Stefan Decker

Mining Maximal Frequent Patterns in Transactional Databases and Dynamic Data Streams: A Spark-based Approach

Abstract Mining maximal frequent patterns (MFPs) in transactional databases (TDBs) and dynamic data streams (DDSs) is substantially important for business intelligence. MFPs, as the smallest set of patterns, help to reveal customers’ purchase rules and market basket analysisxa0(MBA). Although, numerous studies have been carried out in this area, most of them extend the main-memory based Apriori or FP-growth algorithms. Therefore, these approaches are not only unscalable but also lack parallelism. Consequently, ever increasing big data sources requirements cannot be met. In addition, mining performance in some existing approaches degrade drastically due to the presence of null transactions. We, therefore, proposed an efficient way to mining MFPs with Apache Spark to overcome these issues. For the faster computation and efficient utilization of memory, we utilized a prime number based data transformation technique, in which values of individual transaction have been preserved. After removing null transactions and infrequent items, the resulting transformed dataset becomes denser compared to the original distributions. We tested our proposed algorithms in both real static TDBs and DDSs. Experimental results and performance analysis show that our approach is efficient and scalable to large dataset sizes.

Knowledge Representation on the Web Revisited : The Case for Prototypes

Recently, RDF and OWL have become the most common knowledge representation languages in use on the Web, propelled by the recommendation of the W3C. In this paper we examine an alternative way to represent knowledge based on Prototypes. This Prototype-based representation has different properties, which we argue to be more suitable for data sharing and reuse on the Web. Prototypes avoid the distinction between classes and instances and provide a means for object-based data sharing and reuse.

Smart Medical Information Technology for Healthcare (SMITH)

Summary Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. “Smart Medical Information Technology for Healthcare (SMITH)” is one of four consortia funded by the German Medical Informatics Initiative (MI-I) to create an alliance of universities, university hospitals, research institutions and IT companies. SMITH’s goals are to establish Data Integration Centers (DICs) at each SMITH partner hospital and to implement use cases which demonstrate the usefulness of the approach. Objectives: To give insight into architectural design issues underlying SMITH data integration and to introduce the use cases to be implemented. Governance and Policies: SMITH implements a federated approach as well for its governance structure as for its information system architecture. SMITH has designed a generic concept for its data integration centers. They share identical services and functionalities to take best advantage of the interoperability architectures and of the data use and access process planned. The DICs provide access to the local hospitals’ Electronic Medical Records (EMR). This is based on data trustee and privacy management services. DIC staff will curate and amend EMR data in the Health Data Storage. Methodology and Architectural Framework: To share medical and research data, SMITH’s information system is based on communication and storage standards. We use the Reference Model of the Open Archival Information System and will consistently implement profiles of Integrating the Health Care Enterprise (IHE) and Health Level Seven (HL7) standards. Standard terminologies will be applied. The SMITH Market Place will be used for devising agreements on data access and distribution. 3LGM 2 for enterprise architecture modeling supports a consistent development process. The DIC reference architecture determines the services, applications and the standards-based communication links needed for efficiently supporting the ingesting, data nourishing, trustee, privacy management and data transfer tasks of the SMITH DICs. The reference architecture is adopted at the local sites. Data sharing services and the market place enable interoperability. Use Cases: The methodological use case “Phenotype Pipeline” (PheP) constructs algorithms for annotations and analyses of patient-related phenotypes according to classification rules or statistical models based on structured data. Unstructured textual data will be subject to natural language processing to permit integration into the phenotyping algorithms. The clinical use case “Algorithmic Surveillance of ICU Patients” (ASIC) focusses on patients in Intensive Care Units (ICU) with the acute respiratory distress syndrome (ARDS). A model-based decision-support system will give advice for mechanical ventilation. The clinical use case HELP develops a “hospital-wide electronic medical record-based computerized decision support system to improve outcomes of patients with blood-stream infections” (HELP). ASIC and HELP use the PheP. The clinical benefit of the use cases ASIC and HELP will be demonstrated in a change of care clinical trial based on a step wedge design. Discussion: SMITH’s strength is the modular, reusable IT architecture based on interoperability standards, the integration of the hospitals’ information management departments and the public-private partnership. The project aims at sustainability beyond the first 4-year funding period.

