A5103110179- Advanced Database Systems and Queries
- Semantic Web and Ontologies
- Data Management and Algorithms
- Bat Biology and Ecology Studies
- UAV Applications and Optimization
- Marine animal studies overview
- Distributed systems and fault tolerance
- Energy Efficient Wireless Sensor Networks
- Time Series Analysis and Forecasting
- Gaussian Processes and Bayesian Inference
- Context-Aware Activity Recognition Systems
- Animal Vocal Communication and Behavior
- Scientific Computing and Data Management
- Data Quality and Management
- Opportunistic and Delay-Tolerant Networks
- Mobile Ad Hoc Networks
- Logic, Reasoning, and Knowledge
Friedrich-Alexander-Universität Erlangen-Nürnberg
2015-2020
Recent advances in animal tracking technology have ushered a new era biologging. However, the considerable size of many sophisticated biologging devices restricts their application to larger animals, whereas older techniques often still represent state-of-the-art for studying small vertebrates. In industrial applications, low-power wireless sensor networks (WSNs) fulfill requirements similar those needed monitor behavior at high resolution and low tag mass. We developed network (WBN), which...
In this paper, the BATS project is presented, which aims to track behavior of bats via an ultra-low power wireless sensor network. An overview about whole and its parts like node design, tracking grid software infrastructure given evaluation shown. The includes a lightweight that attached combines multiple features. Communication among nodes allows bat encounters. Flight trajectories individual tagged can be recorded at high spatial temporal resolution by ground grid. To increase...
Abstract Recent advances in animal tracking technology have ushered a new era biologging. However, the considerable size of many sophisticated biologging devices restricts their application to larger animals, while old-fashioned techniques often still represent state-of-the-art for studying small vertebrates. In industrial applications, low-power wireless sensor networks fulfill requirements similar those needed monitor behavior at high resolution and low tag weight. We developed network...
SQL queries encapsulate the knowledge of their authors about usage queried data sources. This also contains aspects that cannot be inferred by analyzing contents sources alone. Due to complexity analytical queries, specialized mechanisms are necessary enable user-friendly formulation meta-queries over an existing query log. Currently approaches do not sufficiently consider syntactic and semantic along with contextual information.
Many interesting applications of continuous-query processing are concerned with pattern matching or complex temporal aggregation events. Real-world queries that rely on these operations difficult to implement in current stream-processing systems. The reason seems be a gap between two types existing query languages: Some languages (e. g. CQL) offer small set simple operators can combined order create queries. While provide sound and comprehensible semantics, they lack the expressiveness...
Analytical SQL queries are a valuable source of information. Query log analysis can provide insight into the usage datasets and uncover knowledge that cannot be inferred from schemas or content alone. To unlock this potential, flexible mechanisms for meta-querying required. Syntactic semantic aspects must considered along with contextual
We introduce Query-driven Knowledge-Sharing Systems (QKSS), which extend data management systems with knowledge-sharing capabilities to facilitate collaboration among different teams of scientists. Relevant tacit knowledge about sources is extracted from SQL query logs and externalized support source discovery integration. By studying this collaborative knowledge, scientists are enabled formulate effective analytical queries over unfamiliar sources.
Data-stream processing has continuously risen in importance as the amount of available data has been steadily increasing over last decade. Besides traditional domains such data-center monitoring and click analytics, there is an number network-enabled production machines that generate continuous streams data. Due to their continuous nature, queries on data-streams can be more complex, and distinctly harder under- stand than database queries. As users have consider operational...
Data-stream processing has continuously risen in importance as the amount of available data been steadily increas- ing over last decade. Besides traditional domains such data-center monitoring and click analytics, there is an increasing number network-enabled production machines that generate continuous streams data. Due to their nature, queries on data-streams can be more complex, distinctly harder understand then database queries. As users have consider operational details, maintenance...