Sentiment Analysis (SA) effectively examines big data, such as customer reviews, market research, social media posts, online discussions, and customer feedback evaluation. Arabic Language is a complex and rich language. The main reason for the need to enhance Arabic resources is the existence of numerous dialects alongside the standard version (MSA). This study investigates the impact of stemming and lemmatization methods on Arabic sentiment analysis (ASA) using Machine Learning techniques, specifically the LightGBM classifier. It also employs metaheuristic feature selection algorithms like particle swarm optimization, dragonfly optimization, grey wolf optimization, harris hawks optimizer, and a genetic optimization algorithm to identify the most relevant features to improve LightGBM's model performance. It also employs the Optuna hyperparameter optimization framework to determine the optimal set of hyperparameter values to enhance LightGBM model performance. It also underscores the importance of preprocessing strategies in ASA and highlights the effectiveness of metaheuristic approaches and Optuna hyperparameter optimization in improving LightGBM model performance in ASA. It also applies different stemming and lemmatization methods, Metaheuristic Feature Selection algorithms, and the Optuna hyperparameter optimization on eleven datasets with different Arabic dialects. The findings indicate that metaheuristics feature selection with the LightGBM classifier, using suitable stemming and lemmatization or combining them, enhances LightGBM's accuracy by between 0 and 8%. Still, Optuna hyperparameter optimization with the LightGBM classifier, using suitable stemming and lemmatization or combining them, depending on data characteristics, improves LightGBM's accuracy by between 2 and 11%. It achieves superior results than metaheuristics feature selection in more than 90% of cases. This study is of significant importance in the field of ASA, providing valuable insights and directions for future research.

Securing digital data from unauthorized access throughout its entire lifecycle has been always a critical concern. A robust data security system should protect the information assets of any organization against cybercriminal activities. The Twofish algorithm is one of the well-known symmetric key block cipher cryptographic algorithms and has been known for its rapid convergence. But when it comes to security, it is not the pre-ferred cryptographic algorithm to use compared to other algorithms that have shown better security. Many applications and social platforms have adopted other symmetric key block cipher cryptographic algorithms such as the Advanced Encryption Standard (AES) algorithm to construct their main security wall. In this paper, a new modification for the original Twofish algorithm is proposed to strengthen its security and to take advantage of its fast convergence. The new algorithm has been named Split-n-Swap (SnS). Performance analysis of the new modification algorithm has been performed using different measurement metrics. The experimental results show that the complexity of the SnS algorithm exceeds that of the original Twofish algorithm while maintaining reasonable values for encryption and decryption times as well as memory utilization. A detailed analysis is given with the strength and limitation aspects of the proposed algorithm.

In many fields, complicated issues can now be solved with the help of Artificial Intelligence (AI) and Machine Learning (ML). One of the more modern Metaheuristic (MH) algorithms used to tackle numerous issues in various fields is the Beluga Whale Optimization (BWO) method. However, BWO has a lack of diversity, which could lead to being trapped in local optimaand premature convergence. This study presents two stages for enhancing the fundamental BWO algorithm. The initial stage of BWO’s Opposition-Based Learning (OBL), also known as OBWO, helps to expedite the search process and enhance the learning methodology to choose a better generation of candidate solutions for the fundamental BWO. The second step, referred to as OBWOD, combines the Dynamic Candidate Solution (DCS) and OBWO based on the k-Nearest Neighbor (kNN) classifier to boost variety and improve the consistency of the selected solution by giving potential candidates a chance to solve the given problem with a high fitness value. A comparison study with present optimization algorithms for single-objective bound-constraint optimization problems was conducted to evaluate the performance of the OBWOD algorithm on issues from the 2022 IEEE Congress on Evolutionary Computation (CEC’22) benchmark test suite with a range of dimension sizes. The results of the statistical significance test confirmed that the proposed algorithm is competitive with the optimization algorithms. In addition, the OBWOD algorithm surpassed the performance of seven other algorithms with an overall classification accuracy of 85.17% for classifying 10 medical datasets with different dimension sizes according to the performance evaluation matrix.

