Benedikt Kämpgen

Transforming statistical linked data for use in OLAP systems

The amount of available Linked Data on the Web is increasing, and data providers start to publish statistical datasets that comprise numerical data. Such statistical datasets differ significantly from the currently predominant network-style data published on the Web. We explore the possibility of integrating statistical data from multiple Linked Data sources. We provide a mapping from statistical Linked Data into the Multidimensional Model used in data warehouses. We use an extract-transform-load (ETL) pipeline to convert statistical Linked Data into a format suitable for loading into an open-source OLAP system, and thus demonstrate how standard OLAP infrastructure can be used for elaborate querying and visualisation of integrated statistical Linked Data. We discuss lessons learned from three experiments and identify areas which require future work to ultimately arrive at a well-interlinked set of statistical data from multiple sources which is processable with standard OLAP systems.

Interacting with Statistical Linked Data via OLAP Operations

Online Analytical Processing (OLAP) promises an interface to analyse Linked Data containing statistics going beyond other interaction paradigms such as follow-your-nose browsers, faceted-search interfaces and query builders. Transforming statistical Linked Data into a star schema to populate a relational database and applying a common OLAP engine do not allow to optimise OLAP queries on RDF or to directly propagate changes of Linked Data sources to clients. Therefore, as a new way to interact with statistics published as Linked Data, we investigate the problem of executing OLAP queries via SPARQL on an RDF store. First, we define projection, slice, dice and roll-up operations on single data cubes published as Linked Data reusing the RDF Data Cube vocabulary and show how a nested set of operations lead to an OLAP query. Second, we show how to transform an OLAP query to a SPARQL query which generates all required tuples from the data cube. In a small experiment, we show the applicability of our OLAP-to-SPARQL mapping in answering a business question in the financial domain.

No Size Fits All - Running the Star Schema Benchmark with SPARQL and RDF Aggregate Views

Statistics published as Linked Data promise efficient extraction, transformation and loading (ETL) into a database for decision support. The predominant way to implement analytical query capabilities in industry are specialised engines that translate OLAP queries to SQL queries on a relational database using a star schema (ROLAP). A more direct approach than ROLAP is to load Statistical Linked Data into an RDF store and to answer OLAP queries using SPARQL. However, we assume that general-purpose triple stores – just as typical relational databases – are no perfect fit for analytical workloads and need to be complemented by OLAP-to-SPARQL engines. To give an empirical argument for the need of such an engine, we first compare the performance of our generated SPARQL and of ROLAP SQL queries. Second, we measure the performance gain of RDF aggregate views that, similar to aggregate tables in ROLAP, materialise parts of the data cube.

Querying the Global Cube: Integration of Multidimensional Datasets from the Web

National statistical indicators such as the Gross Domestic Product per Capita are published on the Web by various organisations such as Eurostat, the World Bank and the International Monetary Fund. Uniform access to such statistics will allow for elaborate analysis and visualisations. Though many datasets are also available as Linked Data, heterogeneities remain since publishers use several identifiers for common dimensions and differing levels of detail, units, and formulas. For queries over the Global Cube, i.e., the integration of available datasets modelled in the RDF Data Cube Vocabulary, we extend the well-known Drill-Across operation over data cubes to consider implicit overlaps between datasets in Linked Data. To evaluate more complex mappings we define the Convert-Cube operation over values from a single dataset. We generalise the two operations for arbitrary combinations of multiple datasets with the Merge-Cubes operation and show the feasibility of the analytical operations for integrating government statistics.

OLAP4LD – A Framework for Building Analysis Applications Over Governmental Statistics

Although useful governmental statistics have been published as Linked Data, there are query processing and data pre-processing challenges to allow citizens exploring such multidimensional datasets in pivot tables. In this demo paper we present OLAP4LD, a framework for developers of applications over Linked Data sources reusing the RDF Data Cube Vocabulary. Our demonstration will let visiting developers and dataset publishers explore European statistics with the Linked Data Cubes Explorer (LDCX), will explain how LDCX makes use of OLAP4LD, and will show common dataset modelling errors.

