scholarly journals Data-sharing markets for integrating IoT data processing functionalities

2021 ◽  
Vol 3 (1) ◽  
pp. 76-93
Author(s):  
Nasr Kasrin ◽  
Aboubakr Benabbas ◽  
Golnaz Elmamooz ◽  
Daniela Nicklas ◽  
Simon Steuer ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Management ◽  
Data Sharing ◽  
Information Exchange ◽  
Smart Cities ◽  
Model Management ◽  
Sensor Data ◽  
Integration Model ◽  
Iot Platform ◽  
Iot Platforms

AbstractThe recent evolution of the Internet of Things into a cyber-physical reality has spawned various challenges from a data management perspective. In addition, IoT platform designers are faced with another set of questions. How can platforms be extended to smoothly integrate new data management functionalities? Currently, data processing related tasks are typically realized by manually developed code and functions which creates difficulties in maintenance and growth. Hence we need to explore other approaches to integration for IoT platforms. In this paper we cover both these aspects: (1) we explore several emerging data management challenges, and (2) we propose an IoT platform integration model that can combine disparate functionalities under one roof. For the first, we focus on the following challenges: sensor data quality, privacy in data streams, machine learning model management, and resource-aware data management. For the second, we propose an information-integration model for IoT platforms. The model revolves around the concept of a Data-Sharing Market where data management functionalities can share and exchange information about their data with other functionalities. In addition, data-sharing markets themselves can be combined into networks of markets where information flows from one market to another, which creates a web of information exchange about data resources. To motivate this work we present a use-case application in smart cities.

scholarly journals Raising Awareness of Data Sharing Consent Through Knowledge Graph Visualisation

2021 ◽  
Author(s):  
Christof Bless ◽  
Lukas Dötlinger ◽  
Michael Kaltschmid ◽  
Markus Reiter ◽  
Anelia Kurteva ◽  
...  
Keyword(s):  
Data Sharing ◽  
Large Scale ◽  
User Study ◽  
Smart Cities ◽  
Sensor Data ◽  
Knowledge Graph ◽  
Raising Awareness ◽  
Share Data ◽  
Knowledge Graphs ◽  
Machine Readable

Knowledge graphs facilitate systematic large-scale data analysis by providing both human and machine-readable structures, which can be shared across different domains and platforms. Nowadays, knowledge graphs can be used to standardise the collection and sharing of user information in many different sectors such as transport, insurance, smart cities and internet of things. Regulations such as the GDPR make sure that users are not taken advantage of when they share data. From a legal standpoint it is necessary to have the user’s consent to collect information. This consent is only valid if the user is aware about the information collected at all times. To increase this awareness, we present a knowledge graph visualisation approach, which informs users about the activities linked to their data sharing agreements, especially after they have already given their consent. To visualise the graph, we introduce a user-centred application which showcases sensor data collection and distribution to different data processors. Finally, we present the results of a user study conducted to find out whether this visualisation leads to more legal awareness and trust. We show that with our visualisation tool data sharing consent rates increase from 48% to 81.5%.

IoT Platforms and Technologies Driving Spatial Planning and Analytics

2019 ◽  
pp. 84-112
Author(s):  
Lorenzo Bottaccioli ◽  
Edoardo Patti ◽  
Anna Osello ◽  
Tania Cerquitelli ◽  
Enrico Macii ◽  
...  
Keyword(s):  
Web Service ◽  
Spatial Planning ◽  
Smart Cities ◽  
Research Projects ◽  
Realistic Case ◽  
The Core ◽  
Iot Platform ◽  
Service Oriented ◽  
Iot Platforms ◽  
Access To Data

The continuous evolution of internet of things technologies is constantly evolving the concept of smart cities as well as the surrounding environments. From pervasive sensors through computational nodes at the edge of the network to the cloud and final user applications, the data flow chain makes available to the user a very large and heterogeneous amount of data. IoT platforms are at the core of this chain, providing seamless access to data independently from the hardware devices and making possible the interoperability with other data sources (e.g., GIS, SIM, etc.). Despite the availability of IoT platform solutions either commercial and open-source, research is still very active to design and implement flexible, easy-to-use, and efficient web-service-oriented software infrastructures. This chapter will review the current IoT platform infrastructures making also reference to state-of-the-art solutions in literature and proposed in recent research projects. The chapter will outline the main challenges and directions about future platforms, putting them in the context of realistic case studies.

scholarly journals The Need for Machine-Processable Agreements in Health Data Management

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 87
Author(s):  
George Konstantinidis ◽  
Adriane Chapman ◽  
Mark J. Weal ◽  
Ahmed Alzubaidi ◽  
Lisa M. Ballard ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Management ◽  
Data Sharing ◽  
Medical Information ◽  
Service Providers ◽  
Personal Data ◽  
Health Data ◽  
Coarse Grained ◽  
Semantic Web Technologies ◽  
Fine Grained