Chronic kidney Disease (CKD), also known as chronic renal disease, is an illness that affects the majority of adults and is defined by a progressive decrease in kidney function over time, particularly in those with diabetes and high blood pressure. Metaheuristic (MH) algorithms based machine learning classifiers have become reliable for medical treatment. The weIghted meaN oF vectOrs (INFO) is a recently developed MH but suffers from a fall into local optimal and slow convergence speed. Therefore, to improve INFO, a modified INFO (mINFO) with two enhancement strategies has been developed. The developed variant utilizes the Opposition-Based Learning (OBL) to improve the local search ability to avoid trapping into the local optimum, and the Dynamic Candidate Solution (DCS) is used to overcome the premature convergence problem in INFO and achieve the appropriate balance between exploration and exploitation ability. The performance of the proposed mINFO based on the k-Nearest Neighbor (kNN) classifier is evaluated on the complex CEC'22 test suite and applied to predict Chronic Kidney Disease (CKD) on datasets extracted from UCI. The statistical results revealed the superiority of mINFO compared with several well-known MH algorithms, including the Harris Hawks Optimization (HHO), the Hunger Games Search (HGS) algorithm, the Moth-Flame Optimization (MFO) algorithm, the Whale Optimization Algorithm (WOA), the Sine Cosine Algorithm (SCA), the Gradient-Based Optimizer (GBO), and the original INFO algorithm. According to our knowledge, this paper is the first of its sort to try employing the proposed mINFO for solving the CEC'22 test suite. Furthermore, the experimental results of mINFO-kNN for classifying two CKD datasets demonstrated its superiority with an overall classification accuracy of 93.17% on two CKD datasets over other competitors.

This paper analyzes users reactions on Twitter to the COVID-19 pandemic, using machine learning and data mining algorithms to classify tweets according to economic and health fears. A large dataset of tweets is explored, extracted, transformed, loaded, cleansed, and analyzed. The proposed framework improves prediction quality with a proposed dictionary that is used to classify tweets. The study compares four supervised machine learning algorithms and finds that people discuss the pandemics dangers from economic and health perspectives with equal frequency. The Naive Bayes algorithm achieves the highest percentage of correct predictions.

As healthcare data becomes increasingly available from various sources, including clinical institutions, patients, insurance companies, and pharmaceutical industries, machine learning (ML) services are becoming more significant in healthcare-facing domains. Therefore, it is imperative to ensure the integrity and reliability of ML models to maintain the quality of healthcare services. Particularly due to the growing need for privacy and security, healthcare data has resulted in each Internet of Things (IoT) device being treated as an independent source of data, isolated from other devices. Moreover, the limited computational and communication capabilities of wearable healthcare devices hinder the applicability of traditional ML. Federated Learning (FL) is a paradigm that maintains data privacy by storing only learned models on a server and advances with data from scattered clients, making it ideal for healthcare applications where patient data must be safeguarded. The potential of FL to transform healthcare is significant, as it can enable the development of new ML-powered applications that can enhance the quality of care, lower costs, and improve patient outcomes. However, the accuracy of current Federated Learning aggregation methods suffers greatly in unstable network situations due to the high volume of weights transmitted and received. To address this issue, we propose an alternative approach to Federated Average (FedAvg) that updates the global model by gathering score values from learned models primarily utilized in Federated Learning, using an improved version of Particle Swarm Optimization (PSO) called FedImpPSO. This approach boosts the robustness of the algorithm in erratic network conditions. To further enhance the speed and efficiency of data exchange within a network, we modify the format of the data clients send to servers using the FedImpPSO method. The proposed approach is evaluated using the CIFAR-10 and CIFAR-100 datasets and a Convolutional Neural Network (CNN). We found that it yielded an average accuracy improvement of 8.14% over FedAvg and 2.5% over Federated PSO (FedPSO). This study evaluates the use of FedImpPSO in healthcare by training a deep-learning model over two case studies to evaluate the effectiveness of our approach in healthcare. The first case study involves the classification of COVID-19 using public datasets (Ultrasound and X-ray) and achieved an F1-measure of 77.90% and 92.16%, respectively. The second case study was conducted over the cardiovascular dataset, where our proposed FedImpPSO achieves 91.18% and 92% accuracy in predicting the existence of heart diseases. As a result, our approach demonstrates the effectiveness of using FedImpPSO to improve the accuracy and robustness of Federated Learning in unstable network conditions and has potential applications in healthcare and other domains where data privacy is critical.

