Kjell Orsborn

Representing Matrices Using Multi-Directional Foreign Functions

New application areas for database technology such as computer-aided design and analysis systems require a semantically rich data model, high performance, extensibility, and application oriented queries. We have built a system for finite element analysis using a functional database management system to represent multiple matrix representations. The Functional Data Model is well suited for managing the data complexity and representation needs of such applications. Our application domain needs multiple customized numerical data representations, user-defined (foreign) functions, multiply inherited types, and overloading of multi-argument functions. Type inheritance and overloading require the support for late binding of function calls in queries. Furthermore, queries may use functions as relationships, which means that the system needs to process inverses of both tabulated and foreign functions, multi-directional functions. It is shown how to model matrix algebra operators using multi-directional foreign functions and how to process queries to these functions.

Computational database technology applied to option pricing via finite differences

Computational database technology spans the two research fields data-base technology and scientific computing. It involves development of database capabilities that support computational-intensive applications found in science and engineering. This includes support for representing and processing of mathematical models within the database environment without any significant performance loss compared to conventional implementations. This paper describes how an existing database management system, AMOS II, is extended with capabilities to solve the Black–Scholes equation commonly used in option pricing. The numerical method used is finite differences, and a flexible database framework that can deal with complex mathematical objects and numerical methods is created. We describe how computational data representations and operations are adapted to the database management system and the approach is evaluated with respect to performance, extensibility, and ease of use.

VisDM–A Data Stream Visualization Platform

Visual Data stream Monitor (VisDM) is a new approach to integrate a visual programming language with a data stream management system (DSMS) to support the construction, configuration, and visualization of data stream applications through a set of building blocks, Visual Data Flow Components (VDFCs). This functionality is provided by extending the LabVIEW visual programming platform to support easy declarative specification of continuous visualizations of continuous query results. With actor-based data flows, visualization of data stream output becomes more accessible.

Framework for real-time clustering over sliding windows

Clustering queries over sliding windows require maintaining cluster memberships that change as windows slide. To address this, the Generic 2-phase Continuous Summarization framework (G2CS) utilizes a generation based window maintenance approach where windows are maintained over different time intervals. It provides algorithm independent and efficient sliding mechanisms for clustering queries where the clustering algorithms are defined in terms of queries over cluster data represented as temporal tables. A particular challenge for real-time detection of a high number of fastly evolving clusters is efficiently supporting smooth re-clustering in real-time, i.e. to minimize the sliding time with increasing window size and decreasing strides. To efficiently support such re-clustering for clustering algorithms where deletion of expired data is not supported, e.g. BIRCH, G2CS includes a novel window maintenance mechanism called Sliding Binary Merge (SBM), which maintains several generations of intermediate window instances and does not require decremental cluster maintenance. To improve real-time sliding performance, G2CS uses generation-based multi-dimensional indexing. Extensive performance evaluation on both synthetic and real data shows that G2CS scales substantially better than related approaches.