Microblogging platforms such as Twitter have been recently frequently used for detecting real-time events. The spatial component, as reflected by user location, usually plays a key role in such systems. However, an often neglected source of spatial information are location mentions expressed in tweet contents. In this paper we demonstrate a novel visualization system for analyzing how Twitter users collectively talk about space and for uncovering correlations between geographical locations of Twitter users and the locations they tweet about. Our exploratory analysis is based on the development of a model of spatial information extraction and representation that allows building effective visual analytics framework for large scale datasets. We show visualization results based on half a year long dataset of Japanese tweets and a four months long collection of tweets from USA. The proposed system allows observing many space related aspects of tweet messages including the average scope of spatial attention of social media users and variances in spatial interest over time. The analytical framework we provide and the findings we outline can be valuable for scientists from diverse research areas and for any users interested in geographical and social aspects of shared online data.

Microblogging platforms such as Twitter have recently received much attention as great sources for live web sensing, real-time event detection and opinion analysis. Previous works usually assumed that tweets mainly describe "what's happening now". However, a large portion of tweets contains time expressions that refer to time frames within the past or the future. Such messages often reflect expectations or memories of social media users. In this work we investigate how microblogging users collectively refer to time. In particular, we analyze half a year long portion of Japanese and four months long collection of US tweets and we quantify collective temporal attention of users as well as other related temporal characteristics. This kind of knowledge is helpful in the context of growing interest for detection and prediction of important events within social media. The exploratory analysis we perform is possible thanks to the development of visual analytics framework for robust overview and easy detection of various regularities in the past and future-oriented thinking of Twitter users. We believe that the visualizations we provide and the findings we outline can be also valuable for sociologists and computer scientists to test and refine their models about time in natural language.

We present the WebdamLog system for managing distributed data on the Web in a peer-to-peer manner. We demonstrate the main features of the system through an application called Wepic for sharing pictures between attendees of the sigmod conference. Using Wepic, the attendees will be able to share, download, rate and annotate pictures in a highly decentralized manner. We show how WebdamLog handles heterogeneity of the devices and services used to share data in such a Web setting. We exhibit the simple rules that define the Wepic application and show how to easily modify the Wepic application.

This paper addresses the challenges faced by everyday Web users, who interact with inherently heterogeneous and distributed information. Managing such data is currently beyond the skills of casual users. We describe ongoing work that has as its goal the development of foundations for declarative distributed data management. In this approach, we see the Web as a knowledge base consisting of distributed logical facts and rules. Our objective is to enable automated reasoning over this knowledge base, ultimately improving the quality of service and of data. For this, we use Webdamlog, a Datalog-style language with rule delegation. We outline ongoing efforts on the Web dam Exchange platform that combines Webdamlog evaluation with communication and security protocols.

The demonstration presents the WebdamExchange system, a distributed knowledge base management system with access rights, localization and provenance. This system is based on the exchange of logical statements that describe documents, collections, access rights, keys and localization information and updates of this data. We illustrate how the model can be used in a social-network context to help users keep control on their data on the web. In particular, we show how users within very different schemes of data-distribution (centralized, dht, unstructured P2P, etc.) can still transparently collaborate while keeping a good control over their own data.

There is a new trend to use Datalog-style rule-based languages to specify modern distributed applications, notably on the Web. We introduce here such a language for a distributed data model where peers exchange messages (i.e. logical facts) as well as rules. The model is formally defined and its interest for distributed data management is illustrated through a variety of examples. A contribution of our work is a study of the impact on expressiveness of "delegations" (the installation of rules by a peer in some other peer) and explicit timestamps. We also validate the semantics of our model by showing that under certain natural conditions, our semantics converges to the same semantics as the centralized system with the same rules. Indeed, we show this is even true when updates are considered.