Service Mashups Research Articles

Augmented latent Dirichlet allocation model via word embedded clusters for mashup service clustering

AbstractAlong with the growing number of mashups, there have been many changes in their diversity as well. With the creation of a wide variety of mashup services, it has become difficult to select and recommend appropriate web‐based APIs or services to launch an application based on the principle of mashup technology. In keeping with the methods already in place, we realized that the input/output parameters and text description which are key features directly from WSDL specification documents are increasingly being used in the mashup service innovation process. The quality of these methods can be enhanced by capturing hidden topic content from WSDL documents through the LDA (latent Dirichlet allocation). In this paper, the performance as concerns the LDA model can be enhanced by using some auxiliary features rather than limiting it to some extent. The reason for this is that the word vectors achieved by the word2vec tool is of advanced quality than the word vectors of the word gained by the LDA model. So, we proposed to innovate the novel mashup service innovation and clustering method with better efficiency and higher accuracy through word2vec. Here developed an improved LDA model with a word embedded cluster (called AUG‐LDA) that has a major impact on word vector quality. In this proposed approach, the LDA's training process can be semimonitored by first acquiring the word vectors through the word2vec tool and then merging the word clusters with the K‐means++ algorithm. This will enable better representations for the distribution of mashup services. This can be obtained by crawling the dataset from the website calledwww.programmableweb.comto perform a comprehensive experiment and demonstrate the proposed method. It has been proven that there is a better increase by comparing our approach with different metrics and the clustering accuracy values obtained by other methods.

Semantic Knowledge for Autonomous Smart Farming

A higher degree of automation - and autonomization - of agricultural processes is expected to lead to productivity gains, especially in light of more environmentally-friendly farming practices, while improving the safety of agricultural processes. To exploit the potential of this development, it should be possible to flexibly integrate devices and services within service mashups, and thereby enable them to provide higher-value services together, However, current farm automation tools instead tend to reinforce vertical functional silos and tight coupling within often proprietary systems that manage the farm environment information. We propose to describe capabilities of individual devices and services and interlink them across components and with the description of the farm environment. We posit that this will better enable autonomous agents - software agents as well as humans - to perform complex agricultural tasks while integrating heterogeneous devices and services across multiple vendors. Concretely, we describe - and demonstrate in a laboratory setting - the usage of a Knowledge Graph to describe the environment and equipment used to perform farming tasks. We show how a multi-agent-based automation system for smart farming uses this graph to reason about the state of the environment and the agents to plan the achievement of user-specified goals. Furthermore, we show how such knowledge-driven autonomous systems may include human agents alongside artificial agents as first-class citizens, towards realizing “Social Machines” in the agriculture domain.

Attentional matrix factorization with context and co-invocation for service recommendation

Easy accessibility of data and functions are the main advantages to develop mashups from abundant sources of Web APIs. However, it simultaneously brings difficulties to choose suitable APIs for a mashup. Existing probabilistic matrix factorization (PMF) recommender systems can effectively exploit the latent features of the invocations with the same weight but not all features are equally significant and predictive, and the useless features may bring noises to the model. In this work, we propose the Attentional PMF Model (AMF), which leverages a neural attentional network to learn the significance of latent features. We then inject the attentional scores and the mashup-API context similarity into the matrix factorization structure for training. Furthermore, our model exploits the relationship between APIs from both their context and co-invocation history as regularization terms to improve its prediction performance. Our experiments are evaluated on ProgrammableWeb. The results show that our model outperforms some state-of-art recommender systems in mashup service applications.

