New Information in Trending Topics of Tweets by Labelled Clusters

2015 ◽  
Vol 14 (03) ◽  
pp. 1550019
Author(s):  
Amina Madani ◽  
Omar Boussaid ◽  
Djamel Eddine Zegour
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Exponential Growth ◽  
Research Topic ◽  
Important Research ◽  
New Approach ◽  
New Information ◽  
Important Research Topic ◽  
Trending Topics ◽  
The Web

Twitter is a popular micro-blogging service, and one of the main means of spreading ideas and information throughout the web. In this system, participants post short status messages called tweets that are often available publicly. Recently, the exponential growth of tweets has started to draw the attention of researchers from various disciplines. Numerous research approaches in the data mining field have examined Twitter. How to automatically extract useful information from tweets has therefore become an important research topic. The aim of this paper is to bring up what's up which is a new approach of tweets mining. It is a more general approach that discovers many different trending topics from tweets in real-time. Trending topics have generated big interest not only for the users of Twitter but also for information seekers. Our trending topics are detected for a specific geographic town and compared with the top trending topics shown on Twitter. They are presented by labelled clusters that constitute an accurate description of each trending topic. Each cluster is labelled by an emerging trending topic and is composed of keywords that represent the properties of the trending topic.

Mining Indirect Temporal Sequential Patterns in Large Transaction Databases

2013 ◽  
Vol 385-386 ◽  
pp. 1362-1365
Author(s):  
Wei Min Ouyang ◽  
Qin Hua Huang
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Research Topic ◽  
Sequential Pattern ◽  
The Other ◽  
Sequential Patterns ◽  
Important Research ◽  
Data Set ◽  
Important Research Topic ◽  
The One

Sequential pattern is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining sequential patterns focus on the frequent sequences, which do not consider the infrequent sequences and lifespan of each sequence. On the one hand, some infrequent patterns can provide very useful insight view into the data set, on the other hand, without taking lifespan of each sequence into account, not only some discovered patterns may be invalid, but also some useful patterns may not be discovered. So, we extend the sequential patterns to the indirect temporal sequential patterns, and put forward an algorithm to discover indirect temporal sequential patterns in this paper.

Empirical Comparative Study of Boosting and its Relatives

2013 ◽  
Vol 312 ◽  
pp. 667-672
Author(s):  
Fang Jun Wu
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Transfer Learning ◽  
Research Topic ◽  
Performance Difference ◽  
Important Research ◽  
Knowledge And Skills ◽  
Statistical Framework ◽  
Important Research Topic ◽  
F Measure

Transfer learning is an important research topic in machine learning and data mining that focuses on utilizing knowledge and skills learned in previous tasks to a novel but related task. This paper contributes to comparison between boosting for transfer learning and boosting. The results, in terms of the accuracy, weighted F-Measure, G-Mean, weighted GMPR, weighted precision and weighted AUC, are rigorously tested using the statistical framework proposed by Janez Demsar. Results show that the performance difference between TrAdaBoost and AdaBoost is less significant.

Conformal Optimization Algorithm for Undevelopable Surfaces and its Application

2014 ◽  
Vol 602-605 ◽  
pp. 3570-3574
Author(s):  
Zhen Hua Luo ◽  
Fen Jiang
Keyword(s):  
Approximation Algorithms ◽  
Optimization Algorithm ◽  
Manufacturing Process ◽  
Research Topic ◽  
Important Research ◽  
Computer Aided Geometric Design ◽  
Industrial Manufacturing ◽  
Important Research Topic ◽  
Computer Aided ◽  
Planar Materials

In the industrial manufacturing process, most kinds of surfaces are processed by planar materials, but undevelopable surfaces are difficult develop to the plane. The approximation algorithms to develop a undevelopable surface is an important research topic in Computer Aided Geometric Design (CAGD). In this paper, we propose a new approximation algorithms based optimization algorithm. We guarantee the deformation vector make the minimum changes during the developing process. In the paper, some numerical example are given and the can illustrate the our method is effective.

scholarly journals The happy victimizer phenomenon: Not found here

2014 ◽  
Vol 46 (1) ◽  
pp. 145-161
Author(s):  
Ana Jevtic ◽  
Jovan Miric
Keyword(s):  
Emotional Development ◽  
Positive Emotions ◽  
Negative Emotions ◽  
Age Groups ◽  
Research Topic ◽  
Important Research ◽  
Important Research Topic ◽  
Significant Difference ◽  
Moral Explanations

Children?s attribution of emotions to a moral transgressor is an important research topic in the psychology of moral and emotional development. This is especially because of the so-called Happy Victimizer Phenomenon (HVP) where younger children attribute positive emotions to a moral transgressor described in a story. In the two studies that we have conducted (children aged 5, 7 and 9, 20 of each age; 10 of each age in the second study) we have tested the possible influence of the fear of sanctions and the type of transgression (stealing and inflicting body injuries) on the attribution of emotions. Children were presented with stories that described transgressions and they were asked to answer how the transgressor felt. The fear of sanctions did not make a significant difference in attribution but the type of transgression did - more negative emotions were attributed for inflicting body injuries than for stealing. Positive emotions were explained with situational-instrumental explanations in 84% of cases while negative emotions were explained with moral explanations in 63,5%. Girls attributed more positive emotions (61%) than boys (39%). However, our main finding was that, for the aforementioned age groups, we did not find the HVP effect although it has regularly been registered in foreign studies. This finding denies the generalizability of the phenomenon and points to the significance of disciplining styles and, even more so, culture for children?s attribution of emotions to moral transgressors.

