Big Data is an emerging paradigm and has currently become a strong attractor of global interest, specially within the transportation industry. The combination of disruptive technologies and new ...concepts such as the Smart City upgrades the transport data life cycle. In this context, Big Data is considered as a new pledge for the transportation industry to effectively manage all data this sector required for providing safer, cleaner and more efficient transport means, as well as for users to personalize their transport experience. However, Big Data comes along with its own set of technological challenges, stemming from the multiple and heterogeneous transportation/mobility application scenarios. In this survey we analyze the latest research efforts revolving on Big Data for the transportation and mobility industry, its applications, baselines scenarios, fields and use case such as routing, planning, infrastructure monitoring, network design, among others. This analysis will be done strictly from the Big Data perspective, focusing on those contributions gravitating on techniques, tools and methods for modeling, processing, analyzing and visualizing transport and mobility Big Data. From the literature review a set of trends and challenges is extracted so as to provide researchers with an insightful outlook on the field of transport and mobility.
Despite the publicity regarding big data and analytics (BDA), the success rate of these projects and strategic value created from them are unclear. Most literature on BDA focuses on how it can be ...used to enhance tactical organizational capabilities, but very few studies examine its impact on organizational value. Further, we see limited framing of how BDA can create strategic value for the organization. After all, the ultimate success of any BDA project lies in realizing strategic business value, which gives firms a competitive advantage. In this study, we describe the value proposition of BDA by delineating its components. We offer a framing of BDA value by extending existing frameworks of information technology value, then illustrate the framework through BDA applications in practice. The framework is then discussed in terms of its ability to study constructs and relationships that focus on BDA value creation and realization. We also present a problem-oriented view of the framework-where problems in BDA components can give rise to targeted research questions and areas for future study. The framing in this study could help develop a significant research agenda for BDA that can better target research and practice based on effective use of data resources.
To date, health care industry has not fully grasped the potential benefits to be gained from big data analytics. While the constantly growing body of academic research on big data analytics is mostly ...technology oriented, a better understanding of the strategic implications of big data is urgently needed. To address this lack, this study examines the historical development, architectural design and component functionalities of big data analytics. From content analysis of 26 big data implementation cases in healthcare, we were able to identify five big data analytics capabilities: analytical capability for patterns of care, unstructured data analytical capability, decision support capability, predictive capability, and traceability. We also mapped the benefits driven by big data analytics in terms of information technology (IT) infrastructure, operational, organizational, managerial and strategic areas. In addition, we recommend five strategies for healthcare organizations that are considering to adopt big data analytics technologies. Our findings will help healthcare organizations understand the big data analytics capabilities and potential benefits and support them seeking to formulate more effective data-driven analytics strategies.
•A big data analytics architecture for healthcare organizations is built.•We identify five big data analytics capabilities from 26 big data cases.•We present several strategies for being successful with big data analytics in healthcare settings.•We provide a comprehensive understanding of the potential benefits of big data analytics.
Every day a large number of Earth observation (EO) spaceborne and airborne sensors from many different countries provide a massive amount of remotely sensed data. Those data are used for different ...applications, such as natural hazard monitoring, global climate change, urban planning, etc. The applications are data driven and mostly interdisciplinary. Based on this it can truly be stated that we are now living in the age of big remote sensing data. Furthermore, these data are becoming an economic asset and a new important resource in many applications. In this paper, we specifically analyze the challenges and opportunities that big data bring in the context of remote sensing applications. Our focus is to analyze what exactly does big data mean in remote sensing applications and how can big data provide added value in this context. Furthermore, this paper describes the most challenging issues in managing, processing, and efficient exploitation of big data for remote sensing problems. In order to illustrate the aforementioned aspects, two case studies discussing the use of big data in remote sensing are demonstrated. In the first test case, big data are used to automatically detect marine oil spills using a large archive of remote sensing data. In the second test case, content-based information retrieval is performed using high-performance computing (HPC) to extract information from a large database of remote sensing images, collected after the terrorist attack to the World Trade Center in New York City. Both cases are used to illustrate the significant challenges and opportunities brought by the use of big data in remote sensing applications.
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