Privacy-Preserving Mining of Web Service Conversations

Organizations and businesses are exporting their applications as Web services seeking more collaboration opportunities. These services are generally not used in silos. Indeed, the invocation of a service is often conditioned by the invocation of other services. We refer to the precedence relationships between service invocations as conversations or choreographies. As clients interact with Web services, they exchange an important quantity of sensitive data, hence raising the challenge to keep the privacy of various interactions. In addition to the data exchanged with Web services, users may consider the information about service usage as sensitive and would like to hide that information from third parties. However, conversation relationships may complicate the task of keeping such information secret. In this Thesis, we extend the traditional concept of k-anonymity introduced for databases to Web service conversations. The goal is to determine the extent to which the invocation of a service can be inferred from downstream invocations. We first use the FP-Growth algorithm for mining service invocation logs. The mining process returns the probabilities of service conversations. We then define a probabilistic k-anonymity technique for Web service conversations based on the results of the mining process. The proposed approach assists users in selecting Web services that best satisfy their anonymity requirements. We conducted extensive experiments using realworld Web services to prove the efficiency of the proposed approach.Master of ScienceComputer and Information Science, College of Engineering and Computer ScienceCollege of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/138104/1/Privacy-Preserving Mining of Web Service Conversations.pdfDescription of Privacy-Preserving Mining of Web Service Conversations.pdf : Thesi

JQPro : Join query processing in a distributed system for big RDF data using the hash-merge join technique

In the last decade, the volume of semantic data has increased exponentially, with the number of Resource Description Framework (RDF) datasets exceeding trillions of triples in RDF repositories. Hence, the size of RDF datasets continues to grow. However, with the increasing number of RDF triples, complex multiple RDF queries are becoming a significant demand. Sometimes, such complex queries produce many common sub-expressions in a single query or over multiple queries running as a batch. In addition, it is also difficult to minimize the number of RDF queries and processing time for a large amount of related data in a typical distributed environment encounter. To address this complication, we introduce a join query processing model for big RDF data, called JQPro. By adopting a MapReduce framework in JQPro, we developed three new algorithms, which are hash-join, sort-merge, and enhanced MapReduce-join for join query processing of RDF data. Based on an experiment conducted, the result showed that the JQPro model outperformed the two popular algorithms, gStore and RDF-3X, with respect to the average execution time. Furthermore, the JQPro model was also tested against RDF-3X, RDFox, and PARJs using the LUBM benchmark. The result showed that the JQPro model had better performance in comparison with the other models. In conclusion, the findings showed that JQPro achieved improved performance with 87.77% in terms of execution time. Hence, in comparison with the selected models, JQPro performs better

Social Intelligence Approach for Service-Oriented Software

Service-oriented computing allows companies to break down functionalities into individual, autonomous services. The last decade has seen a surge of services in the form of APIs in a variety of domains. The API economy is growing rapidly, and companies are making APIs an integral part of their software development strategies. The reuse of existing APIs allows developers to build large-scale applications referred to as mashups or composite services. Service Composition or Mashup is a technique to construct value-added services by integrating pre-existing APIs. Recent technologies such as cloud computing, big data, and Internet of Things (IoT) boosted the popularity of service composition and mashups. This is because of the ability of services to interact and work together despite their heterogeneity and autonomy. Integrating multiple APIs created by various third parties require a wide array of technical skills such as Web, data management, software engineering, programming, and security. To overcome these challenges, developers often turn to programming communities or crowd (e.g., StackOverflow, GitHub) to share practices, knowledge, experience, and brainpower in solving intricate problems. This growing trend adds a "social" dimension to software development allowing programmers to share experiences on a daily basis through blogs, wikis, tutorials, bug reporting, and discussion forums. As a result, the data related to APIs (both technical and non-technical) are scattered in different platforms and with various formats. Our vision in this research is to leverage artificial intelligence techniques (e.g., machine learning, natural language processing) in order to turn API-related "social" data into useful information to support the creation of future mashups and service compositions. We use the term social intelligence (combination of social computing and artificial intelligence) to refer to the proposed approach. In this dissertation, we propose a social intelligence-based approach for service composition. First, we introduce CrowdMashup, a crowdsourcing approach for mashup team recommendation. We analyze online developer communities and API directories to infer developers' interests in APIs through natural language processing. We propose three techniques to generate teams of developers that best fulfill mashup requirements: graph-based, clustering-based, and search-based. Second, we propose FAME (inFluencer Apis in developer coMmunitiEs), a multi-dimensional influencer model for APIs in service-oriented environments. The proposed model helps API providers to increase the visibility of their APIs and API consumers to select the best-in-class APIs. We introduce a linear regression technique to predict the evolution of influence scores and correlate API features to those scores. Third, we propose TEAM (qualiTy of Experience-based Api recoMmendation), an approach that leverages prior API development experiences to recommend APIs. We extract structured and unstructured information from various developer communities to build a Quality of Experience (QoE) model. We train three different classifiers to recommend APIs and predict their usage: random forest, support vector machine, and deep neural net- work. Finally, we conduct extensive experiments on real-world and large datasets extracted from developer communities to evaluate the proposed approaches.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/171925/1/Faisal Binzagr Final Dissertation.pdfDescription of Faisal Binzagr Final Dissertation.pdf : Dissertatio

AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data

Our research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often require arduous manual feature extraction or computationally demanding deep learning (DL) techniques. To address this, we introduce an optimized model tailored specifically for UAV-based applications. Our alterations to the YOLOv5s model, which include advanced attention modules, expanded cross-stage partial network (CSP) modules, and refined multiscale feature extraction mechanisms, enable precise pest detection and classification. Inspired by the efficiency and versatility of UAVs, our study strives to revolutionize pest management in sustainable agriculture while also detecting and preventing crop diseases. We conducted rigorous testing on a medium-scale dataset, identifying five agricultural pests, namely ants, grasshoppers, palm weevils, shield bugs, and wasps. Our comprehensive experimental analysis showcases superior performance compared to various YOLOv5 model versions. The proposed model obtained higher performance, with an average precision of 96.0%, an average recall of 93.0%, and a mean average precision (mAP) of 95.0%. Furthermore, the inherent capabilities of UAVs, combined with the YOLOv5s model tested here, could offer a reliable solution for real-time pest detection, demonstrating significant potential to optimize and improve agricultural production within a drone-centric ecosystem

Intelligent Deep Learning for Anomaly-Based Intrusion Detection in IoT Smart Home Networks

The Internet of Things (IoT) is a tremendous network based on connected smart devices. These networks sense and transmit data by using advanced communication standards and technologies. The smart home is one of the areas of IoT networks, where home appliances are connected to the internet and smart grids. However, these networks are at high risk in terms of security violations. Different kinds of attacks have been conducted on these networks where the user lost their data. Intrusion detection systems (IDSs) are used to detect and prevent cyberattacks. These systems are based on machine and deep learning techniques and still suffer from fitting or overfitting issues. This paper proposes a novel solution for anomaly-based intrusion detection for smart home networks. The proposed model addresses overfitting/underfitting issues and ensures high performance in terms of hybridization. The proposed solution uses feature selection and hyperparameter tuning and was tested with an existing dataset. The experimental results indicated a significant increase in performance while minimizing misclassification and other limitations as compared to state-of-the-art solutions

Imperative Role of Digital Twin in the Management of Hospitality Services

Digital twin implementation enables more effective terms of evaluation and planning, and also effective utilization of resources with a flood of knowledge to improve the real-time services. The hospitality industry settings utilize digital twin technologies to introduce new ideas with sensor, actuators, AR/VR improve production, and improve customer services. Currently, the hospitality industry is focused to create a fast, virtual world space where customers can get a real world of hospitality. The technologically digital twin of a vast inn office can be implemented to create both discrete and continuous event recreations in order to precisely conceptualize the events that occur in distinct frameworks. Based on the above facts, the adoption of the digital twin in the hospitality industry has gained significant attention. With this motivation, the study aims to investigate the significance and application of the digital twins in the hospitality industry for establishing innovative and digital infrastructure. In addition to this, the study discusses different elements that are significant for the digital twin. Finally, the article summarizes and recommends vital recommendation in the adoption of digital twin in hospitality industry

