2017 IEEE International Conference on Big Data (Big Data)

11-14 Dec. 2017

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  • [Front cover]

    Publication Year: 2017, Page(s): 1
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  • IEEE Big Data 2017 welcome message from the organizers

    Publication Year: 2017, Page(s):1 - 2
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  • Committee members

    Publication Year: 2017, Page(s):1 - 2
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  • Program committee

    Publication Year: 2017, Page(s):1 - 14
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  • [Copyright notice]

    Publication Year: 2017, Page(s): 1
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  • Author index

    Publication Year: 2017, Page(s):1 - 32
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  • Human-in-the-loop applied machine learning

    Publication Year: 2017, Page(s): 1
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  • A more open efficient future for AI development and data science with an introduction to Julia

    Publication Year: 2017, Page(s): 2
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  • Contextual reinforcement learning

    Publication Year: 2017, Page(s): 3
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  • Large-scale graph representation learning

    Publication Year: 2017, Page(s): 4
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  • Being “BYTES-oriented” in HPC leads to an open big data/AI ecosystem and further advances into the post-moore era

    Publication Year: 2017, Page(s): 5
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  • TextScope: Enhance human perception via text mining

    Publication Year: 2017, Page(s): 6
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  • Collective subjective logic: Scalable uncertainty-based opinion inference

    Publication Year: 2017, Page(s):7 - 16
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (746 KB) | HTML iconHTML

    Subjective Logic (SL), as one of the state-of-the-art belief models, has been proposed to model an opinion that explicitly deals with its uncertainty. SL offers a variety of operators to update opinions consisting of belief, disbelief, and uncertainty. However, SL operators lack scalability to derive opinions from a large-scale network data due to the sequential procedures of combining two opinion... View full abstract»

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  • Quality-aware aggregation & predictive analytics at the edge

    Publication Year: 2017, Page(s):17 - 26
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (250 KB) | HTML iconHTML

    We investigate the quality of aggregation and predictive analytics in edge computing environments. Edge analytics require pushing processing and inference to the edge of a network of sensing & actuator nodes, which enables huge amount of contextual data to be processed in real time that would be prohibitively complex and costly to transfer on centralized locations. We propose a quality-aware, time... View full abstract»

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  • Robust multi-label semi-supervised classification

    Publication Year: 2017, Page(s):27 - 36
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (645 KB) | HTML iconHTML

    The lack of labels and the poor quality of data present a common challenge in many data mining and machine learning problems. The model performance might be limited if only a few labeled samples are available for training. Moreover, the data may be noisy in reality, which disturbs the data distribution and further hinders the learning performance. These problems become even more critical in multi-... View full abstract»

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  • Lifelong multi-task multi-view learning using latent spaces

    Publication Year: 2017, Page(s):37 - 46
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (189 KB) | HTML iconHTML

    In this paper, we study the problem of MTMV learning in a lifelong learning framework. Lifelong machine learning, like human lifelong learning, learns multiple tasks over time. Lifelong multi-task multi-view (Lifelong MTMV) learning is a new data mining and machine learning problem where new tasks and/or new views may come in anytime during the learning process. Our goal is to efficiently learn a ... View full abstract»

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  • Compact multi-class boosted trees

    Publication Year: 2017, Page(s):47 - 56
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (677 KB) | HTML iconHTML

    Gradient boosted decision trees are a popular machine learning technique, in part because of their ability to give good accuracy with small models. We describe two extensions to the standard tree boosting algorithm designed to increase this advantage. The first improvement extends the boosting formalism from scalar-valued trees to vector-valued trees. This allows individual trees to be used as mul... View full abstract»

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  • Constraint-aware dynamic truth discovery in big data social media sensing

    Publication Year: 2017, Page(s):57 - 66
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (314 KB) | HTML iconHTML

    Social media sensing has emerged as a new big data application paradigm to collect observations and claims about the measured variables in physical environment from common citizens. A fundamental problem in social media sensing applications lies in estimating the evolving truth of claims and the reliability of data sources without knowing either of them a priori, which is referred to as dynamic tr... View full abstract»

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  • Standardizing big earth datacubes

    Publication Year: 2017, Page(s):67 - 73
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (676 KB) | HTML iconHTML

    Geo data like satellite imagery and climate simulation output constitute major contributors to today's Big Data deluge. The datacube paradigm is currently being considered not only for getting data analysis ready, but also to offer advanced functionality in an easy-to-use way while allowing for highly effective server-side optimizations. Datacube standards can help substantially in unifying access... View full abstract»

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  • Enhancing data quality by cleaning inconsistent big RDF data

    Publication Year: 2017, Page(s):74 - 79
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (469 KB) | HTML iconHTML

    We address the problem of dealing with inconsistencies in fusion of big data sources using Resource Description Framework (RDF) and ontologies. We propose a scalable approach ensuring data quality for query answering over big RDF data in a distributed way on a Spark ecosystem. In so doing, the cleaning inconsistent big RDF data approach is built on the following steps (1) modeling consistency rule... View full abstract»

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  • Iterative matrix correlation for bisection clustering

    Publication Year: 2017, Page(s):80 - 87
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (195 KB) | HTML iconHTML

    We introduce and theoretically study the convergence behavior of iterative matrix correlation computation and show how it can be leveraged to derive a novel bisection clustering algorithm with unique characteristics. A correlation matrix is a symmetric n × n matrix for n vectors, where the (i, j)-th entry is the Pearson correlation coefficient between vectors i and j. We observe that in gen... View full abstract»

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  • Entropic determinants of massive matrices

    Publication Year: 2017, Page(s):88 - 93
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (146 KB) | HTML iconHTML

    The ability of many powerful machine learning algorithms to deal with large data sets without compromise is often hampered by computationally expensive linear algebra tasks, of which calculating the log determinant is a canonical example. In this paper we demonstrate the optimality of Maximum Entropy methods in approximating such calculations. We prove the equivalence between mean value constraint... View full abstract»

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  • Big active learning

    Publication Year: 2017, Page(s):94 - 101
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (365 KB) | HTML iconHTML

    Active learning is a common strategy to deal with large-scale data with limited labeling effort. In each iteration of active learning, a query is ready for oracle to answer such as what the label is for a given unlabeled data. Given the method, we can request the labels only for those data that are essential and save the labeling effort from oracle. We focus on pool-based active learning where a s... View full abstract»

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  • A novel approach to optimization of iterative machine learning algorithms: Over heap structure

    Publication Year: 2017, Page(s):102 - 109
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2283 KB) | HTML iconHTML

    Iterative machine learning algorithms, i.e., k-means (KM), expectation maximization (EM), become overwhelmed with big data since all data points are being continually and indiscriminately visited while a cost is being minimized. In this work, we demonstrate (1) an optimization approach to reduce training run-time complexity of iterative machine learning algorithms and (2) implementation of this fr... View full abstract»

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  • Multi-view graph learning with adaptive label propagation

    Publication Year: 2017, Page(s):110 - 115
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (214 KB) | HTML iconHTML

    Graphs play an essential role in many data mining paradigms, such as semi-supervised classification. Conventional graph learning methods mainly focus on constructing graphs from single-view data. Nowadays data can be collected from multiple views using various sensors. How to construct a robust and reliable graph from multi-view data is still an open problem. In this paper, we propose a multi-view... View full abstract»

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