Referencias | References
Referencias completas de vocabulario, eventos, crónicas, evidencias y otros contenidos utilizados en los proyectos relacionados con biotecnología y neurociencia de la KW Foundation.
Full references of vocabulary, events, chronicles, evidences and other contents used in KW Projects related to biotechnology and neuroscience.
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Una macro (del griego makro, que significa ‘grande’) es una abreviatura del término macroinstrucción. Contiene una secuencia de comandos, ejecutables mediante una sola orden. Esto permite la automatización de tareas repetitivas.
Magia Blanca 
Se denomina magia blanca a aquellos actos de liturgia mágica cuya naturaleza, métodos u objetivos son comúnmente aceptados por la sociedad donde se producen. Se utiliza como antónimo de magia negra.
Una computadora central (en inglés mainframe) posee gran tamaño. Potente y costosa, es utilizada principalmente por compañías que requieren el procesamiento confiable de una gran cantidad de datos; por ejemplo, para el procesamiento de transacciones bancarias.
Make the right choice between Hadoop clusters and a data warehouse 
Make the right choice between Hadoop clusters and a data warehouse
by David Loshin
Consultant David Loshin outlines a process for comparing specific criteria and variables to help guide decisions on deploying Hadoop or using an enterprise data warehouse.
A major motivation for the ongoing evolution of the Hadoop software stack is a desire to position it for broader use in enterprise computing environments. And the appetite for Hadoop and related technologies among user organizations is growing -- justifiably so, because the economics of Hadoop clusters provide the ability to implement a scalable data management architecture with a low barrier to entry and to incremental investments as processing needs increase.
At the same time, much confusion remains about what Hadoop is and does, and how it differs from -- or is the same as -- an enterprise data warehouse (EDW). In many cases, this confusion becomes more acute as additional buzz terms are introduced into the Hadoop vs. data warehouse conversation, such as data lake or data reservoir. In addition, the confusion surrounding what capabilities are or are not part of Hadoop adds additional complexity.
There's no doubt that Hadoop has a place in the enterprise, especially as big data applications take hold. But the venerable EDW has a well-established presence in data centers, and after years of refinement plays a significant role in meeting the reporting and analytics needs of most organizations. Does the emergence of Hadoop mean it's time to abandon the EDW? Some IT and data management professionals are aching to use Hadoop as a replacement for the data warehouse -- but are companies really prepared to abandon their decades-long investments in EDW infrastructure, software, staffing and development?
Criteria to help drive buying decisions
To determine when one technology is better than another for a particular task, it's worth examining the criteria that are important to consider. In this case, they can be grouped into four high-level categories:
Operational performance. Examples include query response times, data integration and loading throughput, the volume of data that can be stored, the ability to support a mixed workload of applications and the number of simultaneous users.
Support for desired functionality. This includes things like SQL support, the ability to ingest and manage both structured and unstructured data, high availability and fault-tolerance features, integrated security and data protection, data compression and stream processing functionality.
Cost factors. As a matter of course, you should take into account elements such as the base system price, the average cost per storage volume unit and the cost of scaling up systems, as well as maintenance and support costs.
Strategic value. Utilizing a single platform for both data management and high-performance computing that powers advanced analytics applications might be beneficial to an organization. Other issues that could come into play include replacing legacy systems for which there is a shrinking pool of skilled workers and being able to use data virtualization techniques that allow you to pull together different data sets without physically moving them.
Hadoop or data warehouse, side by side
You can compare the different technologies by lining up the choices side by side and seeing how they each address the specific criteria that the people in your organization care most deeply about, in accordance with the planned applications. As an example, consider "SQL compliance," which might be relevant for a more traditional reporting application. A relational database running on a data warehouse appliance is likely to satisfy that requirement. On the other hand, even though query engines that layer SQL interfaces on top of Hadoop have become available, they may still need some additional refinement and tweaking before they can be trusted to execute all types of SQL queries correctly and efficiently.
Another example is "cost to scale," which might be relevant in the context of an application for analyzing a growing number of text documents. For a traditional EDW running on a mainframe or large Unix server, the cost of scaling up the system is likely to be is high. A Hadoop cluster based on commodity hardware is better positioned to scale incrementally, and the required investment in a few more compute nodes and storage devices should be relatively small.
For each business requirement, you can repeat this evaluation process for all the criteria relevant to the desired outcome. Create a grid in which the technology choices form the columns and the performance measures form the rows. If you'd like, add a weighting factor to each of the criteria. Assess how well the corresponding technology satisfies each criterion. When you're done, you can use your weightings to formulate a comparative score that can guide the decision on how to move forward.
Eventually, a broader mix of traditional data management, reporting and analytics tools will be adapted to and deployed on the low-cost, high-performance framework that Hadoop embodies. It's safe to say that, in a way that mirrors the maturity curve for any new technology, the underlying Hadoop platform will gel into an environment that is production-hardened and satisfies the dimensional analysis, high-performance computing and advanced analytics needs that span the corporate user community. But that may take some time -- and in the interim, choices between Hadoop clusters and data warehouses must be made. Considering performance criteria and specific variables will help you decide which technology is best suited to solving a particular business problem.
About the author: David Loshin is president of Knowledge Integrity Inc., a consulting and development services company that works with clients on big data, business intelligence and data management projects. He also is the author or co-author of various books, including Using Information to Develop a Culture of Customer Centricity. Email him at firstname.lastname@example.org.
Making Big Data Actionable 
Harvard Business Review Key Summary: Making Big Data Actionable
With many organizations still using very basic tools to visualize their data, they are missing a huge opportunity. To make big data actionable and profitable, firms must find new and innovative ways to fully leverage their data and change their business through analytics.
Bill Franks, Chief Analytics Officer of Teradata discusses how to make big data more actionable by using compelling data visualization tools and techniques.
Please read the attached whitepaper.
Malvertisement (malicious advertisement or malvertising)
Posted by Margaret Rouse
A malvertisement (malicious advertisement) is an advertisement on the Internet that delivers a malicious payload.
A malvertisement (malicious advertisement) is an advertisement on the Internet that is capable of infecting the viewer's computer with malware. According to the network security company Blue Coat Systems Inc., malvertising is the current computer hijacking technique of choice for organized crime. Compromised computers can be used to create powerfulbotnets that can be used to carry out identity theft, corporate espionage or other illegal activity.
Malvertisements are commonly placed on a website in one of these two ways:
Legitimate advertisements: Initially, a criminal may place a series of malware-free advertisements on a trusted site that runs third-party ads and leave them alone for several months in order to establish a good reputation.Later on, the criminal will inject a malicious payload into the ad, infecting as many computers as possible in a short amount of time before removing the malicious code or discontinuing the ad. This type of attack is often run on websites that run third-party ads.
Pop-up ads: A pop-up ad can deliver a malicious payload as soon as the ad appears on the viewer’s screen. Scareware, which is malicious code disguised as an anti-virus application, is often delivered through pop-up ads. In some cases, the malware will execute when the viewer clicks the “X” to close the pop-up window.
By infiltrating popular syndicated online ad services, thousands of sites can be infected at once. Unfortunately, websites that run third-party ads can do little to protect their visitors because syndicated ads are not under their direct control. In fact, the company from whom they receive the ads may use ads from other publishers, so the original source of the advertisements can be several parties removed. Malvertisement infections are becoming so prevalent that many security experts recommend that users block all pop-up ads and create an application whitelist that will only allow their computer to run programs that have been positively approved.
Continue Reading About malvertisement (malicious advertisement or malvertising)
Man of Steel 
Managing Identity Theft 
After a Breach: Managing Identity Theft Effectively
This white paper from LifeLock Business Solutions notes that FIs in addition to managing fraud should strive to turn a negative event for customers into a positive, relationship-building experience. Learn why ID theft has soared and what steps banks can take in response.
Please read the attached whitepaper.
Máquina del Tiempo 
La máquina del tiempo (The Time Machine) es una novela de ficción del escritor británico Herbert George Wells, publicada por primera vez en Londres en el año 1895. Consta de dieciséis capítulos y un epílogo. Está basada en la teoría del Eternalismo. Al contrario que Julio Verne, padre del detalle y la explicación minuciosa, Wells describe (a propósito) la máquina de modo superficial, con algunas pinceladas (comenta que tenía partes de metal, cristal de roca y marfil), lo cual deja al lector con curiosidad por saber más del invento y su mecanismo.