Recent advances in data mining and machine learning techniques are focused on exploiting location data. These advances, combined with the increased availability of location-acquisition technology, have encouraged social networking services to offer to their users different ways to share their location information. These social networks, called location-based social networks (LBSNs), have attracted millions of users and the attention of the research community. One fundamental task in the LBSN context is the friendship prediction due to its role in different applications such as recommendation systems. In the literature exists a variety of friendship prediction methods for LBSNs, but most of them give more importance to the location information of users and disregard the strength of relationships existing between these users. The contributions of this article are threefold, we: 1) carried out a comprehensive survey of methods for friendship prediction in LBSNs and proposed a taxonomy to organize the existing methods; 2) put forward a proposal of five new methods addressing gaps identified in our survey while striving to find a balance between optimizing computational resources and improving the predictive power; and 3) used a comprehensive evaluation to quantify the prediction abilities of ten current methods and our five proposals and selected the top-5 friendship prediction methods for LBSNs. We thus present a general panorama of friendship prediction task in the LBSN domain with balanced depth so as to facilitate research and real-world application design regarding this important issue.

/pdf/the-role-of-location-and-social-strength-for-friendship-15r7ihhelo.pdf

The role of location and social strength for friendship prediction in location-based social networks

An essential goal of recommendation systems is to provide users with accurate and personalized recommendations that meet their preferences. With the rapid growth of IoT-connected sensors, the availability of contextual information has increased, and this has necessitated the fast development of Context-Aware Recommendation Systems (CARS). Context-Aware recommenders are different from traditional recommenders because of their ability to predict the ratings of target users/items by exploiting the knowledge of contextual information. Context-aware recommenders define the context as any information that characterizes the situations of items and users at a particular interaction. They are essential for some contexts where prediction can be more precise in generating specific personalized recommendations. This paper provides a comprehensive review of context-based recommendation systems in IoT environments, namely IoT-CARS, and sheds light on their requirements, characteristics, and applications. We characterize context-aware recommenders in terms of the different IoT contexts and how these contexts are modeled. We also highlight the used metrics to evaluate the performance of various context-based recommenders.

/pdf/context-aware-recommendation-systems-in-the-iot-environment-1001n8xijd.pdf

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

Agenda 2030 of Sustainable Development Goals (SDGs) 9 and 11 recognizes tourism as one of the central industries to global development to tackle global challenges. With the transformation of information and communication technologies (ICT), e-tourism has evolved globally to establish commercial relationships using the Internet for offering tourism-related products, including giving personalised suggestions. The contextual suggestion has emerged as a modified recommendation system that is integrated with information-retrieval techniques within large databases to provide tourists with a list of suggestions based on contexts, such as location, time of day, or day of the week (weekdays or weekends). This study surveyed literature in the field of contextual suggestion and recommendation systems with a focus on e-tourism. The concerns linked with approaches used in contextual suggestion and recommendation systems are highlighted in this systematic review, while motivations, recommendations, and practical implications in e-tourism are also discussed in this paper. A query search using the keywords “contextual suggestion system”, “recommendation system”, and “tourism” identified 143 relevant articles published from 2012 to 2020. Four major repositories are considered for searching, namely, (i) Science Direct, (ii) Scopus, (iii) IEEE, and (iv) Web of Science. This review was carried out under the protocols of four phases, namely, (i) query searching in major article repositories, (ii) removal of duplicates, (iii) scan of title and abstract, and (iv) complete reading of articles. To identify the gaps in current research, a taxonomy analysis was exemplified into categories and subcategories. The main categories were highlighted as (i) review articles, (ii) model/framework, and (iii) applications. Critical analysis was carried out on the basis of the available literature on the limitations of approaches used in contextual suggestion and recommendation systems. In conclusion, the approaches used are mainly based on content-based filtering, collaborative filtering, preference-based product ranking, and language modelling. The evaluation measures for the contextual suggestion system include precision, normalized discounted cumulative, and mean reciprocal rank, while test collections comprise Internet resources. Given that the tourism industry contributed to the environmental and social-economic development, contextual suggestion and recommendation systems have presented themselves to be relevant in integrating and achieving SDG 9 and SDG 11 in many ways such as web-based e-services by the government sector and smart gadgets based on reliable and real-time data and information for city planners as well as law enforcement personnel in a sustainable city.

https://www.mdpi.com/2071-1050/13/15/8141/pdf

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

In present circumstances, the large enterprises and hospitals are dependent on data, these data will manage through the database server. There is a challenging task to manage the clinical data, which takes more time to retrieve the records of the patient. Therefore, we use Data mining techniques to resolve this type of problem and proposed the Medical Information System that easily manage the daily patient records. These databases are useful for conducting daily clinical data and routine work of the hospitals. There is a huge competition in the market which conduct to start the data mining and warehousing concepts to analyze the clinical data to derive new techniques and discover better ways in carrying out the routine work of the hospitals. The mining procedure will be applied to the consistent databases, which will be converted into the warehouses. In this paper, we propose a methodology for the construction of a data warehouse for a medical information system using data mining techniques. The data warehouse is based on the informational database which is built by the transformation of the operational database. A data analyst can analyze the data and take decisions. They propose the suggestions and predict the diseases with the help of data. We also propose the clinical data warehouse model which is based upon data mining techniques. The overall system is implemented to serve as a data repository for the medical information system.

Design of A Data Warehouse for Medical Information System Using Data Mining Techniques

The article describes the main fields of big data tools implementation in socially economic researches, demonstrates the process of a query construction for data retrieval from social networks on the example of Twitter using Twitter API and Python Libraries, further information processing via the selected parameters and illustrates the possibility of data visualisation using standard charts.

Big Data Tools in Processing Information from Open Sources

Healthcare applications, such as patient record, patient – doctor mapping service, various disease symptoms etc. The major challenges is processing and integrating the real time data. Following-up with analyzing the data would involve moving the data in Data Lake to a Cloud to known about the status of the patient and for tracking purpose. In this research, we recommend a cloud based information integration framework using Data Lake in Cloud. This work extends the information integration architecture by designing with Data Lake in Cloud, using mathematical model (Petri Net) to verify architecture. This approach has the ability to scale up and down in real time data, and leading to efficient way of using data resources in the Cloud.

/pdf/verification-of-cloud-based-information-integration-1w767ayy3n.pdf

Verification of Cloud Based Information Integration Architecture using Colored Petri Nets

Recommender systems play an important role in our day-to-day life. A recommender system automatically suggests an item to a user that he/she might be interested in. Small-scale datasets are used to provide recommendations based on location, but in real time, the volume of data is large. We have selected Foursquare dataset to study the need for big data in recommendation systems for location-based social network (LBSN). A few quality parameters like parallel processing and multimodal interface have been selected to study the need for big data in recommender systems. This paper provides a study and analysis of quality parameters of recommendation systems for LBSN with big data.

https://cyberleninka.org/article/n/1334078.pdf

A study and analysis of recommendation systems for location-based social network (LBSN) with big data

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Verification of Cloud Based Information Integration Architecture using Colored Petri Nets

A study and analysis of recommendation systems for location-based social network (LBSN) with big data