scholarly journals Geolocation for research purposes: geotags as a data source

Inter ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 65-80
Author(s):  
Anna V. Strelnikova ◽  
Anastasia A. Burova
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Applied Research ◽  
Huge Amount ◽  
Ability To Work ◽  
Online Data ◽  
Research Opportunities ◽  
Amount Of Information ◽  
Or Groups ◽  
Data Source

The worldwide spread of social networks provides new opportunities to collect data for various applied research. The geotagging function, which is present in most social networks, is extremely useful in the case of spatial research. For urban researchers, this is more than just an opportunity to collect a huge amount of information about the behavior of people in space, but also to analyze the space itself. However, this type of data has both significant advantages and limitations. The article includes highlighting the most significant characteristics that urban researchers need to consider working with social networks: their volume (the ability to work with them as big data), non-reactivity (they are reproduced by the users themselves) and additional interpretations (they allow us to understand the meanings of certain elements of space for individual individuals or groups). The authors also illustrate the research opportunities provided by online data in combination with geotagging with various empirical examples.

scholarly journals Assessment of the accessibility of improvement facilities for low-mobility groups of the population based on reviews in social networks

2021 ◽  
Author(s):  
Boris Abdullochonovich Nizomutdinov ◽  
Vladimir Andreevich Kazak
Keyword(s):  
Social Networks ◽  
Urban Environment ◽  
Population Based ◽  
Database Search ◽  
Huge Amount ◽  
Google Maps ◽  
Object A ◽  
Amount Of Information ◽  
The City ◽  
New Facilities

Today, social networks play a huge role in the life of a modern person Moreover, people can not only communicate in this way, but also share tips, professional skills, promote their services, buy goods, and so on. Every day, a huge amount of information appears in social networks. People ask questions about their future trips, share their travel experiences, talk about local attractions, post photos, praise or scold restaurants, museums and any other objects of the urban environment. In this paper, we consider a method for assessing the availability of urban improvement facilities for low-mobility groups of the population based on data from their social networks The study was conducted on the example of the Petrogradsky district of St. Petersburg. 25 objects of urban improvement (parks, gardens, squares) were selected, and reviews from Google Maps were uploaded for each object. A dictionary describing the accessibility of objects was prepared and a database search was conducted to find problems related to accessibility for low-mobility groups of the population that users write about. All reviews were depersonalized and depersonalized. Such data can be used by the city authorities when planning new facilities, or when implementing targeted programs for the development of improvement facilities.

A Research Approach to Detect Unreliable Information in Online Professional Social Networks

2015 ◽  
Vol 6 (4) ◽  
pp. 39-56
Author(s):  
Nan Jing ◽  
Mengdi Li ◽  
Su Zhang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Networking ◽  
Research Approach ◽  
Web Pages ◽  
Huge Amount ◽  
Pagerank Algorithm ◽  
Amount Of Information ◽  
Professional Network ◽  
Cross Site

Professional social network gives companies a platform to post hiring information and locate professional talents. However, the professional network has a great number of users who generate huge amount of information every day, which makes it difficult for the hiring company to distinguish reliability of users' information and evaluate their professional abilities. In this context, this article bases on LinkedIn Mobile as the online professional social network and proposes a research approach to effectively identify unreliable information and evaluate users' abilities. First, the authors look for relevant social network profiles for a cross-site check. Second, on a single professional social networking they site, the authors check the similarity between the user's background and his connections' backgrounds, to detect any possible unreliable information. Third, they propose an algorithm to rank the trustfulness of users' recommendations based on a PageRank algorithm that was traditionally to evaluate the importance of web pages.

scholarly journals Big Data Analytics With Reference To Wide Variety of Domains

2019 ◽  
Vol 8 (10) ◽  
pp. 1100-1104
Keyword(s):  
Health Care ◽  
Social Networks ◽  
Big Data ◽  
Data Analytics ◽  
High Speed ◽  
Big Data Analytics ◽  
Huge Amount ◽  
Government Sector ◽  
Big Data Technologies ◽  
Survey Results

In the current day scenario, a huge amount of data is been generated from various heterogeneous sources like social networks, business apps, government sector, marketing, health care system, sensors, machine log data which is created at such a high speed and other sources. Big Data is chosen as one among the upcoming area of research by several industries. In this paper, the author presents wide collection of literature that has been reviewed and analyzed. This paper emphasizes on Big Data Technologies, Application & Challenges, a comparative study on architectures, methodologies, tools, and survey results proposed by various researchers are presented

scholarly journals SOCIAL NETWORKS, BIG DATA AND TRANSPORT PLANNING

2016 ◽  
Author(s):  
Tomás Ruiz Sánchez ◽  
María del Lidón Mars Aicart ◽  
María Rosa Arroyo López ◽  
Ainhoa Serna Nocedal
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Social Life ◽  
Quality Data ◽  
Future Prospects ◽  
Huge Amount ◽  
Transport Planning ◽  
High Quality Data ◽  
Potential Sources

The characteristics of people who are related or tied to each individual affects her activitytravel behavior. That influence is especially associated to social and recreational activities, which are increasingly important. Collecting high quality data from those social networks is very difficult, because respondents are asked about their general social life, which is most demanding to remember that specific facts. On the other hand, currently there are different potential sources of transport data, which is characterized by the huge amount of information available, the velocity with it is obtained and the variety of format in which is presented. This sort of information is commonly known as Big Data. In this paper we identify potential sources of social network related big data that can be used in Transport Planning. Then, a review of current applications in Transport Planning is presented. Finally, some future prospects of using social network related big data are highlighted.DOI: http://dx.doi.org/10.4995/CIT2016.2016.4251

scholarly journals A Comparison of recommendation algorithms based on use of linked data and cloud