This review presents various perspectives on converting user keywords into a formal query. Without understanding the dataset's underlying structure, how can a user input a text-based query and then convert this text into semantic protocol and resource description framework query language (SPARQL) that deals with the resource description framework (RDF) knowledge base? The user may not know the structure and syntax of SPARQL, a formal query language and a sophisticated tool for the semantic web (SEW) and its vast and growing collection of interconnected open data repositories. As a result, this study examines various strategies for turning natural language into formal queries, their workings, and their results. In an Internet search engine from a single query, such as on Google, numerous matching documents are returned, with several related to the inquiry while others are not. Since a considerable percentage of the information retrieved is likely unrelated, sophisticated information retrieval systems based on SEW technologies, such as RDF and web ontology language (OWL), can help end users organize vast amounts of data to address this issue. This study reviews this research field and discusses two different approaches to show how users with no knowledge of the syntax of semantic web technologies deal with queries.

Face verification systems are critical in a wide range of applications, such as security systems and biometric authentication. However, these systems are vulnerable to adversarial attacks, which can significantly compromise their accuracy and reliability. Adversarial attacks are designed to deceive the face verification system by adding subtle perturbations to the input images. These perturbations can be imperceptible to the human eye but can cause the system to misclassify or fail to recognize the person in the image. To address this issue, we propose a novel system called VeriFace that comprises two defense mechanisms, adversarial detection, and adversarial removal. The first mechanism, adversarial detection, is designed to identify whether an input image has been subjected to adversarial perturbations. The second mechanism, adversarial removal, is designed to remove these perturbations from the input image to ensure the face verification system can accurately recognize the person in the image. To evaluate the effectiveness of the VeriFace system, we conducted experiments on different types of adversarial attacks using the Labelled Faces in the Wild (LFW) dataset. Our results show that the VeriFace adversarial detector can accurately identify adversarial images with a high detection accuracy of 100%. Additionally, our proposed VeriFace adversarial removal method has a significantly lower attack success rate of 6.5% compared to state-of-the-art removal methods.

Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehensive comparative analysis of various hyperparameter tuning techniques is performed; these are Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). They are used to optimize the accuracy of six machine learning algorithms, namely, Logistic Regression (LR), Ridge Classifier (RC), Support Vector Machine Classifier (SVC), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) classifiers. To test the performance of each hyperparameter tuning technique, the machine learning models are used to solve an Arabic sentiment classification problem. Sentiment analysis is the process of detecting whether a text carries a positive, negative, or neutral sentiment. However, extracting such sentiment from a complex derivational morphology language such as Arabic has been always very challenging. The performance of all classifiers is tested using our constructed dataset both before and after the hyperparameter tuning process. A detailed analysis is described, along with the strengths and limitations of each hyperparameter tuning technique. The results show that the highest accuracy was given by SVC both before and after the hyperparameter tuning process, with a score of 95.6208 obtained when using Bayesian Optimization.

Sentiment Analysis (SA) is an increasingly important field of study, also known as opinion mining, as it has the ability to identify the emotional tone of a source material as being either positive, negative, or neutral. Simply, SA digests natural languages and extracts insights, making it extremely useful to gain an overview of the public opinion in a certain issue, topic, or even a product. With the vast amount of information over social network platforms, shared views, website reviews, and blogs comes the importance of SA. Arabic is a rich language with extremely complex inflectional and derivational morphology making sentiment analysis in Arabic text more challenging. In this paper, we propose a novel approach to enhance the accuracy of Arabic Sentiment Analysis (ASA). In this aspect, nine supervised machine learning algorithms have been implemented for ASA. Three of these classifiers have never been used before in ASA classification, namely, they are Ridge, Gradient Boosting, and Multi-layer Perceptron. The performance of all nine classifiers is tested using our constructed dataset. The dataset contains 6318 reviews written in different forms of Arabic language and prepared manually by gathering hotel reviews from Booking.com website.

