Performance Evaluation of Non-Relational Data on Big Data Environments

Big data management is a real challenge for traditional systems. The experimental evaluation is performed on measurement of performance of five different databases with an open non-relational dataset. It was structured and tested separately in each store, giving some advantages and limitations to them from a practical point of view. The results are drawn based on the throughput per number of users executed respectively. It was loaded and executed more than a million of records in each and every database. Following its semi-persistent model, Redis performed better than other databases

A need for an integrative security model for semantic stream reasoning systems

State-of-the-art security frameworks have been extensively addressing security issues for web resources, agents and services in the Semantic Web. The provision of Stream Reasoning as a new area spanning Semantic Web and Data Stream Management Systems has eventually opened up new challenges. Namely, their decentralized nature, the metadata descriptions, the number of users, agents, and services, make securing Stream Reasoning systems difficult to handle. Thus, there is an inherent need of developing new security models which will handle security and automate security mechanisms to a more autonomous system that supports complex and dynamic relationships between data, clients and service providers. We plan to validate our approach on a typical application of stream data, on Wireless Sensor Networks (WSNs). In particular, WSNs for water quality monitoring will serve as a case study. The paper describes the initial findings and research plan for building a consistent security model for stream reasoning systems

A need for an integrative security model for semantic stream reasoning systems

State-of-the-art security frameworks have been extensively addressing security issues for web resources, agents and services in the Semantic Web. The provision of Stream Reasoning as a new area spanning Semantic Web and Data Stream Management Systems has eventually opened up new challenges. Namely, their decentralized nature, the metadata descriptions, the number of users, agents, and services, make securing Stream Reasoning systems difficult to handle. Thus, there is an inherent need of developing new security models which will handle security and automate security mechanisms to a more autonomous system that supports complex and dynamic relationships between data, clients and service providers. We plan to validate our approach on a typical application of stream data, on Wireless Sensor Networks (WSNs). In particular, WSNs for water quality monitoring will serve as a case study. The paper describes the initial findings and research plan for building a consistent security model for stream reasoning systems

SMIA: Improving Kosovo\u27s education management system

Successful management of today’s education systems requires effective policy-making and system monitoring through data and information. To this end, countries around the world have invested significant resources into collecting, processing, and managing more and better data through education management information systems (EMIS). SMIA is a modern EMIS, which offers easier management of educational process in Kosovo. It facilitates decision-making, resource planning, strategy building, monitoring and evaluation of educational system through a rich set of analytical and statistical tools. For over six years of activity, the system has efficiently tracked and thus helped the development of the educational process for every institution including kindergarten, preprimary, primary and middle school. This paper will describe the system tools and features, which has enabled real-time monitoring of pupils and teachers performance. This information has provided a timely intervention from the high management to make balanced dissemination of didactic resources

Multithreaded Approach for Using Blockchain as an Automated Versioning Tool

Blockchain is a new and growing technology with a bright future ahead. It can be implemented in many different ways and different industries like banking, cryptocurrencies, health information systems etc. Its powerfulness in security and systematic tracking of different items makes it one of its kind and attractive to work with. This technology is also a suitable environment for multithreaded programming to be adopted in, while using blocks and items to be tracked by single threads. This paper presents an innovative approach of using blockchain as a versioning control tool for personal or commercial usage

LMS Solution: Evidence of Google Classroom Usage in Higher Education

Background: Learning Management Systems (LMS) represent one of the main technology to support learning in HE institutions. However, every educational institution differs in its experience with the usage of these systems. South East European University’s LMS experience is longer than a decade. From last year SEE – University is adopting Google Classroom (GC) as an LMS solution. Objectives: Identifying factors which encourage LMS activities, with special emphasis on SEEU, might be of crucial importance for Higher Education academic leaders as well as software developers who design tools related to fostering LMS. Methods/Approach: This paper introduces new approach of investigating the usage of LMS, i.e. identifying the determinants of increasing usage of LMS activities, by conducting empirical analysis for the case of SEEU. We apply appropriate estimation technique such as OLS methodology. Results: Using SEEU Usage Google Classroom Report & Analysis Data for spring semester (2016–2017) and winter semester (2017–2018) - SUGCR dataset 2017, we argue that (i) LMS activities are affected by demographic characteristics and (ii) the students’ LMS usage is affected by level and resources of instructors’ LMS usage. Conclusions: The empirical results show positive relationship between student and instructors’ LMS usage

