http://dl.gi.de/handle/20.500.12116/11567 2026-07-23T12:19:30Z 2026-07-23T12:19:30Z Sehili, Ziad Rahm, Erhard http://dl.gi.de/handle/20.500.12116/11791 2018-03-29T12:36:14Z 2016-01-01T00:00:00Z

Speeding up Privacy Preserving Record Linkage for Metric Space Similarity Measures Sehili, Ziad; Rahm, Erhard The analysis of person-related data in Big Data applications faces the tradeoff of finding useful results while preserving a high degree of privacy. This is especially challenging when person-related data from multiple sources need to be integrated and analyzed. Privacy-preserving record linkage (PPRL) addresses this problem by encoding sensitive attribute values such that the identification of persons is prevented but records can still be matched. In this paper we study how to improve the efficiency and scalability of PPRL by restricting the search space for matching encoded records. We focus on similarity measures for metric spaces and investigate the use of M‑trees as well as pivot-based solutions. Our evaluation shows that the new schemes outperform previous filter approaches by an order of magnitude.

2016-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11784 2018-03-29T12:36:14Z 2016-01-01T00:00:00Z

Dissertationen

2016-01-01T00:00:00Z Sips, Mike Köthur, Patrick Eggert, Daniel http://dl.gi.de/handle/20.500.12116/11785 2018-03-29T12:36:14Z 2016-01-01T00:00:00Z

Toward a Visual Analytics Approach to Support Multi-Sensor Analysis in Remote Sensing Science Sips, Mike; Köthur, Patrick; Eggert, Daniel Multi-sensor analysis is a novel scientific approach in remote sensing science. The basic idea is to enable users to combine various satellite mission data (called scenes) into a common data set. This combination produces millions of high-resolution time series (one time series for each pixel) from which users want to extract potentially interesting spatio-temporal patterns. A challenge of multi-sensor analysis is that users often experience difficulties interpreting the extracted patterns. We use Visual Analytics (VA) to help users understand these patterns. We learned from our interdisciplinary cooperation in the GeoMultiSens project that VA has to support the assessment and selection of scenes suitable for the current application scenario and question to achieve this goal. The contribution of this paper is twofold. First, we describe how we devised a VA approach that supports users in the assessment and selection of remote sensing data based on a user and task analysis. We demonstrate how our VA approach helps users to select and assess scenes to study forest cover change in Europe between 2010 and 2016. The study of forest cover change is an important scientific scenario because the loss of forest cover has negative effects on the environment, such as undermining the capacity of ecosystems to maintain fresh water, loosing the ability to regulate the climate, and poorer air quality. Second, we discuss the Scientific Data Explorer, our research vision for VA to enable users to effectively develop VA approaches for a variety of scientific scenarios.

2016-01-01T00:00:00Z Behrend, Andreas Schmiegelt, Philip Dohr, Andreas http://dl.gi.de/handle/20.500.12116/11792 2018-03-29T12:36:14Z 2016-01-01T00:00:00Z

Supporting Situation Awareness in Spatio-Temporal Databases Behrend, Andreas; Schmiegelt, Philip; Dohr, Andreas Situation awareness refers to the capability of systems to perceive an existing or predicted context that determines the values of variables in a changing environment. Despite the enhanced support for managing temporal data, current database systems still lack mechanisms for handling highly dynamic situations in which data may change frequently. We present first results from an ongoing research project investigating these missing database features. In particular, we identify (i) the requirements for representing complex spatio-temporal data, (ii) the reasoning capabilities needed for detecting valid relationships between situations, and (iii) the operators necessary for supporting situation-based reasoning. Our investigations are based on a new perception concept, which comprises interval timestamped data derived from observed events and processed using the sequenced semantics. Perceptions provide a high level (and qualitative) description of past and current situations, complemented by projections into the future.

2016-01-01T00:00:00Z