Accepting the XBRL Challenge with Linked Data for Financial Data Integration

Analysts spend a disproportionate amount of time with financial data curation before they are able to compare company performances in an analysis. The Extensible Business Reporting Language (XBRL) for annotating financial facts is suited for automatic processing to increase information quality in financial analytics. Still, XBRL does not solve the problem of data integration as required for a holistic view on companies. Semantic Web technologies promise benefits for financial data integration, yet, existing literature lacks concrete case studies. In this paper, we present the Financial Information Observation System (FIOS) that uses Linked Data and multidimensional modelling based on the RDF Data Cube Vocabulary for accessing and representing relevant financial data. FIOS fulfils the information seeking mantra of “overview first, zoom and filter, then details on demand”, integrates yearly and quarterly balance sheets, daily stock quotes as well as company and industry background information and helps analysts creating their own analyses with Excel-like functionality.

Toward cognitive pipelines of medical assistance algorithms

PurposeAssistance algorithms for medical tasks have great potential to support physicians with their daily work. However, medicine is also one of the most demanding domains for computer-based support systems, since medical assistance tasks are complex and the practical experience of the physician is crucial. Recent developments in the area of cognitive computing appear to be well suited to tackle medicine as an application domain.MethodsWe propose a system based on the idea of cognitive computing and consisting of auto-configurable medical assistance algorithms and their self-adapting combination. The system enables automatic execution of new algorithms, given they are made available as Medical Cognitive Apps and are registered in a central semantic repository. Learning components can be added to the system to optimize the results in the cases when numerous Medical Cognitive Apps are available for the same task. Our prototypical implementation is applied to the areas of surgical phase recognition based on sensor data and image progressing for tumor progression mappings.ResultsOur results suggest that such assistance algorithms can be automatically configured in execution pipelines, candidate results can be automatically scored and combined, and the system can learn from experience. Furthermore, our evaluation shows that the Medical Cognitive Apps are providing the correct results as they did for local execution and run in a reasonable amount of time.ConclusionThe proposed solution is applicable to a variety of medical use cases and effectively supports the automated and self-adaptive configuration of cognitive pipelines based on medical interpretation algorithms.

Representing Interoperable Provenance Descriptions for ETL Workflows

The increasing availability of data on the Web provided by the emergence of Web 2.0 applications and, more recently by Linked Data, brought additional complexity to data management tasks, where the number of available data sources and their associated heterogeneity drastically increases. In this scenario, where data is reused and repurposed on a new scale, the pattern expressed as Extract-Transform-Load (ETL) emerges as a fundamental and recurrent process for both producers and consumers of data on the Web. In addition to ETL, provenance, the representation of source artifacts, processes and agents behind data, becomes another cornerstone element for Web data management, playing a fundamental role in data quality assessment, data semantics and facilitating the reproducibility of data transformation processes. This paper proposes the convergence of these two Web data management concerns, introducing a principled provenance model for ETL processes in the form of a vocabulary based on the Open Provenance Model (OPM) standard and focusing on the provision of an interoperable provenance model for ETL environments. The proposed ETL provenance model is instantiated in a real-world sustainability reporting scenario.

DC proposal: online analytical processing of statistical linked data

The amount of Linked Data containing statistics is increasing; and so is the need for concepts of analysing these statistics. Yet, there are challenges, e.g., discovering datasets, integrating data of different granularities, or selecting mathematical functions. To automatically, flexibly, and scalable integrate statistical Linked Data for expressive and reliable analysis, we propose to use expressive Semantic Web ontologies to build and evolve a well-interlinked conceptual model of statistical data for Online Analytical Processing.

Enterprise Knowledge Structures

One of the major aims of knowledge management has always been to facilitate the sharing and reuse of knowledge. Over the years a long list of technologies and tools pursuing this aim have been proposed, using different types of conceptual structures to capture the knowledge that individuals and groups communicate and exchange. This chapter is concerned with these knowledge structures and their development, maintenance and use within corporate environments. Enterprise knowledge management as we know it today often follows a predominantly community-driven approach to meet its organizational and technical challenges. It builds upon the power of mass collaboration and social software combined with intelligent machine-driven information management technology delivered though formal semantics. The knowledge structures underlying contemporary enterprise knowledge management platforms are diverse, from database tables deployed company-wide to files in proprietary formats used by scripts, from loosely defined folksonomies describing content through tags to highly formalized ontologies through which new enterprise knowledge can be automatically derived. Leveraging such structures requires a knowledge management environment which not only exposes them in an integrated fashion, but also allows knowledge workers to adjust and customize them according to their specific needs. We discuss how the Semantic MediaWiki provides such an environment – not only as an easy-to-use, highly versatile communication and collaboration medium, but also as an integration and knowledge engineering tool targeting the full range of enterprise knowledge structures currently used.