Prediction, Recommendation and Group Analytics Models in the domain of Mashup Services and Cyber-Argumentation Platform

Mashup application development is becoming a widespread software development practice due to its appeal for a shorter application development period. Application developers usually use web APIs from different sources to create a new streamlined service and provide various features to end-users. This kind of practice saves time, ensures reliability, accuracy, and security in the developed applications. Mashup application developers integrate these available APIs into their applications. Still, they have to go through thousands of available web APIs and chose only a few appropriate ones for their application. Recommending relevant web APIs might help application developers in this situation. However, very low API invocation from mashup applications creates a sparse mashup-web API dataset for the recommendation models to learn about the mashups and their web API invocation pattern. One research aims to analyze these mashup-specific critical issues, look for supplemental information in the mashup domain, and develop web API recommendation models for mashup applications. The developed recommendation model generates useful and accurate web APIs to reduce the impact of low API invocations in mashup application development. Cyber-Argumentation platform also faces a similarly challenging issue. In large-scale cyber argumentation platforms, participants express their opinions, engage with one another, and respond to feedback and criticism from others in discussing important issues online. Argumentation analysis tools capture the collective intelligence of the participants and reveal hidden insights from the underlying discussions. However, such analysis requires that the issues have been thoroughly discussed and participant’s opinions are clearly expressed and understood. Participants typically focus only on a few ideas and leave others unacknowledged and underdiscussed. This generates a limited dataset to work with, resulting in an incomplete analysis of issues in the discussion. One solution to this problem would be to develop an opinion prediction model for cyber-argumentation. This model would predict participant’s opinions on different ideas that they have not explicitly engaged. In cyber-argumentation, individuals interact with each other without any group coordination. However, the implicit group interaction can impact the participating user\u27s opinion, attitude, and discussion outcome. One of the objectives of this research work is to analyze different group analytics in the cyber-argumentation environment. The objective is to design an experiment to inspect whether the critical concepts of the Social Identity Model of Deindividuation Effects (SIDE) are valid in our argumentation platform. This experiment can help us understand whether anonymity and group sense impact user\u27s behavior in our platform. Another section is about developing group interaction models to help us understand different aspects of group interactions in the cyber-argumentation platform. These research works can help develop web API recommendation models tailored for mashup-specific domains and opinion prediction models for the cyber-argumentation specific area. Primarily these models utilize domain-specific knowledge and integrate them with traditional prediction and recommendation approaches. Our work on group analytic can be seen as the initial steps to understand these group interactions

Exploiting Social Annotation for Automatic Resource Discovery

Information integration applications, such as mediators or mashups, that require access to information resources currently rely on users manually discovering and integrating them in the application. Manual resource discovery is a slow process, requiring the user to sift through results obtained via keyword-based search. Although search methods have advanced to include evidence from document contents, its metadata and the contents and link structure of the referring pages, they still do not adequately cover information sources -- often called ``the hidden Web''-- that dynamically generate documents in response to a query. The recently popular social bookmarking sites, which allow users to annotate and share metadata about various information sources, provide rich evidence for resource discovery. In this paper, we describe a probabilistic model of the user annotation process in a social bookmarking system del.icio.us. We then use the model to automatically find resources relevant to a particular information domain. Our experimental results on data obtained from \emph{del.icio.us} show this approach as a promising method for helping automate the resource discovery task.Comment: 6 pages, submitted to AAAI07 workshop on Information Integration on the We

Deep Learning Framework for Online Interactive Service Recommendation in Iterative Mashup Development

Recent years have witnessed the rapid development of service-oriented computing technologies. The boom of Web services increases the selection burden of software developers in developing service-based systems (such as mashups). How to recommend suitable follow-up component services to develop new mashups has become a fundamental problem in service-oriented software engineering. Most of the existing service recommendation approaches are designed for mashup development in the single-round recommendation scenario. It is hard for them to update recommendation results in time according to developers' requirements and behaviors (e.g., instant service selection). To address this issue, we propose a deep-learning-based interactive service recommendation framework named DLISR, which aims to capture the interactions among the target mashup, selected services, and the next service to recommend. Moreover, an attention mechanism is employed in DLISR to weigh selected services when recommending the next service. We also design two separate models for learning interactions from the perspectives of content information and historical invocation information, respectively, as well as a hybrid model called HISR. Experiments on a real-world dataset indicate that HISR outperforms several state-of-the-art service recommendation methods in the online interactive scenario for developing new mashups iteratively.Comment: 15 pages, 6 figures, and 3 table

