Günter Ladwig

FedBench: a benchmark suite for federated semantic data query processing

In this paper we present FedBench, a comprehensive benchmark suite for testing and analyzing the performance of federated query processing strategies on semantic data. The major challenge lies in the heterogeneity of semantic data use cases, where applications may face different settings at both the data and query level, such as varying data access interfaces, incomplete knowledge about data sources, availability of different statistics, and varying degrees of query expressiveness. Accounting for this heterogeneity, we present a highly flexible benchmark suite, which can be customized to accommodate a variety of use cases and compare competing approaches. We discuss design decisions, highlight the flexibility in customization, and elaborate on the choice of data and query sets. The practicability of our benchmark is demonstrated by a rigorous evaluation of various application scenarios, where we indicate both the benefits as well as limitations of the state-of-the-art federated query processing strategies for semantic data.

Linked data query processing strategies

Recently, processing of queries on linked data has gained attention. We identify and systematically discuss three main strategies: a bottom-up strategy that discovers new sources during query processing by following links between sources, a top-down strategy that relies on complete knowledge about the sources to select and process relevant sources, and a mixed strategy that assumes some incomplete knowledge and discovers new sources at run-time. To exploit knowledge discovered at run-time, we propose an additional step, explicitly scheduled during query processing, called correct source ranking. Additionally, we propose the adoption of stream-based query processing to deal with the unpredictable nature of data access in the distributed Linked Data environment. In experiments, we show that our implementation of the mixed strategy leads to early reporting of results and thus, more responsive query processing, while not requiring complete knowledge.

SIHJoin: querying remote and local linked data

The amount of Linked Data is increasing steadily. Optimized top-down Linked Data query processing based on complete knowledge about all sources, bottom-up processing based on run-time discovery of sources as well as a mixed strategy that combines them have been proposed. A particular problem with Linked Data processing is that the heterogeneity of the sources and access options lead to varying input latency, rendering the application of blocking join operators infeasible. Previous work partially address this by proposing a non-blocking iterator-based operator and another one based on symmetric-hash join. Here, we propose detailed cost models for these two operators to systematically compare them, and to allow for query optimization. Further, we propose a novel operator called the Symmetric Index Hash Join to address one open problem of Linked Data query processing: to query not only remote, but also local Linked Data. We perform experiments on real-world datasets to compare our approach against the iterator-based baseline, and create a synthetic dataset to more systematically analyze the impacts of the individual components captured by the proposed cost models.

Browsing-oriented semantic faceted search

Faceted search enables users to browse and discover relevant items from a large collection such as the Web of data. Existing faceted search solutions assume a precise information need, and thus optimise relevance, interestingness, and costs of fulfilling an information need. In this paper, we propose a complementary solution. Instead of assuming a search scenario (i.e., a user has a precise information need), our solution targets a browsing scenario (i.e., a user has a fuzzy need). We aimto support users in exploring an unknown collection of items, thereby allowing them to discover new or unfamiliar items of interest. Our approach comprises mechanisms for grouping facets and facet values and facet ranking. Via a task-based evaluation, we demonstrate that the proposed solution enables more effective browsing compared to the state-of-the-art, given fuzzy information needs.

Generating and visualizing a soccer knowledge base

This demo abstract describes the SmartWeb Ontology-based Annotation system (SOBA). A key feature of SOBA is that all information is extracted and stored with respect to the SmartWeb Integrated Ontology (SWIntO). In this way, other components of the systems, which use the same ontology, can access this information in a straightforward way. We will show how information extracted by SOBA is visualized within its original context, thus enhancing the browsing experience of the end user.

Index structures and top-k join algorithms for native keyword search databases

For supporting keyword search on structured data, current solutions require large indexes to be built that redundantly store subgraphs called neighborhoods. Further, for exploring keyword search results, large graphs have to be loaded into memory. We propose a solution, which employs much more compact index structures for neighborhood lookups. Using these indexes, we reduce keyword search result exploration to the traditional database problem of top-k join processing, enabling results to be computed efficiently. In particular, this computation can be performed on data streams successively loaded from disk (i.e., does not require the entire input to be loaded at once into memory). For supporting this, we propose a top-k procedure based on the rank join operator, which not only computes the k-best results, but also selects query plans in a top-k fashion during the process. In experiments using large real-world datasets, our solution reduced storage requirements and also outperformed the state-of-the-art in terms of performance and scalability.

Combining query translation with query answering for efficient keyword search

Keyword search has been regarded as an intuitive paradigm for searching not only documents but also data, especially when the users are not familiar with the data and the query language. Two types of approaches can be distinguished. Answers to keywords can be computed by searching for matching subgraphs directly in the data. The alternative to this is keyword translation, which is based on searching the data schema for matching join graphs, which are then translated to queries. Answering these queries is performed in the later stage. While clear advantages have been shown for the approaches based on query translation, we observe that processing done during query translation has some overlaps with the processing needed for query answering. We propose a tight integration of query translation with query answering. Instead of using the schema, we employ a bisimulation-based structure index graph. Searching this index for matching subgraphs results not only in queries, but also candidate answers. We propose a set of algorithms which allow for an incremental process, where intermediate results computed during query translation can be reused for query answering. In experiments, we show that this integrated approach consistently outperforms the state of the art.

Approximate and incremental processing of complex queries against the web of data

The amount of data on the Web is increasing. Current exact and complete techniques for matching complex query pattern against graph-structured web data have limits. Considering web scale, exactness and completeness might have to be traded for responsiveness. We propose a new approach, allowing an affordable computation of an initial set of (possibly inexact) results, which can be incrementally refined as needed. It is based on approximate structure matching techniques, which leverage the notion of neighborhood overlap and structure index. For exact and complete result computation, evaluation results show that our incremental approach compares well with the state of the art. Moreover, approximative results can be computed in much lower response time, without compromising too much on precision.

Semantische Suche: Planungsdaten des KTBL finden und maschinell weiterverarbeiten

The effort to investigate relevant data for planning purposes and preparation of labour and investments in agricultural production as well as reworking and entering them for reuse in calculation tools and farm management information systems are major challenges for decisions based on data. The following paper presents a solution which on the one hand simplifies targeted finding of planning data within KTBL’s data sets using a semantic search engine and on the other hand enables simple reuse and processing of these data by providing them using Linked Open Data principles.