사용자 중심의 대화형 IoT 서비스 매쉬업 모델과 엔진

IoT 환경에서 사용자들은 IoT 기기를 활용한 서비스를 제공받을 뿐만 아니라 서비스 매쉬업을 통해 자신만의 애플리케이션을 생성하고자 한다. 이를 위해 시각적인 방식으로 IoT 서비스 매쉬업을 생성하는 방법들이 제안되었으나, 시각적으로 표현되는 매쉬업의 실행 흐름을 이해하는 것은 쉽지 않기 때문에 사용자들은 매쉬업을 만드는 데 어려움을 겪는다. 본 연구에서는 프로그래밍 경험이 없는 일반 사용자들이 IoT 서비스 매쉬업을 생성하도록 자연어를 통해 상호작용하는 대화형 서비스 매쉬업 모델 및 엔진을 제안한다. 대화형 서비스 매쉬업 모델은 사용자 명령을 파악하기 위한 네 종류 키워드로 구성되며, 엔진은 인터랙션 관리자, 시맨틱 매칭 모듈, 서비스 매쉬업 모듈로 구성된다. 본 연구에서는 스마트 홈 환경에서의 시나리오를 바탕으로 사례 연구를 진행하여, 일반 사용자들이 대화형 서비스 매쉬업 모델 및 엔진을 통해 더 쉽고 편리하게 필요한 서비스 매쉬업을 생성할 수 있음을 확인하였다.

A real-world service mashup platform based on data integration, information synthesis, and knowledge fusion

ABSTRACT Service-oriented architecture (SOA) provides a flexible IT infrastructure in order to deal with global competition. However, real-world services change their architectures and functions frequently. This work uses data integration, information synthesis, and knowledge fusion to analyze SOA evolution and complexity. A quantificational approach is provided to estimate the change effects on a service-oriented system. We established a cloud service community to provide rich cloud services and a service mashup platform that connects API service providers and service users. The platform user can invoke all API services on the platform without jumping to the third-party website. The platform users can filter and accept the services recommended by the platform, and also freely compose the services which are real-world services. Data integration, information synthesis, and knowledge fusion are applied to mashups that users constructed, helping users avoid complexity and unreliability that is our objective. We also showed that our method achieved significant quantitative results.

Web Service Instant Recommendation for Sustainable Service Mashup

Service Mashups can help users to integrate data of multiple sources based on Web services composition. Considering a kind of sustainable service Mashup whose data requirement cannot be predetermined, so users need to choose and compose services in a tentative manner. Meanwhile, users can choose and compose services continually to obtain more data based on existing composition results. In such Mashups, a Web service is chosen according to the data provided by the service. Because it is difficult for users to choose from large amounts of services manually, it is a challenge to recommend services instantly for users during the construction of a sustainable service Mashup. This paper proposes an approach to recommend Web services instantly for a sustainable service Mashup. According to the services used in the service Mashup under construction, candidate services are chosen based on the Mashups that are similar to the constructing Mashup, as well as the parameter correlations of services from the perspective of actual operations of Web service composition. Experimental results indicate that the proposed approach has better precision, recall, and coverage values compared to existing state-of-the-art approaches, and therefore, it is more suitable for instant service recommendation of sustainable service Mashups.

Mashup tag completion with attention-based topic model

The past few years have witnessed a substantial increase in functional rich API services and their compositions (e.g., Mashup services) on the Internet, which as a result proposes new requirement of organization and management methods for better service understanding, discovery and usage. Tagging is known to be efficient for this purpose, and many tag recommender systems for API services have been proposed in the past. Existing approaches targeted at API services usually recommend several similar tags, which is insufficient for Mashup services that normally demonstrate diverse functionalities. In this paper, we propose a novel approach for Mashup service tag completion that can recommend tags revealing the functional features of Mashup services more comprehensively. It first extracts candidate tags for the target Mashup from API services in different functional domains. Then, it adopts a hybrid filtering procedure to recommend the most relevant tags. To support efficient tag extraction and recommendation, an attention-based topic model called Att-LDA is proposed that can highlight the functional oriented features in descriptions of services for accurate functional semantic learning. Experiments and validations on a real-word dataset demonstrate the performance of our approach improved 7.1% compared with other state-of-the-art methods.

Automating Mashup Service Recommendation via Semantic and Structural Features

Increasing physical objects connected to the Internet make it possible for smart things to access all kinds of cloud services. Mashup has been an effective way to the rapid IoT (Internet of Things) application development. It remains a big challenge to bridge the semantic gap between user expectations and application functionality with the development of mashup services. This paper proposes a mashup service recommendation approach via merging semantic features from API descriptions and structural features from the mashup-API network. To validate our approach, large-scale experiments are conducted based on a real-world accessible service repository, ProgrammableWeb. The results show the effectiveness of our proposed approach.

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.

