Conference
Databases, Knowledge, and Data Applications
About: Databases, Knowledge, and Data Applications is an academic conference. The conference publishes majorly in the area(s): Computer science & Relational database. Over the lifetime, 163 publications have been published by the conference receiving 892 citations.
Topics: Computer science, Relational database, Database design, Graph database, Relational database management system
Papers
1 Mar 2009
TL;DR: This paper proposes a conceptual framework for the problem space of object-relational impedance mismatch and consequently distinguishes four kinds of impedance mismatch, showing that each kind of mismatch needs to be addressed using a different object- Relational mapping strategy.
Abstract: Object and relational technologies are grounded in different paradigms. Each technology mandates that those who use it take a particular view of a universe of discourse. Incompatibilities between these views manifest as problems of an object-relational impedance mismatch. In this paper we propose a conceptual framework for the problem space of object-relational impedance mismatch and consequently distinguish four kinds of impedance mismatch. We show that each kind of impedance mismatch needs to be addressed using a different object-relational mapping strategy. Our framework provides a mechanism to explore issues of fidelity, integrity and completeness in the design and implementation of existing and new strategy choices. Our framework will be of benefit to standards bodies, tool vendors, designers and programmers as it will provide them with new insights into how to address problems of an object-relational impedance mismatch both at the most appropriate levels of abstraction and in the most appropriate way.
117 citations
Proceedings Article•
29 Feb 2012TL;DR: This document summarizes current capabilities, research and operational priorities, and plans for further studies that were established at the 2015 USGS workshop on quantitative hazard assessments of earthquake-triggered landsliding and liquefaction.
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82 citations
11 Apr 2010
TL;DR: This paper compares the characteristics and constraints of relational databases with LOD, trying to understand the latter as a Web-scale database.
Abstract: While Linked Open Data (LOD) has gained much attention in the recent years, requirements and the challenges concerning its usage from a database perspective are lacking. We argue that such a perspective is crucial for increasing acceptance of LOD. In this paper, we compare the characteristics and constraints of relational databases with LOD, trying to understand the latter as a Web-scale database. We propose LOD-specific requirements beyond the established database rules and highlight research challenges, aiming to combine future efforts of the database research community and the Linked Data research community in this area.
57 citations
1 Mar 2009
TL;DR: This paper demonstrates in the context of a project case study that data mining (DM) is a well suited approach to detect hidden patterns in malware data and thus to support SIEM.
Abstract: Enterprise information infrastructures are generally characterized by a multitude of information systems which support decision makers in fulfilling their duties. The object of information security management is the protection of these systems, whereas security information and event management (SIEM) addresses those information management tasks which focus on the short term handling of events, as well as on the long term improvement of the entire information security architectures. This is carried out based on those data which can be logged and collected within the enterprise information security infrastructure. An especially interesting type of log data is data created by anti-malware software. This paper demonstrates in the context of a project case study that data mining (DM) is a well suited approach to detect hidden patterns in malware data and thus to support SIEM.
32 citations
11 Apr 2010
TL;DR: A simple and efficient data mining-based solution for anti-money laundering developed as a tool and some preliminary experiment results with real transaction datasets are presented.
Abstract: Today, money laundering poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché, of drug trafficking to financing terrorism and surely not forgetting personal gain. Most international financial institutions have been implementing anti-money laundering solutions to fight investment fraud. However, traditional investigative techniques consume numerous man-hours. Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting money laundering activities. Within the scope of a collaboration project for the purpose of developing a new solution for the anti-money laundering Units in an international investment bank, we proposed a simple and efficient data mining-based solution for anti-money laundering. In this paper, we present this solution developed as a tool and show some preliminary experiment results with real transaction datasets.
32 citations
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 3 |
| 2020 | 9 |
| 2019 | 2 |
| 2018 | 3 |
| 2017 | 6 |
| 2016 | 4 |