Maya Barnea

Recommending social media content to community owners

Online communities within the enterprise offer their leaders an easy and accessible way to attract, engage, and influence others. Our research studies the recommendation of social media content to leaders (owners) of online communities within the enterprise. We developed a system that suggests to owners new content from outside the community, which might interest the community members. As online communities are taking a central role in the pervasion of social media to the enterprise, sharing such recommendations can help owners create a more lively and engaging community. We compared seven different methods for generating recommendations, including content-based, member-based, and hybridization of the two. For member-based recommendations, we experimented with three groups: owners, active members, and regular members. Our evaluation is based on a survey in which 851 community owners rated a total of 8,218 recommended content items. We analyzed the quality of the different recommendation methods and examined the effect of different community characteristics, such as type and size.

Swimming against the streamz: search and analytics over the enterprise activity stream

Activity streams have become prevalent on the web and are starting to emerge in enterprises. In this work, we present Streamz, a novel application that uses a faceted search approach to provide employees with advanced capabilities of search, navigation, attention management, and other types of analytics on top of an enterprise activity stream. We provide a detailed description of the Streamz tool as well as usage analysis based on user interface logs and interviews of active users.

Application Generation Framework for Real-Time Complex Event Processing

We propose to develop a framework which provides the ability to apply complex event processing in realtime domains, thus allowing an easier process of developing and maintaining specific solutions for real-time event-based systems, while upholding the real time requirements of the system. Specifically, we propose to develop a framework that includes an integrated development environment for defining rules, and, given a set of rules, generates code for a complex event processing application for which it is able to determine time bounds on the response of this application to a set of supported events. In particular, the tool helps determine a time bound for the execution time of the code corresponding to each rule. Many Service Oriented Architecture (SOA) applications, in domains such as financial services, manufacturing, gaming and military/aerospace, have real-time performance requirements. We present real-life industry use cases from these domains as motivation for the potential benefit in developing real-time complex event processing applications. In support of a feasibility argument for the proposed approach we present some preliminary experimental results obtained on a partially implemented tool.

Finger on the Pulse: The Value of the Activity Stream in the Enterprise

The activity stream, which syndicates user activities across social media, has been gaining popularity on the web. With social media infiltrating the enterprise and higher portions of the workforce becoming accustomed to consuming information through activity streams, it also has the potential to play a key role in shaping the workplace. This work provides a first comprehensive study of an enterprise activity stream. We analyze different characteristics of the stream, its usage through a faceted search-based application, and the way users search it compared to traditional enterprise search. We also discuss various use cases of the stream, both from an individual employee’s perspective and from an organizational perspective, exposing the potential value and role of the activity stream in the enterprise of the future.

Increasing Activity in Enterprise Online Communities Using Content Recommendation

Although online communities have become popular both on the web and within enterprises, many of them often experience low levels of activity and engagement from their members. Previous studies identified the important role of community leaders in maintaining the health and vitality of their communities. One of their key means for doing so is by contributing relevant content to the community. In this paper, we study the effects of recommending social media content on enterprise community leaders. We conducted a large-scale user survey with four recommendation rounds, in which community leaders indicated their willingness to share social media items with their communities. They also had the option to instantly share these items. Recommendations were generated based on seven types of community interest profiles that were member-based, content-based, or hybrid. Our results attest that providing content recommendations to leaders can help uplift activity within their communities.

Social Media-Based Expertise Evidence

Social media provides a fertile ground for expertise location. The public nature of the data supports expertise inference with little privacy infringement and, in addition, presentation of direct and detailed evidence for an expert’s skillfulness in the queried topic. In this work, we study the use of social media for expertise evidence. We conducted two user surveys of enterprise social media users within a large global organization, in which participants were asked to rate anonymous experts based on artificial and real evidence originating from different types of social media data. Our results indicate that the social media data types perceived most convincing as evidence are not necessarily the ones from which expertise can be inferred most precisely or effectively. We describe these results in detail and discuss implications for designers and architects of expertise location systems.

Generating real-time complex event-processing applications

We propose to exploit the technology for complex event processing (CEP) embodied in the rule-based engine known as IBM Active Middleware Technology™ and extend it to the development of real-time CEP applications. Specifically, we propose to develop a framework that includes an integrated development environment (IDE) for defining rules, and, given a set of rules, generates code for a CEP application and enables us to determine time bounds on the response of this application to a set of supported events. In particular, the IDE helps determine a time bound for the execution time of the code corresponding to each rule. The calculation of time bounds is based on a set of benchmark measurements to be performed on the target hardware and involves code segments corresponding to basic operations. Although we assume the code generation phase produces Java™ code, the same approach can be applied to any other suitable programming language. In support of a feasibility argument for the proposed approach, we present some preliminary experimental results obtained on a partially implemented tool.

What You See And Do Is What You Get: A Human-Centric Design Approach to Human-Centric Process

Designing human-centric processes is complex. It involves the definition of interactions between humans and machines, interactions between machines and machines, information transfer, and scenarios based on decisions taken by both humans and machines. Traditionally, designing such processes is performed by design experts who define the processes in a way that mimics a bird’s eye view of it, usually expressed by a graph composed of nodes and arrows. In this work, we suggest a design approach based on the way that a process is perceived by the users who participate in it. We present a novel approach termed “What You See And Do Is What You Get” that enables defining an entire human-centric process with a lowered expertise entry bar for process designers. Further, we present a model-driven, web-based tool that realizes the presented design approach and enables fast development of applications that support human-centric processes.

Orient Me!: Important Event Identification in an Enterprise Activity Stream

Social media platforms such as blogs, wikis and file sharing have become very popular in enterprises. Despite their effectiveness in increasing collaboration in the organization, employees are overloaded with information originating from these many sources and find it hard to orient themselves in the stream of events occurring in their organizational news feed. In this paper we identify what makes an event in an organizational social media platform important to employees. Once important factors of an event to an employee are identified, the stream of events can be personalized and prioritized based on those and thus reduce the overload and assist in work efficiency. Through interviews and two extensive user surveys, the first hypothetical and the second empirical, we identified which factors of an event make it important and compare results from the hypothetical and empirical surveys.

Personal Recommendations for Raising Social Eminence in an Enterprise

Social media sites have become very popular within large enterprises. Still, employees are experiencing difficulties in engaging efficiently. In this paper, we present a study of a personalized action recommendation system in an enterprise social network. Following a previous study on how to raise ones social eminence in the enterprise and a set of interviews, we built an innovative recommendation system which provides employees with concrete personalized recommendations on how and where to engage. Differently from other systems, it presents recommendations in context of limiting social network behavioral patterns. The recommendations goal is to assist employees in growing out of these patterns. The paper presents the interview findings, the innovative recommendation system and results of a wide survey investigating the effectiveness of such a system.