An Interoperable Open Data Framework for Discovering Popular Tours Based on Geo-Tagged Tweets

2017 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Giuseppe Psaila ◽  
Maurizio Toccu ◽  
Gloria Bordogna ◽  
Luca Frigerio ◽  
Alfredo Cuzzocrea
Keyword(s):  
Open Data ◽  
Data Framework

scholarly journals Technical Note: The Linked Paleo Data framework – a common tongue for paleoclimatology

2015 ◽  
Vol 11 (5) ◽  
pp. 4309-4327
Author(s):  
N. P. McKay ◽  
J. Emile-Geay
Keyword(s):  
Open Data ◽  
Technical Note ◽  
Structured Data ◽  
Data Standards ◽  
Knowledge Representations ◽  
Standard Data ◽  
Data Framework ◽  
Online Databases ◽  
Scientific Endeavor ◽  
Paleoclimate Data

Abstract. Paleoclimatology is a highly collaborative scientific endeavor, increasingly reliant on online databases for data sharing. Yet, there is currently no universal way to describe, store and share paleoclimate data: in other words, no standard. Data standards are often regarded by scientists as mere technicalities, though they underlie much scientific and technological innovation, as well as facilitating collaborations between research groups. In this article, we propose a preliminary data standard for paleoclimate data, general enough to accommodate all the proxy and measurement types encountered in a large international collaboration (PAGES2K). We also introduce a vehicle for such structured data (Linked Paleo Data, or LiPD), leveraging recent advances in knowledge representations (Linked Open Data). The LiPD framework enables quick querying and extraction, and we expect that it will facilitate the writing of open-source, community codes to access, analyze, model and visualize paleoclimate observations. We welcome community feedback on this standard, and encourage paleoclimatologists to experiment with the format for their own purposes.

scholarly journals A probabilistic approach to estimating residential losses from different flood types

2020 ◽  
Author(s):  
Dominik Paprotny ◽  
Heidi Kreibich ◽  
Oswaldo Morales-Nápoles ◽  
Dennis Wagenaar ◽  
Attilio Castellarin ◽  
...  
Keyword(s):  
Probabilistic Approach ◽  
Open Data ◽  
Continuous Variables ◽  
Flood Events ◽  
Damage Models ◽  
Data Framework ◽  
Wide Range ◽  
Flood Losses ◽  
Building Level

AbstractResidential assets, comprising buildings and household contents, are a major source of direct flood losses. Existing damage models are mostly deterministic and limited to particular countries or flood types. Here, we compile building-level losses from Germany, Italy and the Netherlands covering a wide range of fluvial and pluvial flood events. Utilizing a Bayesian network (BN) for continuous variables, we find that relative losses (i.e. loss relative to exposure) to building structure and its contents could be estimated with five variables: water depth, flow velocity, event return period, building usable floor space area and regional disposable income per capita. The model’s ability to predict flood losses is validated for the 11 flood events contained in the sample. Predictions for the German and Italian fluvial floods were better than for pluvial floods or the 1993 Meuse river flood. Further, a case study of a 2010 coastal flood in France is used to test the BN model’s performance for a type of flood not included in the survey dataset. Overall, the BN model achieved better results than any of 10 alternative damage models for reproducing average losses for the 2010 flood. An additional case study of a 2013 fluvial flood has also shown good performance of the model. The study shows that data from many flood events can be combined to derive most important factors driving flood losses across regions and time, and that resulting damage models could be applied in an open data framework.

A linked open data framework to enhance the discoverability and impact of culture heritage

2018 ◽  
Vol 45 (6) ◽  
pp. 756-766 ◽  
Author(s):  
Gustavo Candela ◽  
Pilar Escobar ◽  
Rafael C Carrasco ◽  
Manuel Marco-Such
Keyword(s):  
Information Retrieval ◽  
Open Data ◽  
Knowledge Bases ◽  
Linked Open Data ◽  
Miguel De Cervantes ◽  
Data Framework ◽  
Retrieval Systems ◽  
All Solutions ◽  
Information Retrieval Systems ◽  
Culture Heritage

Cultural heritage institutions have recently begun to consider the benefits of sharing their collections using linked open data to disseminate and enrich their metadata. As datasets become very large, challenges appear, such as ingestion, management, querying and enrichment. Furthermore, each institution has particular features related to important aspects such as vocabularies and interoperability, which make it difficult to generalise this process and provide one-for-all solutions. In order to improve the user experience as regards information retrieval systems, researchers have identified that further refinements are required for the recognition and extraction of implicit relationships expressed in natural language. We introduce a framework for the enrichment and disambiguation of locations in text using open knowledge bases such as Wikidata and GeoNames. The framework has been successfully used to publish a dataset based on information from the Biblioteca Virtual Miguel de Cervantes, thus illustrating how semantic enrichment can help information retrieval. The methods applied in order to automate the enrichment process, which build upon open source software components, are described herein.

scholarly journals Linked Open Data Framework for Ethnic Groups in Thailand Learning

2020 ◽  
Vol 15 (10) ◽  
pp. 140
Author(s):  
Wirapong Chansanam ◽  
Kulthida Tuamsuk ◽  
Juthatip Chaikhambung
Keyword(s):  
Best Practices ◽  
Ethnic Groups ◽  
Open Data ◽  
Linked Open Data ◽  
Guidance System ◽  
Public And Private ◽  
Pilot Model ◽  
Data Framework ◽  
Enormous Number ◽  
General Guidance

The key significant worldview of the Semantic Web is Linked Open Data, another period of the World Wide Web that capacities to carry suggestions to information. An enormous number of both public and private foundations have dis-tributed their information following the Linked Open Data philosophies, or have done as such with information from different associations. To this degree, since the generation and production of Linked Open Data are thorough designing procedures that require high consideration so as to achieve high caliber, and since experience has uncovered that current general guidance is not constantly adequate to be applied to each area, this paper presents a lot of guidance system for creating and distributing Linked Open Data with regards to ethnic groups in Thailand to outside (TEG-LOD Framework). This framework offers an exhaustive depiction of the undertakings to perform, including a rundown of steps, tools that help in accomplishing the errand, different alternatives for achievement of the assignment, and best practices and proposals. Also, this paper exhibits a pilot model on the generation and distribution of Linked Open Data about ethnic groups in Thai-land, adhering to the available guidance, where the ethnic groups in Thailand are the property of the Princess Maha Chakri Sirindhorn Anthropology Center (SAC) have been made and distributed as Linked Open Data.

scholarly journals Technical note: The Linked Paleo Data framework – a common tongue for paleoclimatology

2016 ◽  
Vol 12 (4) ◽  
pp. 1093-1100 ◽  
Author(s):  
Nicholas P. McKay ◽  
Julien Emile-Geay
Keyword(s):  
Open Data ◽  
Technical Note ◽  
Structured Data ◽  
Research Groups ◽  
Data Standards ◽  
Standard Data ◽  
Data Framework ◽  
Online Databases ◽  
Scientific Endeavor ◽  
Paleoclimate Data

Abstract. Paleoclimatology is a highly collaborative scientific endeavor, increasingly reliant on online databases for data sharing. Yet there is currently no universal way to describe, store and share paleoclimate data: in other words, no standard. Data standards are often regarded by scientists as mere technicalities, though they underlie much scientific and technological innovation, as well as facilitating collaborations between research groups. In this article, we propose a preliminary data standard for paleoclimate data, general enough to accommodate all the archive and measurement types encountered in a large international collaboration (PAGES 2k). We also introduce a vehicle for such structured data (Linked Paleo Data, or LiPD), leveraging recent advances in knowledge representation (Linked Open Data).The LiPD framework enables quick querying and extraction, and we expect that it will facilitate the writing of open-source community codes to access, analyze, model and visualize paleoclimate observations. We welcome community feedback on this standard, and encourage paleoclimatologists to experiment with the format for their own purposes.

scholarly journals An Open Data Framework for the San Francisco Estuary

2020 ◽  
Vol 18 (2) ◽  
Author(s):  
Melinda Baerwald ◽  
Brittany Davis ◽  
Sarah Lesmeister ◽  
Brian Mahardja ◽  
Rachel Pisor ◽  
...  
Keyword(s):  
San Francisco ◽  
Open Data ◽  
San Francisco Estuary ◽  
Data Framework

scholarly journals The Politics of Open Government Data: Understanding Organizational Responses to Pressure for More Transparency

2019 ◽  
Vol 50 (3) ◽  
pp. 260-274 ◽  
Author(s):  
Erna Ruijer ◽  
Francoise Détienne ◽  
Michael Baker ◽  
Jonathan Groff ◽  
Albert J. Meijer
Keyword(s):  
Public Management ◽  
Open Data ◽  
Analytical Framework ◽  
Open Government ◽  
Institutional Pressures ◽  
Strategic Responses ◽  
Government Transparency ◽  
Data Framework ◽  
Open Government Data ◽  
Government Data

