scholarly journals Discovering User’s Trends and Routines from Location Based Social Networks

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1222
Author(s):  
Sergio Salomón ◽  
Rafael Duque ◽  
José Montaña
Keyword(s):  
Dynamic Bayesian Network ◽  
User Preferences ◽  
Bayesian Network Model ◽  
Real World Data ◽  
Location Data ◽  
User Context ◽  
Location Based Social Network ◽  
Location Based Social Networks ◽  
Source Of Information

Location data is a powerful source of information to discover user’s trends and routines. A suitable identification of the user context can be exploited to provide automatically services adapted to the user preferences. In this paper, we define a Dynamic Bayesian Network model and propose a method that processes location annotated data in order to train the model. Finally, our model enables us to predict future location contexts from the user patterns. A case study evaluates the proposal using real-world data of a location-based social network.

scholarly journals Evaluation of Tourist Activities and Destination Attraction Capacity Using Geotags

2019 ◽  
Vol 18 (32) ◽  
pp. 34-43
Author(s):  
Vladimir Sergeevich Tikunov ◽  
Vitalii Semenovich Belozerov ◽  
Stanislav Olegovich Antipov
Keyword(s):  
Mineral Waters ◽  
Alternative Source ◽  
The Public ◽  
Tourist Destinations ◽  
Location Data ◽  
Age Related ◽  
Geographic Origins ◽  
Gender And Age ◽  
Source Of Information

Tourist location data accessible through social networks is a new source of information which is opening up great opportunities for studying tourism activities. We used tourist geotags from the VKontakte social network to evaluate the attractiveness of tourist destinations around the Caucasian Mineral Waters. Based on the data received from the public profiles of tourists publishing geotags, we analyzed their gender- and age-related structure, seasonal preferences, and geographic origins. The resort of Pyatigorsk was used as a case study to develop a classification of recreational areas for local people and tourists. Women prevailed among those displaying their photos – 64.7%. The two major seasons for attaching photos were spring and summer – 64.8 % of the photos. The specific feature of the Caucasian Mineral Waters viewed as a tourist destination implied the prevalence of intraregional geotags over interregional ones. The major interregional tourist flow came from the cities of Moscow, St. Petersburg, Rostov-on-Don, and Krasnodar. The geotags on photos, therefore, may be used as an alternative source of information when evaluating the tourist activity and attractiveness of these destinations.

scholarly journals A Unified View of Causal and Non-causal Feature Selection

2021 ◽  
Vol 15 (4) ◽  
pp. 1-46
Author(s):  
Kui Yu ◽  
Lin Liu ◽  
Jiuyong Li
Keyword(s):  
Feature Selection ◽  
Bayesian Network ◽  
Synthetic Data ◽  
Selection Methods ◽  
Bayesian Network Model ◽  
Real World Data ◽  
Feature Sets ◽  
Unified View ◽  
Optimal Feature ◽  
Different Levels

In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data.

scholarly journals Exploring Factors Influencing Scenarios Evolution of Waste NIMBY Crisis: Analysis of Typical Cases in China

2021 ◽  
Vol 18 (4) ◽  
pp. 2006
Author(s):  
Ling He ◽  
Qing Yang ◽  
Xingxing Liu ◽  
Lingmei Fu ◽  
Jinmei Wang
Keyword(s):  
Network Theory ◽  
Development Process ◽  
Dynamic Bayesian Network ◽  
Impact Factors ◽  
Social Stability ◽  
Ground Theory ◽  
Evolution Path ◽  
Dynamic Scenario ◽  
The Impact

