Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The massive volume of data generated in modern applications can overwhelm our ability to conveniently transmit, store, and index it. For many scenarios, building a compact summary of a dataset that is vastly smaller enables flexibility and efficiency in a range of queries over the data, in exchange for some approximation. This comprehensive introduction to data summarization, aimed at practitioners and students, showcases the algorithms, their behavior, and the mathematical underpinnings of their operation. The coverage starts with simple sums and approximate counts, building to more advanced probabilistic structures such as the Bloom Filter, distinct value summaries, sketches, and quantile summaries. Summaries are described for specific types of data, such as geometric data, graphs, and vectors and matrices. The authors offer detailed descriptions of and pseudocode for key algorithms that have been incorporated in systems from companies such as Google, Apple, Microsoft, Netflix and Twitter.

Big data. --- Big data

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Diese Arbeit hat sich zum Ziel gesetzt, Methoden aufzuzeigen, "Big-Data"-Archive zu organisieren und zentrale Elemente der enthaltenen Informationen zu visualisieren. Anhand von drei wissenschaftlichen Experimenten werde ich zwei "Big-Data"- Herausforderungen, Datenvolumen (Volume) und Heterogenität (Variety), untersuchen und eine Visualisierung im Browser präsentieren, die trotz reduzierter Datenrate die wesentliche Information in den Datensätzen enthält. The scope of this research focuses on managing Big Data and eventually visualising the core information of the data itself. Specifically, I study three large-scale experiments that feature two Big Data challenges: large data size (Volume) and heterogeneous data (Variety), and provide the final visualisation through the web browser in which the size of the input data has to be reduced while preserving the vital information.

Big data. --- Big data

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Big data.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Annotation, The conference will focus on, but not limit to, the following cutting edge topics 1 Geospatial data mining and knowledge discovery Geospatial or spatiotemporal data mining algorithms Spatiotemporal data reasoning Spatial statistics and data mining Visual spatial data mining Geographical stream and video mining Data cleaning & preprocessing methods Feature extraction, selection and dimension Reduction Extraction knowledge from Big Data Mining on volunteered geographic data Mining semi structured Data Anomaly & outlier detection Mining spatial data sets with constraints Mining with Data Clouds Mining Complex Datasets Mining on Emerging Architectures 2 Geographical process modeling and Decision Spatiotemporal analysis and modeling methods Spatial decision support systems Spatial data warehouse and spatial OLAP Modeling and analysis of digital elevation or topography Modeling uncertainty in geo spatial information Virtual modeling of large geographic areas Time series and non stationa.

Big data

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Big data