Ralf Schenkel

Top-k query evaluation with probabilistic guarantees

Top-k queries based on ranking elements of multidimensional datasets are a fundamental building block for many kinds of information discovery. The best known general-purpose algorithm for evaluating top-k queries is Fagins threshold algorithm (TA). Since the users goal behind top-k queries is to identify one or a few relevant and novel data items, it is intriguing to use approximate variants of TA to reduce run-time costs. This paper introduces a family of approximate top-k algorithms based on probabilistic arguments. When scanning index lists of the underlying multidimensional data space in descending order of local scores, various forms of convolution and derived bounds are employed to predict when it is safe, with high probability, to drop candidate items and to prune the index scans. The precision and the efficiency of the developed methods are experimentally evaluated based on a large Web corpus and a structured data collection.

FedX: optimization techniques for federated query processing on linked data

Motivated by the ongoing success of Linked Data and the growing amount of semantic data sources available on theWeb, new challenges to query processing are emerging. Especially in distributed settings that require joining data provided by multiple sources, sophisticated optimization techniques are necessary for efficient query processing. We propose novel join processing and grouping techniques to minimize the number of remote requests, and develop an effective solution for source selection in the absence of preprocessed metadata. We present FedX, a practical framework that enables efficient SPARQL query processing on heterogeneous, virtually integrated Linked Data sources. In experiments, we demonstrate the practicability and efficiency of our framework on a set of real-world queries and data sources from the Linked Open Data cloud. With FedX we achieve a significant improvement in query performance over state-of-the-art federated query engines.

Efficient top-k querying over social-tagging networks

Online communities have become popular for publishing and searching content, as well as for finding and connecting to other users. User-generated content includes, for example, personal blogs, bookmarks, and digital photos. These items can be annotated and rated by different users, and these social tags and derived user-specific scores can be leveraged for searching relevant content and discovering subjectively interesting items. Moreover, the relationships among users can also be taken into consideration for ranking search results, the intuition being that you trust the recommendations of your close friends more than those of your casual acquaintances. Queries for tag or keyword combinations that compute and rank the top-k results thus face a large variety of options that complicate the query processing and pose efficiency challenges. This paper addresses these issues by developing an incremental top-k algorithm with two-dimensional expansions: social expansion considers the strength of relations among users, and semantic expansion considers the relatedness of different tags. It presents a new algorithm, based on principles of threshold algorithms, by folding friends and related tags into the search space in an incremental on-demand manner. The excellent performance of the method is demonstrated by an experimental evaluation on three real-world datasets, crawled from deli.cio.us, Flickr, and LibraryThing.

HOPI: An Efficient Connection Index for Complex XML Document Collections

In this paper we present HOPI, a new connection index for XML documents based on the concept of the 2–hop cover of a directed graph introduced by Cohen et al. In contrast to most of the prior work on XML indexing we consider not only paths with child or parent relationships between the nodes, but also provide space– and time–efficient reachability tests along the ancestor, descendant, and link axes to support path expressions with wildcards in our XXL search engine. We improve the theoretical concept of a 2–hop cover by developing scalable methods for index creation on very large XML data collections with long paths and extensive cross–linkage. Our experiments show substantial savings in the query performance of the HOPI index over previously proposed index structures in combination with low space requirements.

Efficient creation and incremental maintenance of the HOPI index for complex XML document collections

The HOPI index, a connection index for XML documents based on the concept of a 2-hop cover, provides space- and time-efficient reachability tests along the ancestor, descendant, and link axes to support path expressions with wildcards in XML search engines. This paper presents enhanced algorithms for building HOPI, shows how to augment the index with distance information, and discusses incremental index maintenance. Our experiments show substantial improvements over the existing divide-and-conquer algorithm for index creation, low space overhead for including distance information in the index, and efficient updates.

