Assisted mashup development: On the discovery and recommendation of mashup composition knowledge

Over the past few years, mashup development has been made more accessible with tools such as Yahoo! Pipes that help in making the development task simpler through simplifying technologies. However, mashup development is still a difficult task that requires knowledge about the functionality of web APIs, parameter settings, data mappings, among other development efforts. In this work, we aim at assisting users in the mashup process by recommending development knowledge that comes in the form of reusable composition knowledge. This composition knowledge is harvested from a repository of existing mashup models by mining a set of composition patterns, which are then used for interactively providing composition recommendations while developing the mashup. When the user accepts a recommendation, it is automatically woven into the partial mashup model by applying modeling actions as if they were performed by the user. In order to demonstrate our approach we have implemented Baya, a Firefox plugin for Yahoo! Pipes that shows that it is indeed possible to harvest useful composition patterns from existing mashups, and that we are able to provide complex recommendations that can be automatically woven inside Yahoo! Pipes' web-based mashup editor

Message Correlation for Conversation Reconstruction in Service Interaction Logs

The problem of understanding the behavior of business processes and of services is rapidly becoming a priority in medium and large companies. To this end, recently, analysis tools as well as variations of data mining techniques have been applied to process and service execution logs to perform OLAP-style analysis and to discover behavioral (process and protocol) models out of execution data. All these approaches are based on one key assumption: events describing executions and stored in process and service logs include identifiers that allow associating each event to the process or service execution they belong to (e.g., can correlate all events related to the processing of a certain purchase order or to the hiring of a given employee). In reality, however, such information rarely exists. In this paper, we present a framework for discovering correlations among messages in service logs. We characterize the problem of message correlation and propose novel algorithms and techniques based on heuristics on the characteristics of conversations and of message attributes that can act as identifier for such conversations. As we will show, there is no right or wrong way to correlate messages, and such correlation is necessarily subjective. To account for this subjectiveness, we propose an approach where algorithms suggest candidate correlators, provide measures that help users understand the implications of choosing a given correlators, and organize candidate correlators in such a way to facilitate visual exploration. The approach has been implemented and experimental results show its viability and scalability on large synthetic and real-world datasets. We believe that message correlation is a very important and challenging area of research that will witness many contributions in the near future due to the pressing industry needs for process and service execution analysis

eAssistant: Cognitive Assistance for Identification and Auto-Triage of Actionable Conversations

The browser and screen have been the main user interfaces of the Web and mobile apps. The notification mechanism is an evolution in the user interaction paradigm by keeping users updated without checking applications. Conversational agents are posed to be the next revolution in user interaction paradigms. However, without intelligence on the triage of content served by the interaction and content differentiation in applications, interaction paradigms may still place the burden of information overload on users. In this paper, we focus on the problem of intelligent identification of actionable information in the content served by applications, and in particular in productivity applications (such as email, chat, messaging, social collaboration tools, etc.). We present eAssistant, which offers a novel fine-grained action identification method in an adaptive, personalizable, and online-trainable manner, and a cognitive agent/API that uses action information and user-centric conv ersation characteristics to auto-triage user conversations. The introduced method identifies individual actions and associated metadata; it is extensible in terms of the number of action classes; it learns in an online and continuous manner via user interactions and feedback, and it is personalizable to different users. We have evaluated the proposed method using real-world datasets. The results show that the method achieves higher accuracy compared to traditional ways of formulating the problem, while exhibiting additional desired properties of online, personalized, and adaptive learning. In eAssistant, we introduce a multi-dimensional learning model of conversations auto-triage, defined based on a user study and NLP-based information extraction techniques, to auto-triage user conversations on social collaboration and productivity tools