Armin Haller

A declarative recommender system for cloud infrastructure services selection

The cloud infrastructure services landscape advances steadily leaving users in the agony of choice. Therefore, we present CloudRecommender, a new declarative approach for selecting Cloud-based infrastructure services. CloudRecommender automates the mapping of users specified application requirements to cloud service configurations. We formally capture cloud service configurations in ontology and provide its implementation in a structured data model which can be manipulated through both regular expressions and SQL. By exploiting the power of a visual programming language (widgets), CloudRecommender further enables simplified and intuitive cloud service selection. We describe the design and a prototype implementation of CloudRecommender, and demonstrate its effectiveness and scalability through a service configuration selection experiment on most of todays prominent cloud providers including Amazon, Azure, and GoGrid.

Investigating decision support techniques for automating Cloud service selection

The compass of Cloud infrastructure services advances steadily leaving users in the agony of choice. To be able to select the best mix of service offering from an abundance of possibilities, users must consider complex dependencies and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal on investigating an intelligent decision support system for selecting Cloud-based infrastructure services (e.g. storage, network, CPU). The outcomes of this will be decision support tools and techniques, which will automate and map users specified application requirements to Cloud service configurations.

Relationship-Based Top-K Concept Retrieval for Ontology Search

With the recent growth of Linked Data on the Web there is an increased need for knowledge engineers to find ontologies to describe their data. Only limited work exists that addresses the problem of searching and ranking ontologies based on a given query term. In this paper we introduce DWRank, a two-staged bi-directional graph walk ranking algorithm for concepts in ontologies. We apply this algorithm on the task of searching and ranking concepts in ontologies and compare it with state-of-the-art ontology ranking models and traditional information retrieval algorithms such as PageRank and tf-idf. Our evaluation shows that DWRank significantly outperforms the best ranking models on a benchmark ontology collection for the majority of the sample queries defined in the benchmark.

Ontology Search: An Empirical Evaluation

Much of the recent work in Semantic Search is concerned with addressing the challenge of finding entities in the growing Web of Data. However, alongside this growth, there is a significant increase in the availability of ontologies that can be used to describe these entities. Whereas several methods have been proposed in Semantic Search to rank entities based on a keyword query, little work has been published on search and ranking of resources in ontologies. To the best of our knowledge, this work is the first to propose a benchmark suite for ontology search. The benchmark suite, named CBRBench, includes a collection of ontologies that was retrieved by crawling a seed set of ontology URIs derived from prefix.cc and a set of queries derived from a real query log from the Linked Open Vocabularies search engine. Further, it includes the results for the ideal ranking of the concepts in the ontology collection for the identified set of query terms which was established based on the opinions of ten ontology engineering experts. n nWe compared this ideal ranking with the top-k results retrieved by eight state-of-the-art ranking algorithms that we have implemented and calculated the precision at k, the mean average precision and the discounted cumulative gain to determine the best performing ranking model. Our study shows that content-based ranking models outperform graph-based ranking models for most queries on the task of ranking concepts in ontologies. However, as the performance of the ranking models on ontologies is still far inferior to the performance of state-of-the-art algorithms on the ranking of documents based on a keyword query, we put forward four recommendations that we believe can significantly improve the accuracy of these ranking models when searching for resources in ontologies.

A note on software tools and techniques for monitoring and prediction of cloud services

Senior Research Scientist and CSIRO Julius Fellow, CSIRO CCI Division, GPO Box 664, Canberra, ACT 2601 Director, Cloud Computing and Distributed Systems (CLOUDS) Lab, Department of Computing and Information Systems, The University of Melbourne, Australia University of Zurich, Department of Informatics, Binzmuhlestrasse 14, CH-8050 Zurich, Switzerland Research Scientist, CSIRO CCI Division, GPO Box 664, Canberra, ACT 2601 Institute AIFB Building 11.40, Karlsruhe Institute of Technology, D-76128 Karlsruhe

RaUL: RDFa user interface language - a data processing model for web applications

In this paper we introduce RaUL, the RDFa User Interface Language, a user interface markup ontology that is used to describe the structure of a web form as RDF statements. RaUL separates the markup of the control elements on a web form, the form model, from the data model that the form controls operate on. Form controls and the data model are connected via a data binding mechanism. The form elements include references to an RDF graph defining the data model. For the rendering of the instances of a RaUL model on the client-side we propose ActiveRaUL, a processor that generates XHTML+RDFa elements for displaying the model on the client.

Web Information Systems Engineering - WISE 2011 and 2012 workshops: combined WISE 2011 and WISE 2012 workshops; Sydney, Australia, October 12-14, 2011 and Paphos, Cyprus, November 28-30, 2012: revised selected papers

High performance is a key requirement for a number of Semantic Web applications. Reasoning over large and extra-large data is the most challenging class of Semantic Web applications in terms of the performance demands. For such problems, parallelization is a key technology for achieving high performance and scalability. However. development of parallel application is still a challenge for the majority of Semantic Web algorithms due to their implementation in Java – a programming language whose design does not allow the porting to the High Performance Computing Infrastructures to be trivially achieved. The Message-Passing Interface (MPI) is a well-known programming paradigm, applied beneficially for scientific applications written in the “traditional” programming languages for High Performance Computing, such as C, C++ and Fortran. We describe an MPI based approach for implementing parallel Semantic Web applications and evaluate the performance of a pilot Semantical Statistics application random indexing over large text volumes.

Generating Workflow Models from OWL-S Service Descriptions with a Partial-Order Plan Construction

In this work we construct partial order plans from a pool of atomic services described in OWL-S. We make extensions to Partial Order Planning to allow multiple conditional effects in action definitions. The purpose is to handle the uncertain behavior of Web services with incomplete initial information. We post-process the partial order plan to auto-generate a workflow model. We developed a method to identify a subset of workflow patterns from the solution plan to create a workflow diagram.

Automated Table Understanding Using Stub Patterns

Tables in documents are a rich source of information, but not yet well-utilised computationally because of the difficulty of extracting their structure and data automatically. In this paper, we progress the state-of-the-art in automatic table extraction by identifying common patterns in table headers to develop rules and heuristics for determining table structure. We describe and evaluate a table understanding system using these patterns and rules.

DWRank: Learning concept ranking for ontology search

With the recent growth of Linked Data on the Web there is an increased need for knowledge engineers to find ontologies to describe their data. Only limited work exists that addresses the problem of searching and ranking ontologies based on a given query term. In this paper we introduce DWRank, a two-staged bi-directional graph walk ranking algorithm for concepts in ontologies. DWRank characterises two features of a concept in an ontology to determine its rank in a corpus, the centrality of the concept to the ontology within which it is defined (HubScore) and the authoritativeness of the ontology in which it is defined (AuthorityScore). DWRank then uses a Learning to Rank approach to learn the feature weights for the two aforementioned ranking strategies. We compare DWRank with state-of-the-art ontology ranking models and traditional information retrieval algorithms. This evaluation shows that DWRank significantly outperforms the best ranking models on a benchmark ontology collection for the majority of the sample queries defined in the benchmark. In addition, we compare the eectiveness of the HubScore part of our algorithm with the state-of-the-art ranking model to determine a concept centrality and show the improved performance of DWRank in this aspect. Finally, we evaluate the eectiveness of the design decisions made for the AuthorityScore method in DWRank to find missing inter-ontology links and present a graph-based analysis of the ontology corpus that shows the increased connectivity of the ontology corpus after extraction of the implicit inter-ontology links.