Notable Channels: #javascript, #frontend, #backend, #php, #mobile, #design, #devops, #career, #code-review, #webtopic, #desktopic, #community-projects, #paid-work Notable Channels: #looking-for-dev, #challenges, #python, #javascript, #c-family, #java, #html-css-web-js #dotnet, #databases, #hosting Some Discord counterpart communities to Stack Overflow. Click on the or icon to visit the community's official website or git repository page.A quick lookup from browser ctrl + f may help to find communities by a topic.Read this FAQ about Discord's privacy policy and answers to other frequent questions. A community may offer channels dedicated to more than one spoken languages that Language is there for. Notable channels are exploratory, and can be used for looking up a particular topic. For starters take a look at the available badges. If you're familiar with other popular awesome lists, you may find this list a little anomalous. This is not a Discord listing, promotional, or affiliated site! Contents Discord at heart, is a community-oriented instant messaging, voice and video call application. A curated list of awesome Discord communities for developers, game engineers, hackers, hobbyists, mathematicians and tech enthusiasts. 3(March 2003), 993–1022 (2003)Ī gentle introduction to topic modeling using R. Accessed įile Information Tool Set (FITS). Integrated Rule-Oriented Data System (iRODS). Accessed ĭigital Record Object Identification (DROID) tool. Arora, Applying lessons from e-discovery to process big data using HPC, in Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment, ACM, New York, 2014.
Eichenwald, Conspiracy of Fools: A True Story (Broadway Books, New York, 2005)
This process is experimental and the keywords may be updated as the learning algorithm improves.Į-Discovery Reference Model (EDRM).
These keywords were added by machine and not by the authors. Hence, the discussion on these techniques as presented in this chapter could be relevant to a wide range of disciplines that are grappling with the deluge of Big Data. The various techniques that the eDiscovery practitioners have adopted to meet the Big Data challenge are transferrable to other domains as well. In this chapter, we discuss the strategies for data management and analysis from the legal eDiscovery domain, and also discuss the advantages of using HTC for eDiscovery. In addition to using such optimal strategies, the time-to-results during eDiscovery can be further reduced by taking advantage of the High-Throughput Computing (HTC) paradigm on High Performance Computing (HPC) platforms. Therefore, they use optimal strategies for data analysis and management to meet the time and quality requirements of their business. The eDiscovery practitioners are typically required to produce results with short turnaround times, utilizing mostly commodity solutions that are available at their disposal, while still conforming to legally defensible standards of data management. Legal Electronic Discovery ( eDiscovery) is a business domain that utilizes large volumes of data, in a variety of structured and unstructured formats to discover evidence that may be pertinent to legal proceedings, compliance needs, litigation or other investigations.
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