As the impact factors of the waste Not-In-My-Back Yard (NIMBY) crisis are complex, and the scenario evolution path of it is diverse. Once the crisis is not handled properly, it will bring adverse effects on the construction of waste NIMBY facilities, economic development and social stability. Consequently, based on ground theory, this paper takes the waste NIMBY crisis in China from 2006 to 2019 as typical cases, through coding analysis, scenario evolution factors of waste NIMBY crisis are established. Furtherly, three key scenarios were obtained, namely, external situation (E), situation state (S), emergency management (M), what is more, scenario evolution law of waste NIMBY crisis is revealed. Then, the dynamic Bayesian network theory is used to construct the dynamic scenario evolution network of waste NIMBY crisis. Finally, based on the above models, Xiantao waste NIMBY crisis is taken as a case study, and the dynamic process of scenario evolution network is visually displayed by using Netica. The simulation results show that the scenario evolution network of Xiantao waste NIMBY crisis is basically consistent with the actual incident development process, which confirms the effectiveness and feasibility of the model.

scholarly journals Mining Open Government Data for Business Intelligence Using Data Visualization: A Two-Industry Case Study

2021 ◽  
Vol 16 (4) ◽  
pp. 1042-1065
Author(s):  
Anne Gottfried ◽  
Caroline Hartmann ◽  
Donald Yates
Keyword(s):  
Data Visualization ◽  
Business Intelligence ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Open Government ◽  
Open Government Data ◽  
Market Opportunities ◽  
Government Data ◽  
Source Of Information

The business intelligence (BI) market has grown at a tremendous rate in the past decade due to technological advancements, big data and the availability of open source content. Despite this growth, the use of open government data (OGD) as a source of information is very limited among the private sector due to a lack of knowledge as to its benefits. Scant evidence on the use of OGD by private organizations suggests that it can lead to the creation of innovative ideas as well as assist in making better informed decisions. Given the benefits but lack of use of OGD to generate business intelligence, we extend research in this area by exploring how OGD can be used to generate business intelligence for the identification of market opportunities and strategy formulation; an area of research that is still in its infancy. Using a two-industry case study approach (footwear and lumber), we use latent Dirichlet allocation (LDA) topic modeling to extract emerging topics in these two industries from OGD, and a data visualization tool (pyLDAVis) to visualize the topics in order to interpret and transform the data into business intelligence. Additionally, we perform an environmental scanning of the environment for the two industries to validate the usability of the information obtained. The results provide evidence that OGD can be a valuable source of information for generating business intelligence and demonstrate how topic modeling and visualization tools can assist organizations in extracting and analyzing information for the identification of market opportunities.

scholarly journals Neural networks for increased accuracy of allergenic pollen monitoring

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marcel Polling ◽  
Chen Li ◽  
Lu Cao ◽  
Fons Verbeek ◽  
Letty A. de Weger ◽  
...  
Keyword(s):  
Neural Networks ◽  
Airborne Pollen ◽  
Pollen Grains ◽  
Allergenic Pollen ◽  
Herbarium Specimens ◽  
Pollen Monitoring ◽  
Pollen Concentrations ◽  
Source Of Information ◽  
Urticaceae Pollen

AbstractMonitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this process has become feasible. A challenge that persists, however, is that many pollen grains cannot be distinguished beyond the genus or family level using a microscope. Here, we assess the use of Convolutional Neural Networks (CNNs) to increase taxonomic accuracy for airborne pollen. As a case study we use the nettle family (Urticaceae), which contains two main genera (Urtica and Parietaria) common in European landscapes which pollen cannot be separated by trained specialists. While pollen from Urtica species has very low allergenic relevance, pollen from several species of Parietaria is severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these without the often used acetolysis step to train the CNN model. The models show that unacetolyzed Urticaceae pollen grains can be distinguished with > 98% accuracy. We then apply our model on before unseen Urticaceae pollen collected from aerobiological samples and show that the genera can be confidently distinguished, despite the more challenging input images that are often overlain by debris. Our method can also be applied to other pollen families in the future and will thus help to make allergenic pollen monitoring more specific.

scholarly journals Development of an Innovative Real-World Evidence Registry for the Herpes Simplex Virus: Case Study

