scholarly journals WikiOlapBase: A collaborative tool for open data processing and integration

2020 ◽  
Author(s):  
Pedro Bernardo ◽  
Ismael Silva ◽  
Glívia Barbosa ◽  
Flávio Coutinho ◽  
Evandrinho Barros
Keyword(s):  
Open Data ◽  
Relevant Information ◽  
Knowledge Generation ◽  
Knowledge Dissemination ◽  
Technical Knowledge ◽  
Great Volume ◽  
Collaborative Tool ◽  
Technological Advances ◽  
Different Sources ◽  
Cross Reference

The technological advances have made data sharing and knowledge generation possible in several areas. In order to support information extraction and knowledge generation, several datasets have been made publicly available, giving rise to the concept of open data. However, while such data are available, the processing, visualization, and analysis of them by society, in general, can be considered difficult tasks. Data are available to a great volume, in different files and formats, making it difficult to cross-reference and analyze them to obtain relevant information without the support of appropriate tools. Inspired by this scenario, this paper presents WikiOlapBase, a collaborative tool capable of processing, integrating and making feasible the analysis of open data from different sources, even by people without technical knowledge. WikiOlapBase contributes to the expansion of open data analysis, since it favors a greater information sharing and knowledge dissemination.

scholarly journals How Does Inter-Organizational Cooperation Impact Organizations’ Scientific Knowledge Generation? Evidence from the Biomass Energy Field

2020 ◽  
Vol 13 (1) ◽  
pp. 191
Author(s):  
Liu Li ◽  
Chaoying Tang
Keyword(s):  
Scientific Knowledge ◽  
Biomass Energy ◽  
Knowledge Generation ◽  
Degree Centrality ◽  
Technical Knowledge ◽  
Technical Cooperation ◽  
Scientific Cooperation ◽  
Structural Holes ◽  
Cooperation Network ◽  
Positive Effect

Previous studies have demonstrated that accessing external knowledge is important for organizations’ knowledge generation. The main purpose of this study is to investigate how the diversity and amount of organizations’ external scientific knowledge influence their scientific knowledge generation. We also consider the moderating effect of the redundant industrial scientific knowledge and the amount of technical knowledge from external technical cooperators. The social network analysis method is used to establish both ego- and industrial-scientific cooperation network, and ego-technical cooperation network in order to analyze the external scientific knowledge and technical knowledge. The empirical analysis is based on patent and article data of 106 organizations in the biomass energy industry (including firms, universities and research institutes), and the results show that organizations’ structural holes and degree centrality of scientific cooperation network have positive effects on their scientific knowledge generation. In addition, organizations’ degree centrality of technical cooperation network positively moderates the relationship between their degree centrality of scientific cooperation network and scientific knowledge generation. Furthermore, density of industrial scientific cooperation network decreases the positive effect of organizations’ structural holes on their scientific knowledge generation, while it strengthens the positive effect of degree centrality of scientific cooperation network on their scientific knowledge generation. Academic contributions and practical suggestions are discussed.

Development of a flow-based planning support system based on open data for the City of Atlanta

2017 ◽  
Vol 46 (2) ◽  
pp. 207-224
Author(s):  
Ge Zhang ◽  
Wenwen Zhang ◽  
Subhrajit Guhathakurta ◽  
Nisha Botchwey
Keyword(s):  
Support System ◽  
Open Data ◽  
Community Planning ◽  
Relevant Information ◽  
Easy Access ◽  
Data Movement ◽  
Planning Support ◽  
Planning Support System ◽  
Analytical Tools ◽  
The City

Open data have come of age with many cities, states, and other jurisdictions joining the open data movement by offering relevant information about their communities for free and easy access to the public. Despite the growing volume of open data, their use has been limited in planning scholarship and practice. The bottleneck is often the format in which the data are available and the organization of such data, which may be difficult to incorporate in existing analytical tools. The overall goal of this research is to develop an open data-based community planning support system that can collect related open data, analyze the data for specific objectives, and visualize the results to improve usability. To accomplish this goal, this study undertakes three research tasks. First, it describes the current state of open data analysis efforts in the community planning field. Second, it examines the challenges analysts experience when using open data in planning analysis. Third, it develops a new flow-based planning support system for examining neighborhood quality of life and health for the City of Atlanta as a prototype, which addresses many of these open data challenges.

