Many business Web-based applications do not offer APIs to enable other applications to access their data and functions in a programmatic manner. This makes their composition difficult for instance to synchronize data between two applications. To address this challenge, this paper presents Abmash, an approach to facilitate the integration of such legacy Web applications by automatically imitating human interactions with them. By automatically interacting with the GUI of Web applications, the system supports all forms of integrations including bidirectional interactions and is able to interact with AJAX-based applications. Furthermore, the integration programs are easy to write because they deal with end-user, visual UI elements. The integration code is simple enough to be called a 'mash-up'. Copyright © 2013 John Wiley & Sons, Ltd.
Apart from those three main types of mashups, the literature also refers to enterprise mashups'' (e.g. @cite_28 ) for mashups created in an enterprise environment with some associated business value, and to mashup agents'' @cite_9 for agents capable of semantically determining relevant information sources with respect to a specific concern. Finally, as others programming activities, mashup development is also related to development environments @cite_7 @cite_31 and debugging @cite_4 .
@cite_4 @cite_7 @cite_28 @cite_9 @cite_31
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This chapter presents a survey of six mashup development environments and looks at how mashups fit into the vision of the smart internet. The fast-paced expansion of mashup development environments has resulted in a wealth of features and approaches. To provide an overview of End User Development support in current mashup development environments, we explore, summarize and compare their features across six different themes (Levels of Abstraction, Learning Support, Community Support, Discoverability, User Interface Design and Software Engineering Techniques). We found that the mashup development environments provide many features to support end users, but there is still much room for further improvement, especially in relation to the smart internet. We believe that by connecting matters of concern to mashups, mashup development environments can become an essential part of the smart internet. Such a connection would enable mining of mashup elements, which could facilitate automatic mashup creation and customization. The evolution of the Web over the past few years has fostered the growth of a handful of new technologies (e.g. Blogs, Wiki’s, Web Services). Recently web mashups have emerged as the newest Web technology and have gained lots of momentum and attention from both academic and industry communities. Current mashup literature focuses on a wide array of issues, which can be partially explained by how new the topic is. However, to date, mashup literature lacks an articulation of the different subtopics of web mashup research. This study presents a broad review of mashup literature to help frame the 1subtopics in mashup research. In this paper we describe a new release of a Web scale entity graph that serves as the backbone of Microsoft Academic Service (MAS), a major production effort with a broadened scope to the namesake vertical search engine that has been publicly available since 2008 as a research prototype. At the core of MAS is a heterogeneous entity graph comprised of six types of entities that model the scholarly activities: field of study, author, institution, paper, venue, and event. In addition to obtaining these entities from the publisher feeds as in the previous effort, we in this version include data mining results from the Web index and an in-house knowledge base from Bing, a major commercial search engine. As a result of the Bing integration, the new MAS graph sees significant increase in size, with fresh information streaming in automatically following their discoveries by the search engine. In addition, the rich entity relations included in the knowledge base provide additional signals to disambiguate and enrich the entities within and beyond the academic domain. The number of papers indexed by MAS, for instance, has grown from low tens of millions to 83 million while maintaining an above 95 accuracy based on test data sets derived from academic activities at Microsoft Research. Based on the data set, we demonstrate two scenarios in this work: a knowledge driven, highly interactive dialog that seamlessly combines reactive search and proactive suggestion experience, and a proactive heterogeneous entity recommendation. In Semantic Textual Similarity (STS), systems rate the degree of semantic equivalence, on a graded scale from 0 to 5, with 5 being the most similar. This year we set up two tasks: (i) a core task (CORE), and (ii) a typed-similarity task (TYPED). CORE is similar in set up to SemEval STS 2012 task with pairs of sentences from sources related to those of 2012, yet different in genre from the 2012 set, namely, this year we included newswire headlines, machine translation evaluation datasets and multiple lexical resource glossed sets. TYPED, on the other hand, is novel and tries to characterize why two items are deemed similar, using cultural heritage items which are described with metadata such as title, author or description. Several types of similarity have been defined, including similar author, similar time period or similar location. The annotation for both tasks leverages crowdsourcing, with relative high interannotator correlation, ranging from 62 to 87 . The CORE task attracted 34 participants with 89 runs, and the TYPED task attracted 6 teams with 14 runs.