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Big Data Cogn. Comput., Volume 2, Issue 2 (June 2018)

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Cover Story (view full-size image) With the widespread use of wearable sensors, cloud services, social networking services (SNS), [...] Read more.
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Open AccessFeature PaperArticle The Development of Data Science: Implications for Education, Employment, Research, and the Data Revolution for Sustainable Development
Big Data Cogn. Comput. 2018, 2(2), 14; https://doi.org/10.3390/bdcc2020014
Received: 28 May 2018 / Revised: 16 June 2018 / Accepted: 16 June 2018 / Published: 19 June 2018
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Abstract
In Data Science, we are concerned with the integration of relevant sciences in observed and empirical contexts. This results in the unification of analytical methodologies, and of observed and empirical data contexts. Given the dynamic nature of convergence, the origins and many evolutions
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In Data Science, we are concerned with the integration of relevant sciences in observed and empirical contexts. This results in the unification of analytical methodologies, and of observed and empirical data contexts. Given the dynamic nature of convergence, the origins and many evolutions of the Data Science theme are described. The following are covered in this article: the rapidly growing post-graduate university course provisioning for Data Science; a preliminary study of employability requirements, and how past eminent work in the social sciences and other areas, certainly mathematics, can be of immediate and direct relevance and benefit for innovative methodology, and for facing and addressing the ethical aspect of Big Data analytics, relating to data aggregation and scale effects. Associated also with Data Science is how direct and indirect outcomes and consequences of Data Science include decision support and policy making, and both qualitative as well as quantitative outcomes. For such reasons, the importance is noted of how Data Science builds collaboratively on other domains, potentially with innovative methodologies and practice. Further sections point towards some of the most major current research issues. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2018)
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Open AccessArticle Risks of Deep Reinforcement Learning Applied to Fall Prevention Assist by Autonomous Mobile Robots in the Hospital
Big Data Cogn. Comput. 2018, 2(2), 13; https://doi.org/10.3390/bdcc2020013
Received: 30 April 2018 / Revised: 2 June 2018 / Accepted: 7 June 2018 / Published: 17 June 2018
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Abstract
Our previous study proposed an automatic fall risk assessment and related risk reduction measures. A nursing system to reduce patient accidents was also developed, therefore reducing the caregiving load of the medical staff in hospitals. However, there are risks associated with artificial intelligence
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Our previous study proposed an automatic fall risk assessment and related risk reduction measures. A nursing system to reduce patient accidents was also developed, therefore reducing the caregiving load of the medical staff in hospitals. However, there are risks associated with artificial intelligence (AI) in applications such as assistant mobile robots that use deep reinforcement learning. In this paper, we discuss safety applications related to AI in fields where humans and robots coexist, especially when applying deep reinforcement learning to the control of autonomous mobile robots. First, we look at a summary of recent related work on robot safety with AI. Second, we extract the risks linked to the use of autonomous mobile assistant robots based on deep reinforcement learning for patients in a hospital. Third, we systematize the risks of AI and propose sample risk reduction measures. The results suggest that these measures are useful in the fields of clinical and industrial safety. Full article
(This article belongs to the Special Issue Applied Deep Learning: Business and Industrial Applications)
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Open AccessArticle Memory Recall Support System Based on Active Acquisition and Accumulation of Memory Fragments
Big Data Cogn. Comput. 2018, 2(2), 12; https://doi.org/10.3390/bdcc2020012
Received: 23 March 2018 / Revised: 23 April 2018 / Accepted: 13 May 2018 / Published: 17 May 2018
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Abstract
With the widespread use of wearable sensors, cloud services, social networking services (SNS), etc., there are various applications and systems that record information on users’ daily activities and support recalling these activities. In various situations in everyday life, it is useful to recall
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With the widespread use of wearable sensors, cloud services, social networking services (SNS), etc., there are various applications and systems that record information on users’ daily activities and support recalling these activities. In various situations in everyday life, it is useful to recall and refer to past events by utilizing such information; therefore, there are increasing expectations surrounding a memory recall system that supports users’ activities. In this research, we aim to realize a system that acquires records of users’ experiences, transforms these records as Active Information Resources, autonomously manages the accumulated records based on the record’s metadata, and supports users’ human memory recall. In this paper, we describe the design and implementation of a basic framework for accumulating records on daily activities and providing information related to past experiences according to the user’s request. We also present evaluation experiments using the implemented system. Full article
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Open AccessArticle Large Scale Product Recommendation of Supermarket Ware Based on Customer Behaviour Analysis
