2015 IEEE International Congress on Big Data

June 27 2015-July 2 2015

Filter Results

Displaying Results 1 - 25 of 133
  • [Front cover]

    Publication Year: 2015, Page(s): C4
    Request permission for commercial reuse | PDF file iconPDF (1469 KB)
    Freely Available from IEEE
  • [Title page i]

    Publication Year: 2015, Page(s): i
    Request permission for commercial reuse | PDF file iconPDF (13 KB)
    Freely Available from IEEE
  • [Title page iii]

    Publication Year: 2015, Page(s): iii
    Request permission for commercial reuse | PDF file iconPDF (117 KB)
    Freely Available from IEEE
  • [Copyright notice]

    Publication Year: 2015, Page(s): iv
    Request permission for commercial reuse | PDF file iconPDF (126 KB)
    Freely Available from IEEE
  • Table of contents

    Publication Year: 2015, Page(s):v - xvi
    Request permission for commercial reuse | PDF file iconPDF (156 KB)
    Freely Available from IEEE
  • Message from the General Chairs

    Publication Year: 2015, Page(s): xvii
    Request permission for commercial reuse | PDF file iconPDF (41 KB) | HTML iconHTML
    Freely Available from IEEE
  • Message from the Program Committee Chairs

    Publication Year: 2015, Page(s): xviii
    Request permission for commercial reuse | PDF file iconPDF (36 KB) | HTML iconHTML
    Freely Available from IEEE
  • Organizing Committee

    Publication Year: 2015, Page(s):xix - xx
    Request permission for commercial reuse | PDF file iconPDF (40 KB)
    Freely Available from IEEE
  • Program Committee

    Publication Year: 2015, Page(s):xxi - xxiv
    Request permission for commercial reuse | PDF file iconPDF (43 KB)
    Freely Available from IEEE
  • External Reviewers

    Publication Year: 2015, Page(s): xxv
    Request permission for commercial reuse | PDF file iconPDF (28 KB)
    Freely Available from IEEE
  • IEEE Computer Society Technical Committee on Services Computing

    Publication Year: 2015, Page(s): xxvi
    Request permission for commercial reuse | PDF file iconPDF (74 KB)
    Freely Available from IEEE
  • A Scalable Approach for Online Hierarchical Big Data Mining

    Publication Year: 2015, Page(s):1 - 8
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (282 KB) | HTML iconHTML

    We study online compound decision problems in the context of sequential prediction of real valued sequences. In particular, we consider finite state (FS) predictors that are constructed based on the sequence history, whose length is quite large for applications involving big data. To mitigate over training problems, we define hierarchical equivalence classes and apply the exponentiated gradient (E... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Parallel Distributed Weka Framework for Big Data Mining Using Spark

    Publication Year: 2015, Page(s):9 - 16
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (991 KB) | HTML iconHTML

    Effective Big Data Mining requires scalable and efficient solutions that are also accessible to users of all levels of expertise. Despite this, many current efforts to provide effective knowledge extraction via large-scale Big Data Mining tools focus more on performance than on use and tuning which are complex problems even for experts. Weka is a popular and comprehensive Data Mining workbench wit... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Geometrical and Topological Modelling: A Fast Computation of Spatial 3D TLS Data Selections

    Publication Year: 2015, Page(s):17 - 24
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1517 KB) | HTML iconHTML

    Underground caves and their specific structures are important for geomorphological studies. In this paper we present a new tool to identify and map speleothems by surveying cave chambers interiors. One of the research problems that we had to solve was that we were dealing with a great number of points that resulted from the Laser scan. The cave chamber was surveyed using Terrestrial Laser Scanning... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • PaWI: Parallel Weighted Itemset Mining by Means of MapReduce

    Publication Year: 2015, Page(s):25 - 32
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (186 KB) | HTML iconHTML

    Frequent item set mining is an exploratory data mining technique that has fruitfully been exploited to extract recurrent co-occurrences between data items. Since in many application contexts items are enriched with weights denoting their relative importance in the analyzed data, pushing item weights into the item set mining process, i.e., Mining weighted item sets rather than traditional item sets... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Distributed SPARQL over Big RDF Data: A Comparative Analysis Using Presto and MapReduce

    Publication Year: 2015, Page(s):33 - 40
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (765 KB) | HTML iconHTML

    The processing of large volumes of RDF data require an efficient storage and query processing engine that can scale well with the volume of data. The initial attempts to address this issue focused on optimizing native RDF stores as well as conventional relational databases management systems. But as the volume of RDF data grew to exponential proportions, the limitations of these systems became app... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A GPU Based SVM Method with Accelerated Kernel Matrix Calculation

    Publication Year: 2015, Page(s):41 - 46
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (196 KB) | HTML iconHTML

    Support vector machine (SVM) is a popular classifier dealing with small-scale datasets. It has outstanding performance compared to other classifiers. However the execution time is extremely long when training Big Data. The Graphics Processing Unit (GPU) is a massively parallel device which performs very well as a co-processor. NVIDIA proposed a programming platform, CUDA, in 2006, which makes it m... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Clustered Approach for Fast Computation of Betweenness Centrality in Social Networks

    Publication Year: 2015, Page(s):47 - 54
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (612 KB) | HTML iconHTML

    In the last few years, the data generated by social networking systems have become interesting to analyze local and global social phenomena. A useful metric to identify influential people or opinion leaders is the between ness centrality index. The computation of this index is a very demanding task since its exact calculation exhibits O(nm) time complexity for unweighted graphs. This complexity ha... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Semantic Recommender for Micro-blog Users

    Publication Year: 2015, Page(s):55 - 62
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (330 KB) | HTML iconHTML

    In this paper we propose a Twitter recommender based on a semantic description of users' interests. To express interests we use friendship information, which is readily available in users' profiles, not only in Twitter but in the majority of Social Networks, thus presenting substantial advantage in terms of computational complexity with respect to methods based on content mining. To obtain a synth... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Incorporating Tie Strength in Robust Social Recommendation

    Publication Year: 2015, Page(s):63 - 70
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3426 KB) | HTML iconHTML

    In this paper, we present a novel method in making recommendations by leveraging Tie Strength, an integrated social relationship measurement calculated from various user information gathered from social media. Moreover, the proposed method adopts Least Absolute Errors in factorization scheme to reduce the sensitivity to data outliers. We have conducted comprehensive experiments over the real datas... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Can We Rank Emotions? A Brand Love Ranking System for Emotional Terms

    Publication Year: 2015, Page(s):71 - 78
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (759 KB) | HTML iconHTML

    In this paper we examine customers' emotional attachment to a brand name utilizing content extracted from social media. More specifically, we consider the emotions associated to brand love appearing in the form of terms in users' Twitter posts. Building on existing work that identifies seven dimensions in brand love, we propose a probabilistic network scheme that employs a topic identification met... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Matrix Inter-joint Factorization - A New Approach for Topic Derivation in Twitter

    Publication Year: 2015, Page(s):79 - 86
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (399 KB) | HTML iconHTML

    Amongst all the social media platforms available, Twitter is rapidly becoming the main one used for communications about real-time events. As a result, there is a lot of interest in monitoring Twitter and understanding the topics of conversations. However, the fact that tweets are short in content makes topics derivation a challenge, as most existing methods use content features only, sometimes in... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Deriving Topics in Twitter by Exploiting Tweet Interactions

    Publication Year: 2015, Page(s):87 - 94
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (482 KB) | HTML iconHTML

    Twitter as a big data social network becomes one of the most important sources for capturing the up-to-date events happening in the world. Topic derivation from Twitter is important for various applications such as situation awareness, market analysis, content filtering, and recommendations. However, tweets are short messages, which makes topic derivation challenging. Current methods employ variou... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Privacy Preserving Data Analysis in Mental Health Research

    Publication Year: 2015, Page(s):95 - 101
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (184 KB) | HTML iconHTML

    The digitalization of mental health records and psychotherapy notes has made individual mental health data more readily accessible to a wide range of users including patients, psychiatrists, researchers, statisticians, and data scientists. However, increased accessibility of highly sensitive mental records threatens the privacy and confidentiality of psychiatric patients. The objective of this stu... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Enabling Privacy Mechanisms in Apache Storm

    Publication Year: 2015, Page(s):102 - 109
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (375 KB) | HTML iconHTML

    To analyze data that is streamed into a real-time computation system has gained traction and is very useful in use cases where for example telecom networks should be optimized dynamically. For this analysis lots of data i.e., Big data is used. This nevertheless also poses privacy risks as this data usually also contains personal data and not only core applications of an organization want to access... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.