Efficient Web Service Research Articles

Python's Role in Accelerating Web Application Development with Django

Efficient, secure, and scalable web services are critical in today's digital environment. This study explores the field of web service development and addresses issues such system efficiency, project duration, and changing requirements. The goal of this study is to ensure reliable and efficient web services by streamlining the development process and utilizing the Django framework. The study highlights the application of Django's Model-Template-View (MTV) design pattern, which is customized for a listing management system. Smooth data interaction and effective system performance are ensured by the study's seamless integration of Django's capabilities with MySQL for database management. To further improve system performance, focus is given to standardizing data sharing protocols and expediting user authentication procedures. The automation of web page construction with Python, HTML, and CSS modules, which increases system efficacy, is a crucial component of the research. Furthermore, the study shows how crucial security measures are, such as encryption for data confidentiality and integrity, which are made possible by the REST API interaction with the front end and the Django REST framework. Along with discussing key attacks and weaknesses that are frequent in web technology, the article also offers strategies to reduce these risks. Through the use of suitable SDLC models and testing procedures, it recognizes the crucial role that software engineering processes play in the development process. Focusing attention to Django's feature set, scalability, and wide library support, the study proves its advantages over competing web frameworks. The goal of the research is to provide developers and students with the skills they need to fully utilize Django's capabilities, making it easier to create dependable, successful and safe online applications. This study advances knowledge in the field of web technology and service development by lighting the development, difficulties, and current solutions in these fields. The study's ultimate goal is to open the door for comprehensive, modern, and efficient web service creation in order to satisfy the growing need for effective web solutions in the current digital era.

Toward an efficient web service composition based on an improved BTLBO algorithm

Toward an efficient web service composition based on an improved BTLBO algorithm

An Integrated Framework for More Efficient Web Services Selection Using an Improved Fuzzy AHP

With the rapid development of Cloud Computing and Service Oriented Computing, the process of selecting web services which gives the same functionality with different quality of service (QoS) become an important issue. To deal with the large number of Web services candidates, K-representative Skyline is appeared as a Skyline variant to find the short list of the most relevant Web services that represent a summary about the full skyline Web services result. However, it returns generally a conflicting result. To rank-order K-representative Skyline Web Services, we propose an improved Fuzzy AHP which allows to: i) elicit the QoS importance level using linguistic terms based on natural language, asking fewer efforts to users, ii) reduce the number of inputs and generate automatically all pair-wise matrix of Skyline Web services with respect to each attribute. The experimental evaluation conducted on real world dataset illustrates the feasibility and the effectiveness of our framework in comparison with existing works.

QoS based soft computing techniques for evaluating efficient web service recommendation

QoS based soft computing techniques for evaluating efficient web service recommendation

A semantic web services discovery approach integrating multiple similarity measures and k-means clustering

Web service (WS) discovery is an essential task for implementing complex applications in a service oriented architecture (SOA), such as selecting, composing, and providing services. This task is limited semantically in the incorporation of the customer’s request and the web services. Furthermore, applying suitable similarity methods for the increasing number of WSs is more relevant for efficient web service discovery. To overcome these limitations, we propose a new approach for web service discovery integrating multiple similarity measures and k-means clustering. The approach enables more accurate services appropriate to the customer's request by calculating different similarity scores between the customer's request and the web services. The global semantic similarity is determined by applying k-means clustering using the obtained similarity scores. The experimental results demonstrated that the proposed semantic web service discovery approach outperforms the state-of-the approaches in terms of precision (98%), recall (95%), and F-measure (96%). The proposed approach is efficiently designed to support and facilitate the selection and composition of web services phases in complex applications.

Collaborative Filtering Method of Web Service Recommendation Based on Location Aware and Personalized

With the rapid growth of the number of Web services, it is necessary to build an efficient web service recommendation system in the face of massive web services. In order to recommend high-quality services to users, the key problem is how to obtain the s value of Web services. This paper proposes a collaborative web service recommendation method based on location clustering. Firstly, users are clustered according to the autonomous system by using the correlation between QoS and user location. According to the clustering results, the system fills in the vacancy Qos value; Then, the vacancy Qos value is filled in in advance and the similarity between active users and each user is calculated. Based on this, to P-K algorithm is used to obtain the most similar Qos value to predict the unknown service for active users to complete the recommendation. The method proposed in this paper can effectively solve the problem of data sparsity and cold start of Web services. At the same time, a better balance between accuracy and coverage is obtained.

