Universität Rostock, Lehrstuhl Datenbank- und Informationssysteme: Dbis Repository
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940 research outputs found
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Inverse im Forschungsdatenmanagement: Eine Kombination aus Provenance Management, Schema- und Daten-Evolution
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Bildarchive nutzbar machen - Eine Studie am Beispiel des Bildarchives des Freilichtmuseums Mueß
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Effizientes Reverse Engineering von Keys und Quasi-Keys aus komplexen, heterogenen und verrauschten NoSQL-Daten
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De-Anonymisierungsverfahren: Kategorisierung und Anwendung für Datenbankanfragen (De-Anonymization: Categorization and Use-Cases for Database Queries)
Get PDFKonzeption eines Polystores für relationale Datenbanken, Graphdatenbanken und NoSQL-Datenbanken und Definition beschreibender Metadaten
No full textRewriting Complex Queries from Cloud to Fog under Capability Constraints to Protect the Users' Privacy
Get PDFIn this paper we show how existing query rewriting and query containment techniques can be used to achieve an efficient and privacy-aware processing of queries. To achieve this, the whole network structure, from data producing sensors up to cloud computers, is utilized to create a database machine consisting of billions of devices from the Internet of Things. Based on previous research in the field of database theory, especially query rewriting, we present a concept to split a query into fragment and remainder queries. Fragment queries can operate on resource limited devices to filter and preaggregate data. Remainder queries take these data and execute the last, complex part of the original queries on more powerful devices. As a result, less data is processed and forwarded in the network and the privacy principle of data minimization is accomplished
Machine Learning on Large Databases: Transforming Hidden Markov Models to SQL Statements
Get PDFMachine Learning is a research field with substantial relevance for many applications in different areas. Because of technical improvements in sensor technology, its value for real life applications has even increased within the last years. Nowadays, it is possible to gather massive amounts of data at any time with comparatively little costs. While this availability of data could be used to develop complex models, its implementation is often narrowed because of limitations in computing power. In order to overcome performance problems, developers have several options, such as improving their hardware, optimizing their code, or use parallelization techniques like the MapReduce framework. Anyhow, these options might be too cost intensive, not suitable, or even too time expensive to learn and realize. Following the premise that developers usually are not SQL experts we would like to discuss another approach in this paper: using transparent database support for Big Data Analytics. Our aim is to automatically transform Machine Learning algorithms to parallel SQL database systems. In this paper, we especially show how a Hidden Markov Model, given in the analytics language R, can be transformed to a sequence of SQL statements. These SQL statements will be the basis for a (inter-operator and intra-operator) parallel execution on parallel DBMS as a second step of our research, not being part of this paper