Answer First or Google First? Using the Internet in ways that Enhance, not Impair, One’s Subsequent Retention of Needed Information

2020 ◽  
pp. 147572572096159
Author(s):  
Saskia Giebl ◽  
Stefany Mena ◽  
Benjamin C. Storm ◽  
Elizabeth Ligon Bjork ◽  
Robert A. Bjork
Keyword(s):  
Relevant Information ◽  
Choice Test ◽  
Cognitive Resources ◽  
Related Information ◽  
Needed Information ◽  
Technological Advances ◽  
New Information ◽  
Subsequent Recall ◽  
Programming Knowledge ◽  
Problem Solving Task

Technological advances have given us tools—Google, in particular—that can both augment and free up our cognitive resources. Research has demonstrated, however, that some cognitive costs may arise from our reliance on such external memories. We examined whether pretesting—asking participants to solve a problem before consulting Google for needed information—can enhance participants’ subsequent recall for the searched-for content as well as for relevant information previously studied. Two groups of participants, one with no programming knowledge and one with some programming knowledge, learned several fundamental programming concepts in the context of a problem-solving task. On a later multiple-choice test with transfer questions, participants who attempted the task before consulting Google for help out-performed participants who were allowed to search Google right away. The benefit of attempting to solve the problem before googling appeared larger with some degree of programming experience, consistent with the notion that some prior knowledge can help learners integrate new information in ways that benefit its learning as well as that of previously studied related information.

scholarly journals Dermatopathology and Social Media

2017 ◽  
Vol 142 (2) ◽  
pp. 184-190 ◽  
Author(s):  
Erin Carlquist ◽  
Nathan E. Lee ◽  
Sara C. Shalin ◽  
Michael Goodman ◽  
Jerad M. Gardner
Keyword(s):  
Social Media ◽  
Medical Profession ◽  
Relevant Information ◽  
Medical Professionals ◽  
Patient Confidentiality ◽  
Collaborative Tool ◽  
The World ◽  
The Future ◽  
Use Of Social Media

Context.— Use of social media in the medical profession is an increasingly prevalent and sometimes controversial practice. Many doctors believe social media is the future and embrace it as an educational and collaborative tool. Others maintain reservations concerning issues such as patient confidentiality, and legal and ethical risks. Objective.— To explore the utility of social media as an educational and collaborative tool in dermatopathology. Design.— We constructed 2 identical surveys containing questions pertaining to the responders' demographics and opinions regarding the use of social media for dermatopathology. The surveys were available on Twitter and Facebook for a period of 10 days. Results.— The survey was completed by 131 medical professionals from 29 different countries: the majority (81%, 106 of 131) were 25 to 45 years of age. Most replied that they access Facebook or Twitter several times a day (68%, 89 of 131) for both professional and social purposes (77%, 101 of 131). The majority agreed that social media provides useful and relevant information, but stated limitations they would like addressed. Conclusions.— Social media is a powerful tool with the ability to instantaneously share dermatopathology with medical professionals across the world. This study reveals the opinions and characteristics of the population of medical professionals currently using social media for education and collaboration in dermatopathology.

scholarly journals SENSING SLOW MOBILITY AND INTERESTING LOCATIONS FOR LOMBARDY REGION (ITALY): A CASE STUDY USING POINTWISE GEOLOCATED OPEN DATA

2016 ◽  
Vol XLI-B2 ◽  
pp. 603-607 ◽  
Author(s):  
M. A. Brovelli ◽  
D. Oxoli ◽  
M. A. Zurbarán
Keyword(s):  
Regional Scale ◽  
Open Data ◽  
Temporal Analysis ◽  
Lombardy Region ◽  
Horse Riding ◽  
Share Data ◽  
Community Platforms ◽  
Gps Tracks ◽  
Different Sources