SMS: A Framework for Service Discovery by Incorporating Social Media Information

© 2008-2012 IEEE. With the explosive growth of services, including Web services, cloud services, APIs and mashups, discovering the appropriate services for consumers is becoming an imperative issue. The traditional service discovery approaches mainly face two challenges: 1) the single source of description documents limits the effectiveness of discovery due to the insufficiency of semantic information; 2) more factors should be considered with the generally increasing functional and nonfunctional requirements of consumers. In this paper, we propose a novel framework, called SMS, for effectively discovering the appropriate services by incorporating social media information. Specifically, we present different methods to measure four social factors (semantic similarity, popularity, activity, decay factor) collected from Twitter. Latent Semantic Indexing (LSI) model is applied to mine semantic information of services from meta-data of Twitter Lists that contains them. In addition, we assume the target query-service matching function as a linear combination of multiple social factors and design a weight learning algorithm to learn an optimal combination of the measured social factors. Comprehensive experiments based on a real-world dataset crawled from Twitter demonstrate the effectiveness of the proposed framework SMS, through some compared approaches

Modeling Users Feedback Using Bayesian Methods for Data-Driven Requirements Engineering

Data-driven requirements engineering represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. App developers now receive abundant user feedback from user comments in app stores and social media, i.e., explicit feedback, to feedback from usage data and system logs, i.e, implicit feedback. In this dissertation, we describe two novel Bayesian approaches that utilize the available user\u27s to support requirements decisions and activities in the context of applications delivered through software marketplaces (web and mobile). In the first part, we propose to exploit implicit user feedback in the form of usage data to support requirements prioritization and validation. We formulate the problem as a popularity prediction problem and present a novel Bayesian model that is highly interpretable and offers early-on insights that can be used to support requirements decisions. Experimental results demonstrate that the proposed approach achieves high prediction accuracy and outperforms competitive models. In the second part, we discuss the limitations of previous approaches that use explicit user feedback for requirements extraction, and alternatively, propose a novel Bayesian approach that can address those limitations and offer a more efficient and maintainable framework. The proposed approach (1) simplifies the pipeline by accomplishing the classification and summarization tasks using a single model, (2) replaces manual steps in the pipeline with unsupervised alternatives that can accomplish the same task, and (3) offers an alternative way to extract requirements using example-based summaries that retains context. Experimental results demonstrate that the proposed approach achieves equal or better classification accuracy and outperforms competitive models in terms of summarization accuracy. Specifically, we show that the proposed approach can capture 91.3% of the discussed requirement with only 19% of the dataset, i.e., reducing the human effort needed to extract the requirements by 80%

Proceedings of the First International Workshop on Mashup Personal Learning Environments

Wild, F., Kalz, M., & Palmér, M. (Eds.) (2008). Proceedings of the First International Workshop on Mashup Personal Learning Environments (MUPPLE08). September, 17, 2008, Maastricht, The Netherlands: CEUR Workshop Proceedings, ISSN 1613-0073. Available at http://ceur-ws.org/Vol-388.The work on this publication has been sponsored by the TENCompetence Integrated Project (funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org]) and partly sponsored by the LTfLL project (funded by the European Commission's 7th Framework Programme, priority ISCT. Contract 212578 [http://www.ltfll-project.org

A Survey on the Web of Things

The Web of Things (WoT) paradigm was proposed first in the late 2000s, with the idea of leveraging Web standards to interconnect all types of embedded devices. More than ten years later, the fragmentation of the IoT landscape has dramatically increased as a consequence of the exponential growth of connected devices, making interoperability one of the key issues for most IoT deployments. Contextually, many studies have demonstrated the applicability of Web technologies on IoT scenarios, while the joint efforts from the academia and the industry have led to the proposals of standard specifications for developing WoT systems. Through a systematic review of the literature, we provide a detailed illustration of the WoT paradigm for both researchers and newcomers, by reconstructing the temporal evolution of key concepts and the historical trends, providing an in-depth taxonomy of software architectures and enabling technologies of WoT deployments and, finally, discussing the maturity of WoT vertical markets. Moreover, we identify some future research directions that may open the way to further innovation on WoT systems

Automated Rule-Based Selection and Instantiation of Layout Templates for Widget-Based Microsites

