2017, Weiler, Andreas, Grossniklaus, Michael, Scholl, Marc H.

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.

2016, Hölsch, Jürgen, Grossniklaus, Michael, Scholl, Marc H.

A key promise of SQL is that the optimizer will find the most efficient execution plan, regardless of how the query is formulated. In general, query optimizers of modern database systems are able to keep this promise, with the notable exception of nested queries. While several optimization techniques for nested queries have been proposed, their adoption in practice has been limited. In this paper, we argue that the NF² (non-first normal form) algebra, which was originally designed to process nested tables, is a better approach to nested query optimization as it fulfills two key requirements. First, the NF² algebra can represent all types of nested queries as well as both existing and novel optimization techniques based on its equivalences. Second, performance benefits can be achieved with little changes to existing transformation-based query optimizers as the NF² algebra is an extension of the relational algebra.

2015, Weiler, Andreas, Grossniklaus, Michael, Scholl, Marc H.

Nowadays, there are plenty of sources generating massive amounts of text data streams in a continuous way. For example, the increasing popularity and the active use of social networks result in voluminous and fast-flowing text data streams containing a large amount of user-generated data about almost any topic around the world. However, the observation and tracking of the ongoing evolution of topics in these unevenly distributed text data streams is a challenging task for analysts, news reporters, or other users. This paper presents “Stor-e- Motion” a shape-based visualization to track the ongoing evolution of topics’ frequency (i.e., importance), sentiment (i.e., emotion), and context (i.e., story) in user-defined topic channels over continuous flowing text data streams. The visualization supports the user in keeping the overview over vast amounts of streaming data and guides the perception of the user to unexpected and interesting points or periods in the text data stream. In this work, we mainly focus on the visualization of text streams from the social microblogging service Twitter, for which we present a series of case studies (e.g., the observation of cities, movies, or natural disasters) applied on real-world data streams collected from the public timeline. However, to further evaluate our visualization, we also present a baseline case study applied on the text stream of a fantasy book series.

2015, Weiler, Andreas, Grossniklaus, Michael, Scholl, Marc H.

Twitter’s increasing popularity as a source of up to date news and information about current events has spawned a body of research on event detection techniques for social media data streams. Although all proposed approaches provide some evidence as to the quality of the detected events, none relate this task-based performance to their run-time performance in terms of processing speed or data throughput. In particular, neither a quantitative nor a comparative evaluation of these aspects has been performed to date. In this paper, we study the run-time and task-based performance of several state-of-the-art event detection techniques for Twitter. In order to reproducibly compare run-time performance, our approach is based on a general-purpose data stream management system, whereas task-based performance is automatically assessed based on a series of novel measures.

2016-12, Weiler, Andreas, Grossniklaus, Michael, Scholl, Marc H.

Twitter׳s increasing popularity as a source of up-to-date news and information about current events has spawned a body of research on event detection techniques for social media data streams. Although all proposed approaches provide some evidence as to the quality of the detected events, none relate this task-based performance to their run-time performance in terms of processing speed, data throughput, or memory usage. In particular, neither a quantitative nor a comparative evaluation of these aspects has been performed to date. In this article, we study the run-time and task-based performance of several state-of-the-art event detection techniques for Twitter. In order to reproducibly compare run-time performance, our approach is based on a general-purpose data stream management system, whereas task-based performance is automatically assessed based on a series of novel measures.

2015, Kircher, Lukas, Grossniklaus, Michael, Grün, Christian, Scholl, Marc H.

In order to manage XML documents, native XML databases use specific encodings that map the hierarchical structure of a document to a flat representation. Several encodings have been proposed that differ in terms of their support for certain query workloads. While some encodings are optimized for query processing, others focus on data manipulation. For example, the Pre/Dist/Size XML encoding has been designed to support queries over all XPath axes efficiently, but processing atomic updates in XML documents can be costly. In this paper, we present a technique, so-called structural bulk updates, that works in concert with the XQuery Update Facility to support efficient updates on the Pre/Dist/Size encoding. We demonstrate the benefits of our technique in a detailed performance evaluation based on the XMark benchmark.

2015, Grossniklaus, Michael, Scholl, Marc H., Weiler, Andreas

2016-04, Weiler, Andreas, Grossniklaus, Michael, Scholl, Marc H.

The continuous growth of social networks and the active use of social media services result in massive amounts of user-generated data. Our goal is to leverage social media users as “social sensors” in order to increase the situational awareness within and about urban areas. In addition to the well-known challenges of event and topic detection and tracking, this task involves a spatial and temporal dimension. In this paper, we present a visualization that supports analysts in monitoring events/topics and emotions both in time and in space. The visualization uses a clock-face metaphor to encode temporal and spatial relationships, a color map to reflect emotion, and tag clouds for events and topics. A hierarchy of these clock-faces supports drilling down to finer levels of granularity as well as rolling up the vast and fast flow of information. In order to showcase these functionalities of our visualization, we discuss several case studies that use the live data stream of the Twitter microblogging service. Finally, we demonstrate the usefulness and usability of the visualization in a user study that we conducted.

2015, Weiler, Andreas, Grossniklaus, Michael, Scholl, Marc H.

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.

2015, Wörteler, Leonard, Grossniklaus, Michael, Grün, Christian, Scholl, Marc H.

Originally developed as a query language for XML databases, XQuery has evolved into a complete functional programming language. In order to unlock all optimization opportunities, XQuery processors therefore need to combine traditional query optimization with techniques used in optimizing compilers. In this paper, we discuss how the well-known technique of function inlining can be applied to XQuery. We present an implementation of function inlining based on the query processor of BaseX, an open-source XML database. Finally, a detailed quantitative evaluation demonstrates that the performance benefits obtained by blending compiler and query optimizer techniques surpass results from any one single technique.