The emergence of social media allows people to interact with others all over the world. During interaction, people leave many traces behind that can reveal things about themselves, or about how they perceive others: having many followers may indicate that one is an influencer; forum answers that gain high ranking, are likely to testify for expertise; people who gain high ranking in eCommerce sites are likely to be trustworthy. In this paper, we examine whether public online traces can be used for inferring the reputation of a person as perceived by others in relation to trustworthiness, influence, expertise, and impact. We describe a study performed on indicators of reputation that employees leave in a rich organizational social media platform. We compare different indicators, and report the results of an extensive user study with over 500 participants who provided their perception of thousands of others through a set of hypothetical scenarios.

A proliferation of travel-related web sites enable people to share their travel plans, review hotels, offer advice, and more. In this paper we study social travel planning in the enterprise. While business travelers and leisure travelers have different preferences and needs, employees may benefit from sharing information and travel plans within the enterprise. We present a study collecting the requirements for social travel from employees, detail the design principles of a social travel application in the enterprise, and present Voyage, the outcome. We evaluated Voyage based on qualitative and quantitative data and discuss the results using four perspectives: collaborative activities, social information impact, usage patterns, and sharing behavior. Employees expressed their growing satisfaction from the social information contributed by fellow employees. Moreover, we observed that Voyage shortens the reservation time, thus saving costs for the enterprise.

Feed readers have emerged as one of the salient applications that characterize Web 2.0. Lately, some of the available readers introduced social features, analogously to other Web 2.0 applications, such as recommendations and tagging. Yet, most of the readers lack collaborative features, such as the ability to share feeds in a community or divide the reading task among community members. In this paper we describe CoffeeReader, a web-based feed reader, which combines social and collaborative features, and is deployed in a small community within our company. CoffeeReader provides awareness of other users' feed lists and read status; it enables information sharing such as tags and recommendations; and aims to support coordination of filtering through feeds to locate important items. We compare these group collaboration features of CoffeeReader with emerging features in publicly available feed readers; present the outcomes of using CoffeeReader within our community; and discuss our findings and their implications on making feed readers more collaborative.

Application messaging focuses on enabling integration, linking and extension of business applications. This focus has led to intelligent middleware development that encompasses a modular, plug and play components approach to providing an open message broker framework. A message broker is a value-added application-to-application middleware service that comprises one or more software facilities. It is capable of one-to-many, many-to-one and many-to-many message distribution. A message broker further enables one to decompose information transformation and routing requirements into message flows, which are a series of stepwise operations that can be connected using visual composition tools. A message flow is a new programming paradigm that supports message transformation, database integration, message warehousing, and message routing. In this paper we introduce the message flow concept and its execution model. Next, we concentrate on the definition of test adequacy criteria suitable for message flows and the description of a coverage analysis tool, based on the defined coverage criteria. The coverage analysis tool collects execution details from trace files, analyzes them and presents the coverage results to the user in an intuitive manner, both visually and textually.