Analyzing big data is very common requirement of today; all such requirements become difficult to access when analyzing bulk of data source such as social networking sites which are having a lot of information on daily basis. Twitter is the micro blogging and popular site providing social networking services today. Analyzing big amounts of tweets from twitter to get different patterns and extract relevant information is a big challenge. Apache Spark is the platform that is used to analyze big data efficiently. Therefore, this paper proposes a novel metaheuristic method based on Particle Swarm Optimization and K-means (PSOK). The main idea of our proposed method is to find the optimum cluster-heads from the streaming tweets. The efficiency of our proposed method has been tested on different Twitter datasets by comparing our results with Particle Swarm Optimization (PSO) and Cuckoo Search with K-means (CSK) methods.

Rheumatoid arthritis (RA) usually occurs as a symmetrical disease, which mainly affects the small joints of the hands and feet. The correlation of handedness with radiological changes shows significantly greater radiological changes in the dominant hand than in the non-dominant one. Additionally, the dominant hand is more severely affected in terms of strength, function and deformity. Our objective is to evaluate the influence of handedness on musculoskeletal ultrasound (US), Ritchie articular index (RAI) and digital dynamometer findings in patients with active RA (early, group B, vs. established, group A). A total number of 113 patients with established RA and 44 patients with early RA with active disease (DAS28-ESR >3.2) were included in the study. US assessments of both hands were performed to assess synovitis, tenosynovitis, and erosions. RAI was used to evaluate three joint groups in each hand. Handgrip strength was measured with a digital dynamometer. The US5 score showed that the dominant hand was more affected than the non-dominant one. This was significant in group A for the synovitis Power Doppler (PD) mode (p=0.032) and tenosynovitis PD (p=0.005) scores, and in group B for synovitis Grey Scale (GS) mode (p<0.001), synovitis PD (p=0.037) and erosions (p=0.027) scores. RAI was significantly higher in the dominant hand (p=0.013) in group A and even greater in group B (p=0.011). The dominant hand was stronger than the non-dominant hand in both groups. The dominant hand is generally affected in early RA. Subsequently, the disease tends to become more symmetrical with disease progression.

Introducing Cloud computing to the globe has changed many conceptual and infrastructural bases for today’s and tomorrow’s computing. It has made the global thinking migrates rapidly towards cloud based architecture. Clouds bring out a variety of benefits including computing resources configurability, cost controllability, sustainability, mobility and service flexibility. However, the new concepts that clouds introduce such as outsourcing, multi-tenancy, and resource sharing create new challenges and raise a broad range of security and privacy issues. Cryptography is the art-of-science of protecting data privacy by converting it to unreadable format using standard mathematical techniques. This paper provides a comprehensive study for eight of the most common symmetric cryptographic algorithms, namely, DES, 3DES, Blowfish, Twofish, RC2, RC5, RC6 and AES. A comparative analysis based on the structure of the algorithm, encryption and decryption times, throughput and memory utilization has been performed to examine the performance of each algorithm.

The vast availability of information, that added in a very fast pace, in the data repositories creates a challenge in extracting correct and accurate information. Which has increased the competition among developers in order to gain access to technology that seeks to understand the intent researcher and contextual meaning of terms. While the competition for developing an Arabic Semantic Search systems are still in their infancy, and the reason could be traced back to the complexity of Arabic Language. It has a complex morphological, grammatical and semantic aspects, as it is a highly inflectional and derivational language. In this paper, we try to highlight and present an Ontological Search Engine called IBRI-CASONTO for Colleges of Applied Sciences, Oman. Our proposed engine supports both Arabic and English language. It is also employed two types of search which are a keyword-based search and a semantics-based search. IBRI-CASONTO is based on different technologies such as Resource Description Framework (RDF) data and Ontological graph. The experiments represent in two sections, first it shows a comparison among Entity-Search and the Classical-Search inside the IBRI-CASONTO itself, second it compares the Entity-Search of IBRI-CASONTO with currently used search engines, such as Kngine, Wolfram Alpha and the most popular engine nowadays Google, in order to measure their performance and efficiency.

