Javascript playground for Waterbear allows you to create Waterbear scripts, see the Javascript it will generate, and run it right in the browser!

version control

free ebook w/github supported edits - Data-Intensive Text Processing with MapReduce #datascience #text

User Experience Design Guidelines for Windows Phone

would enjoy Zork again for sure.

Finally, some new (or is it? http://www.google.com/press/pressrel/outride.html ) functionality in google's personalized search options.

startup profiles for R

links to the apps and sites in the Missing Manual book for Lion

audio & presentations from ApacheCon North America 2011

OpenNLP is an organizational center for open source projects related to natural language processing. Its primary role is to encourage and facilitate the collaboration of researchers and developers on such projects.

OpenNLP also hosts a variety of java-based NLP tools which perform sentence detection, tokenization, pos-tagging, chunking and parsing, named-entity detection, and coreference using the OpenNLP Maxent machine learning package

The Apache Tika™ toolkit detects and extracts metadata and structured text content from various documents using existing parser libraries.

Unstructured Information Management applications are software systems that analyze large volumes of unstructured information in order to discover knowledge that is relevant to an end user. An example UIM application might ingest plain text and identify entities, such as persons, places, organizations; or relations, such as works-for or located-at.


UIMA enables applications to be decomposed into components, for example "language identification" => "language specific segmentation" => "sentence boundary detection" => "entity detection (person/place names etc.)". Each component implements interfaces defined by the framework and provides self-describing metadata via XML descriptor files. The framework manages these components and the data flow between them. Components are written in Java or C++; the data that flows between components is designed for efficient mapping between these languages.

Chukwa is an open source data collection system for monitoring large distributed systems. Chukwa is built on top of the Hadoop Distributed File System (HDFS) and Map/Reduce framework and inherits Hadoop’s scalability and robustness. Chukwa also includes a flexible and powerful toolkit for displaying, monitoring and analyzing results to make the best use of the collected data.

Hive is a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL.

Apache Rave is a new web and social mashup engine. It will provide an out-of-the-box as well as an extendible lightweight Java platform to host, serve and aggregate (Open)Social Gadgets and services through a highly customizable and Web 2.0 friendly front-end. Rave is targeted as engine for internet and intranet portals and as building block to provide context-aware personalization and collaboration features for multi-site/multi-channel (mobile) oriented and content driven websites and (social) network oriented services and platforms. For the OpenSocial container and services the (Java) Apache Shindig will be integrated. At a later stage further generalization is envisioned to also transparently support W3C Widgets using Apache Wookie.

docs

examining large datasets for Benford's Law

we need to boost when examining recommendation items by CS people

gestures with the mouse or trackpad

Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets.

Scribe is a server for aggregating streaming log data. It is designed to scale to a very large number of nodes and be robust to network and node failures. There is a scribe server running on every node in the system, configured to aggregate messages and send them to a central scribe server (or servers) in larger groups. If the central scribe server isn’t available the local scribe server writes the messages to a file on local disk and sends them when the central server recovers. The central scribe server(s) can write the messages to the files that are their final destination, typically on an nfs filer or a distributed filesystem, or send them to another layer of scribe servers.

Scribe is unique in that clients log entries consisting of two strings, a category and a message. The category is a high level description of the intended destination of the message and can have a specific configuration in the scribe server, which allows data stores to be moved by changing the scribe configuration instead of client code. The server also allows for configurations based on category prefix, and a default configuration that can insert the category name in the file path. Flexibility and extensibility is provided through the “store” abstraction. Stores are loaded dynamically based on a configuration file, and can be changed at runtime without stopping the server. Stores are implemented as a class hierarchy, and stores can contain other stores. This allows a user to chain features together in different orders and combinations by changing only the configuration.

Scribe is implemented as a thrift service using the non-blocking C++ server. The installation at facebook runs on thousands of machines and reliably delivers tens of billions of messages a day.

Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. Its main goal is to deliver data from applications to Apache Hadoop's HDFS. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. The system is centrally managed and allows for intelligent dynamic management. It uses a simple extensible data model that allows for online analytic applications.

Apache Solr 3.1 Cookbook looks great