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Design Choices when Architecting Visualizations
Design Choices when Architecting Visualizations

... through the analysis cycle, new data may be collected, the format of the data being collected may change, and so on. Users do not care about tradeoffs or where their data or meta-data is stored; they only want to be able to import all of the data into the visualization system and then proceed with t ...
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... Spatial data mining is an extraordinarily demanding field referring to extraction of implicit knowledge, spatial relationships, which are not explicitly stored into geographical databases [7]. In fact, the collected data can exceed human's capacity to analyze and extract interesting knowledge from l ...
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... In this section, we describe a new data organization technique that extends traditional table partitioning [5] and can be used to efficiently index highly dynamic column-oriented data. The rationale is that by creating more granular (smaller) partitions that segment the dataset on every possible dim ...
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... and are readily available. For these reasons, they are heavily used for data analytics, and implementations of new algorithms are often integrated. In addition, these languages and environments provide data visualizations and are well-suited for exploration and interactive analytics. However, their ...
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... processes is highly desirable for ease of use but also because querying information as a unit allows parallel and online processing of partial results. Moreover, scientists want transparent access to all data. Automatic integration assumes customizable tools implementing generic integration solution ...
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Big data



Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making. And better decisions can mean greater operational efficiency, cost reduction and reduced risk.Analysis of data sets can find new correlations, to ""spot business trends, prevent diseases, combat crime and so on."" Scientists, business executives, practitioners of media and advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, and biological and environmental research.Data sets grow in size in part because they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers, and wireless sensor networks. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 exabytes (2.5×1018) of data were created; The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.Work with big data is necessarily uncommon; most analysis is of ""PC size"" data, on a desktop PC or notebook that can handle the available data set.Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires ""massively parallel software running on tens, hundreds, or even thousands of servers"". What is considered ""big data"" varies depending on the capabilities of the users and their tools, and expanding capabilities make Big Data a moving target. Thus, what is considered ""big"" one year becomes ordinary later. ""For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.""
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