Peter Haase

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.

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.

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.

Optique: Zooming in on Big Data

Optique overcomes problems in current ontology-based data access systems pertaining to installation overhead, usability, scalability, and scope by integrating a user-oriented query interface, semi-automated managing methods, new query rewriting techniques, and temporal and streaming data processing in one platform.

An evaluation of approaches to federated query processing over linked data

The Web has evolved from a global information space of linked documents to a web of linked data. The Web of Data enables answering complex, structured queries that could not be answered by a single data source alone. While the current procedure to work with multiple, distributed linked data sources is to load the desired data into a single RDF store and process queries in a centralized way against the merged data set, such an approach may not always be practically feasible or desired.n In this paper, we analyze alternative approaches to federated query processing over linked data and how different design alternatives affect the performance and practicality of query processing. To this end, we define a benchmark for federated query processing, comprising a selection of data sources in various domains and representative queries. Using the benchmark, we perform experiments with different federation alternatives and provide insights about their advantages and disadvantages.

Optique: Towards OBDA Systems for Industry

The recently started EU FP7-funded project Optique will develop an end-to-end OBDA system providing scalable end-user access to industrial Big Data stores. This paper presents an initial architectural specification for the Optique system along with the individual system components.

GovWILD: integrating open government data for transparency

Many government organizations publish a variety of data on the web to enable transparency, foster applications, and to satisfy legal obligations. Data content, format, structure, and quality vary widely, even in cases where the data is published using the wide-spread linked data principles. Yet within this data and their integration lies much value: We demonstrate GovWILD, a web-based prototype that integrates and cleanses Open Government Data at a large scale. Apart from the web-based interface that presents a use case of the created dataset at govwild.org, we provide all integrated data as a download. This data can be used to answer questions about politicians, companies, and government funding.

Semantic technologies for enterprise cloud management

Enterprise clouds apply the paradigm of cloud computing to enterprise IT infrastructures, with the goal of providing easy, flexible, and scalable access to both computing resources and IT services. Realizing the vision of the fully automated enterprise cloud involves addressing a range of technological challenges. In this paper, we focus on the challenges related to intelligent information management in enterprise clouds and discuss how semantic technologies can help to fulfill them. In particular, we address the topics of data integration, collaborative documentation and annotation and intelligent information access and analytics and present solutions that are implemented in the newest addition to our eCloudManager product suite: The Intelligence Edition.

How to Best Find a Partner? An Evaluation of Editing Approaches to Construct R2RML Mappings

R2RML defines a language to express mappings from relational data to RDF. That way, applications built on top of the W3C Semantic Technology stack can seamlessly integrate relational data. A major obstacle to using R2RML, though, is the effort for manually curating the mappings. In particular in scenarios that aim to map data from huge and complex relational schemata (e.g., [5]) to more abstract ontologies efficient ways to support the mapping creation are needed.

Optique: OBDA Solution for Big Data

Accessing the relevant data in Big Data scenarios is increasingly difficult both for end-user and IT-experts, due to the volume, variety, and velocity dimensions of Big Data.This brings a hight cost overhead in data access for large enterprises. For instance, in the oil and gas industry, IT-experts spend 30-70% of their time gathering and assessing the quality of data [1]. The Optique project ( http://www.optique-project.eu/ ) advocates a next generation of the well known Ontology-Based Data Access (OBDA) approach to address the Big Data dimensions and in particular the data access problem. The project aims at solutions that reduce the cost of data access dramatically.