Tomasz Wiktor Wlodarczyk

Privacy Preserving Data Analytics for Smart Homes

A framework for maintaining security & preserving privacy for analysis of sensor data from smart homes, without compromising on data utility is presented. Storing the personally identifiable data as hashed values withholds identifiable information from any computing nodes. However the very nature of smart home data analytics is establishing preventive care. Data processing results should be identifiable to certain users responsible for direct care. Through a separate encrypted identifier dictionary with hashed and actual values of all unique sets of identifiers, we suggest re-identification of any data processing results. However the level of re-identification needs to be controlled, depending on the type of user accessing the results. Generalization and suppression on identifiers from the identifier dictionary before re-introduction could achieve different levels of privacy preservation. In this paper we propose an approach to achieve data security & privacy through out the complete data lifecycle: data generation/collection, transfer, storage, processing and sharing.

Industrial Cloud: Toward Inter-enterprise Integration

Industrial cloud is introduced as a new inter-enterprise integration concept in cloud computing. The characteristics of an industrial cloud are given by its definition and architecture and compared with other general cloud concepts. The concept is then demonstrated by a practical use case, based on Integrated Operations (IO) in the Norwegian Continental Shelf (NCS), showing how industrial digital information integration platform gives competitive advantage to the companies involved. Further research and development challenges are also discussed.

Overview of Time Series Storage and Processing in a Cloud Environment

In this paper we provide a short overview of Time Series Storage and Processing in a Cloud Environment. We focus on four main developments: Chukwa, OpenTSDB, TempoDB and Squwk. We compare them in terms of storage infrastructure, data acquisition, GUI and support for advanced analysis. In this comparison OpenTSDB emerges as the most interesting option for projects requiring support for advanced analysis. At the same time, TempoDB offers all the necessary basic functionality and can be a better option for many project which lack full IT support. We also describe other works in the field that have not yet developed in full frameworks. Some of them can significantly contribute to the existing platforms.

SWRL-F: a fuzzy logic extension of the semantic web rule language

Enhancing Semantic Web technologies with ability to express uncertainty and imprecision is widely discussed topic. While SWRL can provide additional expressivity to OWL-based ontologies, it does not provide any way to handle uncertainty or imprecision. There is a pressing need to provide a standard-based, simple and functioning solution. We describe an extension of SWRL called SWRL-F that we believe can provides such a solution. SWRL-F is based on SWRL rule language and uses SWRLs strong semantic foundation as its formal underpinning. We extend it with a SWRL-F ontology to enable fuzzy reasoning in the rule base basing on the fuzzy control systems approach. The resulting language provides small but powerful set of fuzzy operations that do not introduce inconsistencies in the host ontology. We present it basing on the example of risk assessment in oil and gas industry which was a driving use case for this project.

Performance Considerations of Data Acquisition in Hadoop System

Data have become more and more important these years, especially for big companies, and it is of great benefit to mine useful information in these data. Oil & Gas industry has to deal with vast amounts of data, both in real-time and historical context. As the amount of data is significant, it is usually infeasible or very time consuming to actually process the data. In our project we investigate usage of Hadoop to solve this problem. In order to perform Hadoop jobs, data must first exist in the Hadoop file system, which creates the problem of data acquisition. In this paper, two solutions are investigates, performance comparison is performed and solution based on Chukwa is demonstrated to be more efficient than a naïve implementation in particular for bigger file sizes.

An Initial Survey on Integration and Application of Cloud Computing to High Performance Computing

In this paper we survey state-of-the-art of integration and application of Cloud Computing (CC) to High Performance Computing (HPC). Motivation and general application areas are presented demonstrating particular focus on commoditization of HPC resources. Current experiments usually show significant performance differences between CC and HPC infrastructures and also programming models. However, recent research efforts aim at finding a common ground between those approaches. A conclusion emerges that some level of synthesis of CC and HPC is inevitable and probably beneficial for both, however, it requires further significant research efforts.

Performance Analysis of Hadoop for Query Processing

Query processing using mostly various NoSQL languages becomes a significant application area for Hadoop. Despite significant work on performance improvement of these languages the performance dependence on basic configuration parameters seems not to be fully considered. In this paper we present a relatively comprehensive study into influence the basic configuration parameters have on performance of typical types of queries. We choose three queries from Lehigh University Benchmark that can represent the most typical challenges and we analyze their dependence on parameters such as: dataset size, number of nodes, number of reducers and loading overhead. The results indicate strong dependence on the amount of reducers and IO performance of the cluster, which proves the common opinion that MapReduce is IO bound. These results can help to compare performance behavior of different languages and serve as a basis for understanding the influence of configuration parameters on the final performance.

New Instructional Models for Building Effective Curricula on Cloud Computing Technologies and Engineering

This paper presents ongoing work to develop advanced education and training course on the Cloud Computing technologies foundation and engineering by a cooperating group of universities and the professional education partners. The central part of proposed approach is the Common Body of Knowledge in Cloud Computing (CBK-CC) that defines the professional level of knowledge in the selected domain and allows consistent curricula structuring and profiling. The paper presents the structure of the course and explains the principles used for developing course materials, such as Blooms Taxonomy applied for technical education, and andragogy instructional model for professional education and training. The paper explains the importance of using the strong technical foundation to build the course materials that can address interests of different categories of stakeholders and roles/responsibilities in the Cloud Computing services provisioning and operation. The paper provides a short description of summary of the used Cloud Computing related architecture concepts and models that allow consistent mapping between CBK-CC, stakeholder roles/responsibilities and required skills, explaining also importance of the requirements engineering stage that provides a context for cloud based services design. The paper refers to the ongoing development of the educational course on Cloud Computing at the University of Amsterdam, University of Stavanger and provides suggestions for building advanced online training course for IT professionals.

A Scalable K-Anonymization Solution for Preserving Privacy in an Aging-in-Place Welfare Intercloud

Aging-in-Place solutions are becoming increasingly prevalent in our society. New age big data technologies can harness upon enormous amount of data generated from sensors in smart homes to provide enabling services. Added care and preventive services can be furnished through interoperability and bidirectional dataflow across the value chain. However the nature of the problem domain which although allows establishing better care through sharing of information also risks disclosing complete living behavior of individuals. In this paper, we introduce and evaluate a novel scalable k-anonymization solution based upon the distributed map-reduce paradigm for preserving privacy of the shared data in a welfare intercloud. Our evaluation benchmarks both information loss and data quality metrics and demonstrates better scalability/performance than any other available solutions.

Evaluation of some optimisation techniques for semantic query answering on shared-nothing architecture

In this paper, we present an evaluation of several optimisation techniques for semantic query answering for large datasets. We investigate influence of cluster configuration parameters on performance of different types of queries. We also compare data materialisation against query rewriting demonstrating that for more complex queries performance of query rewriting is not satisfactory. We also test performance of different types of encoding demonstrating that integer encoding can significantly improve the performance, but it is sensitive to implementation of its internal methods.