New Problems and Approaches Related to Large Databases in Astronomy

Author(s):  
Fionn Murtagh ◽  
Alex Aussem
Keyword(s):  
2008 ◽  
Author(s):  
Bradley C. Stolbach ◽  
Frank Putnam ◽  
Melissa Perry ◽  
Karen Putnam ◽  
William Harris ◽  
...  

2012 ◽  
Vol 1 (1) ◽  
pp. 51-56
Author(s):  
Katarzyna Pukowiec

Abstract The activities in name of tourist development in Wodzislaw poviat are the reason to evaluate the tourist land development. The evaluation was prepared on the basis of selected indexes characterizing the level of tourist infrastructure development. It considered: the number of lodgings per km2, the number of restaurants per km2, the amount of additional attractions per km2 and the density of tourist tracks. This database was analyzed by the use of GIS tools. Using GIS software allowed working with large databases and provided the possibility to create a graphic representation of the results. The level of tourist land development is diversified and depends on it function. The cities with the best developed tourist infrastructure are Wodzislaw Slaski, Radlin, Pszow, Rydultowy and town in Odra Valley: Olza, Bukow and Nieboczowy. Pszow, Gorzyce and Godow commons have the biggest density of tourist tracks.


1997 ◽  
Vol 24 (1-2) ◽  
pp. 3
Author(s):  
LUIZ ERNESTO RENUNCIO ◽  
CARLOS LOCH

The daily increase of inhabitants in our cities, when associated with the commercial and industrial development, leads to larger water consumption demand. Despite the larger demand the resource may maintain its quality. The solutions to these difficult problem frequently require the capacity to store manage and analyze large databases, spatially distributed. This work presents the methodology adopted to plan and locate the best site for a water supply reservoir, with the aggregation of a GIS structured database, regarding a microbasin as the planning unit. This was held in the municipality of Cocal do Sul, Brazil. The result was a previous location for the water supply reservoir, considering also other uses for the water that should be stored.


2021 ◽  
Vol 21 (2) ◽  
pp. 1-31
Author(s):  
Bjarne Pfitzner ◽  
Nico Steckhan ◽  
Bert Arnrich

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.


2021 ◽  
Vol 12 ◽  
pp. 204209862110128
Author(s):  
Hanan Khalil ◽  
Dimi Hoppe ◽  
Nabil Ameen

Background: Retrospective analyses of large databases of treated patients can provide useful links to the presence of drug misuse or rare and infrequent adverse effects, such as agranulocytosis, diabetic ketoacidosis or neuroleptic malignant syndrome. The aim of this study is to describe the adverse effects to antipsychotics reported in the Australian Database of Adverse Event Notifications (DAEN). Methods: Data were collected from the DAEN – a spontaneous reporting database. The database, which covered the period from January 2004 to December 2017, was obtained from the Therapeutic Goods Administration (TGA) website ( www.TGA.gov ). The drugs selected for this investigation are the following: aripiprazole, clozapine, olanzapine, paliperidone, risperidone, ziprasidone, quetiapine, haloperidol and pimozide. All data were analysed descriptively. Comparison of reporting and management of adverse events between adults (older than 20 years) and children (5–19 years) was undertaken using chi squared test, where p < 0.05 is significant. Results: A total of 7122 adverse events associated with the antipsychotics aripiprazole, clozapine, haloperidol, olanzapine, paliperidone, pimozide, quetiapine and risperidone were reported to the TGA between January 2004 and December 2017. On average, there were 2.6 adverse events reported for each case. The most common adverse event reported for antipsychotics was neuroleptic malignant syndrome. There were no significant differences in the number of co-medications, formulations, indications, therapeutic dose, hospital admission and overdose among the antipsychotics between paediatric and adult populations. However, there were significant differences between causality, death and the management of adverse events between adult and paediatric populations (5–19 years) ( p < 0.05, chi squared test). Conclusion: The antipsychotic drug associated with the highest adverse events in adults was clozapine, followed by olanzapine. The most common adverse event in adults, and reported with a number of antipsychotic drugs, was neuroleptic malignant syndrome. In children, the highest numbers of adverse events reported in the database were associated with risperidone, clozapine and olanzapine. Plain language summary Adverse events reported of antipsychotics Background: Retrospective analyses of large databases of treated patients can provide useful clues to the presence of drug misuse or rare and infrequent adverse effects associated with antipsychotics. The drugs selected for this investigation are the following: aripiprazole, clozapine, olanzapine, paliperidone, risperidone, ziprasidone, quetiapine, haloperidol and pimozide. Methods: All data were analysed descriptively and investigated for any associations between the variables collected. Comparison of reporting and management of adverse events between adults (older than 20 years) and children (5–19 years) was undertaken using chi squared test, where p < 0.05 is significant. Results: The antipsychotic drug associated with the highest adverse events was clozapine, followed by olanzapine. In children, the highest numbers of adverse events reported in the database were associated with risperidone, clozapine and olanzapine. The most common adverse event in adults, and reported with a number of antipsychotic drugs, was neuroleptic malignant syndrome. Conclusion: There were significant differences between causality, death and the management of adverse events between adult and paediatric populations (5–19 years).Keywords: Antipsychotics, adverse effects, adverse events, safety