Partout: a distributed engine for efficient RDF processing

The increasing interest in Semantic Web technologies has led not only to a rapid growth of semantic data on the Web but also to an increasing number of backend applications relying on efficient query processing. Confronted with such a trend, existing centralized state-of-the-art systems for storing RDF and processing SPARQL queries are no longer sufficient. In this paper, we introduce Partout, a distributed engine for fast RDF processing in a cluster of machines. We propose an effective approach for fragmenting RDF data sets based on a query log and allocating the fragments to hosts in a cluster of machines. Furthermore, Partouts query optimizer produces efficient query execution plans for ad-hoc SPARQL queries.

Exploiting social relations for query expansion and result ranking

Online communities have recently become a popular tool for publishing and searching content, as well as for finding and connecting to other users that share common interests. The content is typically user-generated and includes, for example, personal blogs, bookmarks, and digital photos. A particularly intriguing type of content is user-generated annotations (tags) for content items, as these concise string descriptions allow for reasonings about the interests of the user who created the content, but also about the user who generated the annotations. This paper presents a framework to cast the different entities of such networks into a unified graph model representing the mutual relationships of users, content, and tags. It derives scoring functions for each of the entities and relations. We have performed an experimental evaluation on two real-world datasets (crawled from deli.cio.us and Flickr) where manual user assessments of the query result quality show that our unified graph framework delivers high-quality results on social networks.

TopX: efficient and versatile top-k query processing for semistructured data

Recent IR extensions to XML query languages such as Xpath 1.0 Full-Text or the NEXI query language of the INEX benchmark series reflect the emerging interest in IR-style ranked retrieval over semistructured data. TopX is a top-k retrieval engine for text and semistructured data. It terminates query execution as soon as it can safely determine the k top-ranked result elements according to a monotonic score aggregation function with respect to a multidimensional query. It efficiently supports vague search on both content- and structure-oriented query conditions for dynamic query relaxation with controllable influence on the result ranking. The main contributions of this paper unfold into four main points: (1) fully implemented models and algorithms for ranked XML retrieval with XPath Full-Text functionality, (2) efficient and effective top-k query processing for semistructured data, (3) support for integrating thesauri and ontologies with statistically quantified relationships among concepts, leveraged for word-sense disambiguation and query expansion, and (4) a comprehensive description of the TopX system, with performance experiments on large-scale corpora like TREC Terabyte and INEX Wikipedia.

Efficient and self-tuning incremental query expansion for top-k query processing

We present a novel approach for efficient and self-tuning query expansion that is embedded into a top-k query processor with candidate pruning. Traditional query expansion methods select expansion terms whose thematic similarity to the original query terms is above some specified threshold, thus generating a disjunctive query with much higher dimensionality. This poses three major problems: 1) the need for hand-tuning the expansion threshold, 2) the potential topic dilution with overly aggressive expansion, and 3) the drastically increased execution cost of a high-dimensional query. The method developed in this paper addresses all three problems by dynamically and incrementally merging the inverted lists for the potential expansion terms with the lists for the original query terms. A priority queue is used for maintaining result candidates, the pruning of candidates is based on Fagins family of top-k algorithms, and optionally probabilistic estimators of candidate scores can be used for additional pruning. Experiments on the TREC collections for the 2004 Robust and Terabyte tracks demonstrate the increased efficiency, effectiveness, and scalability of our approach.

FedX: a federation layer for distributed query processing on linked open data

Driven by the success of the Linked Open Data initiative todays Semantic Web is best characterized as a Web of interlinked datasets. Hand in hand with this structure new challenges to query processing are arising. Especially queries for which more than one data source can contribute results require advanced optimization and evaluation approaches, the major challenge lying in the nature of distribution: Heterogenous data sources have to be integrated into a federation to globally appear as a single repository. On the query level, though, techniques have to be developed to meet the requirements of efficient query computation in the distributed setting.We present FedX, a project which extends the Sesame Framework with a federation layer that enables efficient query processing on distributed Linked Open Data sources. We discuss key insights to its architecture and summarize our optimization techniques for the federated setting. The practicability of our system will be demonstrated in various scenarios using the Information Workbench.