A Bayesian learning model for design-phase service mashup popularity prediction

Using web services as building blocks to develop software applications, i.e., service mashups, not only reuses software development efforts to minimize development cost, but also leverages user groups and marketing efforts of those services to attract users and improve profits. This has significantly encouraged the development of a large number of service mashups in various domains. However, using existing services, even popular ones, does not guarantee the success of a mashup. In fact, a large portion of existing mashups fail to attract a good number of users, making the mashup development effort less effective. Design-phase popularity prediction can help avoid unpromising mashup developments by providing early-on insight into the potential popularity of a mashup. In this paper, we investigate the factors that can affect the popularity of a mashup through a comprehensive analysis on one of the largest mashup repository (i.e., ProgrammableWeb). We further propose a novel Bayesian approach that offers early-on insight to developers into the potential popularity of a mashup using design-phase features only. Besides identifying those relevant features, the Bayesian learning model can provide a confidence level for each prediction. This provides useful guidance to developers for successful mashup development. Experimental results demonstrate that the proposed approach achieves high prediction accuracy and outperforms competitive models.

Integrated Content and Network-Based Service Clustering and Web APIs Recommendation for Mashup Development

The rapid growth in the number and diversity of Web APIs, coupled with the myriad of functionally similar Web APIs, makes it difficult to find most suitable Web APIs for users to accelerate and accomplish Mashup development. Even if the existing methods show improvements in Web APIs recommendation, it is still challenging to recommend Web APIs with high accuracy and good diversity. In this paper, we propose an integrated content and network-based service clustering and Web APIs recommendation method for Mashup development. This method, first develop a two-level topic model by using the relationship among Mashup services to mine the latent useful and novel topics for better service clustering accuracy. Moreover, based on the clustering results of Mashups, it designs a collaborative filtering (CF) based Web APIs recommendation algorithm. This algorithm, exploits the implicit co-invocation relationship between Web APIs inferred from the historical invocation history between Mashups clusters and the corresponding Web APIs, to recommend diverse Web APIs for each Mashups clusters. The method is expected to not only find much better matched Mashups with high accuracy, but also diversify the recommendation result of Web APIs with full coverage. Finally, based on a real-world dataset from ProgrammableWeb, we conduct a comprehensive evaluation to measure the performance of our method. Compared with existing methods, experimental results show that our method significantly improves the accuracy and diversity of recommendation results in terms of precision, recall, purity, entropy, DCG and HMD.

A context-driven geospatial web service approach towards the design of situational awareness maps

Abstract. The recent development of internet-based map services has dramatically change how geospatial information is presented to users, novice or professional, for improving the quality and convenience of daily lives. Especially with the innovated internet and computer graphic technology, current map services are designed with comprehensive cartographic considerations and can be simultaneously accessed by multiple clients, with which users can metaphorically establish their understanding towards the phenomena in reality, regardless of where they are. Despite location-based services become increasingly popular, the serviced contents are nevertheless often “static” and the illustration seldom or even fail to adapt to the needs of presenting continuously or periodically changing situations in the real world. From the perspective of intuitive map reading, we argue it is necessary to include the needs of “situational awareness” into the design of map services, so as to improve the human computer interactions in the clients’ application environments. A context-driven approach for the design and mashup of map services are proposed. We first identify three types of map services, namely, reference map services, application-oriented map services and atomic map services. The context of each map services are analysed based on the considerations of theme, time, weather and transportation and described by a set of pre-defined “keywords.” These standardized keywords enable application developers to define rules to select map services with correct contexts and mashup them to meet particular situation needs, e.g., a rainy day map or an emergency medical map services. As the selected map services are already defined with context considerations, their mashup results would be automatically adjusted according to the situations acquired from the clients. By enriching the situational context and enhancing the transparent access of individual map services, a flexible and powerful bridging relationship can be built between the application developers and domain service providers. The proposed approach is hence not only an innovated thinking for designing map services, but should also be seen as a crucial strategy for facilitating interoperable application environments for the ecosystem of the National Spatial Data Infrastructure.

