Authors:
Philip Schmiegelt
1
;
Jingquan Xie
2
;
Gereon Schüller
1
and
Andreas Behrend
3
Affiliations:
1
Fraunhofer FKIE, Germany
;
2
Fraunhofer IAIS, Germany
;
3
University of Bonn, Germany
Keyword(s):
Monitoring, Data Streams, Event Processing, Temporal Databases, Provenance, Knowledge Management, Declarative Programming.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Analytics
;
Data Engineering
;
Databases and Data Security
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Query Processing and Optimization
;
Symbolic Systems
Abstract:
Databases are able to store, manage, and retrieve large amounts and a broad variety of data. However, the task of understanding and reacting to the data is often left to tools or user applications outside the database. As a consequence, monitoring applications are often relying on problem-specific imperative code for data analysis, scattering the application logic. This usually leads to island solutions which are hard to maintain, give raise to security and performance problems due to the separation of data storage and analysis. In this paper, we identify missing database functionalities which overcome these problems by allowing data processing on a higher level of abstraction. Such functionalities would allow to employ a database system even for the complex analysis tasks required in evolving monitoring scenarios.