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Knowledge about data completeness is essentially in data-supported decision making. In this thesis we present a framework for metadata-based assessment of database completeness. We discuss how to express information about data completeness and how to use such information to draw conclusions about the completeness of query answers. In particular, we introduce formalisms for stating completeness for parts of relational databases. We then present techniques for drawing inferences between such statements and statements about the completeness of query answers, and show how the techniques can be extended to databases that contain null values. We show that the framework for relational databases can be transferred to RDF data, and that a similar framework can also be applied to spatial data. We also discuss how completeness information can be verified over processes, and introduce a data-aware process model that allows this verification.
The Virtual Observatory has reached sufficient maturity for its routine scientific exploitation by astronomers. To prove this statement, here I present a brief description of the complete VO-powered PhD thesis entitled Galactic and extragalactic rese
This Dissertation presents results of a thorough study of ultracold bosonic and fermionic gases in three-dimensional and quasi-one-dimensional systems. Although the analyses are carried out within various theoretical frameworks (Gross-Pitaevskii, Bet
Data Lake (DL) is a Big Data analysis solution which ingests raw data in their native format and allows users to process these data upon usage. Data ingestion is not a simple copy and paste of data, it is a complicated and important phase to ensure t
Probabilistic databases play a preeminent role in the processing and management of uncertain data. Recently, many database research efforts have integrated probabilistic models into databases to support tasks such as information extraction and labeli
With new emerging technologies, such as satellites and drones, archaeologists collect data over large areas. However, it becomes difficult to process such data in time. Archaeological data also have many different formats (images, texts, sensor data)