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Avoiding Unintended Flows of Personally Identifiable Information : Enterprise Identity Management and Online Social Networks

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ISBN: 9783731500940 Year: Pages: XVIII, 196 p. DOI: 10.5445/KSP/1000036269 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Computer Science
Added to DOAB on : 2019-07-30 20:02:01

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This work addresses potentially occurring unintended flows of personally identifiable information (PII) within two fields of research, i.e., enterprise identity management and online social networks. For that, we investigate which pieces of PII can how often be gathered, correlated, or even be inferred by third parties that are not intended to get access to the specific pieces of PII. Furthermore, we introduce technical measures and concepts to avoid unintended flows of PII.

Data Privacy and Trust in Cloud Computing

Authors: --- --- ---
Book Series: Palgrave Studies in Digital Business & Enabling Technologies ISBN: 9783030546601 Year: Pages: 149 DOI: 10.1007/978-3-030-54660-1 Language: English
Publisher: Springer Nature
Subject: Business and Management --- Computer Science
Added to DOAB on : 2020-11-19 00:19:05
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This open access book brings together perspectives from multiple disciplines including psychology, law, IS, and computer science on data privacy and trust in the cloud. Cloud technology has fueled rapid, dramatic technological change, enabling a level of connectivity that has never been seen before in human history. However, this brave new world comes with problems. Several high-profile cases over the last few years have demonstrated cloud computing's uneasy relationship with data security and trust. This volume explores the numerous technological, process and regulatory solutions presented in academic literature as mechanisms for building trust in the cloud, including GDPR in Europe. The massive acceleration of digital adoption resulting from the COVID-19 pandemic is introducing new and significant security and privacy threats and concerns. Against this backdrop, this book provides a timely reference and organising framework for considering how we will assure privacy and build trust in such a hyper-connected digitally dependent world. This book presents a framework for assurance and accountability in the cloud and reviews the literature on trust, data privacy and protection, and ethics in cloud computing.

The Cloud-to-Thing Continuum

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Book Series: Palgrave Studies in Digital Business & Enabling Technologies ISBN: 9783030411107 Year: Pages: 161 DOI: 10.1007/978-3-030-41110-7 Language: English
Publisher: Springer Nature
Subject: Business and Management --- Computer Science
Added to DOAB on : 2020-07-15 23:58:19
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The Internet of Things offers massive societal and economic opportunities while at the same time significant challenges, not least the delivery and management of the technical infrastructure underpinning it, the deluge of data generated from it, ensuring privacy and security, and capturing value from it. This Open Access Pivot explores these challenges, presenting the state of the art and future directions for research but also frameworks for making sense of this complex area. This book provides a variety of perspectives on how technology innovations such as fog, edge and dew computing, 5G networks, and distributed intelligence are making us rethink conventional cloud computing to support the Internet of Things. Much of this book focuses on technical aspects of the Internet of Things, however, clear methodologies for mapping the business value of the Internet of Things are still missing. We provide a value mapping framework for the Internet of Things to address this gap. While there is much hype about the Internet of Things, we have yet to reach the tipping point. As such, this book provides a timely entrée for higher education educators, researchers and students, industry and policy makers on the technologies that promise to reshape how society interacts and operates.

Measuring the Business Value of Cloud Computing

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Book Series: Palgrave Studies in Digital Business & Enabling Technologies ISBN: 9783030431983 Year: Pages: 125 DOI: 10.1007/978-3-030-43198-3 Language: English
Publisher: Springer Nature
Subject: Business and Management --- Computer Science
Added to DOAB on : 2020-09-23 00:02:40
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The importance of demonstrating the value achieved from IT investments is long established in the Computer Science (CS) and Information Systems (IS) literature. However, emerging technologies such as the ever-changing complex area of cloud computing present new challenges and opportunities for demonstrating how IT investments lead to business value. Recent reviews of extant literature highlights the need for multi-disciplinary research. This research should explore and further develops the conceptualization of value in cloud computing research. In addition, there is a need for research which investigates how IT value manifests itself across the chain of service provision and in inter-organizational scenarios. This open access book will review the state of the art from an IS, Computer Science and Accounting perspective, will introduce and discuss the main techniques for measuring business value for cloud computing in a variety of scenarios, and illustrate these with mini-case studies.