A Google Classroom-Based Learning Management System: Empirical Evidence from SEEU

The use of e-learning in a higher education institution is identified by the implementation of Learning Management Systems (LMS). South East European University’s LMS experience is longer than a decade. From last year SEE – University is adopting Google Classroom (GC). However, despite adoption of these systems, there are considerable challenges facing the usage of the systems. Hence, a tool has been developed to track the activity of the teachers in the system and to analyze the factors that maximize its usage. Moreover, a module for course and users’ management was also implemented. The purpose of this paper is to introduce a new approach of investigating the usage of GC, i.e. identifying the determinants of undertaking GC activities, by conducting empirical analysis for the case of SEEU. Using SEEU Usage Google Classroom Report &amp; Analysis Data for 2016–2017 (SUGCR dataset 2017), we argue that (i) GC activities are affected by demographic characteristics and (ii) level, number of courses, and department affect the usage of GC. We apply appropriate estimation technique such as mlogit methodology. Identifying factors which encourage GC activities, with special emphasis on SEEU, might be of crucial importance for Higher Education academic leaders as well as software developers who design tools related to fostering GC. This work is licensed under a&nbsp;Creative Commons Attribution-NonCommercial 4.0 International License.</p

Design and development of the recommendation mechanism with performance high and scalable using Kafka, Spring Boot and algorithms collaborative filtering

Recommender systems have become an indispensable part of human daily life due to the large amount of information where in this case information filtering must be implemented that limits the capabilities of the recommender system and improving the user experience by helping users to get what they themselves want. The goal of this implementation is the recommendation mechanis that is extremely efficient and scalable, this document includes details about the integration of Kafka,Spring Boot and collaborative filtering algorithms in the recommendation mechanism. This integration shows the latest model of the recommender mechanism that aims to provide recommendations as personalized and accurate as possible. With this combination of high techonologies it creates a modern mechanism designed to provide personalized recommendations to users effectively.The current state of technology has brought significant changes in the way users interact with the system, leading to the need for systems more advanced recommendations. These systems use interactivity and user preferences to create personalized suggestions, improving the user experience across different platforms. To meet this requirement, the integration of Kafka,Spring Boot and collaborative filtering algorithms is the most compact, fast and powerful solution to meet the needs of the advanced recommendation mechanism.Kafka, serving as a distributed streaming platform, is the focal point of the application architecture facilitating real-time data collection and distribution.Kafka’s ability to scale and asynchronize the system effectively makes it suitable for managing large amounts of data and user interactions. Spring Boot on the other hand is a powerful framework for developing applications and complements Kafka by enabling the rapid creation of microservices with the modular structure and automation also increases the flexibility and ease of maintenance of the recommendation system. Within the application, with the implementation of collaborative filtering algorithms, interactions are examined to discover connections and similarities between users and items with the use of techniques such as matrix factorization or nearest neighbor methods, accurate recommendations are provided. By combining these technologies, the recommendation mechanism with high performance and scalability is achieved

Design and implementation of digital learning resources using cloud services – ORA case

Kosova’s primary and secondary school poor results in local and international level show that there is a need of including new tools and methodologies in order to transform education using technology. Providing digital learning materials have proved to be very useful. Main purpose of this research is to identify technological possibilities in providing such materials through cloud services for the grades I-XII. Its main research question is: “How to design a platform that fulfills the needs of the material also being competitive to other stuff on Internet, such as video games and social networks?” Moreover, we address the issues on deciding about the best techniques to be used on providing such materials and the underlying hardware and software requirements. Cloud services are large in number and advantageous when comparing it to traditional ones e.g. scalability, processing power, availability, to name a few. The Microservices architecture cloud platform described in this paper ensures student’s attractiveness and competitiveness at the same time by including video games for learning purposes. As per the case for the learning material of the curriculum for grad I-XII of Kosovo education system, it was estimated that the required storage space is around 32 TB. Besides the current results, for future work is planned to include applications that stimulate experiments in chemistry, biology, geography, and Virtual Labs (through Virtual Reality technology)