in the era of information, data which are worthwhile asset of human, organizations and enterprises have become such sophisticated that the conventional approaches and methods are not usable anymore, or not efficient at least. Such complexity which is known as the Big Data problem is the affordable extraction of value from big data sets that we are encountered in many recent applications e.g., e-business, scientific research, monitoring, search engines, social networking, etc.. Big Data complexities are instantiated by three major dimensions, high Volume, high Variety, and high Velocity (a.k.a.3Vs). The first and most essential step in data management (also for Big Data management) is designing and employing a proper data model, as the footstone of the other data management activities such as R& D of DB languages, DBMSs, tools, methods, algorithms, etc.. In this paper, a proper data model for Big Data is designed and proposed in which the properties required for Big Data problem (i.e., to be integrated, complete, scalable, flexible, compatible, and efficient) are considered. As a data model, data representation is designed and implicit integrity constraints are presented for the proposed HNG (Hyper Nested Graph) data model. Experimental evaluation results show that the proposed data model outperforms other currently used data models such as the document-based, graph document-based, and graph- based data models in terms of response time.

A novel smart vaccination method is proposed in this paper to distribute a limited number of vaccines among the people of a large community, such as a country, consisting of smaller communities like cities or provinces. The proposed method is comprised of two phases; A vaccine allocation phase and a targeted vaccination phase. In the first phase, the available vaccines are allocated to the communities based on demographics and the effectiveness of each type of vaccine. In the second phase, each community is modelled as a contact graph, and the vaccines available to the community are administered to the individuals whose vaccination has the greatest impact on breaking the chain of transmission. As a result of utilizing the Node2Vec graph embedding algorithm, the complexity of the proposed method increases linearly with the number of people in the community, as opposed to common centrality based methods, the complexities of which increase with the square or cube of the number of individuals. Furthermore, the proposed method can distribute multiple types of vaccines with different probabilities of effectiveness. The performance of the proposed method is comparable to the common centrality based vaccination methods, while its complexity is lower. The results of the simulation show a 20% decrease in the peak number of infected individuals.

Abstract— Telecommunications companies rely on recommendation systems to deliver personalized services and enhance customer satisfaction. Traditional methods, such as Collaborative Filtering (CF) and Content-Based Filtering (CBF), often fall short in capturing the complex relationships and social influences inherent in large telecom networks. In this paper, we propose a novel Graph Neural Network (GNN)-based recommendation system that integrates customer profiles with graph data representing customer interactions (e.g., calls, messages). The system uses the GraphSAGE architecture to aggregate information from each customer’s network, enabling it to learn from both direct and indirect relationships. By combining customer demographic and usage data with interaction networks, our model provides more accurate and personalized service recommendations. We evaluate the system on a real-world telecom dataset, comparing it with traditional models, including CF, CBF, and Matrix Factorization (MF). The GNN-based system achieves a significant performance boost, with a precision of 0.81 and an F1-score of 0.80, outperforming all baselines. These results highlight the ability of GNNs to capture social and communication patterns, making them highly effective for telecom recommendations. Future work will explore the scalability of the system and its application to real-time data, further enhancing its potential for customer retention and revenue growth.

Abstract— Telecommunications companies rely on recommendation systems to deliver personalized services and enhance customer satisfaction. Traditional methods, such as Collaborative Filtering (CF) and Content-Based Filtering (CBF), often fall short in capturing the complex relationships and social influences inherent in large telecom networks. In this paper, we propose a novel Graph Neural Network (GNN)-based recommendation system that integrates customer profiles with graph data representing customer interactions (e.g., calls, messages). The system uses the GraphSAGE architecture to aggregate information from each customer’s network, enabling it to learn from both direct and indirect relationships. By combining customer demographic and usage data with interaction networks, our model provides more accurate and personalized service recommendations. We evaluate the system on a real-world telecom dataset, comparing it with traditional models, including CF, CBF, and Matrix Factorization (MF). The GNN-based system achieves a significant performance boost, with a precision of 0.81 and an F1-score of 0.80, outperforming all baselines. These results highlight the ability of GNNs to capture social and communication patterns, making them highly effective for telecom recommendations. Future work will explore the scalability of the system and its application to real-time data, further enhancing its potential for customer retention and revenue growth.

A proper and an accurate evaluation of sediment concentration are required for managers and planners for the suitable and effective design of hydraulic structures. The design of most of the soil and water conservation structures is made inaccurately, since the number of studies conducted in the field of sediment temporal distribution is limited. The problem mentioned above is particularly exaggerated in developing countries where more effort is needed in research to achieve appropriate solutions for problems encountered. The main objective of conducting the present research was to develop a model through which the temporal distribution of the watershed sediment i.e. the sediment graph during a storm event can be estimated. The model was developed on the basis of the easily accessible hydrological data of the Amameh watershed in Iran comprising an area of 3712 ha. A water discharge rating curve, precipitation-runoff relationship and sediment rating curves were developed for the watershed study for the completion and refinement of the collected hydrological data. From the Sediment Rating Curve (SRC), it was observed that in most of the cases there was more than one value of sediment discharge for the same value of runoff discharge located at the rising and falling limbs of the hydrograph. Therefore, the development of two separate regression equations for these sets of points was attempted by using regression and confidence area ellipse approaches. The approach based on the hydrological data was then used for the development of a storm-wise temporal distribution prediction model for sediment yield. Based on the results of factorial scoring, it was found that the model developed on the basis of the concept of Unit Sediment Graph and convolution into direct sediment graph using the sediment mobilized could be supposed as an acceptable performed model for the prediction of the sediment graph in the study watershed.