DOBBS: Towards a Comprehensive Dataset to Study the Browsing Behavior of Online Users

The investigation of the browsing behavior of users provides useful information to optimize web site design, web browser design, search engines offerings, and online advertisement. This has been a topic of active research since the Web started and a large body of work exists. However, new online services as well as advances in Web and mobile technologies clearly changed the meaning behind "browsing the Web" and require a fresh look at the problem and research, specifically in respect to whether the used models are still appropriate. Platforms such as YouTube, Netflix or last.fm have started to replace the traditional media channels (cinema, television, radio) and media distribution formats (CD, DVD, Blu-ray). Social networks (e.g., Facebook) and platforms for browser games attracted whole new, particularly less tech-savvy audiences. Furthermore, advances in mobile technologies and devices made browsing "on-the-move" the norm and changed the user behavior as in the mobile case browsing is often being influenced by the user's location and context in the physical world. Commonly used datasets, such as web server access logs or search engines transaction logs, are inherently not capable of capturing the browsing behavior of users in all these facets. DOBBS (DERI Online Behavior Study) is an effort to create such a dataset in a non-intrusive, completely anonymous and privacy-preserving way. To this end, DOBBS provides a browser add-on that users can install, which keeps track of their browsing behavior (e.g., how much time they spent on the Web, how long they stay on a website, how often they visit a website, how they use their browser, etc.). In this paper, we outline the motivation behind DOBBS, describe the add-on and captured data in detail, and present some first results to highlight the strengths of DOBBS

Virtual Location-Based Services: Merging the Physical and Virtual World

Location-based services gained much popularity through providing users with helpful information with respect to their current location. The search and recommendation of nearby locations or places, and the navigation to a specific location are some of the most prominent location-based services. As a recent trend, virtual location-based services consider webpages or sites associated with a location as 'virtual locations' that online users can visit in spite of not being physically present at the location. The presence of links between virtual locations and the corresponding physical locations (e.g., geo-location information of a restaurant linked to its website), allows for novel types of services and applications which constitute virtual location-based services (VLBS). The quality and potential benefits of such services largely depends on the existence of websites referring to physical locations. In this paper, we investigate the usefulness of linking virtual and physical locations. For this, we analyze the presence and distribution of virtual locations, i.e., websites referring to places, for two Irish cities. Using simulated tracks based on a user movement model, we investigate how mobile users move through the Web as virtual space. Our results show that virtual locations are omnipresent in urban areas, and that the situation that a user is close to even several such locations at any time is rather the normal case instead of the exception

Multiterm keyword search in NoSQL systems

Distributed NoSQL systems aim to provide high availability for large volumes of data but lack the inherent support of complex queries often required by overlying applications. Common solutions based on inverted lists for single terms perform poorly in large-scale distributed settings. The authors thus propose a multiterm indexing technique that can store the inverted lists of combinations of terms. A query-driven mechanism adaptively stores popular term combinations derived from the recent query history. Experiments show that this approach reduces the overall bandwidth consumption by half, significantly improving the NoSQL system's capacity and response time with only marginal overhead in terms of additional, but cheaper, required (storage) resources

A Unifying Framework for Behavior-based Trust Models

Abstract. Trust models have been touted to facilitate cooperation among unknown entities. Existing behavior-based trust models typically include a fixed evaluation scheme to derive the trustworthiness of an entity from knowledge about its behavior in previous interactions. This paper in turn proposes a framework for behavior-based trust models for open environments with the following distinctive characteristic. Based on a relational representation of behavior-specific knowledge, we propose a trust-policy algebra allowing for the specification of a wide range of trust-evaluation schemes. A key observation is that the evaluation of the standing of an entity in the network of peers requires centrality indices, and we propose a first-class operator of our algebra for computation of centrality measures. This paper concludes with some preliminary performance experiments that confirm the viability of our approach.

Nudging Users to Slow Down the Spread of Fake News in Social Media

10.1109/ICMEW46912.2020.91060032020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW