4 free data tools for journalists (and snoops) - O'Reilly Radar Note: The following is an excerpt from Pete Warden’s free ebook “Where are the bodies buried on the web? Big data for journalists.” There’s been a revolution in data over the last few years, driven by an astonishing drop in the price of gathering and analyzing massive amounts of information. The technology is also getting easier to use. What does this mean for journalists? Many of you will already be familiar with WHOIS, but it’s so useful for research it’s still worth pointing out. You can also enter numerical IP addresses here and get data on the organization or individual that owns that server. Blekko The newest search engine in town, one of Blekko’s selling points is the richness of the data it offers. The first tab shows other sites that are linking to the current domain, in popularity order. The other handy tab is “Crawl stats,” especially the “Cohosted with” section: This tells you which other websites are running from the same machine. bit.ly Then click on the ‘Info Page+’ link:
RelFinder - Visual Data Web Are you interested in how things are related with each other? The RelFinder helps to get an overview: It extracts and visualizes relationships between given objects in RDF data and makes these relationships interactively explorable. Highlighting and filtering features support visual analysis both on a global and detailed level. Check out the following links for some examples: The RelFinder can easily be configured to work with different RDF datasets. The RelFinder can also be more deeply integrated with your project: Integrating the RelFinder See the following examples of how the RelFinder is integrated into other projects: Ontotext applies the RelFinder to enable an exploration of relationships in the biomedical domain. The RelFinder is readily configured to access RDF data of the DBpedia project and only requires a Flash Player plugin to be executed (which is usually already installed in web browsers). All tools on this website are research prototypes that might contain errors.
Kundenservice auf dem Weg in das Outernet: Willkommen in der Augmented Reality | Blog von Prof. Dr. Heike Simmet i 7 Votes istock Photo Prof. Der Kundenservice in Deutschland befindet sich in einer tiefgreifenden Umbruchphase. Doch die technologische Entwicklung beschleunigt sich zunehmend weiter. Während viele traditionell aufgestellte Call Center noch über die Pros und Cons der Nutzung von Social Media und Social Media Monitoring diskutieren und Apps erst versuchsweise in ihre Serviceprozesse integrieren, sind andere Branchen bereits viel weiter (Sohn 2012). Individueller Kundenservice durch Kontextinformationen Das exponentielle Ansteigen der Informationsflut und das Entstehen des so genannten Big Data Phänomens führt zu einer neuen Generation der smarten Informationsverarbeitung. Umfassende Serviceleistungen durch die neue App Economy Die nächste Generation an intelligenten Devices führt zu einer noch stärkeren virtuellen Erweiterung der Realität. Integration von Social Networks Der Kundenservice der Zukunft integriert zudem die bereits etablierten Social Networks. Bildnachweis: istockphoto Prof.
NodeXL: Network Overview, Discovery and Exploration for Excel Maintenance Management Management of Maintenance Complexity Across a Global Footprint Verisae optimizes facility management and equipment maintenance departments by improving operational efficiency and cutting costs. Verisae offers a comprehensive software solution that helps organizations monitor, measure, track, and manage their facility and equipment maintenance processes. Verisae's Computerized Maintenance Management System (CMMS) enables organizations to maximize many facilities and equipment maintenance management processes on a single software and services platform. This derives the greatest value in the shortest amount of time with the least amount of resource usage. Top Global Retailers Use Verisae's CMMS Software Verisae’s facility management and equipment maintenance software is used by some of the largest retailers in the world. The Verisae CMMS system actively tracks over three million individual assets across more than 28,000 sites worldwide.
tf–idf One of the simplest ranking functions is computed by summing the tf–idf for each query term; many more sophisticated ranking functions are variants of this simple model. Motivation Suppose we have a set of English text documents and wish to determine which document is most relevant to the query "the brown cow". A simple way to start out is by eliminating documents that do not contain all three words "the", "brown", and "cow", but this still leaves many documents. However, because the term "the" is so common, this will tend to incorrectly emphasize documents which happen to use the word "the" more frequently, without giving enough weight to the more meaningful terms "brown" and "cow". Mathematical details tf–idf is the product of two statistics, term frequency and inverse document frequency. The inverse document frequency is a measure of whether the term is common or rare across all documents. with Then tf–idf is calculated as Example of tf–idf Idf is a bit more involved:
Lost Customer Research: What Is It and When Is It Necessary Popular Today in Business: All Popular Articles This blog post is the first of a series of posts that I will be writing over the next few weeks about how to quickly and efficiently plan-out and execute a lost customer research project. This week’s post will explain what lost customer research is, why it is a useful market research tool, and when companies should consider launching this type of an initiative. What Is Lost Customer Research? Lost customer research is the process of gathering, analyzing, and interpreting the root causes as to why an individual customer or a group of customers has canceled their service contract and/or ceased using a company’s product. The purpose of this type of research is to attempt to identify trends in these losses, which can be used to improve a company’s overall understanding of its target customer and competitive positioning in the market place, while at the same time identifying product, service, and performance gaps and issues.
4 Promising Curation Tools That Help Make Sense of the Web Steven Rosenbaum is a curator, author, filmmaker and entrepreneur. He is the CEO of Magnify.net, a real-time video curation engine for publishers, brands, and websites. His book Curation Nation is slated to be published this spring by McGrawHill Business. As the volume of content swirling around the web continues to grow, we're finding ourselves drowning in a deluge of data. Where is the relevant material? The solution on the horizon is curation. In the past 90 days alone, there has been an explosion of new software offerings that are the early leaders in the curation tools category. 1. Storify co-founder Burt Herman worked as a reporter for the Associated Press during a 12-year career, six of those in news management as a bureau chief and supervising correspondent. At the AP, editors sending messages to reporters asking them to do a story would regularly write, “Can u pls storify?” Storify is currently invite only. 2. Scoop.it is often described as Tumblr without the blog. 3. 4.
SCADA - Supervisory Control and Data Acquisition ActiveWarehouse: Extract-Transform-Load Tool The ActiveWarehouse ETL component provides a means of getting data from multiple data sources into your data warehouse. The links in the side bar provide additional information on ETL. Here’s how to get rolling: Install the Gem Get to your command line and type sudo gem install activewarehouse-etl on Linux or OS X or type gem install activewarehouse-etl on Windows. ActiveWarehouse ETL depends on ActiveSupport, ActiveRecord, adapter_extensions and FasterCSV. You can also download the packages in Zip, Gzip, or Gem format from the ActiveWarehouse files section on RubyForge. Create Control Files Create the ETL control files. Execute the etl command Execute the etl command passing the control file name as the argument. Right now the ETL component has the following functionality: Fixed-width and delimited file parsing File and database source File and database destination Virtual source fields, which can be populated via output from Ruby code Support for pre- and post-processing code Transform pipeline