Automated topic labelling brings benefits for users aiming at analysing and understanding document collections, as well as for search engines targetting at the linkage between groups of words and their inherent topics. Current approaches to achieve this suffer in quality, but we argue their performances might be improved by setting the focus on the structure in the data. Building upon research for concept disambiguation and linking to DBpedia, we are taking a novel approach to topic labelling by making use of structured data exposed by DBpedia. We start from the hypothesis that words co-occuring in text likely refer to concepts that belong closely together in the DBpedia graph. Using graph centrality measures, we show that we are able to identify the concepts that best represent the topics. We comparatively evaluate our graph-based approach and the standard text-based approach, on topics extracted from three corpora, based on results gathered in a crowd-sourcing experiment. Our research shows that graph-based analysis of DBpedia can achieve better results for topic labelling in terms of both precision and topic coverage.

Sensor networks have evolved to a powerful infrastructure component for event monitoring in many application scenarios. In addition to simple filter and aggregation operations, an important task in processing sensor data is data mining - the identification of relevant information and patterns. Limited capabilities of sensor nodes in terms of storage and processing capacity, battery lifetime, and communication demand a power-efficient, preferably sensor-local processing. In this paper, we present AnduIN, a system for developing, deploying, and running in-network data mining tasks. The system consists of a data stream processing engine, a library of operators for sensor-local processing, a box-and-arrow editor for specifying data mining tasks and deployment, a GUI providing the user with current information about the network and running queries, and an alerter notifying the user if a better query execution plan is available. At the demonstration site, we plan to show our system in action using burst detection as example application.

Typical approaches for querying structured Web Data collect (crawl) and pre-process (index) large amounts of data in a central data repository before allowing for query answering. However, this time-consuming pre-processing phase however leverages the benefits of Linked Data -- where structured data is accessible live and up-to-date at distributed Web resources that may change constantly -- only to a limited degree, as query results can never be current. An ideal query answering system for Linked Data should return current answers in a reasonable amount of time, even on corpora as large as the Web. Query processors evaluating queries directly on the live sources require knowledge of the contents of data sources. In this paper, we develop and evaluate an approximate index structure summarising graph-structured content of sources adhering to Linked Data principles, provide an algorithm for answering conjunctive queries over Linked Data on theWeb exploiting the source summary, and evaluate the system using synthetically generated queries. The experimental results show that our lightweight index structure enables complete and up-to-date query results over Linked Data, while keeping the overhead for querying low and providing a satisfying source ranking at no additional cost.

Structured P2P overlays supporting standard database functionalities are a popular choice for building large-scale distributed data management systems. In such systems, estimating the number of answers for structured queries can help approximating query completeness, but is especially challenging. In this paper, we propose to use routing graphs in order to achieve this. We introduce the general approach and briefly discuss further aspects like overhead and guarantees.

A crucial prerequisite for the deployment and success of Peer-to-Peer data management applications is the availability of metadata in a way that makes it easy to access and combine data from different sources and domains.

The idea of collecting and combining large public data sets and services became more and more popular. The special characteristics of such systems and the requirements of the participants demand for strictly decentralized solutions. However, this comes along with several ambitious challenges a corresponding system has to overcome. In this demonstration paper, we present a lightweight distributed universal storage capable of dealing with those challenges, and providing a powerful and flexible way of building Internet-scale public data management systems. We introduce our approach based on a triple storage on top of a distributed hash table (DHT) overlay system, based on the ideas of a universal relation model and the resource description framework (RDF), and outline solved challenges as well as open issues.

In this paper, verification of a synergistic, two-staged method for self-optimization of DHT-based overlay networks is presented. The method, named Fabric, minimizes overlay network stretch during a node insertion by using static, readily available information that does not involve costly periodic probing of many nodes. Furthermore, Fabric enables an overlay to optimize its topology at runtime in an adaptive manner.

Large-scale distributed data management with P2P systems requires the existence of similarity operators for queries as we cannot assume that all users agree on exactly the same schema and value representations and data quality problems due to spelling errors and typos. In this paper, we present an approach for efficient processing of similarity selections and joins in a structured overlay. We show that there are several possible strategies exploiting DHT features to a different extent (i.e., key organization, routing, multicasting) and thus the choice of the best operator implementation in a given situation (selectivity, data distribution, load) should be based on cost information allowing the system to estimate the computation and communication costs of query execution plans. Hence, we present a cost model for similarity operations on structured data in a DHT and demonstrate the efficiency of our proposal by experimental results from a large-scale PlanetLab deployment

Peer data management systems (PDMS) currently gain attention at an emerging scale in order to cope with the needs of growing organizational integration. Efficient query processing, as one of the main requirements in these systems, provides three major challenges: achieving robustness, scalability and self organization. In this paper we deal with the physical aspects of these requirements. We introduce an adaptive maintenance technique based on query feedback for keeping routing filters, used to optimize routing, up-to-date. These filters are applied in conjunction with an iterative query processing strategy and we show that this can improve robustness and scalability of query processing in distributed data management systems.