Data processing agreements in health data management are laid out by organisations in monolithic “Terms and Conditions” documents written in natural legal language. These top-down policies usually protect the interest of the service providers, rather than the data owners. They are coarse-grained and do not allow for more than a few opt-in or opt-out options for individuals to express their consent on personal data processing, and these options often do not transfer to software as they were intended to. In this paper, we study the problem of health data sharing and we advocate the need for individuals to describe their personal contract of data usage in a formal, machine-processable language. We develop an application for sharing patient genomic information and test results, and use interactions with patients and clinicians in order to identify the particular peculiarities a privacy/policy/consent language should offer in this complicated domain. We present how Semantic Web technologies can have a central role in this approach by providing the formal tools and features required in such a language. We present our ongoing approach to construct an ontology-based framework and a policy language that allows patients and clinicians to express fine-grained consent, preferences or suggestions on sharing medical information. Our language offers unique features such as multi-party ownership of data or data sharing dependencies. We evaluate the landscape of policy languages from different areas, and show how they are lacking major requirements needed in health data management. In addition to enabling patients, our approach helps organisations increase technological capabilities, abide by legal requirements, and save resources.

Structuring and visualization of indicators in multidimensional data cubes

2018 ◽  
pp. 79-87
Author(s):  
E. E. Akimkina
Keyword(s):  
Decision Making ◽  
Data Processing ◽  
Data Management ◽  
Multidimensional Data ◽  
End User ◽  
Processing Technologies ◽  
Data Cubes ◽  
Multidimensional Visualization ◽  
Multidimensional Data Cubes ◽  
Practical Recommendations

The problems of structuring of indicators in multidimensional data cubes with their subsequent processing with the help of end-user tools providing multidimensional visualization and data management are analyzed; the possibilities of multidimensional data processing technologies for managing and supporting decision making at a design and technological enterprise are shown; practical recommendations on the use of domestic computer environments for the structuring and visualization of multidimensional data cubes are given.

scholarly journals Integrated sensor data processing for occupancy detection in residential buildings

2021 ◽  
Vol 237 ◽  
pp. 110810
Author(s):  
Chenli Wang ◽  
Jun Jiang ◽  
Thomas Roth ◽  
Cuong Nguyen ◽  
Yuhong Liu ◽  
...  
Keyword(s):  
Data Processing ◽  
Residential Buildings ◽  
Sensor Data ◽  
Integrated Sensor ◽  
Occupancy Detection

Practice Report “Smart Disaster Management” — Combining Smart City Data and Citizen Participation to Increase Disaster Resilience

i-com ◽  
2021 ◽  
Vol 20 (2) ◽  
pp. 177-193
Author(s):  
Daniel Wessel ◽  
Julien Holtz ◽  
Florian König
Keyword(s):  
Citizen Participation ◽  
Smart City ◽  
Development Process ◽  
Smart Cities ◽  
Government Support ◽  
Sensor Data ◽  
Specific Information ◽  
Mutual Support ◽  
The Everyday

Abstract Smart cities have a huge potential to increase the everyday efficiency of cities, but also to increase preparation and resilience in case of natural disasters. Especially for disasters which are somewhat predicable like floods, sensor data can be used to provide citizens with up-to-date, personalized and location-specific information (street or even house level resolution). This information allows citizens to better prepare to avert water damage to their property, reduce the needed government support, and — by connecting citizens locally — improve mutual support among neighbors. But how can a smart city application be designed that is both usable and able to function during disaster conditions? Which smart city information can be used? How can the likelihood of mutual, local support be increased? In this practice report, we present the human-centered development process of an app to use Smart City data to better prepare citizens for floods and improve their mutual support during disasters as a case study to answer these questions.

Bulk Sensor Data Sharing Using Millimeter Wave V2X for Enhanced Safety and Comfort in Mobility

2018 ◽  
Author(s):  
Masataka Irie ◽  
Gaius Wee Yao Huang ◽  
Michael Sim Hong Cheng ◽  
Kazuaki Takahashi
Keyword(s):  
Data Sharing ◽  
Millimeter Wave ◽  
Sensor Data

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

2017 ◽  
Vol 8 (2) ◽  
pp. 88-105 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Sensor Data ◽  
Climate Data ◽  
The Real ◽  
Disease Surveillance System

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

Data Management at Gaia Data Processing Centers

2012 ◽  
pp. 107-115 ◽  
Author(s):  
Pilar de Teodoro ◽  
Alexander Hutton ◽  
Benoit Frezouls ◽  
Alain Montmory ◽  
Jordi Portell ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Management
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