Purpose - The purpose of this paper is to present an efficient and scalable Arabic semantic search engine based on a domain-specific ontological graph for Colleges of Applied Science, Sultanate of Oman ( CASOnto). It also supports the factorial question answering and uses two types of searching: the keyword-based search and the semantics-based search in both languages Arabic and English. This engine is built on variety of technologies such as resource description framework data and ontological graph. Furthermore, two experimental results are conducted; the first is a comparison among entity-search and the classical-search in the system itself. The second compares the CASOnto with well-known semantic search engines such as Kngine, Wolfram Alpha and Google to measure their performance and efficiency.Design/methodology/approach - The design and implementation of the system comprises the following phases, namely, designing inference, storing, indexing, searching, query processing and the user's friendly interface, where it is designed based on a specific domain of the IBRI CAS ( College of Applied Science) to highlight the academic and nonacademic departments. Furthermore, it is ontological inferred data stored in the tuple data base ( TDB) and MySQL to handle the keyword-based search as well as entity-based search. The indexing and searching processes are built based on the Lucene for the keyword search, while TDB is used for the entity search. Query processing is a very important component in the search engines that helps to improve the user's search results and make the system efficient and scalable. CASOnto handles the Arabic issues such as spelling correction, query completion, stop words' removal and diacritics removal. It also supports the analysis of the factorial question answering.Findings - In this paper, an efficient and scalable Arabic semantic search engine is proposed. The results show that the semantic search that built on the SPARQL is better than the classical search in both simple and complex queries. Clearly, the accuracy of semantic search equals to 100 per cent in both types of queries. On the other hand, the comparison of CASOnto with the Wolfram Alpha, Kngine and Google refers to better results by CASOnto. Consequently, it seems that our proposed engine retrieved better and efficient results than other engines. Thus, it is built according to the ontological domain-specific, highly scalable performance and handles the complex queries well by understanding the context behind the query.Research limitations/implications - The proposed engine is built on a specific domain ( CAS Ibri-Oman), and in the future vision, it will highlight the nonfactorial question answering and expand the domain of CASOnto to involve more integrated different domains.Originality/value - The main contribution of this paper is to build an efficient and scalable Arabic semantic search engine. Because of the widespread use of search engines, a new dimension of challenge is created to keep up with the evolution of the semantic Web. Whereas, catering to the needs of users has become a matter of paramount importance in the light of artificial intelligence and technological development to access the accurate and the efficient information in less possible time. However, the research challenges still in its infancy due to lack of research engine that supports the Arabic language. It could be traced back to the complexity of the Arabic language morphological and grammar rules.

Purpose The purpose of this paper is to present an efficient and scalable Arabic semantic search engine based on a domain-specific ontological graph for Colleges of Applied Science, Sultanate of Oman (CASOnto). It also supports the factorial question answering and uses two types of searching: the keyword-based search and the semantics-based search in both languages Arabic and English. This engine is built on variety of technologies such as resource description framework data and ontological graph. Furthermore, two experimental results are conducted; the first is a comparison among entity-search and the classical-search in the system itself. The second compares the CASOnto with well-known semantic search engines such as Kngine, Wolfram Alpha and Google to measure their performance and efficiency. Design/methodology/approach The design and implementation of the system comprises the following phases, namely, designing inference, storing, indexing, searching, query processing and the user’s friendly interface, where it is designed based on a specific domain of the IBRI CAS (College of Applied Science) to highlight the academic and nonacademic departments. Furthermore, it is ontological inferred data stored in the tuple data base (TDB) and MySQL to handle the keyword-based search as well as entity-based search. The indexing and searching processes are built based on the Lucene for the keyword search, while TDB is used for the entity search. Query processing is a very important component in the search engines that helps to improve the user’s search results and make the system efficient and scalable. CASOnto handles the Arabic issues such as spelling correction, query completion, stop words’ removal and diacritics removal. It also supports the analysis of the factorial question answering. Findings In this paper, an efficient and scalable Arabic semantic search engine is proposed. The results show that the semantic search that built on the SPARQL is better than the classical search in both simple and complex queries. Clearly, the accuracy of semantic search equals to 100 per cent in both types of queries. On the other hand, the comparison of CASOnto with the Wolfram Alpha, Kngine and Google refers to better results by CASOnto. Consequently, it seems that our proposed engine retrieved better and efficient results than other engines. Thus, it is built according to the ontological domain-specific, highly scalable performance and handles the complex queries well by understanding the context behind the query. Research limitations/implications The proposed engine is built on a specific domain (CAS Ibri – Oman), and in the future vision, it will highlight the nonfactorial question answering and expand the domain of CASOnto to involve more integrated different domains. Originality/value The main contribution of this paper is to build an efficient and scalable Arabic semantic search engine. Because of the widespread use of search engines, a new dimension of challenge is created to keep up with the evolution of the semantic Web. Whereas, catering to the needs of users has become a matter of paramount importance in the light of artificial intelligence and technological development to access the accurate and the efficient information in less possible time. However, the research challenges still in its infancy due to lack of research engine that supports the Arabic language. It could be traced back to the complexity of the Arabic language morphological and grammar rules.