scholarly journals Facial Landmark Annotation and Fuzzy Model to Detect Players’ Focus

2021 ◽  
Author(s):  
Alisson Steffens Henrique ◽  
Esteban Walter Gonzalez Clua ◽  
Rodrigo Lyra ◽  
Anita Maria da Rocha Fernandes ◽  
Rudimar Luis Scaranto Dazzi
Keyword(s):  
Fuzzy Model ◽  
Research Topic ◽  
Important Research ◽  
Facial Landmark ◽  
Important Research Topic ◽  
Focus Detection ◽  
Data Log ◽  
Game Analytics

Game Analytics is an important research topic in digitalentertainment. Data log is usually the key to understand players’behavior in a game. However, alpha and beta builds may need aspecial attention to player focus and immersion. In this paper, wepropose t he us e of player’s focus detection, through theclassification of pictures. Results show that pictures can be usedas a new source of data for Game Analytics, feeding developerswith a better understanding of players enjoyment while in testingphases .

scholarly journals Demand Supply Oriented Taxi Suggestion System for Vehicular Social Networks with Fuel Charging Mechanism

2019 ◽  
pp. 38-44
Author(s):  
Selvi C ◽  
Keerthana D
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Large Scale ◽  
Recommendation System ◽  
Important Research ◽  
Taxi Drivers ◽  
Charging Station ◽  
Gps Trajectories ◽  
Charging Mechanism ◽  
Supply Level

Data mining depends on large-scale taxi traces is an important research concepts. A vital direction for analyzing taxi GPS dataset is to suggest cruising areas for taxi drivers. The project first investigates the real-time demand-supply level for taxis, and then makes an adaptive tradeoff between the utilities of drivers and passengers for different hotspots. This project constructs a recommendation system by jointly considering the profits of both drivers and passengers. At last, the qualified candidates are suggested to drivers based on analysis. The project also provides a real-time charging station recommendation system for EV taxis via large-scale GPS data mining. By combining each EV taxi’s historical recharging actions and real-time GPS trajectories, the present operational state of each taxi is predicted. Based on this information, for an EV taxi requesting a recommendation, recommend a charging station that leads to the minimal total time before its recharging starts.

Big Data and Privacy State of the Art

2019 ◽  
pp. 104-158
Author(s):  
Amine Rahmani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Exponential Growth ◽  
Scientific Community ◽  
Ethical Issues ◽  
Massive Data ◽  
Medium Term ◽  
Access To Data ◽  
Fast Access ◽  
The Web

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.

Big Data and Privacy State of the Art

2021 ◽  
pp. 947-991
Author(s):  
Amine Rahmani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Exponential Growth ◽  
Scientific Community ◽  
Ethical Issues ◽  
Massive Data ◽  
Medium Term ◽  
Access To Data ◽  
Fast Access ◽  
The Web

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.

Trends and Transformations in European Agricultural Economy, Rural Communities and Food Sustainability in Context of New Common Agricultural Policy (CAP) Reforms

2016 ◽  
pp. 1-24
Author(s):  
Andrei Jean-Vasile ◽  
Alexandra Alecu
Keyword(s):  
Rural Communities ◽  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Research Topic ◽  
Economic Sector ◽  
Important Research ◽  
European Level ◽  
Agricultural Economy ◽  
Security Assurance ◽  
Important Research Topic

Agriculture continues to be quite a debate for the last two and a half decades at least at the European level and for Romania Common Agricultural Policy (CAP) reforms has a big impact in developing the convergence to the European agricultural model. Agriculture becomes nowadays a multirole economic sector, with major implications on rural community's sustainability and on food security assurance. In this context, the transformations in European agricultural economy, rural communities and food sustainability in context of Common Agricultural Policy (CAP) reforms represent an important research topic in the context of EU-28 policy diversification from the larger context of Romanian approach.

Deriving Information System Security and Privacy From Value Cocreation Theory

2019 ◽  
Vol 10 (4) ◽  
pp. 1-25
Author(s):  
Christophe Feltus
Keyword(s):  
Information System ◽  
Value Chain ◽  
System Architecture ◽  
Enterprise Architecture ◽  
Research Topic ◽  
Security And Privacy ◽  
Important Research ◽  
Important Research Topic ◽  
Value Cocreation ◽  
The Relationship

Traditionally, the relationship between the company and its providers have for objective to generate value at the company side in exchange of money. This relationship is largely investigated through the vector of value chain. In this article, security and privacy cocreation (SPCC) is investigated as a specialization of value cocreation. Although it is an important research topic, and despite a plethora of research aiming at depicting the fundamental of SPCC, few contributions have been appeared until now in the area of a language to support SPCC design and deployment. However, such a language is necessary to describe elements of the information system, as well as their underlying dependencies. As a result, this article proposes extending an existing enterprise architecture language to support the process of decision-making and to allow understanding and analysis of the impacts associated to a change of the system architecture as a whole.

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