Big (alternative) data and increasingly sophisticated algorithms:
Parallel Algorithms for Big Data Optimization
... The paper is organized as follows. Section II formally introduces the optimization problem along with the main assumptions under which it is studied. Section III describes our novel general algorithmic framework along ...
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Online Algorithms for Uploading Deferrable Big Data to The Cloud
... charge big data applications with a new, interest- ing percentile based model, leading to new online algorithm design problems for minimizing the traffic cost paid for uploading big data to ...
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A Survey of Clustering Algorithms for Big Data: Taxonomy & Empirical Analysis
... Abstract—Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyse the massive volume of data generated by modern ...categorize data into clusters such ...
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Defending Against Increasingly Sophisticated Cyber Attacks
... These features help improve the security of an enterprise organization’s network systems and data. However, HP NX NGIPS platforms are also designed to meet enterprise requirements concerning scalability and ...
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Veterinarians are increasingly called on to deliver more sophisticated treatments
... The perceived risk of occupational exposure to hazardous drugs is extrapolated from the known mutagenic, teratogenic, and carcinogenic characteristics of certain hazardous drugs; animal toxicology data; and the ...
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Big Data Graph Algorithms
... apply algorithm engineering to optimization problems obtain algorithms that scale to large inputs and machines outperform state-of-the-art open source implementations KaHIP: algo2.iti.kit.edu/kahip KaDraw: ...
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Survey on Big Data Mining Algorithms
... on Big Data Mining Algorithms Anushree Raj 1 , Rio D’Souza 2 1, 2 Department of ...M.Sc. Big Data Analytics, Department of Computer Science & Engineering Abstract: Technology revolution ...
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Novel Algorithms for Big Data Analytics
... 2.4.3 Discussion Now we discuss how we have used other methods to compare with our algorithm. SCALCE version 2.7 executable was used with its default parameters. It encodes sequence data with- out considering the ...
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B669 Sublinear Algorithms for Big Data
... Yes, if you get to know some advanced (and easily implementable) algorithms for handling big data, that will certainly help. (e.g., Google interview questions) But, this is a research-oriented ...
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Algorithms for inverse big data problems
... See [ Gratton, Vicente, Zhang 2014 ] for extension to full domain decomposition and stochastic methods. Also see [ Gratton, Royer, Vicente, Zhang 2014 ] for a complexity analysis of stochastic direct search and [ ...
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Some Algorithms and Paradigms for Big Data
... devices increasingly penetrate our lives and integrate themselves into our lifestyles, their effect has not merely been to render old forms of data digital, but more consequentially, to create new kinds of ...
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Big data tools and analytics are increasingly contributing to the increasing popularity of MOOC.
... To determine the market size of various segments and sub-segments of MOOC market extensive, secondary research is done. The collected data were then verified through primary interviews. Distribution of primary ...
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STATE-OF-THE-ART BIG DATA ANALYTICS AND ALGORITHMS
... Clustering algorithms: Clustering is the process of grouping the data into classes and clusters, so that the objects within the cluster have high similarity in comparison to one another but are very ...
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Evaluating associative classification algorithms for Big Data
... The average ranking for the complexity measure is shown in Table 7. CBA obtained the best results closely followed by its improved version CBA2. CPAR, C4.5 and Ripper obtained the worst results and, among them, there are ...
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Accountability for the Use of Algorithms in a Big Data Environment
... 15 algorithms transcend the realm of data protection, so must the approach in algorithmic ...on data protection principles, nor merely targeted towards a data protection entity as its primary ...
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Clustering-based Algorithms for Big Data Computations
... k-center algorithms for this ...our algorithms, so as to use as few samples as possible, for ...Our algorithms are the first to provide provable guarantees on the quality of the re- turned solution, ...
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Big data and the web: algorithms for data intensive scalable computing
... two algorithms specifically designed for the MapReduce program- ming ...efficient algorithms for MR is not trivial and requires careful blending of several factors to effectively capitalize on the available ...
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Streaming Machine Learning Algorithms with Big Data Systems
... learning algorithms. The idea of processing a high throughput data with less time is one of the key features expected in a stream processing ...learning algorithms. In both algorithms with ...
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Stochastic Optimization for Big Data Analytics: Algorithms and Libraries
... Stochastic Gradient Descent (Pegasos) for L1-SVM (primal) Stochastic Dual Coordinate Ascent (SDCA) for L2-SVM (dual) Stochastic Average Gradient (SAG) for Logistic Regression/Regression?[r] ...
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Parallel Selective Algorithms for Nonconvex Big Data Optimization
... with big data problems it is clearly necessary to design parallel methods able to exploit the computational power of multi-core processors in order to solve many in- teresting ...parallel algorithms ...
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