Veebi avatud arhitektuuron loonud soodsa pinnase veebisolevate andmete kasutamiseks nii keerulisemates kui lihtsamates veebirakendustes. Andmete kogumise ja visualiseerimise lihtsustamiseks lihtsates veebirakendustes on loodud hulganisti tööriistu, mille seas on ka mashup'ide loomise tööriistad. Olemasolevate tööriistadega kõrge kasutatavusega mashup veebilehe loomine võib aga paraku olla keerukas, kuna nõuab erinevate tehnoloogiate ning programmeerimiskeelte tundmist, rääkimata kasutatavuse juhtnööridega kursisolemist. Kuigi osad mashup'ide platvormid, a'la OpenAjax Hub, lihtsustavad olemasolevate komponentide kombineerimist, on lahendamata probleemiks siiani nende rakenduste kasutatavus. Käesolev magistritöö kirjeldab reeglipõhist lahendust andmete visualiseerimise vidinate jaoks sobiva veebilehe malli automaatseks valimiseks vastavalt enimlevinud veebilehtede kasutatavuse juhtnööridele. Selleks laetakse vidinate ning struktuurimallide kirjeldused koos kasutatavuse juhtnööridest saadud reeglitega reeglimootorisse ning kasutatakse reeglimootorit ekspertsüsteemina, mis soovitab sobivamaid malle vastavalt etteantud vidinate komplektile. Lahenduse reeglipõhine ülesehitus võimaldab uute vidinate ning mallide lisandumisel või juhtnööride muutumisel operatiivselt reageerida nendele muutustele reeglibaasi täiendamise kaudu. Väljapakutud lahendus realiseeriti käesoleva töö raames Auto Microsite rakendusena, mis koosneb serveri- ning kliendipoolsest osast. Serveri poolel toimub reeglite abil vidinate komplekti visualiseerimiseks sobiva malli valimine kasutades OO jDREW RuleML reeglimootorit ning rakenduse paketeerimiseks koodi genereerimine. Kliendi poolel kasutatakse OpenAjax Hub raamistikkuvidinate turvaliseks eraldamiseks ning omavahel suhtlemapanemisel. Samuti on kliendi poolel lahendatud genereeritud veebilehe vastavusse viimine brauseri võimalustega. Katsetamaks Auto Microsite rakendust praktikas loodi seda kasutades realisatsioonid kahele lihtsale stsenaariumile. Esimesel juhul viusaliseeriti Euroopa 1997-2008 tööjõukulude (Hourly labour costs in Euros (European Union 1997-2008) ing. k.) andmeid kaardi, tabeli, kokkuvõtte ja menüü vidinatega. Teisel juhul kasutati lisaks andmete visualiseerimise vidinatele ka väliseid andmeallikaid, mis olid realiseeritud mittevisuaalsete vidinatena. Saadud andmed visualiseeriti kahe tabeli ning ühe kaardi vidinaga. Näidisveebilehtede loomise tulemusena järeldub, et rakendus sobib lihtsate veebilehtede loomiseks. Lisaks on võimalik lahendust täiendada keerukamate veebirakenduste automaatseks loomiseks läbi vastavate mallide ning reeglite lisamise.This thesis proposes a rule-based widget and layout template matchmaking solution for widget-based microsites. The solution takes as an input a set of widget descriptions and a set of layout templates with widget placeholders and returns a microsite, where the most suitable template has been instantiated with corresponding widgets. Matchmaking is based on applying a rule engine to metadata of widgets and placeholders about their content categories and dimensions,. Additional usability rules are used to further improve the results with respect to commonly accepted usability guidelines. Such a solution makes it possible to modularly enhance the usability results in the future simply by adding new usability rules and layout templates. Furthermore, the solution can be applied in mashup creation tools for layout selection. The proposed solution has been implemented and is called Auto Microsite in this thesis. The system consists of a server-side and a client-side component. The server-side component matches widgets with layout template placeholders according to the given rules by using the OO jDREW RuleML engine. The client-side is responsible for presenting the mashup appropriately for the client device. The latter is based on OpenAjax Hub 2.0 framework, which enables secure sandboxing and communication of widgets in the generated microsite. Furthermore, OpenAjax Metadata 1.0 specification is used in this thesis to package the widgets such that they could be easily reused. In order to evaluate the Auto Microsite system in practice two proof of concept (PoC) scenarios were implemented. The first scenario visualized "Hourly labour costs in Euros (European Union 1997-2008)" data using widgets for a map, a table and a summary. In the second scenario, also data was queried through a SOAP service and a Web site. In the scenario data was visualized using two table widgets and a map widget. The SOAP service and queries to the Web site were packaged as non-visual widgets to fit the framework. The POCs demonstrate that the Auto Microsite system is able to construct widget-based microsites. Furthermore, the framework is capable of constructing also more complex Web applications, with several pages and more content widgets, by adding new rules and templates

Federated Embedded Systems – a review of the literature in related fields

This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways