2017 International Conference on Big Data Innovations and Applications (Innovate-Data)

21-23 Aug. 2017

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  • [Front cover]

    Publication Year: 2017, Page(s): c1
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  • [Title page i]

    Publication Year: 2017, Page(s): i
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  • [Title page iii]

    Publication Year: 2017, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2017, Page(s): iv
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  • Table of contents

    Publication Year: 2017, Page(s): v
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  • Message from the Innovate-Data 2017 Chairs

    Publication Year: 2017, Page(s): vi
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  • Organizing Committee

    Publication Year: 2017, Page(s): vii
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  • Program Committee

    Publication Year: 2017, Page(s): viii
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  • Invited Talks

    Publication Year: 2017, Page(s):ix - x
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (142 KB)

    The collection, storage, manipulation and retention of massive amounts of data have resulted in serious security and privacy considerations. Various regulations are being proposed to handle big data so that the privacy of the individuals is not violated. For example, even if personally identifiable information is removed from the data, when data is combined with other data, an individual can be id... View full abstract»

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  • Practical String Dictionary Compression Using String Dictionary Encoding

    Publication Year: 2017, Page(s):1 - 8
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    A string dictionary is a data structure for storing a set of strings that maps them to unique IDs. It can manage string data in compact space by encoding them into integers. However, instances have recently emerged in practice where the size of string dictionaries has become a critical problem for very large datasets in many applications. A number of compressed string dictionaries have been propos... View full abstract»

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  • Bringing Big Data into the Car: Does it Scale?

    Publication Year: 2017, Page(s):9 - 16
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1265 KB) | HTML iconHTML

    The increasing velocity of big data captured by various sensors and processed in real-time offers support for a range of new application domains. For car information systems (CIS), data from different sources including IoT needs to be combined to offer an adequate service to the user. In this paper, we introduce a novel CIS big data-centric architecture based on a smart streaming infrastructure in... View full abstract»

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  • Fragmenting Big Data to Boost the Performance of MapReduce in Geographical Computing Contexts

    Publication Year: 2017, Page(s):17 - 24
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (424 KB) | HTML iconHTML

    The last few years have seen a growing demand of distributed Cloud infrastructures able to process big data generated by geographically scattered sources. A key challenge of this environment is how to manage big data across multiple heterogeneous datacenters interconnected through imbalanced network links. We designed a Hierarchical Hadoop Framework (H2F) where a top-level business logic smartly s... View full abstract»

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  • Tabular Data Anomaly Patterns

    Publication Year: 2017, Page(s):25 - 34
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (332 KB) | HTML iconHTML

    One essential and challenging task in data science is data cleaning - the process of identifying and eliminating data anomalies. Different data types, data domains, data acquisition methods, and final purposes of data cleaning have resulted in different approaches in defining data anomalies in the literature. This paper proposes and describes a set of basic data anomalies in the form of anomaly pa... View full abstract»

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  • Near Real-Time Big-Data Processing for Data Driven Applications

    Publication Year: 2017, Page(s):35 - 42
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (2117 KB) | HTML iconHTML

    This paper addresses the context data integration and processing problem for design of data driven application by introducing ASAPCS (Auto-scaling and Adjustment Platform for Cloud-based Systems) platform. Conceptual model, technical architecture and data integration process are described. The ASAPCS platform supports model-driven configuration, separation of context acquisition and application, u... View full abstract»

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  • Document-Oriented Data Schema for Relational Database Migration to NoSQL

    Publication Year: 2017, Page(s):43 - 50
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (423 KB) | HTML iconHTML

    Big data has become a crucial issue and one of the most important technologies in the modern world. The management of a semi-structured data format for big data is an important concern that must be addressed. Most relational database management systems fail to handle the scalability and flexibility of big data. NoSQL, which is a new concept in database technology, offers support a large volume of ... View full abstract»

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  • Author Index

    Publication Year: 2017, Page(s): 51
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  • [Publisher's information]

    Publication Year: 2017, Page(s): 52
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