Middleware Solutions for Wireless Internet of Things

Authors: --- --- ---
ISBN: 9783039210367 9783039210374 Year: Pages: 262 DOI: 10.3390/books978-3-03921-037-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-08-28 11:21:27
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The proliferation of powerful but cheap devices, together with the availability of a plethora of wireless technologies, has pushed for the spread of the Wireless Internet of Things (WIoT), which is typically much more heterogeneous, dynamic, and general-purpose if compared with the traditional IoT. The WIoT is characterized by the dynamic interaction of traditional infrastructure-side devices, e.g., sensors and actuators, provided by municipalities in Smart City infrastructures, and other portable and more opportunistic ones, such as mobile smartphones, opportunistically integrated to dynamically extend and enhance the WIoT environment. A key enabler of this vision is the advancement of software and middleware technologies in various mobile-related sectors, ranging from the effective synergic management of wireless communications to mobility/adaptivity support in operating systems and differentiated integration and management of devices with heterogeneous capabilities in middleware, from horizontal support to crowdsourcing in different application domains to dynamic offloading to cloud resources, only to mention a few. The book presents state-of-the-art contributions in the articulated WIoT area by providing novel insights about the development and adoption of middleware solutions to enable the WIoT vision in a wide spectrum of heterogeneous scenarios, ranging from industrial environments to educational devices. The presented solutions provide readers with differentiated point of views, by demonstrating how the WIoT vision can be applied to several aspects of our daily life in a pervasive manner.

Big Data Research for Social Sciences and Social Impact

Authors: --- ---
ISBN: 9783039282203 9783039282210 Year: Pages: 416 DOI: 10.3390/books978-3-03928-221-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-04-07 23:07:09
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A new era of innovation is enabled by the integration of social sciences and information systems research. In this context, the adoption of Big Data and analytics technology brings new insight to the social sciences. It also delivers new, flexible responses to crucial social problems and challenges. We are proud to deliver this edited volume on the social impact of big data research. It is one of the first initiatives worldwide analyzing of the impact of this kind of research on individuals and social issues. The organization of the relevant debate is arranged around three pillars: Section A: Big Data Research for Social Impact: • Big Data and Their Social Impact; • (Smart) Citizens from Data Providers to Decision-Makers; • Towards Sustainable Development of Online Communities; • Sentiment from Online Social Networks; • Big Data for Innovation. Section B. Techniques and Methods for Big Data driven research for Social Sciences and Social Impact: • Opinion Mining on Social Media; • Sentiment Analysis of User Preferences; • Sustainable Urban Communities; • Gender Based Check-In Behavior by Using Social Media Big Data; • Web Data-Mining Techniques; • Semantic Network Analysis of Legacy News Media Perception. Section C. Big Data Research Strategies: • Skill Needs for Early Career Researchers—A Text Mining Approach; • Pattern Recognition through Bibliometric Analysis; • Assessing an Organization’s Readiness to Adopt Big Data; • Machine Learning for Predicting Performance; • Analyzing Online Reviews Using Text Mining; • Context–Problem Network and Quantitative Method of Patent Analysis. Complementary social and technological factors including: • Big Social Networks on Sustainable Economic Development; Business Intelligence.