Web Service Composition for Executing Workflows: Open Issues, Challenges, Survey and Future Directions

Cloud computing has nowadays become a dominant technology to reduce the computation cost by elastically providing resources to users on a pay-per-use basis. More and more data intensive business and scientific web service applications represented by workflows have been moved or are in active transition to cloud platforms. Therefore, efficient web service compositing for executing workflow are in high demand. This paper conduct extensive survey of various existing web service composition technique for executing workflow. From survey it is identified very limited work is done for minimizing workflow execution processing time with minimal energy dissipation considering task deadline requirement under heterogeneous cloud computational environment. This paper identifies open issues and future challenges and presented possible solution in building efficient web service composition for executing workflows under heterogeneous cloud computational environment

WITHDRAWN: Experimental performance analysis of web recommendation model in web usage mining using KNN page ranking classification approach

Abstract Web service is the technique of detecting suitable service as per requirements and it’s applicable in enterprises, industry and government. In the real time web applications, the web usage mining is mainly focused on determining the knowledge about users and market trends. The purpose of this research is to explore the efficient web service model which plays crucial role for acquiring different services. Initially, an input data is taken from the dataset which consist of different web pages. Then the pre-processing step is included for accessing the web contents by users. For this purpose, the process of tag based pre-processing is performed. Afterwards, the modification method is performed where the user can alter, change or modify the contents in the web pages. From this research, the classification method in web usage mining can be utilized to find visitor activity information in web server logs which helps the organization for getting very useful support information and to specify the evaluation of web applications. In this paper, we introduced a new version of the web page recommendation model in web mining based on KNN page ranking classification algorithm. The recommendation model proposed is validated with social media dataset and the obtained results are tested with necessary parameters. The experimental results show our proposed model offers better performance over the existing algorithms with the high accuracy rate.

Enhancing web service clustering using Length Feature Weight Method for service description document vector space representation

Due to the rapid growth of web services in repositories, discovering the requisite web service is becoming increasingly cumbersome task. It has raised the demand for efficient web service clustering algorithms. In service repositories, when related web services are stored in a clustered way, it enhances the web service discovery process by reducing search space and time. Many eminent researchers have worked in this field and used the Term Frequency – Inverse Document Frequency (TF-IDF) method for representing web services in vector space. In general, there are various limitations of the TF-IDF approach i.e. (1) Not efficient for large documents (2) Position of term and its co-occurrences does not matter (3) Unable to analyze how terms are dispersed in different documents. In the web service scenario, services are represented in short text form. TF-IDF does not work well in web service representation because of the reason that it is unable to effectively find the importance of a term concerning its occurrence in other documents. If we compare two service documents i.e. ‘s1’ and ‘s2’ first having a large and second having small number of terms respectively then TF-IDF does not demonstrate the importance of terms in ‘s1’ as smaller to ‘s2’. Therefore, it is not possible to assign effective weights to the terms. In the lack of effective vector space representation, the performance of the clustering algorithm also degrades. In this paper, we propose a new approach i.e. LFW+K which is based on Length Feature Weight (LFW) for the vectorized representation of service followed by K-Means clustering. The proposed approach helps to find the informative term from web service and assigns the term weight accordingly by considering parameters like the dimension of the web service document, maximum frequency of a term in the document and occurrences of a term in other documents. LFW+K is applied on the datasets of real-world web services and the performance is measured using standard measurement criteria (i.e. precision, recall, F1-score, and accuracy). Results of the proposed approach are compared with K-Means clustering on TF-IDF representation method i.e. TF-IDF+K. Results show that the proposed method outperforms the clustering done by using TF-IDF method for vector space representation of web services.

An accurate and efficient web service QoS prediction model with wide-range awareness

With the wide spread and deepening of service-oriented computing, more and more enterprises and organizations are constructing their applications by integrating third-party Web services in the cloud nowadays. Building high-quality applications has long been a critical research issue. Quality of service (QoS) prediction provides valuable information for making optimal Web service selection from a set of functionally equivalent candidate services. Commonly, collaborative filtering technique like matrix factorization (MF) is implemented for predicting unknown QoS values, and most of them are built via modeling user-service interaction based on QoS data directly or take side information such as geographical location, network autonomous region into account. Due to the overlook of the implicit but important wide-range characteristic of QoS data, existing MF methods might incur high users’ and services’ biases, and their prediction accuracy will not be good enough if we are faced with such wide-range of QoS data. In this work, we first investigate the wide-range characteristic among users and services via real-world Web service QoS dataset, and argue that such observed finding is an essential factor for accurate QoS prediction. We then propose a novel prediction model named Wide-Range Aware Matrix Factorization (WRAMF), which tackles the wide-range influence via bias information combination and an active function mapping explicitly. The proposed WRAMF model is advantageous to existing MF-based models, which optimizes the model by an adaptive learning rate strategy that guides WRAMF to approach the optimal solution accurately, and trains the model by a well-designed parallel stochastic gradient descent algorithm efficiently. Comprehensive experiments are conducted by employing real-world QoS dataset and empirical results show that our WRAMF significantly outperforms the state-of-the-art methods in terms of accuracy and efficiency.