Big Data Cogn. Comput. 2018, 2(2), 11; https://doi.org/10.3390/bdcc2020011
Received: 13 January 2018 / Revised: 3 May 2018 / Accepted: 3 May 2018 / Published: 9 May 2018
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Abstract
In this manuscript, we present a prediction model based on the behaviour of each customer using data mining techniques. The proposed model utilizes a supermarket database and an additional database from Amazon, both containing information about customers’ purchases. Subsequently, our model analyzes these
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In this manuscript, we present a prediction model based on the behaviour of each customer using data mining techniques. The proposed model utilizes a supermarket database and an additional database from Amazon, both containing information about customers’ purchases. Subsequently, our model analyzes these data in order to classify customers as well as products, being trained and validated with real data. This model is targeted towards classifying customers according to their consuming behaviour and consequently proposes new products more likely to be purchased by them. The corresponding prediction model is intended to be utilized as a tool for marketers so as to provide an analytically targeted and specified consumer behavior. Our algorithmic framework and the subsequent implementation employ the cloud infrastructure and use the MapReduce Programming Environment, a model for processing large data-sets in a parallel manner with a distributed algorithm on computer clusters, as well as Apache Spark, which is a newer framework built on the same principles as Hadoop. Through a MapReduce model application on each step of the proposed method, text processing speed and scalability are enhanced in reference to other traditional methods. Our results show that the proposed method predicts with high accuracy the purchases of a supermarket. Full article
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Open AccessReview Fog Computing and the Internet of Things: A Review
Big Data Cogn. Comput. 2018, 2(2), 10; https://doi.org/10.3390/bdcc2020010
Received: 4 March 2018 / Revised: 5 April 2018 / Accepted: 5 April 2018 / Published: 8 April 2018
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Abstract
With the rapid growth of Internet of Things (IoT) applications, the classic centralized cloud computing paradigm faces several challenges such as high latency, low capacity and network failure. To address these challenges, fog computing brings the cloud closer to IoT devices. The fog
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With the rapid growth of Internet of Things (IoT) applications, the classic centralized cloud computing paradigm faces several challenges such as high latency, low capacity and network failure. To address these challenges, fog computing brings the cloud closer to IoT devices. The fog provides IoT data processing and storage locally at IoT devices instead of sending them to the cloud. In contrast to the cloud, the fog provides services with faster response and greater quality. Therefore, fog computing may be considered the best choice to enable the IoT to provide efficient and secure services for many IoT users. This paper presents the state-of-the-art of fog computing and its integration with the IoT by highlighting the benefits and implementation challenges. This review will also focus on the architecture of the fog and emerging IoT applications that will be improved by using the fog model. Finally, open issues and future research directions regarding fog computing and the IoT are discussed. Full article
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Open AccessArticle Development of Framework for Aggregation and Visualization of Three-Dimensional (3D) Spatial Data
Big Data Cogn. Comput. 2018, 2(2), 9; https://doi.org/10.3390/bdcc2020009
Received: 17 February 2018 / Revised: 14 March 2018 / Accepted: 19 March 2018 / Published: 21 March 2018
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Abstract
Geospatial information plays an important role in environmental modelling, resource management, business operations, and government policy. However, very little or no commonality between formats of various geospatial data has led to difficulties in utilizing the available geospatial information. These disparate data sources must
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Geospatial information plays an important role in environmental modelling, resource management, business operations, and government policy. However, very little or no commonality between formats of various geospatial data has led to difficulties in utilizing the available geospatial information. These disparate data sources must be aggregated before further extraction and analysis may be performed. The objective of this paper is to develop a framework called PlaniSphere, which aggregates various geospatial datasets, synthesizes raw data, and allows for third party customizations of the software. PlaniSphere uses NASA World Wind to access remote data and map servers using Web Map Service (WMS) as the underlying protocol that supports service-oriented architecture (SOA). The results show that PlaniSphere can aggregate and parses files that reside in local storage and conforms to the following formats: GeoTIFF, ESRI shape files, and KML. Spatial data retrieved using WMS from the Internet can create geospatial data sets (map data) from multiple sources, regardless of who the data providers are. The plug-in function of this framework can be expanded for wider uses, such as aggregating and fusing geospatial data from different data sources, by providing customizations to serve future uses, which the capacity of the commercial ESRI ArcGIS software is limited to add libraries and tools due to its closed-source architectures and proprietary data structures. Analysis and increasing availability of geo-referenced data may provide an effective way to manage spatial information by using large-scale storage, multidimensional data management, and Online Analytical Processing (OLAP) capabilities in one system. Full article
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