A semantic architecture for preserving and interpreting the information contained in Irish historical vital records

Irish Record Linkage 1864–1913 is a multi-disciplinary project that started in 2014 aiming to create a platform for analyzing events captured in historical birth, marriage, and death records by applying semantic technologies for annotating, storing, and inferring information from the data contained in those records. This enables researchers to, among other things, investigate to what extent maternal and infant mortality rates were underreported. We report on the semantic architecture, provide motivation for the adoption of RDF and Linked Data principles, and elaborate on the ontology construction process that was influenced by both the requirements of the digital archivists and historians. Concerns of digital archivists include the preservation of the archival record and following best practices in preservation, cataloguing, and data protection. The historians in this project wish to discover certain patterns in those vital records. An important aspect of the semantic architecture is the clear separation of concerns that reflects those distinct requirements—the transcription and archival authenticity of the register pages and the interpretation of the transcribed data—that led to the creation of two distinct ontologies and knowledge bases. The advantage of this clear separation is the transcription of register pages resulted in a reusable data set fit for other research purposes. These transcriptions were enriched with metadata according to best practices in archiving for ingestion in suitable long-term digital preservation platforms.

Where to Park?: Predicting Free Parking Spots in Unmonitored City Areas

Several smart cities around the world have begun monitoring parking areas in order to estimate free spots and help drivers that are looking for parking. The current results are indeed promising, however, this approach is limited by the high costs of sensors that need to be installed throughout the city in order to achieve an accurate estimation rate. This work investigates the extension of estimating parking information from areas equipped with sensors to areas that are missing them. To this end, similarity values between city neighborhoods are computed based on background data, i.e., from geographic information systems. Using the derived similarity values, we analyze the adaptation of occupancy rates from monitored- to unmonitored parking areas.

Data Makes the Public Sector Go Round

This article summarizes the results of a systematic literature review on how the new data technologies affect the public sector and what their impact on governments are. The opportunities and the challenges that public administrations face nowadays in a data-driven world are important. Our research retrieved opportunities related to transparency, innovation, public participation and efficiency, while challenges regarding privacy issues, technical difficulties, data management, cultural and political obstacles were found. By overcoming the challenges and empowering the opportunities for data usability in public sector, exploitation of data could become the foundation for innovation and public sector transformation.

Blockchain for Business Applications: A Systematic Literature Review

Blockchain technology is widely known as the technological basis on which bitcoin is built. This technology has created high expectations, as transactions of every kind are executed in a decentralized way, without the need of a trusted third-party. Blockchain real business applications are currently limited mostly to financial services but many R&D projects in companies and corporations try to amplify the areas of blockchain implementation. In this paper, we conduct a systematic survey with the aim of pointing out the areas in which blockchain technology applications and services are being developed both in the public and private sector. In the results, we discuss the disruptive effect that this technology could bring to various business sectors as well as the concerns regarding the development of the blockchain technology.

The Future of the Semantic Web: Prototypes on a Global Distributed Filesystem

An important part of the Semantic Web vision is the idea that data is shared seamlessly and that world wide distributed, accessible, and interlinked knowledge bases can be created. However, the current incarnation of the Semantic Web falls short of this vision: while some necessary infrastructure (e.g., Linked Data) has been put in place, the current use of Linked Data in the Semantic Web is still happening in data silos, and sharing and reusing of knowledge is cumbersome and not straightforward. Recently the idea of prototypical objects was proposed to remedy this situation. This concept, known as Prototypes originates from early Frame systems and is also adopted in programming languages such as Javascript. In this vision paper we describe how a distributed file system forms a natural habitat for prototype knowledge representation, advancing the Semantic Web. In particular, we describe how we envision the deployment of Linked Data and Prototype Knowledge bases atop of the InterPlanetary File System (IPFS), which has several useful features matching the needs for knowledge representation based on prototypes.

A Framework for Applying Data Integration and Curation Pipelines to Support Integration of Migrants and Refugees in Europe

We investigate the benefit of data integration and curation services for the current refugee crisis and proposed an architecture to support development of innovative solutions. We focus on developing a multi- /cross-lingual semantic data curation pipeline enriched with natural language processing capabilities in order to (a) improve decision making capabilities of public authorities with data driven dashboards; (b) stimulate the development of innovative application and services supporting integration of refugees; and (c) improve the use of open data for tackling the societal challenges.