This article contributes to the growing body of literature within public management on open government data by taking a political perspective. We argue that open government data are a strategic resource of organizations and therefore organizations are not likely to share it. We develop an analytical framework for studying the politics of open government data, based on theories of strategic responses to institutional processes, government transparency, and open government data. The framework shows that there can be different organizational strategic responses to open data—varying from conformity to active resistance—and that different institutional antecedents influence these responses. The value of the framework is explored in two cases: a province in the Netherlands and a municipality in France. The cases provide insights into why governments might release datasets in certain policy domains but not in others thereby producing “strategically opaque transparency.” The article concludes that the politics of open government data framework helps us understand open data practices in relation to broader institutional pressures that influence government transparency.

scholarly journals Big Data in Smart City: Management Challenges

2021 ◽  
Vol 11 (10) ◽  
pp. 4557
Author(s):  
Mladen Amović ◽  
Miro Govedarica ◽  
Aleksandra Radulović ◽  
Ivana Janković
Keyword(s):  
Big Data ◽  
Smart City ◽  
Urban Areas ◽  
Management System ◽  
Smart Cities ◽  
Open Data ◽  
Apache Spark ◽  
City Management ◽  
Real Time System ◽  
Data Framework

Smart cities use digital technologies such as cloud computing, Internet of Things, or open data in order to overcome limitations of traditional representation and exchange of geospatial data. This concept ensures a significant increase in the use of data to establish new services that contribute to better sustainable development and monitoring of all phenomena that occur in urban areas. The use of the modern geoinformation technologies, such as sensors for collecting different geospatial and related data, requires adequate storage options for further data analysis. In this paper, we suggest the biG dAta sMart cIty maNagEment SyStem (GAMINESS) that is based on the Apache Spark big data framework. The model of the GAMINESS management system is based on the principles of the big data modeling, which differs greatly from standard databases. This approach provides the ability to store and manage huge amounts of structured, semi-structured, and unstructured data in real time. System performance is increasing to a higher level by using the process parallelization explained through the five V principles of the big data paradigm. The existing solutions based on the five V principles are focused only on the data visualization, not the data themselves. Such solutions are often limited by different storage mechanisms and by the ability to perform complex analyses on large amounts of data with expected performance. The GAMINESS management system overcomes these disadvantages by conversion of smart city data to a big data structure without limitations related to data formats or use standards. The suggested model contains two components: a geospatial component and a sensor component that are based on the CityGML and the SensorThings standards. The developed model has the ability to exchange data regardless of the used standard or the data format into proposed Apache Spark data framework schema. The verification of the proposed model is done within the case study for the part of the city of Novi Sad.

An interoperable open data framework for discovering popular tours based on geo-tagged tweets

2017 ◽  
Vol 10 (3/4) ◽  
pp. 246 ◽  
Author(s):  
Gloria Bordogna ◽  
Alfredo Cuzzocrea ◽  
Luca Frigerio ◽  
Giuseppe Psaila ◽  
Maurizio Toccu
Keyword(s):  
Open Data ◽  
Data Framework

scholarly journals Towards an Open Data Framework for Body Sensor Networks Supporting Bluetooth Low Energy

2016 ◽  
Author(s):  
Ninoshka K. Singh ◽  
Darrell O Ricke
Keyword(s):  
Health Status ◽  
New Technologies ◽  
Emotional Health ◽  
Open Data ◽  
Data Access ◽  
Heterogeneous Data ◽  
Low Energy ◽  
Easy Access ◽  
Bluetooth Low Energy ◽  
Data Framework

AbstractMajor companies, healthcare professionals, the military, and other scientists and innovators are now sensing that fitness and health data from wearable biosensors will likely provide new discoveries and insights into physiological, cognitive, and emotional health status of an individual. Having the ability to collect, process, and correlate data simultaneously from a set of heterogonous biosensor sources may be a key factor in informing the development of new technologies for reducing health risks, improving health status, and possibly preventing and predicting disease. The challenge in achieving this is getting easy access to heterogeneous data from a set of disparate sensors in a single, integrated wearable monitoring system. Often times, the data recorded by commercial biosensing devices are contained within each manufacturer’s proprietary platform. Summary data is available for some devices as free downloads or included only in annual premium memberships. Access to raw measurements is generally unavailable, especially from a custom developed application that may include prototype biosensors. In this paper, we explore key ideas on how to leverage the design features of Bluetooth Low Energy to ease the integration of disparate biosensors at the sensor communication layer. This component is intended to fit into a larger, multi-layered, open data framework that can provide additional data management and analytics capabilities for consumers and scientists alike at all the layers of a data access model which is typically employed in a body sensor network system.

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