A Hybrid User-Centric Approach for Efficient Web Service Selection

Over the last few years, e-commerce has exhibited explosive growth due to the ease of availability of the internet. E-commerce is rapidly changing the way in which businesses are interacting with each other as well as with their consumers. In each innovative e-commerce application, web services are being included as an important component. This leads to the availability of a huge number of web services that provide similar functionalities. The main challenge is to select the appropriate web service which fulfills the end user requirements. So, there is a need for a web service selection method that selects the web services not only based on their functionality, but also considers the nonfunctional requirements. This article proposes a method to preprocess web services using the J48 classification technique. After that, a hybrid weight evaluation mechanism is employed to obtain the weight values of each nonfunctional parameter. In the end, the web services that are near to user expectations are selected out using the ranking method.

RETRACTED: Efficient fuzzy based K-nearest neighbour technique for web services classification

Web services playing a vital role in the World Wide Web and generates huge amount of information across various domains of internet. Due to this evolution data in the form of articles, reports, digital galleries and web data of companies were increased everyday. To handle the huge volume of data each and every day, automatic query classification based on internet is more significant method. Research and development community has developed various techniques for the web services discovery, where it offers the mandated data for the improvement method. With respect to the literature survey, most of the researchers are concentrating to provide the efficient web service discovery. The amount of data that is available in the web is keeps on increasing and also it is used to differentiate the services, explanation and work of art. In order to achieve this method, machine learning algorithm is applied extensively for domain categorization. Various machine learning algorithm like KNN is applied for web service discovery. The systems are effectively learning the input and evaluate the performance accuracy with the given datasets. This paper, proposes an improved fuzzy with KNN algorithm for effective web service classification. This is used to increase an outcome in the form of accuracy and performance measures.

Geographic-aware collaborative filtering for web service recommendation

The explosion of reusable Web services (e.g., open APIs, open data sources, and cloud/IoT services), has become a new opportunity for modern service-composition based applications development. However, this enormous growth of Web services increases the difficulty of selecting the best suitable Web services for a particular application. Hence, the design of an effective and efficient Web service recommendation, primarily based on user feedback, has become a challenge. In the mashup-API recommendation scenario, the most available feedback is the implicit invocation data, i.e., the binary data indicating whether or not a mashup has invoked an API. Various efforts are exploiting potential impact factors, such as the invocation context, to augment the implicit invocation data with the aim to improve service recommendation performance. One significant factor affecting the context of Web service invocations is geographical location, but it has been given less attention in the implicit-based service recommendation. In this paper, we propose a probabilistic matrix factorization based recommendation approach, which considers geographic location information in the derivation of the preference degree underlying a mashup-API interaction. The geographic information, which is integrated with functional descriptions, complements the mashup-API invocation data input for our matrix factorization model. We demonstrate the effectiveness of our approach by conducting extensive experiments on a real dataset crawled from ProgrammableWeb. The evaluation results show that augmenting the implicit data with geographical location information increases the precision of API recommendation for mashup services.

Efficient web service selection with uncertain QoS

The QoS-based service selection in a highly dynamical environment is becoming a challenging issue. In practice, the QoS fluctuations of a service composition entail major difficulties in measuring the degree to which the user requirements are satisfied. In addition, the search space of feasible compositions (i.e., the solutions that preserve the requirements) is generally large and cannot be explored in a limited time; therefore, we need an approach that not only copes with the presence of uncertainty but also ensures a pertinent search with a reduced computational cost. To tackle this problem, we propose a constraint programming framework and a set of ranking heuristics that both reduce the search space and ensure a set of reliable compositions. The conducted experiments show that the ranking heuristics, termed 'fuzzy dominance' and 'probabilistic skyline', outperform almost all existing state-of-the-art methods.

