Stock Cloud began as data mining experiment with a very simple goal — "Could we extract Business Partnerships by tracking press releases?" To accomplish this we selected a press release distribution agency, MarketWire, and began tracking releases. Usi
Stock Cloud began as data mining experiment with a very simple goal — "Could we extract Business Partnerships by tracking press releases?" To accomplish this we selected a press release distribution agency, MarketWire, and began tracking releases. Usi
Amazon Web Service today announced a new AWS Import/Export feature. A potentially huge step forward for data portabilty when using the Amazon Cloud computing infrastructure.
Using RhNav - Rhizome Navigation I wrote a data aggregator for Technorati's API. The first result is a video which visualizes blog domains by analysing Technorati's Cosmos (the blogs which link to a particular URL). The video is a screencast of RhNav fetc
Users will be able to search for documents by date, topic, person, location, etc. and will be able to do "document dives," collaboratively examining large sets of documents. Think of it as a card catalog for primary source documents. DocumentCloud is not meant to be a general document hosting service, like Scribd, Docstoc or Google Docs. Our goal is to build a service that makes source documents easier to find and share regardless of where they are hosted. It is a complement to these services, and not a competitor. the goal is to make documents even easier to find on search engines. DocumentCloud will have information about documents and relations between them, for example what locations, people, or organizations a group of documents have in common. Conceived of by journalists working at ProPublica and The New York Times, DocumentCloud will be managed as an independent nonprofit.
Protocol Buffers allow you to define simple data structures in a special definition language, then compile them to produce classes to represent those structures in the language of your choice. These classes come complete with heavily-optimized code to par
Der Großausfall in der Amazon-Wolke sei nur die Spitze des Eisbergs an Problemen, die Cloud-Computing mit sich bringt, sagte Internetpionier Dave Farber zu ORF.at. Am Montag gab erneut ein Cloud-Anbieter das Geschäftsfeld auf.
Katta is a scalable, failure tolerant, distributed, data storage for real time access.
Katta serves large, replicated, indices as shards to serve high loads and very large data sets. These indices can be of different type. Currently implementations are available for Lucene and Hadoop mapfiles.
* Makes serving large or high load indices easy
* Serves very large Lucene or Hadoop Mapfile indices as index shards on many servers
* Replicate shards on different servers for performance and fault-tolerance
* Supports pluggable network topologies
* Master fail-over
* Fast, lightweight, easy to integrate
* Plays well with Hadoop clusters
* Apache Version 2 License
Following up on KMeans Clustering Now Running on Elastic MapReduce, Stephen Green has generously documented the steps that was necessary to get an example of k-Means clustering up and running on Amazon’s Elastic MapReduce (EMR) on the Apache Lucene Mahout wiki.
X. Ma, Z. Fu, Y. Jiang, M. Yang, und H. Stephen. International Journal of Computer Science and Information Technology (IJCSIT), 9 (3):
31 - 41(Juni 2017)