Accurate information availability is a key factor for knowledge acquisition without going into extraneous information. Understanding searcher intent and the contextual meaning of terms as they appear in the searchable dataspace is a challenge that has been addressed and handled by many semantic search engines. As meaning encoded separately from data in semantic technology, adding, changing and implementing new relationships can be done easily. The evolution of semantic search added a new dimension of challenge due to a lack in support of the Arabic language. In this paper, we figure out the problem and implement a Semantic Search Engine (CASEng) for College of Applied Sciences, Oman. CASEng supports both Arabic and English search. It uses a Resource Description Framework (RDF) data and Lucene for indexing and searching to move from keyword-based search via Google and other engines to semantics-based search. The experiments show that both the spell-checker and the search engine perform well with a set of test queries.

RDF has gained great interest in both academia and industry as an important language to describe graph data. With the increasing amount of RDF data which is becoming available, efficient and scalable nowadays has become a challenge to achieve the semantic web vision. The RDF model has attracted the attention of the database community and researchers to propose various methods to store and query the RDF data efficiently. However, current RDF database suffer from several problems, like, poor performance behavior for querying RDF data.. This paper provides a comparative analysis made on selective RDF databases storages. It provides a precise study on the various means of having a persistent storage and access of RDF graphs. Recently there has been a major development on initiatives in query processing, access protocols and triple-store technologies. In the evaluation the use of a nonmemory and a non-native store Sesame, a native store Allegro graph and Jena API a main-memory based RDF storage system, specifically designed to support fast semantic association discovery. The framework and applications with the ability to store and to query RDF data are analyzed and investigated. Moreover, this paper gives an overview of the features of techniques for storing RDF data and the main purpose of study is to find suitable storage system to store RDF data.

ZigBee or IEEE 802.15.4 is considered as a unique communication standard developed for wireless personal area network. Zigbee is a low-rate (LR) communication network which provides flexibility, very low power consumption, ease of installation, reliable data transfer, short range operation, acceptable battery life, low data rate in an ad hoc self-organizing network among inexpensive fixed, and portable moving devices. It is developed for applications with relaxed throughput requirements which cannot handle the power consumption of heavy protocol stacks. Zigbee can be embedded in a wide range of products and applications across consumer, commercial, industrial and government markets. Zigbee can be considered as a promising technology which has a strong impact of the development of Wireless Sensor Network since this technology is taking care of the power energy and the communication overhead. In this paper, a cluster tree topology for 3 PAN coordinators supported by OPNET 17.5 is implemented. The aim of the study is to tests mobility, nodes leaving a network, and nodes joining a new network by measuring number of performance factors. The measured factors are: throughput, delay, load, data traffic received, end to end delay, number of hops and data traffic sent. The results showed that the PAN 2 behaved as the best tree among the cluster tree topology when compared with the other PAN s networks. The other studied issue is the mobile nodes behavior and moving among the PANs.

Efficient management of RDF data plays an important role in successfully understanding and fast querying data. Although the current approaches of indexing in RDF Triples such as property tables and vertically partitioned solved many issues; however, they still suffer from the performance in the complex self-join queries and insert data in the same table. As an improvement in this paper, we propose an alternative solution to facilitate flexibility and efficiency in that queries and try to reach to the optimal solution to decrease the self-joins as much as possible, this solution based on the idea of "Recursive Mapping of Twin Tables". Our main goal of Recursive Mapping of Twin Tables (RMTT) approach is divided the main RDF Triple into two tables which have the same structure of RDF Triple and insert the RDF data recursively. Our experimental results compared the performance of join queries in vertically partitioned approach and the RMTT approach using very large RDF data, like DBLP and DBpedia datasets. Our experimental results with a number of complex submitted queries shows that our approach is highly scalable compared with RDF-3X approach and RMTT reduces the number of self-joins especially in complex queries 3-4 times than RDF-3X approach