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Unprecedented success and active usage of social media services result in massive amounts of user-generated data. An increasing interest in the contained information from social media data leads to more and more sophisticated analysis and visualization applications. Because of the fast pace and distribution of news in social media data it is an appropriate source to identify events in the data and directly display their occurrence to analysts or other users. This paper presents a method for event identification in local areas using the Twitter data stream. We implement and use a combined log-likelihood ratio approach for the geographic and time dimension of real-life Twitter data in predefined areas of the world to detect events occurring in the message contents. We present a case study with two interesting scenarios to show the usefulness of our approach.

Twitter's popularity as a source of up-to-date news and information is constantly increasing. In response to this trend, numerous event detection techniques have been proposed to cope with the rate and volume of Twitter data streams. Although most of these works conduct some evaluation of the proposed technique, a comparative study is often omitted. In this paper, we present a survey and experimental analysis of state-of-the-art event detection techniques for Twitter data streams. In order to conduct this study, we define a series of measures to support the quantitative and qualitative comparison. We demonstrate the effectiveness of these measures by applying them to event detection techniques as well as to baseline approaches using real-world Twitter streaming data.

Comprehensive data analysis has become indispensable in a variety of environments. Standard OLAP (On-Line Analytical Processing) systems, designed for satisfying the reporting needs of the business, tend to perform poorly or even fail when applied in non-business domains such as medicine, science, or government. The underlying multidimensional data model is restricted to aggregating only over summarizable data, i.e. where each dimensional hierarchy is a balanced tree. This limitation, obviously too rigid for a number of applications, has to be overcome in order to provide adequate OLAP support for novel domains. We present a framework for querying complex multidimensional data, with the major effort at the conceptual level as to transform irregular hierarchies to make them navigable in a uniform manner. We provide a classification of various behaviors in dimensional hierarchies, followed by our two-phase modeling method that proceeds by eliminating irregularities in the data with subsequent transformation of a complex hierarchical schema into a set of well-behaved sub-dimensions. Mapping of the data to a visual OLAP browser relies solely on metadata which captures the properties of facts and dimensions as well as the relationships across dimensional levels. Visual navigation is schemabased, i.e., users interact with dimensional levels and the data instances are displayed on-demand. The power of our approach is exemplified using a real-world study from the domain of academic administration.

This paper presents an approach to exploring multidimensional data cubes with hierarchical visualization techniques. Analysts interact with data in a predominantly "drill-down" fashion, i.e. from coarse grained aggregates towards the desired level of detail. We suggest that visual hierarchies are adequate for mapping the multiscale nature of decomposition as they preserve the results of the entire interaction.We introduce a class of visual structures called Enhanced Decomposition Tree. Every tree level is created by a disaggregation step along a chosen dimension, the nodes contain the corresponding sub-aggregates arranged into a chart and the edges are labeled with their dimensional values. Various layouts are proposed to account for different analysis tasks.Data cubes are queried using a schema-based browser which presents dimensions by the hierarchies of their granularity levels, thus offering an efficient way of generating hierarchical visualizations. Multiple data cubes may be explored in parallel along their shared dimensions. The power of our approach is exemplified using a real-world study from the domain of academic administration.

This work discusses the CROQUE approach to the maintenance problem for materialized views. In a CROQUE database, application-speci ed collections (type extents or classes) themselves need not be materialized. In exchange, the system maintains (redundant) views of the application data that help to minimize query response time. We understand views as functions of database objects and examine algebraic properties of these functions, in particular linearity, t o d e r i v e incremental update plans. It turns out that it is feasible to employ ODMG OQL as a view de nition language { instead of inventing a specialized one { in such a n e n vironment, since the majority of its clauses represent linear functions.

Abstract. Twitter's popularity as a source of up-to-date news and information is constantly increasing. In response to this trend, numerous event detection techniques have been proposed to cope with the rate and volume of social media data streams. Although most of these works conduct some evaluation of the proposed technique, a comparative study is often omitted. In this paper, we present a series of measures that we designed to support the quantitative and qualitative comparison of event detection techniques. In order to demonstrate the effectiveness of these measures, we apply them to state-of-the-art event detection techniques as well as baseline approaches using real-world Twitter streaming data.