The CSFs from the Perspective of Users in Achieving ERP System Implementation and Post-Implementation Success: A Case of Saudi Arabian Food Industry

Enterprise resource planning (ERP) systems have a major impact on the functioning of organizations and the development of business strategy. However, one of the main reasons that cause failure in ERP implementations to achieve the expected benefits is that the system is not fully accepted by end users. User rejection of the system is the second reason after time and budget overrun, while the fourth barrier to ERP post-implementation. Most studies have focused on ERP adoption and installation while neglecting post-implementation evaluation, which omits insights into the priority of ERP systems and CSFs from the stance of ERP users. Therefore, this study identified factors that led to user acceptance of the use of ERP systems at both implementation and post-implementation stages (after installation). In addition, this study assessed the interrelationship between the factors and the most influential factors toward user acceptance. A survey was conducted among pioneers of the food industry in Saudi Arabia, which included 144 ERP system users from assembly and manufacturing, accounts, human resources, warehouse, and sales departments. The descriptive-analytical approach was deployed in this study. As a result, project management, top management support, and user training had significant impacts on the efficacy of ERP system implementation. On the contrary, support for technological changes in new software and hardware, managing changes in systems, procedures, and work steps already in place within the organization, as well as user interfaces and custom code, displayed a direct impact on user acceptance of ERP systems post-implementation. This study is the first research that provides a rating of CSFs from the perspective of its users in Saudi Arabia. It also enables decision makers of food industries to better assess the project risks, implement risk-mitigation methods, create appropriate intervention techniques to discover the strengths and limitations of the ERP users, and value the “best of fit” solutions over “best practice” solutions when determining the most appropriate option for food industries

The CSFs from the Perspective of Users in Achieving ERP System Implementation and Post-Implementation Success: A Case of Saudi Arabian Food Industry

Enterprise resource planning (ERP) systems have a major impact on the functioning of organizations and the development of business strategy. However, one of the main reasons that cause failure in ERP implementations to achieve the expected benefits is that the system is not fully accepted by end users. User rejection of the system is the second reason after time and budget overrun, while the fourth barrier to ERP post-implementation. Most studies have focused on ERP adoption and installation while neglecting post-implementation evaluation, which omits insights into the priority of ERP systems and CSFs from the stance of ERP users. Therefore, this study identified factors that led to user acceptance of the use of ERP systems at both implementation and post-implementation stages (after installation). In addition, this study assessed the interrelationship between the factors and the most influential factors toward user acceptance. A survey was conducted among pioneers of the food industry in Saudi Arabia, which included 144 ERP system users from assembly and manufacturing, accounts, human resources, warehouse, and sales departments. The descriptive-analytical approach was deployed in this study. As a result, project management, top management support, and user training had significant impacts on the efficacy of ERP system implementation. On the contrary, support for technological changes in new software and hardware, managing changes in systems, procedures, and work steps already in place within the organization, as well as user interfaces and custom code, displayed a direct impact on user acceptance of ERP systems post-implementation. This study is the first research that provides a rating of CSFs from the perspective of its users in Saudi Arabia. It also enables decision makers of food industries to better assess the project risks, implement risk-mitigation methods, create appropriate intervention techniques to discover the strengths and limitations of the ERP users, and value the “best of fit” solutions over “best practice” solutions when determining the most appropriate option for food industries

Intelligent deep learning for anomaly-based intrusion detection in IoT smart home networks

The Internet of Things (IoT) is a tremendous network based on connected smart devices. These networks sense and transmit data by using advanced communication standards and technologies. The smart home is one of the areas of IoT networks, where home appliances are connected to the internet and smart grids. However, these networks are at high risk in terms of security violations. Different kinds of attacks have been conducted on these networks where the user lost their data. Intrusion detection systems (IDSs) are used to detect and prevent cyberattacks. These systems are based on machine and deep learning techniques and still suffer from fitting or overfitting issues. This paper proposes a novel solution for anomaly-based intrusion detection for smart home networks. The proposed model addresses overfitting/underfitting issues and ensures high performance in terms of hybridization. The proposed solution uses feature selection and hyperparameter tuning and was tested with an existing dataset. The experimental results indicated a significant increase in performance while minimizing misclassification and other limitations as compared to state-of-the-art solutions.</p