During the past years Web 2.0 technologies have caused the emergence of platforms where users can share data related to their activities which in some cases are then publicly released with open licenses. Popular categories for this include community platforms where users can upload GPS tracks collected during slow travel activities (e.g. hiking, biking and horse riding) and platforms where users share their geolocated photos. However, due to the high heterogeneity of the information available on the Web, the sole use of these user-generated contents makes it an ambitious challenge to understand slow mobility flows as well as to detect the most visited locations in a region. Exploiting the available data on community sharing websites allows to collect near real-time open data streams and enables rigorous spatial-temporal analysis. This work presents an approach for collecting, unifying and analysing pointwise geolocated open data available from different sources with the aim of identifying the main locations and destinations of slow mobility activities. For this purpose, we collected pointwise open data from the Wikiloc platform, Twitter, Flickr and Foursquare. The analysis was confined to the data uploaded in Lombardy Region (Northern Italy) – corresponding to millions of pointwise data. Collected data was processed through the use of Free and Open Source Software (FOSS) in order to organize them into a suitable database. This allowed to run statistical analyses on data distribution in both time and space by enabling the detection of users’ slow mobility preferences as well as places of interest at a regional scale.

scholarly journals Improve Building Façades in Open Lidar Data Using Ground Imagery

2018 ◽  
Author(s):  
Shenman Zhang ◽  
Pengjie Tao
Keyword(s):  
Point Cloud ◽  
Open Data ◽  
Point Clouds ◽  
Lidar Data ◽  
Free Access ◽  
Registration Accuracy ◽  
Incomplete Coverage ◽  
Meta Information ◽  
Coherent Point Drift ◽  
Different Sources

Recent advances in open data initiatives allow us to free access to a vast amount of open LiDAR data in many cities. However, most of these open LiDAR data over cities are acquired by airborne scanning, where the points on façades are sparse or even completely missing due to the viewpoint and object occlusions in the urban environment. Integrating other sources of data, such as ground images, to complete the missing parts is an effective and practical solution. This paper presents an approach for improving open LiDAR data coverage on building façades by using point cloud generated from ground images. A coarse-to-fine strategy is proposed to fuse these two different sources of data. Firstly, the façade point cloud generated from terrestrial images is initially geolocated by matching the SFM camera positions to their GPS meta-information. Next, an improved Coherent Point Drift algorithm with normal consistency is proposed to accurately align building façades to open LiDAR data. The significance of the work resides in the use of 2D overlapping points on the outline of buildings instead of limited 3D overlap between the two point clouds and the achievement to a reliable and precise registration under possible incomplete coverage and ambiguous correspondence. Experiments show that the proposed approach can significantly improve the façades details of buildings in open LiDAR data and improving registration accuracy from up to 10 meters to less than half a meter compared to classic registration methods.

scholarly journals The Challenges of Open Data Movement for Education: What is the Say of Young Employees and Students in Croatia?

2021 ◽  
pp. 374-383
Author(s):  
Branka Mraović
Keyword(s):  
Young People ◽  
Open Data ◽  
Technical Knowledge ◽  
Data Movement ◽  
Data Policy ◽  
Data Portal ◽  
Open Data Movement ◽  
The Impact ◽  
The City ◽  
Shed Light

This paper aims to shed light on how students and young employees in Croatia assess their education for open data and what is their opinion on the compliance of the central Open Data Portal with the needs of young people as well as how they evaluate open data policy related to the young people in Croatia. This research highlights the lack of technical knowledge as a serious obstacle to the productive use of open data. As many as 56% of respondents from companies that have undergone digital transformation believe that they do not have enough knowledge to participate in open data projects, and the same scepticism is expressed by 59.6% of non-technical respondents and 45.7% of students. The data presented in this paper is part of a broader empirical research on the impact of digitalization on the transformation of the Croatian economy, carried out by the author in late 2018 on a sample of 51 young employees from 10 companies in the city of Zagreb and 70 students from 16 technical and non-technical Faculties of Zagreb University.

scholarly journals Cut & recombine: reuse of robot action components based on simple language instructions

