Cleaning Denial Constraint Violations through Relaxation

Data cleaning is a time-consuming process that depends on the data analysis that users perform. Existing solutions treat data cleaning as a separate offline process that takes place before analysis begins. Applying data cleaning before analysis assumes a priori knowledge of the inconsistencies and the query workload, thereby requiring effort on understanding and cleaning the data that is unnecessary for the analysis. We propose an approach that performs probabilistic repair of denial constraint violations on-demand, driven by the exploratory analysis that users perform. We introduce Daisy, a system that seamlessly integrates data cleaning into the analysis by relaxing query results. Daisy executes analytical query-workloads over dirty data by weaving cleaning operators into the query plan. Our evaluation shows that Daisy adapts to the workload and outperforms traditional offline cleaning on both synthetic and real-world workloads.Comment: To appear in SIGMOD 2020 proceeding

ISO/IEEE ve HL7 standartları ile medikal cihaz gözlemlerinin otomatik edinimi ve kulanımı.

The delivery of quality healthcare to all citizens at reasonable costs is an important challenge. With the increase in the aging population, the costs of managing chronic diseases increase. Today, healthcare services tend to shift from recovery to prevention. Remote healthcare monitoring is crucial for prevention and monitoring of chronic diseases since they require continuous and long-term monitoring. The advances in networking, mobile communications and medical device technologies offer a great potential to realize remote healthcare monitoring. However, seamless integration of multi-modal medical devices to the existing healthcare information systems is necessary for the automated use of medical device observations in related applications. The thesis addresses the automatic acquisition and use of multi-modal medical device observations in healthcare information systems. The interoperability of medical devices with healthcare information systems requires both physical connectivity and application level interoperability. Therefore, the thesis concentrates on both the medical device domain and the interoperability efforts on the existing healthcare information systems. It provides an interoperability solution based on ISO/IEEE 11073 and HL7 standards. This work is also realized the automatic acquisition and use of multi-modal medical device observations in an intelligent healthcare monitoring and decision support system which is developed as a part of the IST-027074 SAPHIRE project funded by the European Commission.M.S. - Master of Scienc

Collaborative Business Process Support in eHealth: Integrating IHE Profiles through ebXML Business Process Specification Language

real-world use cases to facilitate the interoperability of healthcare information resources. While realizing a complex real world scenario, IHE Profiles are combined by grouping the related IHE actors. Grouping IHE Actors implies that the associated business processes (IHE Profiles) that the Actors are involved must be combined, that is, the choreography of the resulting collaborative business process must be determined by deciding on the execution sequence of transactions coming from different profiles. There are many IHE profiles and each user or vendor may support a different set of IHE Profiles that fits to its business need. However, determining the precedence of all the involved transaction manually for each possible combination of the profiles is a very tedious task. In this paper, we describe how to obtain the overall business process automatically when IHE Actors are grouped. For this purpose, we represent the IHE Profiles through a standard, machine processable language, namely, OASIS ebXML Business Process Specification (ebBP) Language. We define the precedence rules among the transactions of the IHE Profiles, again, in a machine processable way. Then through a graphical tool, we allow users to select the actors to be grouped and automatically produce the overall business process in a machine processable format

Anlamsal birlikte işlerlik platformu tabanlı akıllı sağlık takibi

TÜBİTAK EEEAG01.09.200