scholarly journals Updated distribution of the invasive Megachile sculpturalis (Hymenoptera: Megachilidae) in Italy and its first record on a Mediterranean island

2020 ◽  
Vol 8 ◽  
Author(s):  
Enrico Ruzzier ◽  
Mattia Menchetti ◽  
Laura Bortolotti ◽  
Marco Selis ◽  
Elisa Monterastelli ◽  
...  
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
First Record ◽  
Solitary Bee ◽  
Mediterranean Island ◽  
Elba Island

Megachile sculpturalis (Smith, 1853) (Hymenoptera: Megachilidae) is an invasive solitary bee that is rapidly spreading all over Europe. The present study aims to update the distribution of this species in Italy. The research led to the collection of 177 records, obtained through bibliographic research and data-mining from websites, blogs and social networks. We here present the first record of M. sculpturalis on a Mediterranean island and discuss its possible effect on the native ecosystem. Given the particular discovery of M. sculpturalis on Elba Island (Tuscany), we suggest possible monitoring, containment and possible eradication measures of the species.

scholarly journals First Record of the Alien Species Procambarus virginalis Lyko, 2017 in Fresh Waters of Sardinia and Insight into Its Genetic Variability

Life ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 606
Author(s):  
Daria Sanna ◽  
Ilenia Azzena ◽  
Fabio Scarpa ◽  
Piero Cossu ◽  
Angela Pira ◽  
...  
Keyword(s):  
Genetic Variability ◽  
Alien Species ◽  
Procambarus Clarkii ◽  
Mitochondrial Gene ◽  
First Record ◽  
Indigenous Species ◽  
Fresh Waters ◽  
Mediterranean Island ◽  
Marbled Crayfish ◽  
Non Indigenous Species

In the fresh waters of Sardinia (Italy), the non-indigenous crayfish species Procambarus clarkii has been reported from 2005, but, starting from 2019, there have been several reports of a new non-indigenous crayfish in southern and central areas of this Mediterranean island, and its morphology suggests that this species may be the marbled crayfish Procambarus virginalis. Forty-seven individuals of this putative species were analyzed, using the mitochondrial gene Cytochrome c Oxidase subunit I as molecular marker to identify this crayfish and investigate the level of genetic variability within the recently established population. Phylogenetic and phylogeographic analyses were carried out on a dataset including sequences from the Sardinian individuals and from all congenerics available in GenBank. Results showed that the new Sardinian crayfish belong to the species P. virginalis. All the sequences belonging to P. virginalis from European countries are identical, with only few exceptions found among Sardinian individuals. In conclusion, this paper highlights the occurrence of a new further alien species in the Sardinian fresh waters, which are already characterized by the high presence of non-indigenous species.

A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks

2013 ◽  
Vol 9 (1) ◽  
pp. 36-53
Author(s):  
Evis Trandafili ◽  
Marenglen Biba
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Networks ◽  
Large Scale ◽  
Viral Marketing ◽  
Social Network Mining ◽  
Business Applications ◽  
Mining Community ◽  
Recent Developments ◽  
Analysis Of Social Networks

Social networks have an outstanding marketing value and developing data mining methods for viral marketing is a hot topic in the research community. However, most social networks remain impossible to be fully analyzed and understood due to prohibiting sizes and the incapability of traditional machine learning and data mining approaches to deal with the new dimension in the learning process related to the large-scale environment where the data are produced. On one hand, the birth and evolution of such networks has posed outstanding challenges for the learning and mining community, and on the other has opened the possibility for very powerful business applications. However, little understanding exists regarding these business applications and the potential of social network mining to boost marketing. This paper presents a review of the most important state-of-the-art approaches in the machine learning and data mining community regarding analysis of social networks and their business applications. The authors review the problems related to social networks and describe the recent developments in the area discussing important achievements in the analysis of social networks and outlining future work. The focus of the review in not only on the technical aspects of the learning and mining approaches applied to social networks but also on the business potentials of such methods.