2019 ◽  
Vol 38 (10-11) ◽  
pp. 1179-1207
Author(s):  
Minija Tamosiunaite ◽  
Mohamad Javad Aein ◽  
Jan Matthias Braun ◽  
Tomas Kulvicius ◽  
Irena Markievicz ◽  
...  
Keyword(s):  
Information Transfer ◽  
Relevant Information ◽  
Human Beings ◽  
Shape Information ◽  
Simple Language ◽  
Small Set ◽  
Data Tables ◽  
Viable Approach ◽  
Different Sources ◽  
Robotic Applications

Human beings can generalize from one action to similar ones. Robots cannot do this and progress concerning information transfer between robotic actions is slow. We have designed a system that performs action generalization for manipulation actions in different scenarios. It relies on an action representation for which we perform code-snippet replacement, combining information from different actions to form new ones. The system interprets human instructions via a parser using simplified language. It uses action and object names to index action data tables (ADTs), where execution-relevant information is stored. We have created an ADT database from three different sources (KUKA LWR, UR5, and simulation) and show how a new ADT is generated by cutting and recombining data from existing ADTs. To achieve this, a small set of action templates is used. After parsing a new instruction, index-based searching finds similar ADTs in the database. Then the action template of the new action is matched against the information in the similar ADTs. Code snippets are extracted and ranked according to matching quality. The new ADT is created by concatenating code snippets from best matches. For execution, only coordinate transforms are needed to account for the poses of the objects in the new scene. The system was evaluated, without additional error correction, using 45 unknown objects in 81 new action executions, with 80% success. We then extended the method including more detailed shape information, which further reduced errors. This demonstrates that cut & recombine is a viable approach for action generalization in service robotic applications.

scholarly journals Application of a Convolutional Neural Network for image classification for the analysis of collisions in High Energy Physics

2019 ◽  
Vol 214 ◽  
pp. 06017 ◽  
Author(s):  
Celia Fernández Madrazo ◽  
Ignacio Heredia ◽  
Lara Lloret ◽  
Jesús Marco de Lucas
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
High Energy Physics ◽  
Open Data ◽  
Feedforward Neural Networks ◽  
Relevant Information ◽  
High Energy ◽  
Particle Collisions ◽  
Learning Framework ◽  
Energy Physics

The application of deep learning techniques using convolutional neural networks for the classification of particle collisions in High Energy Physics is explored. An intuitive approach to transform physical variables, like momenta of particles and jets, into a single image that captures the relevant information, is proposed. The idea is tested using a well-known deep learning framework on a simulation dataset, including leptonic ttbar events and the corresponding background at 7 TeV from the CMS experiment at LHC, available as Open Data. This initial test shows competitive results when compared to more classical approaches, like those using feedforward neural networks.

scholarly journals Modelling variability in cardiac electrophysiology: a moment-matching approach

2017 ◽  
Vol 14 (133) ◽  
pp. 20170238 ◽  
Author(s):  
Eliott Tixier ◽  
Damiano Lombardi ◽  
Blanca Rodriguez ◽  
Jean-Frédéric Gerbeau
Keyword(s):  
Probability Density Function ◽  
Cardiac Electrophysiology ◽  
Maximum Entropy Principle ◽  
Relevant Information ◽  
Ionic Channel ◽  
Moment Matching ◽  
Entropy Principle ◽  
Different Sources ◽  
Probability Density Function Pdf ◽  
Insight Into

The variability observed in action potential (AP) cardiomyocyte measurements is the consequence of many different sources of randomness. Often ignored, this variability may be studied to gain insight into the cell ionic properties. In this paper, we focus on the study of ionic channel conductances and describe a methodology to estimate their probability density function (PDF) from AP recordings. The method relies on the matching of observable statistical moments and on the maximum entropy principle. We present four case studies using synthetic and sets of experimental AP measurements from human and canine cardiomyocytes. In each case, the proposed methodology is applied to infer the PDF of key conductances from the exhibited variability. The estimated PDFs are discussed and, when possible, compared to the true distributions. We conclude that it is possible to extract relevant information from the variability in AP measurements and discuss the limitations and possible implications of the proposed approach.

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