Self-adaptive mobile web service discovery framework for dynamic mobile environment

The advancement in mobile technologies has undoubtedly turned mobile web service (MWS) into a significant computing resource in a dynamic mobile environment (DME). The discovery is one of the critical stages in the MWS life cycle to identify the most relevant MWS for a particular task as per the request's context needs. While the traditional service discovery frameworks that assume the world is static with predetermined context are constrained in DME, the adaptive solutions show potential. Unfortunately, the effectiveness of these frameworks is plagued by three problems. Firstly, the coarse-grained MWS categorization approach that fails to deal with the proliferation of functionally similar MWS. Secondly, context models constricted by insufficient expressiveness and inadequate extensibility confound the difficulty in describing the DME, MWS, and the user’s MWS needs. Thirdly, matchmaking requires manual adjustment and disregard context information that triggers self-adaptation, leading to the ineffective and inaccurate discovery of relevant MWS. Therefore, to address these challenges, a self-adaptive MWS discovery framework for DME comprises an enhanced MWS categorization approach, an extensible meta-context ontology model, and a self-adaptive MWS matchmaker is proposed. In this research, the MWS categorization is achieved by extracting the goals and tags from the functional description of MWS and then subsuming k-means in the modified negative selection algorithm (M-NSA) to create categories that contain similar MWS. The designing of meta-context ontology is conducted using the lightweight unified process for ontology building (UPON-Lite) in collaboration with the feature-oriented domain analysis (FODA). The self-adaptive MWS matchmaking is achieved by enabling the self-adaptive matchmaker to learn MWS relevance using a Modified-Negative Selection Algorithm (M-NSA) and retrieve the most relevant MWS based on the current context of the discovery. The MWS categorization approach was evaluated, and its impact on the effectiveness of the framework is assessed. The meta-context ontology was evaluated using case studies, and its impact on the service relevance learning was assessed. The proposed framework was evaluated using a case study and the ProgrammableWeb dataset. It exhibits significant improvements in terms of binary relevance, graded relevance, and statistical significance, with the highest average precision value of 0.9167. This study demonstrates that the proposed framework is accurate and effective for service-based application designers and other MWS clients

Penemuan Web Service Berdasarkan Kesamaan Struktur, Semantik, dan Perilaku

Web Service mempunyai peran penting pada lingkungan komputing bisnis saat ini untuk mengembangkan aplikasi yang terdistribusi di berbagai jaringan. Banyak pelaku bisnis yang sudah menerapkan proses bisnisnya menggunakan Web Service. Pelaku bisnis tersebut dapat mengganti Web Service yang digunakannya dikarenakan perubahan rekan bisnis atau berhenti berjalan (offline). Seiring waktu, Jumlah web service yang sudah dipublikasikan di web semakin meningkat. Dengan banyaknya web service yang beredar di web, penemuan Web Service yang optimal diperlukan untuk memenuhi kebutuhan Web Service sesuai keinginan user. Web Service mempunyai dua tipe, yaitu atomik dan komposit. Untuk penemuan Web Service atomik, pengukuran kesamaan semantik dan struktur diperlukan. Sedangkan untuk penemuan Web Service komposit, pengukuran kesamaan semantik dan perilaku diperlukan. Untuk penemuan Web Service atomik, dokumen Web Service Definition Language (WSDL) digunakan sebagai masukan dan untuk penemuan Web Service komposit, Business Process Execution Language (BPEL) digunakan sebagai masukan. Penelitian sebelumnya menggunakan Latent Dirichlet Allocation untuk mengukur kesamaan semantik berdasarkan topik. Namun LDA memiliki kelemahan ketika digunakan untuk mengekstraksi topik pada Web Service. Beberapa Web Service yang dideskripsikan dengan WSDL atau BPEL, apabila diekstraksi informasinya maka akan membentuk dokumen pendek. Ketika LDA digunakan untuk mencari kesamaan semantic pada WSDL dan BPEL, metode ini tidak dapat berjalan dengan baik dikarenakan keberagaman kata pada dokumen pendek tidak terlalu banyak sehingga dapat mempengaruhi hasil penggalian topik. Sehingga diperlukan metode varian LDA yang mampu menggali topik pada dokumen pendek yaitu Biterm Topic Model (BTM). Penelitian ini mengusulkan untuk menggunakan WDAG untuk memodelkan struktur dan perilaku Web Service dan menggunakan WDAG Similarity untuk mengukur kesamaan struktur atau perilakunya. BTM digunakan untuk mengukur kesamaan topik antar label node pada WDAG. Analisis pengujian akan dilakukan dengan menggunakan metriks Precision dan Recall. Precision dan Recall adalah dua perhitungan yang banyak digunakan untuk mengukur kinerja dari sistem atau metode yang digunakan pada bidang sistem temu kembali (Information Retrieval). Pendekatan yang diusulkan diharapkan dapat mengurai false positive dan false negative saat melakukan penemuan Web Service sehingga dapat meningkatkan nilai Precision dan Recall. ================================================================= Web Service has an important role on business computing in order to develop distributed application in networks. Many business actors have implemented their business processes using web service. Those business actors can change their web service depending on their conditions like when they change their business partner or their Web Service goes offline. Now, A lot of web services have been published on the web. With this condition, A need of optimal Web Service discovery is increasing in order to find a Web Service that satisfies a business actor. Web Service have two types, which are atomic Web Service and composite Web Service. To discover atomic web services, semantic and structure similarity are needed. To discover composite web services, semantic and behavior similarity are needed. Web Service Definition Language (WSDL) is used as an input on atomic Web Service discovery processes. Business Process Definition Language (BPEL) is used as an input on composite Web Service discovery processes. Previous research used Latent Dirichlet Allocation (LDA) to measure semantic similarity of web services based on topic. But LDA has weakness. Web services are descripted using WSDL or BPEL. When information inside WSDL or BPEL extracted, then some of it will create a short document. LDA performance is not going well on short document because of word co-occurrence sparsity. The word co-occurrence sparsity may affect the performance of LDA in a bad way. As of another variant of LDA that can mine topic on short documents is needed such as Biterm Topic Model (BTM). This research proposed on using Weighted Directed Acyclic Graph (WDAG) to models structure and behaviour of a Web Service and using WDAG Similarity to calculate the similarity of structure and behaviour between web services. Biterm Topic Model is used to mine the latent topic on node label of WDAG and calculate its semantic similarity. The test results are analysed by using Precision and Recall. Precision and Recall are two measurement that mostly used to assess performance of system or method which is used in Information Retrieval. The proposed approach in this research is expected to reduce false positive and false negative in order to increase the Precision and Recall value