Knowledge Discovery from Online Communities

Data Mining ◽  
2013 ◽  
pp. 1230-1252
Author(s):  
Luca Cagliero ◽  
Alessandro Fiori
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
User Behavior ◽  
Network Models ◽  
Social Model ◽  
User Generated Content ◽  
Growth Trend ◽  
User Behaviors ◽  
Application Systems

This chapter presents an overview of social network features such as user behavior, social models, and user-generated content to highlight the most notable research trends and application systems built over such appealing models and online media data. It first describes the most popular social networks by analyzing the growth trend, the user behaviors, the evolution of social groups and models, and the most relevant types of data continuously generated and updated by the users. Next, the most recent and valuable applications of data mining techniques to social network models and user-generated content are presented. Discussed works address both social model extractions tailored to semantic knowledge inference and automatic understanding of the user-generated content. Finally, prospects of data mining research on social networks are provided as well.

Emotional and Subjective Volunteered Geographical Information

2017 ◽  
pp. 97-112 ◽  
Author(s):  
Jiri Panek
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Spatial Data ◽  
Large Scale ◽  
Geographical Information ◽  
Emotional Information ◽  
Spatial Features ◽  
Spatial Locations ◽  
The City

Crowdsroucing of emotional information can take many forms, from social networks data mining to large-scale surveys. The author presents the case-study of emotional mapping in Ostrava´s district Ostrava-Poruba, Czech Republic. Together with the local administration, the author crowdsourced the emotional perceptions of the location from almost 400 citizens, who created 4,051 spatial features. Additional to the spatial data there were 1,244 comments and suggestions for improvements in the district. Furthermore, the author is looking for patterns and hot-spots within the city and if there are any relevant linkages between certain emotions and spatial locations within the city.

Detecting Pharmaceutical Spam in Microblog Messages

2011 ◽  
pp. 101-115
Author(s):  
Kathy J. Liszka ◽  
Chien-Chung Chan ◽  
Chandra Shekar
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Cell Phones ◽  
Pharmaceutical Products ◽  
Spam Detection ◽  
Data Mining Techniques ◽  
Flow Through ◽  
Special Category

Microblogs are one of a growing group of social network tools. Twitter is, at present, one of the most popular forums for microblogging in online social networks, and the fastest growing. Fifty million messages flow through servers, computers, and cell phones on a wide variety of topics exchanged daily. With this considerable volume, Twitter is a natural and obvious target for spreading spam via the messages, called tweets. The challenge is how to determine if a tweet is a spam or not, and more specifically a special category advertising pharmaceutical products. The authors look at the essential characteristics of spam tweets and what makes microblogging spam unique from email or other types of spam. They review methods and tools currently available to identify general spam tweets. Finally, this work introduces a new methodology of applying text mining and data mining techniques to generate classifiers that can be used for pharmaceutical spam detection in the context of microblogging.

Emergent Data Mining Tools for Social Network Analysis

2015 ◽  
pp. 1539-1556
Author(s):  
Dhiraj Murthy ◽  
Alexander Gross ◽  
Alex Takata
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Online Social Networks ◽  
Data Extraction ◽  
Future Directions ◽  
Effective Development ◽  
Mining Tools

This chapter identifies a number of the most common data mining toolkits and evaluates their utility in the extraction of data from heterogeneous online social networks. It introduces not only the complexities of scraping data from the diverse forms of data manifested in these sources, but also critically evaluates currently available tools. This analysis is followed by a presentation and discussion on the development of a hybrid system, which builds upon the work of the open-source Web-Harvest framework, for the collection of information from online social networks. This tool, VoyeurServer, attempts to address the weaknesses of tools identified in earlier sections, as well as prototype the implementation of key functionalities thought to be missing from commonly available data extraction toolkits. The authors conclude the chapter with a case study and subsequent evaluation of the VoyeurServer system itself. This evaluation presents future directions, remaining challenges, and additional extensions thought to be important to the effective development of data mining tools for the study of online social networks.

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