Data dredging Data dredging (data fishing, data snooping, equation fitting) is the use of data mining to uncover relationships in data. The process of data mining involves automatically testing huge numbers of hypotheses about a single data set by exhaustively searching for combinations of variables that might show a correlation. Conventional tests of statistical significance are based on the probability that an observation arose by chance, and necessarily accept some risk of mistaken test results, called the significance. When large numbers of tests are performed, some produce false results, hence 5% of randomly chosen hypotheses turn out to be significant at the 5% level, 1% turn out to be significant at the 1% significance level, and so on, by chance alone. When enough hypotheses are tested, it is virtually certain that some falsely appear statistically significant, since almost every data set with any degree of randomness is likely to contain some spurious correlations. Here is a simple example.
DataMachine - jwpl - Documentation of the JWPL DataMachine - Java-based Wikipedia Library -- An application programming interface for Wikipedia Back to overview page. Learn about the different ways to get JWPL and choose the one that is right for you! (You might want to get fatjars with built-in dependencies instead of the download package on Google Code) Download the Wikipedia data from the Wikimedia Download Site You need 3 files: [LANGCODE]wiki-[DATE]-pages-articles.xml.bz2 OR [LANGCODE]wiki-[DATE]-pages-meta-current.xml.bz2 [LANGCODE]wiki-[DATE]-pagelinks.sql.gz [LANGCODE]wiki-[DATE]-categorylinks.sql.gz Note: If you want to add discussion pages to the database, use [LANGCODE]wiki-[DATE]-pages-meta-current.xml.bz2, otherwise [LANGCODE]wiki-[DATE]-pages-articles.xml.bz2 suffices. Run the transformation: java -jar JWPLDataMachine.jar [LANGUAGE] [MAIN_CATEGORY_NAME] [DISAMBIGUATION_CATEGORY_NAME] [SOURCE_DIRECTORY] or de.tudarmstadt.ukp.wikipedia.datamachine.domain.JWPLDataMachine [LANGUAGE] [MAIN_CATEGORY_NAME] [DISAMBIGUATION_CATEGORY_NAME] [SOURCE_DIRECTORY] LANGUAGE - a language string matching one the JWPL_Languages.
Faceted Metadata Search - Search Tools Report Metadata is information about information: more precisely, it's structured information about resources. This can be a single set of hierarchical subject labels, such as a Yahoo or Open Directory Project category. More often, the metadata has several facets: attributes in various orthogonal sets of categories. Traditional Approaches to Structured Data Access Parametric Search Traditional field-based or parametric search engines for structured data have used a command line or provided a form to fill out: AU:rosenfeld TI:web PB:oreilly or These require a lot of knowledge on the searcher's side: they have to know the values or choose from a popup menu. Full-Text Search Full text search engines can index all HTML metadata or gather data from multiple database fields or tables. Faceted Metadata Search Solution A good solution to these problems involves exposing the facets in dynamic taxonomies, so that the search user can see exactly the options they have available at any time.
Web search engine Software system for finding relevant information on the Web A search engine is a software system that provides hyperlinks to web pages and other relevant information on the Web in response to a user's query. The user inputs a query within a web browser or a mobile app, and the search results are often a list of hyperlinks, accompanied by textual summaries and images. Users also have the option of limiting the search to a specific type of results, such as images, videos, or news. For a search provider, its engine is part of a distributed computing system that can encompass many data centers throughout the world. There have been many search engines since the dawn of the Web in the 1990s, but Google Search became the dominant one in the 2000s and has remained so. In 1945, Vannevar Bush described an information retrieval system that would allow a user to access a great expanse of information, all at a single desk.[3] He called it a memex. 1990s: Birth of search engines [edit] By 2000, Yahoo!
Words Aptly Spoken Quotes by Bob Moorehead “The paradox of our time in history is that we have taller buildings but shorter tempers, wider Freeways, but narrower viewpoints. We spend more, but have less, we buy more, but enjoy less. We have bigger houses and smaller families, more conveniences, but less time. We drink too much, smoke too much, spend too recklessly, laugh too little, drive too fast, get too angry, stay up too late, get up too tired, read too little, watch TV too much, and pray too seldom. We've learned how to make a living, but not a life. We've cleaned up the air, but polluted the soul. These are the times of fast foods and slow digestion, big men and small character, steep profits and shallow relationships. These are the days of two incomes but more divorce, fancier houses, but broken homes. Remember, to spend some time with your loved ones, because they are not going to be around forever. Remember, to say, "I love you" to your partner and your loved ones, but most of all mean it.