SECoG: semantically enhanced mashup of CoAP-based IoT services

One of the noticeable characteristics of the Internet-of-Things (IoT) devices is that they are resource-constrained, which makes them incompatible with the standard Internet protocols, e.g., HTTP. Nevertheless, IoT offers the Constrained Application Protocol (CoAP) as an alternative protocol for IoT devices where it is considered to be lightweight in regard to power consumption, network traffic, and so on. The main challenges associated with CoAP, however, still remain unsolved where most of the state-of-the-art approaches group servers manually and return the exact matches only. To address these issues, we propose a novel Semantically Enhanced CoAP Gateway (SECoG) for complex service mashups. SECoG enables users to create a simple or complex CoAP service mashup via a single HTTP request. Furthermore, a prototype implementation of SECoG was deployed on a real-world testbed to evaluate the proposed approach in terms of the reliability, execution time, network traffic, and accuracy.

Structured Data REST Protocol for End to End Data Mashup

Due to the exponential growth of the data and its services, visiting multiple webs/apps by a user raises three issues—(1) consumption of extra bytes; (2) time killing process of surfing inside the webs/apps; (3) tedious task of remembering address of webs/apps with their credentials. The data mashup is a set of techniques and user-friendly approaches which not only resolves above issues but also allows ordinary user to fetch required data from multiple disparate data sources and to create the integrated view in his defined digital place. In this paper, we have proposed an extension of existing REST protocol called Structured Data REST (SDRest) protocol and user-friendly novel approach which allows even ordinary users to develop end to end data mashup, using the innovative concept of Structured Data Mashup Box (SDMB) and One Time Configuration (OTC)-Any Time Access (ATA) models. Our implementation shows that pre-mashup configuration can easily be performed by an ordinary user and an integrated user interface view of end user data mashup can be created without any technical knowledge or programming. We have also evaluated the proposed work by comparing it with some of the related works and found that the proposed work has developed user friendly configurable approach using the current state of the art techniques to involve not only the ordinary user but also the mashup service provider and the data service provider to develop public, private and hybrid data mashup.

Lightweight Service Mashup Middleware With REST Style Architecture for IoT Applications

Internet of Things (IoT) can provide new value-added service by connecting the physical devices to virtual environments association with their context, and there is also a huge demand in ad hoc services by the end users for IoT applications. By extending mashup concept into IoT applications, we can achieve a novel and more lightweight services creation approach. This paper proposes a lightweight IoT service mashup middleware based on REST-style architecture for IoT applications, and design an uniform sensor devices access and dynamically protocol stack management framework, propose a distributed publish/subscribe based messages distribution service, and situational IoT services mashup approach, which can be integrated easily to create new composite and situational applications, and also apply the REST principles to define an extensible interface to build comprehensive and situational mashup applications. Based on proposed service mashup middleware, the end user can integrate applications and services in a more lightweight manner. We also illustrated the scenarios for RESTful Web service mashups representing for coal mine safety monitoring and control automation. In the experiments, the end-user evaluation has been conducted to evaluate the middleware, and also the performance has been measured and analyzed.

Resource-oriented architecture toward efficient device management for the Internet of Things

The Internet-of-Things concept has evolved from providing network connectivity for devices in our physical world to composing complex tasks with the representations of these things in service mashups. Since most of these devices (e.g. wireless sensors, tagged objects) are resource-constrained, it is easy to realize that such objects provide basic services individually and thus, motivates the need to aggregate such services for complex tasks. However, the existence of fragmentation in protocols and standards for device management and service enablement has been a challenge for the domain. In this work, we propose a resource-oriented device management and service enablement architecture that addresses this problem. We also present a smart home application scenario to demonstrate the performance and feasibility of our approach.

Structure-aware Mashup service Clustering for cloud-based Internet of Things using genetic algorithm based clustering algorithm

An increasing number of physical objects connected to the Internet makes it possible for smart things to access all kinds of cloud services. Mashup technology has been an effective way to the rapid IoT (Internet of Things) application development. However, the number of Mashup services (IoT applications) being so large makes how to discover the desired IoT applications accurately and efficiently become a problem. Service clustering technology can facilitate service discovery effectively, and many different approaches have been proposed. However, many of them only use semantic similarities to guide clustering operations and need the configuration of the number of clusters. Structural similarities are orthogonal to semantic similarities. But they have never been used in service clustering approaches. In this paper, we propose a novel Mashup service clustering approach based on a structural similarity and a genetic algorithm based clustering algorithm. First, it applies a two-mode graph to describe Mashups, Web APIs, and their relations formally. Second, it applies the SimRank algorithm to quantify the structural similarity between every pair of Mashup services. Finally, it introduces a genetic algorithm based clustering algorithm to organize Mashup services into clusters effectively and determines the number of clusters automatically. Empirical results on a real-world Mashup services data set collected from ProgrammableWeb demonstrate that our approach can cluster Mashup services efficiently without any constraints on the number of clusters, and its performance is better than other Mashup service clustering approaches based on semantic metrics.