10.2196/16933 ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. e16933 ◽  
Author(s):  
Michelle Helena van Velthoven ◽  
Ching Lam ◽  
Caroline de Cock ◽  
Terese Stenfors ◽  
Hassan Chaudhury ◽  
...  
Keyword(s):  
Herpes Simplex Virus ◽  
Herpes Simplex ◽  
Real World ◽  
Physical Symptoms ◽  
Use Cases ◽  
Real World Data ◽  
World Data ◽  
Real World Evidence ◽  
Simplex Virus

Background Infection with the herpes simplex virus (HSV) is common but not well understood. Furthermore, there remains a social stigma surrounding HSV that can have psychosocial implications for those infected. Despite many patients infected with HSV experiencing mild-to-severe physical symptoms, only one subeffective treatment is available. A registry collecting real-world data reported by individuals potentially infected with HSV could help patients to better understand and manage their condition. Objective This study aimed to report on the development of a registry to collect real-world data reported by people who might be infected with HSV. Methods A case study design was selected as it provides a systematic and in-depth approach to investigating the planning phase of the registry. The case study followed seven stages: plan, design, prepare, collect, analyze, create, and share. We carried out semistructured interviews with experts, which were thematically analyzed and used to build use cases for the proposed registry. These use cases will be used to generate detailed models of how a real-world evidence registry might be perceived and used by different users. Results The following key themes were identified in the interviews: (1) stigma and anonymity, (2) selection bias, (3) understanding treatment and outcome gaps, (4) lifestyle factors, (5) individualized versus population-level data, and (6) severe complications of HSV. We developed use cases for different types of users of the registry, including individuals with HSV, members of the public, researchers, and clinicians. Conclusions This case study revealed key considerations and insights for the development of an appropriate registry to collect real-world data reported by people who might be infected with HSV. Further development and testing of the registry with different users is required. The registry must also be evaluated for the feasibility and effectiveness of collecting data to support symptom management. This registry has the potential to contribute to the development of vaccines and treatments and provide insights into the impact of HSV on other conditions.

scholarly journals Secured user Participation in Offline Event Marketing using Location Based Social Network and Participant Interest

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3104-3109
Keyword(s):  
Social Media ◽  
User Participation ◽  
Customer Base ◽  
Event Marketing ◽  
Online Social Media ◽  
Customer Interaction ◽  
The People ◽  
Location Based Social Network ◽  
Social Gathering ◽  
Location Based Social Networks

Online event marketing connects the offline market with the online world. This sends invitation to the people by using the online social media indicating about the offline business which helps for social gathering. This paper gives an idea to the marketers to improve effectiveness by carefully choosing the invitation of sponsored offline events through the location-based social networks. This framework also produces a platform where one user can interact with another through a secured chat channel, thus adding the customer base with customer-customer interaction

scholarly journals Why We Go Where We Go: Profiling User Decisions on Choosing POIs

2020 ◽  
Author(s):  
Renjun Hu ◽  
Xinjiang Lu ◽  
Chuanren Liu ◽  
Yanyan Li ◽  
Hao Liu ◽  
...  
Keyword(s):  
Key Factors ◽  
Real World Data ◽  
Decision Factors ◽  
Point Of Interest ◽  
Learning Factor ◽  
Poi Recommendation ◽  
Key Factor ◽  
Selection Of ◽  
Decision Structures

While Point-of-Interest (POI) recommendation has been a popular topic of study for some time, little progress has been made for understanding why and how people make their decisions for the selection of POIs. To this end, in this paper, we propose a user decision profiling framework, named PROUD, which can identify the key factors in people's decisions on choosing POIs. Specifically, we treat each user decision as a set of factors and provide a method for learning factor embeddings. A unique perspective of our approach is to identify key factors, while preserving decision structures seamlessly, via a novel scalar projection maximization objective. Exactly solving the objective is non-trivial due to a sparsity constraint. To address this, our PROUD adopts a self projection attention and an L2 regularized sparse activation to directly estimate the likelihood of each factor to be a key factor. Finally, extensive experiments on real-world data validate the advantage of PROUD in preserving user decision structures. Also, our case study indicates that the identified key decision factors can help us to provide more interpretable recommendations and analyses.

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