Keywords

big data --- illegal accommodation --- institutional innovation --- transaction costs --- housing problem --- building stock management --- Hong Kong --- information diffusion --- community detection --- topic analysis --- sentiment analysis --- social networks --- Social network --- sustainable development --- review voting --- online community --- paradox --- smart citizens --- experimental cities --- smart cities --- technopolitics --- big data --- Barcelona --- data commons --- decision-makers --- policy --- GDPR --- big data --- social sciences --- decision-making --- data analyst --- filtering --- framing --- big data --- maturity model --- temporal analytics --- advanced business analytics --- big data analytic methods --- semantic network analysis --- framings --- NodeXL --- promising technology --- research frontier --- bibliometric analysis --- hype cycle --- technology platforms --- sustainable agri-food systems --- innovation in sustainable agriculture --- online data --- data mining --- TP organics --- big data research --- point of interests (POI) --- sustainability development --- spatial accessibility of residential public services --- Xiamen City --- big data --- sales prediction --- online word-of-mouth --- dynamic topic model --- product attributes --- back-propagation neural network --- systematic and replicable patent analysis method --- problem-solved concept --- context–problem network --- network data analysis --- sustainable wireless energy transmission technology --- big data analytics --- text mining --- association rule --- car review --- skills --- researchers --- early career --- text mining --- social media big data --- lbsn --- check-in density --- spatiotemporal analysis --- KDE --- GWR --- SDE --- Guangzhou --- educational data mining --- learning analytics --- machine learning --- big data --- prediction grades --- destination image --- user-generated content --- online travel review --- big data analytics --- opinion mining --- sentiment analysis --- resource optimisation --- place sustainability --- TripAdvisor --- Greek Attica --- opinion mining --- social media --- social networks --- sentiment analysis --- sentiment polarity classification --- social impact --- big data research --- information systems --- analytics --- decision making --- social sciences --- big data research --- social and humanistic computing --- social sciences --- social good --- social impact --- machine learning --- knowledge management --- web science --- data science --- social inclusive economic growth --- sustainability --- innovation --- innovation networks

Google Earth Engine Applications

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ISBN: 9783038978848 9783038978855 Year: Pages: 420 DOI: 10.3390/books978-3-03897-885-5 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Environmental Technology
Added to DOAB on : 2019-04-25 16:37:17
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In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.

Keywords

Google Earth Engine --- NDVI --- vegetation index --- Landsat --- remote sensing --- phenology --- surface reflectance --- cropland mapping --- cropland areas --- 30-m --- Landsat-8 --- Sentinel-2 --- Random Forest --- Support Vector Machines --- segmentation --- RHSeg --- Google Earth Engine --- Africa --- remote sensing --- semi-arid --- ecosystem assessment --- land use change --- image classification --- seasonal vegetation --- carbon cycle --- Google Earth Engine --- crop yield --- gross primary productivity (GPP) --- data fusion --- Landsat --- MODIS --- MODIS --- Random Forest --- pasture mapping --- Brazilian pasturelands dynamics --- Google Earth Engine --- crop classification --- multi-classifier --- cloud computing --- time series --- high spatial resolution --- BACI --- Enhanced Vegetation Index --- Google Earth Engine --- cloud-based geo-processing --- satellite-derived bathymetry --- image composition --- pseudo-invariant features --- sun glint correction --- empirical --- spatial error --- Google Earth Engine --- low cost in situ --- Sentinel-2 --- Mediterranean --- burn severity --- change detection --- Landsat --- dNBR --- RdNBR --- RBR --- composite burn index (CBI) --- MTBS --- lower mekong basin --- landsat collection --- suspended sediment concentration --- online application --- google earth engine --- Landsat --- Google Earth Engine --- protected area --- forest and land use mapping --- machine learning classification --- China --- temporal compositing --- image time series --- multitemporal analysis --- change detection --- cloud masking --- Landsat-8 --- Google Earth Engine (GEE) --- Google Earth Engine --- LAI --- FVC --- FAPAR --- CWC --- plant traits --- random forests --- PROSAIL --- small-scale mining --- industrial mining --- google engine --- image classification --- land-use cover change --- seagrass --- habitat mapping --- image composition --- machine learning --- support vector machines --- Google Earth Engine --- Sentinel-2 --- Aegean --- Ionian --- global scale --- soil moisture --- Soil Moisture Ocean Salinity --- Soil Moisture Active Passive --- Google Earth Engine --- drought --- cloud computing --- remote sensing --- snow hydrology --- water resources --- Google Earth Engine --- user assessment --- MODIS --- snow cover --- flood --- disaster prevention --- emergency response --- decision making --- Google Earth Engine --- land cover --- deforestation --- Brazilian Amazon --- Bayesian statistics --- BULC-U --- Mato Grosso --- spatial resolution --- Landsat --- GlobCover --- SDG --- surface urban heat island --- Geo Big Data --- Google Earth Engine --- global monitoring service --- Google Earth Engine --- web portal --- satellite imagery --- trends --- earth observation --- wetland --- Google Earth Engine --- Sentinel-1 --- Sentinel-2 --- random forest --- cloud computing --- geo-big data --- cloud computing --- big data analytics --- long term monitoring --- data archival --- early warning systems

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