Efficient web service selection with uncertain QoS

Efficient web service selection with uncertain QoS

Efficient Selection of QoS Based Web Services using Modified TOPSIS Method

Nowadays, due to the ease of availability of internet technology large numbers of people are using the World Wide Web. The companies are changing their way to do business. They are shifting from a data-oriented system to a service-oriented system. Now companies are able to depict their business in the form of web services and make them available on the internet. Due to this number of web services are available for satisfying the user’s need. But to select the best web service that satisfies user specification is a challenging issue. So, it is necessary to consider not only the functional requirement of the web services but also the nonfunctional requirements of the web services. On the other hand, users are not able to specify the exact nonfunctional parameter requirements so, there is a need for QoS processor which can understand the user's need and can extract the parameters for QoS. In this paper, a modified TOPSIS approach based on MCDM is proposed for the selection of efficient web service. The web services that are near to user expectations are selected out using the proposed method. Experimental outcomes show that the proposed approach determines the most promising results.

Ontology-Based Smart Sound Digital Forensics Analysis for Web Services

The big data generated by today Web services makes very fastidious and time-consuming the investigators logs management and analysis tasks. This is due partly to the lack of an efficient web service dedicated log data representation. We introduce, in this paper, an extensible standard based semantic ontology representation of Web service log data to identify hidden information and extract eventual scenario of Cyber-attacks in the web logs. The proposed ontology supports the Web service specification and it satisfies the forensics and admissibility requirements. Through a friendly graphical user interface, the investigator can define validation rules and queries and execute them using a logical reasoner over the proposed ontology to get some comprehensive forensic report ready to present to the court. We also showed how the proposed ontology can facilitate the investigator analysis task, reduce required time, and enhance the forensics process comprehensiveness.

Towards Effective Service Discovery using Feature Selection and Supervised Learning Algorithms

With the rapid development of web service technologies, the number and variety of web services available on the internet are rapidly increasing. Currently, service registries support human classification, which has been observed to have certain limitations, such as poor query results with low precision and recall rates. With the huge amount of available web services, efficient web service discovery has become a challenging issue. Therefore, to support the effective application of web services, automatic web service classification is required. In recent years, many researchers have approached web service classification problems by applying machine learning methods to automatically classify web services. The ultimate goal of our work is to construct a classifier model that can accurately classify previously unseen web services into the proper categories. This paper presents an intensive investigation on the impact of incorporating feature selection methods (filter and wrapper) on the performance of four state-of-the-art machine learning classifiers. The purpose of employing feature selection is to find a subset of features that maximizes classification accuracy and improves the speed of traditional machine learning classifiers. The effectiveness of the proposed classification method has been evaluated through comprehensive experiments on real-world web service datasets. The results demonstrated that our approach outperforms other state-of-the-art methods.

Multi-Dimensional QoS Prediction for Service Recommendations

Advances in mobile Internet technology have enabled the clients of Web services to be able to keep their service sessions alive while they are on the move. Since the services consumed by a mobile client may be different over time due to client location changes, a multi-dimensional spatiotemporal model is necessary for analyzing the service consumption relations. Moreover, competitive Web service recommenders for the mobile clients must be able to predict unknown quality-of-service (QoS) values well by taking into account the target client's service requesting time and location, e.g., performing the prediction via a set of multi-dimensional QoS measures. Most contemporary QoS prediction methods exploit the QoS characteristics for one specific dimension, e.g., time or location, and do not exploit the structural relationships among the multi-dimensional QoS data. This paper proposes an integrated QoS prediction approach which unifies the modeling of multi-dimensional QoS data via multi-linear-algebra based concepts of tensor and enables efficient Web service recommendation for mobile clients via tensor decomposition and reconstruction optimization algorithms. In light of the unavailability of measured multi-dimensional QoS datasets in the public domain, this paper also presents a transformational approach to creating a credible multi-dimensional QoS dataset from a measured taxi usage dataset which contains high dimensional time and space information. Comparative experimental evaluation results show that the proposed QoS prediction approach can result in much better accuracy in recommending Web services than several other representative ones.

Tagging augmented neural topic model for semantic sparse Web service discovery

SummarySearch engine based Web service discovery model suffers from the semantic sparsity problem due to the fact that Web services are described in short texts, which in turn leads to poor recall. To address this issue, external information that enriches the semantics of the Web service and improves discovery performance has been highly concerned. In light of this, we propose a novel Web service discovery approach that uses the neural topic model, which seamlessly integrates tagging information and word embedding for semantic sparsity problem. More specifically, instead of clustering Web services as done in most existing service discovery approaches, we use word embedding to map the words as continuous embeddings to embody external semantics of the service description. We also leverage the neural topic model in service discovery, which takes continuous word distribution as the input and interprets the Web service description as a hierarchical model. Based on the neural topic model and word embedding, we propose an efficient Web service query and ranking approach. Experiments conducted on a real‐world Web service dataset demonstrate the effectiveness of the proposed approach.