Fuzzy Reasoning Approach for Predicting Web Services QoS/QoE with ANFIS

Nowadays, the web service (WS) usage in information systems (IS) includes determining a feasible WS that fulfils a set of non-functional requirements of Quality of Services (QoS) and user’s needs of Quality of Experience (QoE). While most existing studies evaluate WS from one perspective, i.e., users, and are based on data-driven approach, which employs a numerical dataset to learn a reasoning model, they overlook that users express their needs in a non-numerical form. To address these issues, we propose a new fuzzy reasoning approach for predicting WS QoS/QoE with the adaptive neuro-fuzzy inference system (ANFIS) that encompasses multiple viewpoints and perspectives, and is also suitable for linguistic terms. To verify the efficiency, we implemented the proposed approach, conducted two experiments and compared them. The results show a good performance of the proposed approach for predicting WS QoS/QoE, and, consequently, it can be considered a suitable tool for predicting

COLLECT: COLLaborativE ConText-aware service oriented architecture for intelligent decision-making in the Internet of Things

Internet of Things (IoT) has radically transformed the world; currently, every device can be connected to the Internet and provide valuable information for decision-making. In spite of the fast evolution of technologies accompanying the grow of IoT, we are still faced with the challenge of providing a service oriented architecture, which facilitates the inclusion of data coming together from several IoT devices, data delivery among a system’s agents, real-time data processing and service provision to users. Furthermore, context-aware data processing and architectures still pose a challenge, in spite of being key requirements in order to get stronger IoT architectures. To face this challenge, we propose a COLLaborative ConText Aware Service Oriented Architecture (COLLECT), which facilitates both the integration of IoT heterogeneous domain context data — through the use of a light message broker — and easy data delivery among several agents and collaborative participants in the system — making use of an enterprise service bus —. In addition, this architecture provides real-time data processing thanks to the use of a complex event processing engine as well as services and intelligent decision-making procedures to users according to the needs of the domain in question. As a result, COLLECT has a great impact on context-aware decentralized and collaborative reasoning for IoT, promoting context-aware intelligent decision making in such scope. Since context-awareness is key for a wide range of recommender and intelligent systems, the presented novel solution improves decision making in a large number of fields where such systems require to promptly process a variety of ubiquitous collaborative and context-aware data

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

AI ニヨル コベツカ スイセン サービス ノ ジュヨウ メカニズム ニ カンスル タンサクテキ ケンキュウ

本研究では、個別化された商品情報を提供するECサイト/アプリの利用者を対象に、消費者の利用意思決定の過程を明らかにした。推薦サービスの有益性は、商品情報の個別化によって高められていると推測できるが、消費者が個別化推薦サービス（PRS）を利用するメカニズムを解明する実証研究は少ない。本研究では、PRSに対する知覚がサービスの認識と態度を媒介して行動意向に及ぼす影響を検討した。併せて、個人的革新性に着目し、利用者個人の特性がPRSの受容に及ぼす影響も検討した。仮説検証を通じ、PRSに対する知覚がサービスの有用性、信頼性、利用態度を介して行動意向に影響することを明らかにした。さらに、サービスの有用性と信頼性が顧客の利用態度を促す間接効果も確認できた。ただし、個人的革新性の調整効果は認められなかった。最後に、分析結果を踏まえて主要な論点ごとに考察を加え、研究の限界点と今後の課題を示した

Concepts and Methods from Artificial Intelligence in Modern Information Systems – Contributions to Data-driven Decision-making and Business Processes

Today, organizations are facing a variety of challenging, technology-driven developments, three of the most notable ones being the surge in uncertain data, the emergence of unstructured data and a complex, dynamically changing environment. These developments require organizations to transform in order to stay competitive. Artificial Intelligence with its fields decision-making under uncertainty, natural language processing and planning offers valuable concepts and methods to address the developments. The dissertation at hand utilizes and furthers these contributions in three focal points to address research gaps in existing literature and to provide concrete concepts and methods for the support of organizations in the transformation and improvement of data-driven decision-making, business processes and business process management. In particular, the focal points are the assessment of data quality, the analysis of textual data and the automated planning of process models. In regard to data quality assessment, probability-based approaches for measuring consistency and identifying duplicates as well as requirements for data quality metrics are suggested. With respect to analysis of textual data, the dissertation proposes a topic modeling procedure to gain knowledge from CVs as well as a model based on sentiment analysis to explain ratings from customer reviews. Regarding automated planning of process models, concepts and algorithms for an automated construction of parallelizations in process models, an automated adaptation of process models and an automated construction of multi-actor process models are provided