Cognitive Privacy Middleware for Deep Learning Mashup in Environmental IoT

Data mashup is a Web technology that combines information from multiple sources into a single Web application. Mashup applications support new services, such as environmental monitoring. The different organizations utilize data mashup services to merge data sets from the different Internet of Multimedia Things (IoMT) context-based services in order to leverage the performance of their data analytics. However, mashup, different data sets from multiple sources, is a privacy hazard as it might reveal citizens specific behaviors in different regions. In this paper, we present our efforts to build a cognitive-based middleware for private data mashup (CMPM) to serve a centralized environmental monitoring service. The proposed middleware is equipped with concealment mechanisms to preserve the privacy of the merged data sets from multiple IoMT networks involved in the mashup application. In addition, we presented an IoT-enabled data mashup service, where the multimedia data are collected from the various IoMT platforms, and then fed into an environmental deep learning service in order to detect interesting patterns in hazardous areas. The viable features within each region were extracted using a multiresolution wavelet transform, and then fed into a discriminative classifier to extract various patterns. We also provide a scenario for IoMT-enabled data mashup service and experimentation results.

Domain-aware Mashup service clustering based on LDA topic model from multiple data sources

ContextMashup is emerging as a promising software development method for allowing software developers to compose existing Web APIs to create new or value-added composite Web services. However, the rapid growth in the number of available Mashup services makes it difficult for software developers to select a suitable Mashup service to satisfy their requirements. Even though clustering based Mashup discovery technique shows a promise of improving the quality of Mashup service discovery, Mashup service clustering with high accuracy and good efficiency is still a challenge problem. ObjectiveThis paper proposes a novel domain-aware Mashup service clustering method with high accuracy and good efficiency by exploiting LDA topic model built from multiple data sources, to improve the quality of Mashup service discovery. MethodThe proposed method firstly designs a domain-aware Mashup service feature selection and reduction process by refining characterization of their domains to consolidate domain relevance. Then, it presents an extended LDA topic model built from multiple data sources (include Mashup description text, Web APIs and tags) to infer topic probability distribution of Mashup services, which serves as a basis of Mashup service similarity computation. Finally, K-means and Agnes algorithm are used to perform Mashup service clustering in terms of their similarities. ResultsCompared with other existing Mashup service clustering methods, experimental results show that the proposed method achieves a significant improvement in terms of precision, recall, F-measure, purity and entropy. ConclusionThe results of the proposed method help software developers to improve the quality of Mashup service discovery and Mashup-based software development. In the future, there will be a need to extend the method by considering heterogeneous network information among Mashup, Web APIs, tags, users, and applying it to Mashup discovery for software developers.

Lightweight Mashup Middleware for Coal Mine Safety Monitoring and Control Automation

Recently, the frequent coal mine safety accidents have caused serious casualties and huge economic losses. It is urgent for the global mining industry to increase operational efficiency and improve overall mining safety. This paper proposes a lightweight mashup middleware to achieve remote monitoring and control automation of underground physical sensor devices. First, the cluster tree based on ZigBee Wireless Sensor Network (WSN) is deployed in an underground coal mine, and propose an Open Service Gateway initiative (OSGi)-based uniform devices access framework. Then, propose a uniform message space and data distribution model, and also, a lightweight services mashup approach is implemented. With the help of visualization technology, the graphical user interface of different underground physical sensor devices could be created, which allows the sensors to combine with other resources easily. Besides, four types of coal mine safety monitoring and control automation scenarios are illustrated, and the performance has also been measured and analyzed. It has been proved that our lightweight mashup middleware can reduce the costs efficiently to create coal mine safety monitoring and control automation applications.