churn. Financial Services Use Cases Below are a few of the use cases that illustrate how big data and Hadoop are being integrated in the financial services industry, providing companies with insights into their operations, their customers, and their markets. Fraud Detection Financial Services Customers