2021 ◽  
Vol 11 (2) ◽  
pp. 500
Author(s):  
Fabrizio Pilo ◽  
Giuditta Pisano ◽  
Simona Ruggeri ◽  
Matteo Troncia

The energy transition for decarbonization requires consumers’ and producers’ active participation to give the power system the necessary flexibility to manage intermittency and non-programmability of renewable energy sources. The accurate knowledge of the energy demand of every single customer is crucial for accurately assessing their potential as flexibility providers. This topic gained terrific input from the widespread deployment of smart meters and the continuous development of data analytics and artificial intelligence. The paper proposes a new technique based on advanced data analytics to analyze the data registered by smart meters to associate to each customer a typical load profile (LP). Different LPs are assigned to low voltage (LV) customers belonging to nominal homogeneous category for overcoming the inaccuracy due to non-existent coincident peaks, arising by the common use of a unique LP per category. The proposed methodology, starting from two large databases, constituted by tens of thousands of customers of different categories, clusters their consumption profiles to define new representative LPs, without a priori preferring a specific clustering technique but using that one that provides better results. The paper also proposes a method for associating the proper LP to new or not monitored customers, considering only few features easily available for the distribution systems operator (DSO).


2002 ◽  
Vol 21 (1) ◽  
pp. 1-8
Author(s):  
Binshan Lin ◽  
Victoria S. Stasinskaya

Online recruiting is becoming one of the major trends in Human Resource Management. Managers are capable of finding quickly and efficiently qualified candidates to fill variety of professional positions within United States and overseas. Varieties of websites were created online to store resumes for the employer's search in the form of database warehouses and datamarts. Datamarts target specific segments of the employment opportunities. Managers run queries to search and analyze data abstracted from these large databases. Major issues for managers in using online recruitment present accuracy, verifiability, and accountability of the data selected. An obstacle for potential employees using online employment services is the privacy of the data submitted by them from current employers and other websites collecting their personal data without consent for marketing purposes. Another issue in online employment databases remains inefficiencies in the ways the data is be retrieved, stored and analyzed. The lack of personal touch during online employment limits communicational flow between potential employees and the employer, leading to the frustrations of the job candidates and missed opportunities on the behalf of the employers. A follow-up service from the site can serve as a communicational link in the process.


2013 ◽  
Vol 3 (4) ◽  
pp. 120-140 ◽  
Author(s):  
Carson K.S. Leung ◽  
Christopher L. Carmichael ◽  
Patrick Johnstone ◽  
David Sonny Hung-Cheung Yuen

In numerous real-life applications, large databases can be easily generated. Implicitly embedded in these databases is previously unknown and potentially useful knowledge such as frequently occurring sets of items, merchandise, or events. Different algorithms have been proposed for managing and retrieving useful information from these databases. Various algorithms have also been proposed for mining these databases to find frequent sets, which are usually presented in a lengthy textual list. As “a picture is worth a thousand words”, the use of visual representations can enhance user understanding of the inherent relationships among the mined frequent sets. Many of the existing visualizers were not designed to visualize these mined frequent sets. In this journal article, an interactive visual analytic system is proposed for providing visual analytic solutions to the frequent set mining problem. The system enables the management, visualization, and advanced analysis of the original transaction databases as well as the frequent sets mined from these databases.


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