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From Analysis to Communication
Published in Nathalie Henry Riche, Christophe Hurter, Nicholas Diakopoulos, Sheelagh Carpendale, Data-Driven Storytelling, 2018
Fanny Chevalier, Melanie Tory, Bongshin Lee, Jarke van Wijk, Giuseppe Santucci, Marian Dörk, Jessica Hullman
Exploring data requires functionality for data acquisition, cleaning and conversion, analysis, and visualization. From a tooling landscape point of view, two approaches can be distinguished. One is a bottom-up approach. One can use a programming language (Java, C++) or scripting language (JavaScript, Python) to perform all these tasks from scratch. R also fits in this category as a programming language defined to support statistical computation in particular. Flexibility is optimal, but the downsides, especially effort and expertise needed, are clear. This can be compensated by using libraries, plug-ins, and frameworks, reusing expertise and efforts from the community. Functionality to produce graphics is provided by technologies like OpenGL and SVG; on top of these more-focused frameworks are available (like D3 for data visualization); on top of which yet more-focused modules are available, for instance, to produce histograms or other standard visualizations. Reuse can be highly efficient, but finding one’s way in all of the available resources is a skill in itself. Scripting languages have become very popular as they facilitate rapid exploration and prototyping. To increase productivity further, dedicated environments to quickly enter code, see results, etc., have become very popular, with Jupyter Notebook and Zeppelin as great examples. Data scientists are fluent in using these tools, however, journalists and graphics designers often are not.
Workspace Sharing Assembly Robots: Applying IEC 61499 to System Integration and Application Development
Published in Alois Zoitl, Thomas Strasser, Distributed Control Applications, 2017
Matthias Plasch, Ebenhofer Gerhard, Michael Hofmann, Martijn Rooker, Sharath Chandra Akkaladevi, Andreas Pichler
Scripting languages are used in various domains, especially for rapid prototyping and connecting software and web applications. The resulting interoperability of interconnected heterogeneous systems or programming languages can be seen as a major advantage of the usage of scripting languages [22]. LUA is a C based, lightweight, and embeddable scripting language which is mainly used as extension language for software components [14]. Application fields of LUA are video games, mobile and embedded devices and automation applications. As an example for an industrial application, Girder [27], a software toolbox for industrial and home automation, uses LUA as base language. Software integration is eased by the provided LUA C-API. The execution of LUA is sped by using LuaJIT [23], a just-in-time interpreter library which extends the LUA base library package. A brief overview on many well-known scripting languages, including interesting facts about their history, is provided by [2]
Engineering Innovation: Electronics Lab/MakerSpace
Published in M. Ann Garrison Darrin, Jerry A. Krill, Infusing Innovation into Organizations, 2016
With the increase in programming language selection, modern users are now able to utilize scripting languages. Scripting languages are much more novice friendly as they tend to have syntax (programming equivalent of grammar) that is much simpler and easy to learn. Generally speaking, scripting languages abstract away a lot of features that are clunky or difficult to deal with. As such, they are known to carry a lot less baggage at the cost of efficiency and speed. In many instances, a program written in a scripting language can be done with significantly less code, often with down to 10% of older system level languages. What this means is there are a lot fewer places to make mistakes. It also means the maker has a much more approachable way to learn how to program quick and dirty to get done what needs to be done with only minor tradeoffs.
Big data analytics business value and firm performance: linking with environmental context
Published in International Journal of Production Research, 2020
Claudio Vitari, Elisabetta Raguseo
In the second section, we checked once again whether the company had at least one BDA solution, by asking about the kinds of BDA solutions the company had, among the following list, offering a multiple choice possibility: Visual analytics software or other software used to display analytical results in visual formats.Scripting languages or other programming languages that work well with big data (e.g. Python, Pig, and Hive).In-memory analytics software or other processing big data used in computers for greater speed.MapReduce and Hadoop software or other software used to process big data across multiple parallel servers.Machine learning software or other software used to rapidly find the model that best fits a data set.Natural language processing or other software used for texts – information extraction, text summarisation, question answering, or sentiment analysis.Social media analytics software (content-based analytics and structure-based analytics).Predictive analytics software used to extract information from data and predict trends and behaviour patterns.