2018 ◽  
Vol 7 (2.6) ◽  
pp. 126
Author(s):  
Ankita Ranjan ◽  
Vinay M
Keyword(s):  
Big Data ◽  
Data Acquisition ◽  
Linked Data ◽  
User Involvement ◽  
Huge Amount ◽  
Amount Of Information ◽  
Recommendation Algorithms ◽  
Cloud Server

Recommendation generation is a critical need in today's time. With the advent of big data and the increasing number of users, generation of most suitable recommendation is essential. There are many issues already associated with recommendations such as data acquisition, scalability, etc.. Moreover, the users today look to get best recommendations at the minimum effort on their side. Thus it becomes difficult to manage such huge amount of information, extract the needed data and present it to the user with least user involvement. In this research, we surveyed some recommendation algorithms and analyze their applications on an open cloud server which uses linked data to generate automated recommendations.

scholarly journals Application Research: Big Data in Food Industry

Foods ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 2203
Author(s):  
Qi Tao ◽  
Hongwei Ding ◽  
Huixia Wang ◽  
Xiaohui Cui
Keyword(s):  
Big Data ◽  
Food Industry ◽  
Health Management ◽  
Application Research ◽  
Huge Amount ◽  
Nutrition And Health ◽  
Broad Application ◽  
Data Source ◽  
Media Data ◽  
Application Prospects

A huge amount of data is being produced in the food industry, but the application of big data—regulatory, food enterprise, and food-related media data—is still in its infancy. Each data source has the potential to develop the food industry, and big data has broad application prospects in areas like social co-governance, exploit of consumption markets, quantitative production, new dishes, take-out services, precise nutrition and health management. However, there are urgent problems in technology, health and sustainable development that need to be solved to enable the application of big data to the food industry.

A Review on Big Data Analytics in Business

2018 ◽  
pp. 210-214
Author(s):  
Manbir Sandhu ◽  
Purnima, Anuradha Saini
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Smart Devices ◽  
Data Streaming ◽  
Huge Amount ◽  
Business Units ◽  
Enormous Amount ◽  
Tools And Techniques ◽  
Heath Care ◽  
Data Analytic

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.

Impact of Big Data over Telecom Industry

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Muhammad Waqar Khan ◽  
Muhammad Asghar Khan ◽  
Muhammad Alam ◽  
Wajahat Ali
Keyword(s):  
Big Data ◽  
Data Science ◽  
Cell Phones ◽  
Smart Phones ◽  
World Population ◽  
Huge Amount ◽  
Scale Down ◽  
Telecom Industry ◽  
Telecom Sector ◽  
Theoretical Computing

<p>During past few years, data is growing exponentially attracting researchers to work a popular term, the Big Data. Big Data is observed in various fields, such as information technology, telecommunication, theoretical computing, mathematics, data mining and data warehousing. Data science is frequently referred with Big Data as it uses methods to scale down the Big Data. Currently<br />more than 3.2 billion of the world population is connected to internet out of which 46% are connected via smart phones. Over 5.5 billion people are using cell phones. As technology is rapidly shifting from ordinary cell phones towards smart phones, therefore proportion of using internet is also growing. There<br />is a forecast that by 2020 around 7 billion people at the globe will be using internet out of which 52% will be using their smart phones to connect. In year 2050 that figure will be touching 95% of world population. Every device connect to internet generates data. As majority of the devices are using smart phones to<br />generate this data by using applications such as Instagram, WhatsApp, Apple, Google, Google+, Twitter, Flickr etc., therefore this huge amount of data is becoming a big threat for telecom sector. This paper is giving a comparison of amount of Big Data generated by telecom industry. Based on the collected data<br />we use forecasting tools to predict the amount of Big Data will be generated in future and also identify threats that telecom industry will be facing from that huge amount of Big Data.</p>

scholarly journals Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts

2021 ◽  
Vol 11 (13) ◽  
pp. 6047
Author(s):  
Soheil Rezaee ◽  
Abolghasem Sadeghi-Niaraki ◽  
Maryam Shakeri ◽  
Soo-Mi Choi
Keyword(s):  
Big Data ◽  
Augmented Reality ◽  
User Study ◽  
Spatial Information ◽  
Ease Of Use ◽  
Tourist Attraction ◽  
Gender And Education ◽  
Time Distance ◽  
Data Source ◽  
The Right

A lack of required data resources is one of the challenges of accepting the Augmented Reality (AR) to provide the right services to the users, whereas the amount of spatial information produced by people is increasing daily. This research aims to design a personalized AR that is based on a tourist system that retrieves the big data according to the users’ demographic contexts in order to enrich the AR data source in tourism. This research is conducted in two main steps. First, the type of the tourist attraction where the users interest is predicted according to the user demographic contexts, which include age, gender, and education level, by using a machine learning method. Second, the correct data for the user are extracted from the big data by considering time, distance, popularity, and the neighborhood of the tourist places, by using the VIKOR and SWAR decision making methods. By about 6%, the results show better performance of the decision tree by predicting the type of tourist attraction, when compared to the SVM method. In addition, the results of the user study of the system show the overall satisfaction of the participants in terms of the ease-of-use, which is about 55%, and in terms of the systems usefulness, about 56%.

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