Information Awareness Office Total Information Awareness (TIA) was a program of the US Information Awareness Office. It was operated from February until May 2003, before being renamed as the Terrorism Information Awareness Program.[4][5] Based on the concept of predictive policing, TIA aimed to gather detailed information about individuals in order to anticipate and prevent crimes before they are committed.[6] As part of efforts to win the War on Terror, the program searched for all sorts of personal information in the hunt for terrorists around the globe.[7] According to Senator Ron Wyden (D-Ore.), TIA was the "biggest surveillance program in the history of the United States".[8] The program was suspended in late 2003 by the United States Congress after media reports criticized the government for attempting to establish "Total Information Awareness" over all citizens.[9][10][11] History[edit] Early developments[edit] Congressional restrictions[edit] Mission[edit] 1. 2. 3. 4. Scope of surveillance[edit] Criticism[edit]
Getting Started with HtmlUnit Introduction The dependencies page lists all the jars that you will need to have in your classpath. The class com.gargoylesoftware.htmlunit.WebClient is the main starting point. This simulates a web browser and will be used to execute all of the tests. Most unit testing will be done within a framework like JUnit so all the examples here will assume that we are using that. In the first sample, we create the web client and have it load the homepage from the HtmlUnit website. Imitating a specific browser Often you will want to simulate a specific browser. Specifying this BrowserVersion will change the user agent header that is sent up to the server and will change the behavior of some of the JavaScript. Finding a specific element Once you have a reference to an HtmlPage, you can search for a specific HtmlElement by one of 'get' methods, or by using XPath. Below is an example of finding a 'div' by an ID, and getting an anchor by name: Using a proxy server Submitting a form
ROI Of Faceted Navigation? 17 July 2011 Faceted navigation is widespread on the web (a.k.a faceted search and faceted browse). It’s become an expected standard. I’ve written several posts on the subject and also have a popular workshop on faceted navigation. (Next one: 22 Oct 2011 in NYC). I’ve only been able to find a few studies or case studies reporting a measureable ROI of faceted navigation. One helpful sources is Endeca’s case studies. Kiddicare.com: 100% increase in conversion rates; 100% increase in sales; Additional 100% increase in conversion rates with PowerReviewsAutoScout 24: 5% increase in lead generation to dealers; 70% decrease in no results foundOtto Group: 130% increase in conversion rates; Doubled conversion rates for visitors originating from pay-per-click marketing programs; Search failure rate decreased from over 33% to 0.5% If you have such data or evidence in any form, please let me and others know about by commenting here. Some logical arguments include combinations of the following:
Web service A Web service is a method of communications between two electronic devices over a network. It is a software function provided at a network address over the web with the service always on as in the concept of utility computing. The W3C defines a Web service as: a software system designed to support interoperable machine-to-machine interaction over a network. The W3C also states: We can identify two major classes of Web services:REST-compliant Web services, in which the primary purpose of the service is to manipulate XML representations of Web resources using a uniform set of stateless operations; andArbitrary Web services, in which the service may expose an arbitrary set of operations.[2] Explanation[edit] Many organizations use multiple software systems for management. Different software might be built using different programming languages, and hence there is a need for a method of data exchange that doesn't depend upon a particular programming language. Web API[edit] Criticisms[edit]
Online analytical processing Online analytical processing, or OLAP (/ˈoʊlæp/), is an approach to answer multi-dimensional analytical (MDA) queries swiftly in computing.[1] OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining.[2] Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM),[3] budgeting and forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture.[4] The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP).[5] OLAP tools enable users to analyze multidimensional data interactively from multiple perspectives. Overview of OLAP systems[edit] The cube metadata is typically created from a star schema or snowflake schema or fact constellation of tables in a relational database. For example: Multidimensional databases[edit] Aggregations[edit]